[ { "id": "https://openalex.org/W7171497879", "doi": "10.1109/hpsr68369.2026.11615187", "title": "Systems-Level Planning and Coordination of Truck-Drone Collaborative Delivery Networks", "year": 2026, "publication_date": "2026-06-01", "type": "conference-paper", "language": null, "authors": [ "Didem Cicek", "Burak Kantarcı" ], "first_author": "Didem Cicek", "venue": null, "publisher": null, "abstract": "Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication, and scalable control. Truck-drone collaboration has emerged as a networked cyber-physical delivery paradigm that combines the payload capacity and range efficiency of trucks with the agility of drones in congested or access-limited urban environments. This paper proposes a layered planning and coordination framework that structures truck-drone collaborative delivery (TDCD) from a systems and control perspective. The framework consists of five interrelated layers: spatial-demand alignment, collaborative delivery configuration, resource and workflow orchestration, performance evaluation, and scalability analysis, providing a unified view of coordination, control, and system-level performance in networked delivery operations. The proposed framework is evaluated using a realistic urban last-mile delivery scenario derived from the 2021 Amazon Last Mile Routing Research Challenge dataset. The case study demonstrates how coordinated truck-drone operation, enabled by structured task orchestration and inter-agent synchronization, improves end-to-end system efficiency under operational constraints. Results show a 42.4% reduction in total delivery time and a 44.2% reduction in energy consumption compared to a conventional truck-only delivery model. The scalability analysis further highlights how coordination gains persist as system size increases, and shows the importance of efficient control and communication in heterogeneous delivery networks.", "keywords": [ "Key (lock)", "Context (archaeology)", "Component (thermodynamics)", "Identification (biology)", "Process (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.48550/arxiv.2606.08738" }, { "id": "https://openalex.org/W7140410886", "doi": "10.1051/aacus/2026030", "title": "Investigations on drone-mounted active noise control", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Jonas Steiner", "Florian Hilgemann", "Peter Jax" ], "first_author": "Jonas Steiner", "venue": "Acta Acustica", "publisher": "EDP Sciences", "abstract": "Although drones have many potential applications including parcel delivery and surveillance, their deployment in urban areas is limited by their noise levels. As a mitigation approach, this work considers active noise control (ANC) with drone-mounted secondary sound sources and reference sensors. The feasibility of this approach is investigated in a controlled acoustic environment. Because commercially available drones lack the hardware required for ANC, two drones were equipped with loudspeakers and microphones for measurement purposes. ANC filter design requires information about the electro-acoustic paths, which are investigated based on measurements in a hemi-anechoic chamber. This facilitates the development of a multichannel feedforward control structure, and its evaluation through simulations. The investigation indicates the feasibility of such systems, as spatially robust median attenuation of 9.4 dB and a peak attenuation of over 15 dB is achieved in simulations. Although further investigation is needed to verify the results, this work documents a principally viable system and identifies its fundamental challenges.", "keywords": [ "Active noise control", "Loudspeaker", "Feed forward", "Drone", "Noise (video)", "Attenuation", "Noise control", "Filter (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1051/aacus/2026030" }, { "id": "https://openalex.org/W7164235278", "doi": "10.1109/hpsr68369.2026.11615187", "title": "Systems-Level Planning and Coordination of Truck-Drone Collaborative Delivery Networks", "year": 2026, "publication_date": "2026-06-07", "type": "preprint", "language": null, "authors": [ "Didem Cicek", "Burak Kantarcı" ], "first_author": "Didem Cicek", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication, and scalable control. Truck-drone collaboration has emerged as a networked cyber-physical delivery paradigm that combines the payload capacity and range efficiency of trucks with the agility of drones in congested or access-limited urban environments. This paper proposes a layered planning and coordination framework that structures truck-drone collaborative delivery (TDCD) from a systems and control perspective. The framework consists of five interrelated layers: spatial-demand alignment, collaborative delivery configuration, resource and workflow orchestration, performance evaluation, and scalability analysis, providing a unified view of coordination, control, and system-level performance in networked delivery operations. The proposed framework is evaluated using a realistic urban last-mile delivery scenario derived from the 2021 Amazon Last Mile Routing Research Challenge dataset. The case study demonstrates how coordinated truck-drone operation, enabled by structured task orchestration and inter-agent synchronization, improves end-to-end system efficiency under operational constraints. Results show a 42.4% reduction in total delivery time and a 44.2% reduction in energy consumption compared to a conventional truck-only delivery model. The scalability analysis further highlights how coordination gains persist as system size increases, and shows the importance of efficient control and communication in heterogeneous delivery networks.", "keywords": [ "Scalability", "Workflow", "Payload (computing)", "Task (project management)", "Resource allocation", "Heterogeneous network", "Resource efficiency", "Orchestration", "Resource (disambiguation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2606.08738" }, { "id": "https://openalex.org/W4393299865", "doi": "10.48550/arxiv.2403.18091", "title": "The flying sidekick traveling salesman problem with multiple drops: A simple and effective heuristic approach", "year": 2024, "publication_date": "2024-03-26", "type": "preprint", "language": "en", "authors": [ "Sarah K. Schaumann", "Abhishake Kundu", "Juan C. Pina-Pardo", "Matthias Winkenbach", "Ricardo A. Gatica", "Stephan M. Wagner", "Timothy I. Matis" ], "first_author": "Sarah K. Schaumann", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We study the Flying Sidekick Traveling Salesman Problem with Multiple Drops (FSTSP-MD), a multi-modal last-mile delivery model where a single truck and a single drone cooperatively deliver customer packages. In the FSTSP-MD, the drone can be launched from the truck to deliver multiple packages before it returns to the truck for a new delivery operation. The objective is to find the synchronized truck and drone delivery routes that minimize the completion time of the delivery process. We develop a simple and effective heuristic to solve the FSTSP-MD based on an order-first, split-second scheme. The core component of our heuristic is a novel split algorithm that finds FSTSP-MD solutions in polynomial time for a given sequence of customers. We embed this split algorithm into a simple heuristic approach that combines standard local search and diversification techniques. The simplicity of our heuristic does not sacrifice performance: we show that it consistently outperforms state-of-the-art solution approaches developed for both the FSTSP-MD and the FSTSP (i.e., the single-drop case) through extensive numerical experiments. Based on both stylized and real-world instances, we also show that the FSTSP-MD substantially reduces completion times compared to traditional truck-only delivery systems. We provide extensive managerial insights into the impacts of drone capabilities and customer distribution on delivery efficiency. Our discussion compares the benefits of drones with greater payload capacity and those with greater speed. We highlight which service area characteristics increase savings but also require enhanced drone capabilities.", "keywords": [ "Travelling salesman problem", "Heuristic", "Simple (philosophy)", "Mathematical optimization", "Computer science", "Traveling purchaser problem", "2-opt", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2403.18091" }, { "id": "https://openalex.org/W7143493139", "doi": "10.1051/aacus/2026030/pdf", "title": "Investigations on drone-mounted active noise control", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Jonas Steiner", "Florian Hilgemann", "Peter Jax" ], "first_author": "Jonas Steiner", "venue": "RWTH Publications (RWTH Aachen)", "publisher": "RWTH Aachen University", "abstract": "Although drones have many potential applications including parcel delivery and surveillance, their deployment in urban areas is limited by their noise levels. As a mitigation approach, this work considers active noise control (ANC) with drone-mounted secondary sound sources and reference sensors. The feasibility of this approach is investigated in a controlled acoustic environment. Because commercially available drones lack the hardware required for ANC, two drones were equipped with loudspeakers and microphones for measurement purposes. ANC filter design requires information about the electro-acoustic paths, which are investigated based on measurements in a hemi-anechoic chamber. This facilitates the development of a multichannel feedforward control structure, and its evaluation through simulations. The investigation indicates the feasibility of such systems, as spatially robust median attenuation of 9.4 dB and a peak attenuation of over 15 dB is achieved in simulations. Although further investigation is needed to verify the results, this work documents a principally viable system and identifies its fundamental challenges.", "keywords": [ "Active noise control", "Loudspeaker", "Feed forward", "Drone", "Noise (video)", "Attenuation", "Noise control", "Filter (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://publications.rwth-aachen.de/record/1034467" }, { "id": "https://openalex.org/W7147804091", "doi": "10.1109/icei65890.2026.11447876", "title": "Recent Trends, Techniques, and Applications of AI Based Autonomous Drones", "year": 2026, "publication_date": "2026-01-09", "type": "conference-paper", "language": null, "authors": [ "Harshada M. Raghuwanshi", "Gauri Dhopavkar" ], "first_author": "Harshada M. Raghuwanshi", "venue": null, "publisher": null, "abstract": "This paper presents a structured survey and comprehensive synthesis of influential research on autonomous drones, focusing on their development, applications, and performance evaluation. The studies span multiple domains, including medical delivery, agricultural automation, military surveillance, swarm coordination, urban navigation, and last-mile logistics. Techniques such as deep learning, fuzzy logic, reinforcement learning, SLAM, and LLM-based adaptive navigation have been applied and evaluated using both simulations and field deployments. Representative parameters include navigation accuracy, latency, battery life, payload capacity, flight time, and delivery efficiency. To complement this analysis, we highlight real-world drone deployments such as Zipline (medical logistics), Wing (urban parcel delivery), and Matternet (hospital supply chains), thereby linking experimental findings to operational viability. Emerging trends include the rise of AI-driven autonomy, integration with mobile and cloud applications, GPS-denied navigation, and use in fire detection, infrastructure inspection, and healthcare delivery. However, gaps remain in privacy-preserving data usage, AI responsibility, benchmarking standards, and commercial viability assessments. This survey identifies opportunities for future research toward scalable, context-aware, and ethically aligned deployment of autonomous drones.", "keywords": [ "Drone", "Software deployment", "Payload (computing)", "Benchmarking", "Field (mathematics)", "Cloud computing", "Fuzzy logic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icei65890.2026.11447876" }, { "id": "https://openalex.org/W4410322231", "doi": "10.2139/ssrn.5252558", "title": "Challenges of Integrating Commercial Drones in Multimodal Operations: Impact and Recommendations", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Safaa Sindi", "Stewart A. Birrell" ], "first_author": "Safaa Sindi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Computer science", "Business", "Aeronautics", "Process management", "Engineering", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5252558" }, { "id": "https://openalex.org/W4393370580", "doi": null, "title": "Planification stratégique de trafics drones aériens", "year": 2023, "publication_date": "2023-02-03", "type": "dissertation", "language": "en", "authors": [ "Zhengyi Wang" ], "first_author": "Zhengyi Wang", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "Large numbers of Urban Air Mobility (UAM) vehicles are expected to operate in urban airspace in the near future. However, this growth will soon exceed the capacities of current airspace and air traffic management systems and put strains on transportation infrastructure, resulting in undesirable outcomes such as traffic congestion, complex traffic situations, delays, etc. As a result, innovative UAM solutions are required to ensure safe and efficient urban transportation. In this thesis, we focus on strategic planning in unmanned aircraft system traffic management, including airspace organization and management and traffic flow management. Specifically, we develop approaches for UAM route network design and air traffic assignment. The structure of the UAM route network strongly impacts many aspects of the operating traffic flow. The UAM route network is expected to be well-designed for the desired network operations, especially with dense traffic demands. In order to minimize the impact on existing air traffic management systems, this thesis proposes a comprehensive framework to design a UAM route network in low-altitude urban airspace in the presence of obstacles and hazardous airspace. Using a variety of open-source data, this approach is applied as a case study for parcel delivery services using UAVs as UAM vehicles in Singapore's urban airspace. Firstly, The UAM route network is designed as a grid-based network that avoids obstacles and airspace within which the aircraft is prohibited. Then, the link cost is defined in terms of noise impact on populations, airspace safety, and flight efficiency. Unlike most research that only calculates the shortest path, we formulate a k-shortest path problem with diversity to select feasible routes between origin-destination pairs that minimize the route cost. The feasibility of this approach is demonstrated in the experiment, which can also be easily transferred to other scenarios. The impact of different parameter settings for link costs on UAM services is also explored. The feasible routes with low similarity provide more travel options, which can be used as pre-computed routes for air traffic assignment models. To adapt the increasing demand to the current airspace capacity, we proposed several air traffic assignment models, including two static air traffic assignment models for single-layer and multi-layer two-way UAM route networks and a dynamic air traffic assignment model for multi-layer two-way UAM route networks in order to mitigate the congestion and complexity and organize the structure of air traffic flow. Firstly, at the macroscopic level, UAM operations are modeled as flows by aggregating the individual vehicle dynamics to describe the overall flow features with respect to the dense traffic volume. The flows are structured into UAM route networks, in which the air routes are modeled as corridors or volume segments. Then, the air traffic assignment model is formulated as an optimization problem. The objective functions are modeled to describe the air traffic complexity based on the linear dynamical system and the congestion based on energy consumption and traffic density. The simulation-based framework including different optimization approaches is proposed to efficiently solve these problems. Computational experiments are performed on case studies of UAM route networks at various scales. The comparisons with regard to conventional traffic assignment algorithms are presented. The results show that the proposed approach is capable of assigning flows in an efficient and effective manner, significantly reducing the complexity and congestion of the UAM operations. The proposed model can be used to assist regulators and air navigation service providers for air traffic assignments in the strategic planning of UAM operations.", "keywords": [ "Strategic planning", "Business", "Transport engineering", "Engineering", "Marketing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://theses.hal.science/tel-04260145v1/document" }, { "id": "https://openalex.org/W7164042238", "doi": "10.48550/arxiv.2606.08738", "title": "Systems-Level Planning and Coordination of Truck-Drone Collaborative Delivery Networks", "year": 2026, "publication_date": "2026-06-07", "type": "preprint", "language": null, "authors": [ "Didem Cicek", "Burak Kantarcı" ], "first_author": "Didem Cicek", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication, and scalable control. Truck-drone collaboration has emerged as a networked cyber-physical delivery paradigm that combines the payload capacity and range efficiency of trucks with the agility of drones in congested or access-limited urban environments. This paper proposes a layered planning and coordination framework that structures truck-drone collaborative delivery (TDCD) from a systems and control perspective. The framework consists of five interrelated layers: spatial-demand alignment, collaborative delivery configuration, resource and workflow orchestration, performance evaluation, and scalability analysis, providing a unified view of coordination, control, and system-level performance in networked delivery operations. The proposed framework is evaluated using a realistic urban last-mile delivery scenario derived from the 2021 Amazon Last Mile Routing Research Challenge dataset. The case study demonstrates how coordinated truck-drone operation, enabled by structured task orchestration and inter-agent synchronization, improves end-to-end system efficiency under operational constraints. Results show a 42.4% reduction in total delivery time and a 44.2% reduction in energy consumption compared to a conventional truck-only delivery model. The scalability analysis further highlights how coordination gains persist as system size increases, and shows the importance of efficient control and communication in heterogeneous delivery networks.", "keywords": [ "Scalability", "Workflow", "Payload (computing)", "Task (project management)", "Resource allocation", "Heterogeneous network", "Resource efficiency", "Orchestration", "Resource (disambiguation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.48550/arxiv.2606.08738" }, { "id": "https://openalex.org/W4416712995", "doi": "10.4018/979-8-3373-2240-7.ch013", "title": "Ethical and Legal Dimensions of Drones and Robots in Smart Business", "year": 2025, "publication_date": "2025-11-26", "type": "book-chapter", "language": "ng", "authors": [ "Ananya Pandey", "Jipson Joseph" ], "first_author": "Ananya Pandey", "venue": null, "publisher": null, "abstract": "Use of drones and robots are slowly but surely making their way into people's personal and professional lives. International companies are spending millions of dollars on drone research and development for logistics. Companies like Google, Amazon, DHL, and Walmart are actively experimenting on autonomous systems and robotics. Drones and robots are used in the business strategies of these organizations to ensure last-mile delivery, which involves transporting packages straight from a depot to the ultimate customer, maintaining inventory, loading and unloading of trucks, order processing, and the like. But the usage of these advancements poses some critical ethical and legal issues. Authorities must pay attention to these challenges and difficulties in order to ensure their meaningful usage. This chapter, in this perspective, overviews the most significant legal and ethical concerns pertaining to the use of autonomous vehicles in smart businesses and logistics and provides recommendations for their effective utilization in future.", "keywords": [ "Drone", "Order (exchange)", "Robot", "Ethical issues", "Agency (philosophy)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3373-2240-7.ch013" }, { "id": "https://openalex.org/W7108447791", "doi": "10.57945/manara.hbku.30694316.v1", "title": "Drone vs. Bird Classification and Applied Color Transfer Augmentation", "year": 2025, "publication_date": "2025-01-01", "type": "dissertation", "language": null, "authors": [ "Al-Sulaiti, Muna Abdulaziz Th M." ], "first_author": "Al-Sulaiti, Muna Abdulaziz Th M.", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "Unmanned Aerial Vehicles (UAVs), commonly known as drones, have rapidly transformed the landscape of both civilian and military operations. Their usage spans a wide range of applications—from commercial photography and agriculture to tactical reconnaissance and parcel delivery. However, their growing accessibility and capabilities pose significant threats in terms of unauthorized surveillance, smuggling, and potential airborne attacks. A critical concern in automated airspace monitoring is the accurate identification of drones, particularly when differentiating them from birds—natural flying entities that share similar aerial dynamics, shapes, and appearances.Distinguishing between drones and birds in aerial images is inherently complex due to overlapping visual characteristics and varied environmental conditions, such as lighting, motion blur, or sensor noise. This thesis addresses this challenge by proposing a deep learning-based classification framework using advanced convolutional neural network architectures. The system employs ResNet-50 and ResNet-152 through transfer learning, allowing efficient feature extraction and training on limited datasets. Furthermore, color transfer techniques are applied to improve model generalization across different domains, especially when dealing with infrared imagery. Detailed experimentation was conducted using original and color-transferred datasets, comprising a balanced distribution of drone and bird imagery. The models demonstrated high classification accuracy, with ResNet-152 achieving superior performance due to its depth. This research contributes to a scalable, adaptable and defensible approach to drone detection and bird classification, laying the foundation for deployment in real-time surveillance and autonomous security systems.", "keywords": [ "Drone", "Software deployment", "Transfer of learning", "Convolutional neural network", "Generalization", "Identification (biology)", "Feature extraction", "Feature (linguistics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.57945/manara.hbku.30694316.v1" }, { "id": "https://openalex.org/W7130428871", "doi": "10.25911/23w5-xa78", "title": "Spatial Sound Field Modeling and Noise Cancellation for Drones", "year": 2026, "publication_date": "2026-01-01", "type": "dissertation", "language": "en", "authors": [ "Hanwen Bi" ], "first_author": "Hanwen Bi", "venue": null, "publisher": null, "abstract": "Abstract This thesis advances the modeling and reduction of drone noise through the use of on-board microphones and directional active noise control (ANC) systems. In recent years, small-scale unmanned aerial vehicles (UAVs), commonly known as drones, have become increasingly prevalent across a wide array of applications, including parcel delivery, mapping, and emergency response. Despite their growing utility, drone operations contribute significantly to environmental noise pollution, posing risks to both daily comfort and public health. Consequently, the development of effective drone noise mitigation techniques has become a pressing concern. To address this challenge, the thesis first introduces novel methods for drone noise modeling based solely on on-board microphone measurements. These methods generalise the drone noise modeling task as a sound field extrapolation problem. A sound field extrapolation framework based on spherical sector harmonics is proposed, and a comprehensive theoretical analysis is conducted to support its application to sectorial sound field reconstruction. Furthermore, the method is enhanced through the derivation of an explicit mapping between spherical and spherical sector harmonics, improving both accuracy and flexibility. To overcome geometric limitations in microphone placement, we further develop a physics-informed neural network (PINN) architecture called the point neuron net- work. This model embeds the fundamental solution of the wave equation into its structure, ensuring physical consistency. The network handles complex-valued sig- nals directly, improves interpretability, and generalizes effectively across various acoustic conditions. Unlike existing approaches, it does not rely on pre-collected datasets and remains robust to sensor noise and irregular sensor configurations. Building upon the proposed modeling approaches, we develop two directional drone ANC systems that target noise propagation in specific far-field regions. The first sys- tem is based on the spherical sector harmonics extrapolation framework and incorporates constraints on payload and power consumption. We analyze the secondary- to-primary source power ratio for different secondary source configurations by simulations. Additionally, we enhance the ANC algorithm to relax the geometric requirements of the monitoring microphone array and validate the system's performance through hardware experiments. Complementing these systems, we introduce a propeller phase control method aimed at reducing tonal drone noise in the downward far-field region. By adjusting the relative phase of multiple propellers, this technique achieves destructive interference in targeted directions, thereby reducing noise radiation without additional hardware. Overall, the contributions presented in this thesis constitute a comprehensive framework for drone noise modeling and directional noise reduction, offering physically grounded, practically feasible, and experimentally validated solutions to a critical problem in drone operations. Furthermore, the point-neuron learning framework and the spherical sector harmonic-based extrapolation method presented in this thesis are applicable to a broad range of sound field-related problems, including sound field reproduction, interpolation, and extrapolation.", "keywords": [ "Drone", "Noise (video)", "Extrapolation", "Microphone", "Microphone array", "Noise control", "Field (mathematics)", "Noise reduction" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.25911/23w5-xa78" }, { "id": "https://openalex.org/W7167701383", "doi": "10.1201/9781003637714-29", "title": "Enhanced Stability of Drones Using Optimized PID Control Systems", "year": 2026, "publication_date": "2026-07-08", "type": "book-chapter", "language": "en", "authors": [ "Thembelihle Elizabeth Mncube", "Pius Adewale Owolawi", "Chunling Du" ], "first_author": "Thembelihle Elizabeth Mncube", "venue": null, "publisher": null, "abstract": "This systematic review examines advancements in optimizing proportional-integral-derivative (PID) control systems to enhance drone stability, focusing on the integration of advanced techniques such as genetic algorithms (GA), linear quadratic regulators (LQR), active disturbance rejection control (ADRC), and artificial intelligence (AI) methods like machine learning (ML) and reinforcement learning (RL). In accordance with PRISMA standards, the review conducted a structured literature search and analysis. Databases including IEEE Xplore, Scopus, and Web of Science were searched for studies related to PID control, drone stability, and optimization strategies. Studies were selected based on their relevance to enhancing drone stability and adaptability through PID optimization, and they were evaluated for methodological rigor and contributions to the field. The findings suggest that GA-optimized PID controllers, combined with AI-enhanced systems, significantly improve drone stability and performance across diverse environmental conditions. Integrating LQR, ADRC, and AI techniques further enhances adaptability, fault tolerance, and real-time control, especially in dynamic and unpredictable environments. These innovations address key limitations of traditional PID controllers, making them more suitable for high-performance applications such as agriculture, urban logistics, and search and rescue operations. However, the review identifies a gap in research related to the real-time application of these systems in diverse environmental conditions, highlighting the need for future field experiments and real-world validation. This review contributes to the growing body of knowledge on advanced control techniques in unmanned aerial vehicle (UAV) systems, underscoring their potential to develop more stable, adaptive, and reliable drones.", "keywords": [ "Control theory (sociology)", "Stability (learning theory)", "Control system", "Drone", "PID controller" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1201/9781003637714-29" }, { "id": "https://openalex.org/W4410341092", "doi": "10.1109/i3ceet61722.2024.10993565", "title": "Drones in B5G/6G: Investigating Aerial Terminal Facilities", "year": 2024, "publication_date": "2024-09-20", "type": "conference-paper", "language": "en", "authors": [ "Hassan M. Al‐Jawahry", "N. Kalidas", "Vishal Goyal", "Amrita Singh", "Rohit Shrivastava", "Абдусаттар Жураеич Чориев" ], "first_author": "Hassan M. Al‐Jawahry", "venue": null, "publisher": null, "abstract": "The demand of high-fidelity, low-latency last-mile communications, together with developments in internet and organization, makes effective handoff procedures imperative. The potential use advanced cellular meanders as airborne base stations for the next wave of cellular frameworks is investigated in this article. Takeoffing In the field of next-generation high-availability communications, post hoc structures that serve as a clustering of transportable exchanges for the on-demand deployment of broadband start to show potential. Beyond that technology, to the sixth phase, the configuration on flexible phone systems has been impacted by the continuous development and broad use of 5G. Mobile phones made it possible to realize the full potential of the many charity organizations that combine setup and pattern-making. Above all, 6G has to demonstrate cutting-edge access technologies and novel, innovative organization topologies. To ensure significance, the job at hand is organized based on fundamental concepts such as movability, center localization, optimum method of action, handover ampleness through essentialness openness, and contrasting multi-objective optimizations. This research looks at ways to improve the current cellular organize center to lower ad hoc foundation operation costs and bring back neglected use cases.", "keywords": [ "Drone", "Terminal (telecommunication)", "Aeronautics", "Computer science", "Aerospace engineering", "Engineering", "Telecommunications" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/i3ceet61722.2024.10993565" }, { "id": "https://openalex.org/W4411156987", "doi": "10.2139/ssrn.5199048", "title": "Cost-benefit Analysis of Drone Logistics in the E-commerce Sector", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Ayush Verma", "B Bharath", "Roopa Traisa A", "Adhi Nagdhendhiran", "Naman Shah", "H Adhivkaa", "Sahas", "Gopi" ], "first_author": "Ayush Verma", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "E-commerce", "Business", "Operations management", "Commerce", "Industrial organization", "Computer science", "Economics", "World Wide Web" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5199048" }, { "id": "https://openalex.org/W7162825846", "doi": null, "title": "Learning-Based Optimization For Collaborative Truck-Drone Routing And Scheduling", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Ahmad A. Bany Abdelnabi" ], "first_author": "Ahmad A. Bany Abdelnabi", "venue": "Journal of International Crisis and Risk Communication Research", "publisher": null, "abstract": "Modern delivery networks increasingly rely on innovative technologies such as drones working alongside trucks to meet rising demands for fast and efficient last–mile transportation. Hybrid truck–drone delivery offers strong potential, but its effectiveness depends on careful synchronization between trucks and drones. Although drones provide fast, direct travel, their limited endurance and the need to coordinate launch and recovery with trucks create tightly coupled routing and scheduling interactions. Poor dispatch decisions can cause truck waiting, reduce parallelism, and increase mission completion time. These challenges motivate decision frameworks that explicitly model synchronization and develop scalable methods for coordinated truck–drone operations. This dissertation develops learning– and optimization–based methods for synchronization-aware truck–drone delivery across three decision layers. Study I establishes a foundational one-truck one–drone model formulated as an episodic Markov decision process that preserves core endurance and timing constraints. Exact dynamic programming and a compact MILP are used for benchmarking, and a structured reinforcement learning policy with feasibility masking learns efficient joint decisions in this controlled setting. Study II focuses on execution–layer dispatch when the truck follows a fixed route. An event–driven simulator enforces launch, recovery, and single–operator constraints, and a masked PPO policy learns to reduce makespan consistently relative to strong greedy heuristics, especially when endurance levels make synchronization decisions critical. Study III addresses full planning in multi-truck multi–drone systems with flexible launch and recovery stops. A new integrated MILP formulation supports small–instance benchmarking, and a scalable MILP–assisted ALNS matheuristic solves large instances. Results show that coordinated planning with flexible stops provides significant reductions in makespan, particularly in larger and more spatially dispersed service regions. Together, the three studies provide a coherent toolchain for synchronization–aware decision making in hybrid truck–drone logistics.", "keywords": [ "Markov decision process", "Scalability", "Scheduling (production processes)", "Truck", "Synchronization (alternating current)", "Reinforcement learning", "Job shop scheduling", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1084&context=gradstudies_etd_2026" }, { "id": "https://openalex.org/W7131387031", "doi": "10.48550/arxiv.2602.20310", "title": "A unified solution framework for truck-and-drone routing problems", "year": 2026, "publication_date": "2026-02-23", "type": "preprint", "language": null, "authors": [ "Ke Xu", "John Gunnar Carlsson" ], "first_author": "Ke Xu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Coordinated truck-and-drone routing integrates the high capacity and range of ground vehicles with the flexible routing and speed of drones, enabling simultaneous service. Increasingly applied in last-mile delivery, this synchronization helps reduce completion time and operational costs. To improve its efficiency, various coordination modes between trucks and drones have been proposed. Each mode accommodates diverse operational constraints tailored to particular delivery requirements. In existing work, a slight change in the structural framework or operational characteristics could generate a totally different problem variant, which often requires the design of specialized algorithms. Consequently, under the requirement of maintaining structural validation and adapting to multiple operational features, this paper presents a unified three-phase solution framework based on the Lin-Kernighan-Helsgaun algorithm to solve a wide family of truck-and-drone routing problems. To validate its flexibility and effectiveness, we carry out numerical experiments on three problem variants: the Flying Sidekick Traveling Salesman Problem (FSTSP), the Traveling Salesman Problem with Multiple Drones (TSP-mD), and the Vehicle Routing Problem with Drones (VRP-D), benchmarking each against an effective algorithm. Computational results show that the framework can closely match optimal solutions on small-size instances and even improve the best-known solutions for several medium-size instances. Moreover, additional extensions are discussed to further highlight its versatility.", "keywords": [ "Routing (electronic design automation)", "Drone", "Flexibility (engineering)", "Travelling salesman problem", "Range (aeronautics)", "Synchronization (alternating current)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2602.20310" }, { "id": "https://openalex.org/W7134044303", "doi": "10.11993/j.issn.2096-3920.2025-0083", "title": "Unmanned Aerial Vehicle Aeromagnetic Positioning Method for Nearshore Submarine Cables Based on Power Frequency Magnetic Characteristics", "year": 2026, "publication_date": "2026-02-01", "type": "article", "language": "zh", "authors": [ "Yunkun SUN", "You LI", "Xiangdong CAO", "Mei CHEN", "Lei Zhang", "Mingyue Li", "Jie Zhao", "Qi HAN" ], "first_author": "Yunkun SUN", "venue": "DOAJ (DOAJ: Directory of Open Access Journals)", "publisher": null, "abstract": "Aiming at the technical bottleneck of high-precision detection and positioning of the “last mile” of nearshore submarine cables, this study proposed a comprehensive unmanned aerial vehicle(UAV) aeromagnetic detection method integrating the analysis of power frequency magnetic field characteristics. Firstly, a forward model of the power frequency magnetic field of submarine cables was established, and the propagation and attenuation law of the power frequency magnetic characteristic signal of the cables was revealed through numerical simulation. Secondly, an innovative frequency-domain signal extraction algorithm based on power frequency magnetic characteristics was constructed, effectively improving the recognition accuracy of weak magnetic signals in the background of strong environmental noise. Then, a reverse analytical positioning method combined with the geomagnetic direction was proposed to achieve the meter-level spatial inversion of the direction of submarine cables. The experiment adopted the self-developed ultra-low-altitude(flight altitude of 1 meter) rotorcraft UAV magnetic detection system to conduct actual measurement and verification in the coastal waters of Wenzhou City. The results show that the system effectively conducts aerial magnetic detection operations of power frequency magnetic characteristics under the complex terrain conditions of the intertidal zone. Through comparative analysis, it is found that the power frequency characteristic positioning method has significant advantages over the conventional magnetic anomaly positioning method in the nearshore shallow water area. Its positioning error does not exceed 1.8 meters, and it can accurately track the cable burial path. This research provides a new technical paradigm for the inspection and positioning of submarine cable projects.", "keywords": [ "Magnetic anomaly", "Submarine", "Earth's magnetic field", "Magnetic field", "Underwater", "Power (physics)", "Global Positioning System", "Terrain", "Radar" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doaj.org/article/c652e1d14b1a4514b735b3450ece2996" }, { "id": "https://openalex.org/W4409603992", "doi": "10.55248/gengpi.6.0425.1311", "title": "Cost-Benefit Analysis of Drone Logistics in the E-Commerce Sector", "year": 2025, "publication_date": "2025-04-01", "type": "article", "language": "en", "authors": [ "Mr.Bharath B", "Roopa Traisa A", "Ayush Verma", "Adhi Nagdhendhiran", "Naman Shah", "H Adhivkaa", "Sahas Gopi" ], "first_author": "Mr.Bharath B", "venue": "International Journal of Research Publication and Reviews", "publisher": null, "abstract": "The growth of e-commerce has increased the demand for efficient delivery solutions, highlighting the challenges in traditional last-mile logistics.Drone technology offers a potential alternative by providing faster deliveries and reducing costs.This study performs a Cost-Benefit Analysis (CBA) to evaluate the economic feasibility of using drones for logistics in e-commerce.It focuses on essential factors such as operational costs, efficiency, and scalability.The findings offer insights into the viability of drone deliveries in cost-sensitive markets like India, highlighting both opportunities and challenges.The paper's total length is 14pp for 3rdyr and 19pp for 4thyr.A cover sheet is also required in addition to the research paper.Note that the cover sheet is not included in the page limit.PURPOSE: This paper evaluates the economic feasibility of implementing drone logistics in e-commerce by analyzing its cost-effectiveness, potential benefits, and associated challenges. METHODOLOGY:The study utilizes a Cost-Benefit Analysis (CBA) approach incorporating qualitative and quantitative data.Primary data is gathered through expert interviews and surveys with logistics providers, while secondary data includes industry reports, regulatory frameworks, and existing case studies.The analysis covers capital investments, operational costs, efficiency improvements, and long-term profitability.FINDINGS: Research shows that drone logistics can significantly reduce delivery times and lower operational costs, offering a competitive advantage in ecommerce.Nevertheless, substantial initial investments, regulatory issues, and infrastructure needs are significant barriers to widespread adoption.", "keywords": [ "Drone", "E-commerce", "Business", "Cost–benefit analysis", "Commerce", "Computer science", "World Wide Web" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.55248/gengpi.6.0425.1311" }, { "id": "https://openalex.org/W4405882587", "doi": "10.4103/jme.jme_66_24", "title": "Integrating Drones in India’s Healthcare System: Opportunities and Challenges", "year": 2024, "publication_date": "2024-10-01", "type": "article", "language": "en", "authors": [ "Jitender Gairolla", "Shivashish Dobhal", "Prakamya Gupta", "Dilip Vaishnav", "Meenu Singh" ], "first_author": "Jitender Gairolla", "venue": "Journal of Medical Evidence", "publisher": "Medknow", "abstract": "INTRODUCTION Despite efforts to improve healthcare infrastructure and delivery, achieving universal access to essential health services remains a formidable challenge, especially in hard-to-reach geographical terrain.[1] Unmanned aerial vehicles, commonly known as drones, have the potential to revolutionise the healthcare sector by providing a faster, more reliable and more efficient means of delivering medicines to remote or inaccessible areas.[2-4] Drones have been effectively utilised during the COVID-19 pandemic to transport medical samples, aerial disinfection and vaccinations in certain parts of the world.[5,6] In addition, drones can be used to transport blood from a recognised medical facility to remote towns or areas in a timely manner, thereby cutting down the wastage in healthcare facilities.[7] The evidence supporting the integration of drone technology into the healthcare system is scant in low-income nations, and its application in healthcare is still developing. ADVANTAGES OF USING DRONES IN HEALTHCARE Drones are a highly versatile technology that has revolutionised the healthcare industry and expanded their applications into various healthcare domains broadly categorised into three areas: medical emergency surveillance, laboratory diagnostic tests and pre-hospital emergency care. The primary advantage of using drones for medicine delivery lies in their ability to overcome geographical barriers and reach remote or isolated regions that are challenging for conventional transportation methods.[4] In rural or underserved communities, where healthcare facilities may be limited, drones offer a lifeline by ensuring that essential medicines reach those in need, promoting better health outcomes. The speed of drone deliveries significantly reduces the time it takes for healthcare logistics (blood products, vaccines, laboratory samples and automated external defibrillators) to reach their destination.[7-10] The use of drones in healthcare delivery also contributes to cost-effectiveness. Drone deliveries have been found to be more cost-efficient than traditional ground transportation methods in various scenarios. By reducing costs, healthcare providers can allocate resources more efficiently, potentially lowering the overall cost of healthcare delivery.[11,12] In addition, safety and security are paramount considerations in medicine delivery, and drones offer features that enhance both aspects. Modern drones come equipped with advanced navigation systems, obstacle avoidance technologies and real-time tracking capabilities, ensuring the safe and secure transportation of medicines.[13] Healthcare delivery is being revolutionised by drones, particularly in remote locations. Their compact size enables them to safely and swiftly deliver medication to hard-to-reach areas.[14,15] This innovation may improve access to healthcare worldwide. However, certain administrative conundrums and legal issues need to be resolved to use this technology effectively. It can be operated quickly and safely because it does not need a pilot. Beyond visual line of sight (BVLOS) technology allows drones to go farther distances safely. Drones have been used in emergency conditions such as cardiac arrest situations to improve prehospital care through operator-led cardiopulmonary resuscitation.[16] Drone-based transportation of laboratory specimens for coagulation-based testing, biochemistry and haematology has also been studied and no significant disparities in biochemical composition after drone flights were observed in studies.[12,17] In 2016, To improve blood accessibility during emergencies, Rwanda, a country in Central Africa has partnered with Zipline International Inc. to employ drones to transport blood between medical facilities. A study demonstrated the efficacy of drone-mediated blood transportation, revealing negligible variations within a 24-h timeframe.[18] Another promising avenue for drone technology is the transportation of vaccines, particularly in remote areas, where its utilisation has been observed in diverse geographical contexts, notably during the COVID-19 pandemic.[10,19,20] In addition, forensic science increasingly uses drones to enhance investigative processes and data collection. Being equipped with high-resolution cameras and deployment of various sensors can quickly assess and document virtually any area in question where traditional access is challenging, namely mass casualty incidents, natural disasters, traffic accident sites or a crime scene.[21] Providing an aerial overview it helps to plan and prioritize response, search and rescue operations, locating victims or missing persons and identify potential evidence or points of interest. Reconstructing events and establishing accountability are aided by information gathered from accident scenes, such as skid markings, vehicle positions and injury patterns. Since drones are not appropriate for controlled situations, they have not been used for autopsies. They can, however, deliver medical samples to forensic laboratories, supporting inquiries in far-flung locations. It is not recommended to use drones indoors because of possible downwash effect.[22] INTEGRATION OF DRONE TECHNOLOGY IN INDIA’S HEALTHCARE LANDSCAPE Due to the inherent geographical challenges and limitations in last-mile connectivity, several Indian states such as Meghalaya, Uttarakhand, Arunachal Pradesh and Himachal Pradesh have recently conducted successful field trials using drone technology for the delivery of emergency medical supplies and the transport of diagnostic samples.[5,23,24] India is investigating the application of drones for healthcare delivery, yet it confronts ethical and legal dilemmas. It is imperative to establish frameworks considering privacy, airspace management and regulatory adherence to ensure safe and responsible deployment.[25] Establishing standards for incorporating drones into the healthcare supply chain will require cooperation between", "keywords": [ "Drone", "Health care", "Healthcare system", "Business", "Economic growth", "Biology", "Economics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4103/jme.jme_66_24" }, { "id": "https://openalex.org/W4403753861", "doi": "10.48550/arxiv.2409.14173", "title": "An Evolutionary Algorithm For the Vehicle Routing Problem with Drones with Interceptions", "year": 2024, "publication_date": "2024-09-21", "type": "preprint", "language": "en", "authors": [ "Carlos Pambo", "Jacomine Grobler" ], "first_author": "Carlos Pambo", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The use of trucks and drones as a solution to address last-mile delivery challenges is a new and promising research direction explored in this paper. The variation of the problem where the drone can intercept the truck while in movement or at the customer location is part of an optimisation problem called the vehicle routing problem with drones with interception (VRPDi). This paper proposes an evolutionary algorithm to solve the VRPDi. In this variation of the VRPDi, multiple pairs of trucks and drones need to be scheduled. The pairs leave and return to a depot location together or separately to make deliveries to customer nodes. The drone can intercept the truck after the delivery or meet up with the truck at the following customer location. The algorithm was executed on the travelling salesman problem with drones (TSPD) datasets by Bouman et al. (2015), and the performance of the algorithm was compared by benchmarking the results of the VRPDi against the results of the VRP of the same dataset. This comparison showed improvements in total delivery time between 39% and 60%. Further detailed analysis of the algorithm results examined the total delivery time, distance, node delivery scheduling and the degree of diversity during the algorithm execution. This analysis also considered how the algorithm handled the VRPDi constraints. The results of the algorithm were then benchmarked against algorithms in Dillon et al. (2023) and Ernst (2024). The latter solved the problem with a maximum drone distance constraint added to the VRPDi. The analysis and benchmarking of the algorithm results showed that the algorithm satisfactorily solved 50 and 100-nodes problems in a reasonable amount of time, and the solutions found were better than those found by the algorithms in Dillon et al. (2023) and Ernst (2024) for the same problems.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Travelling salesman problem", "Routing algorithm", "Algorithm", "Mathematical optimization", "Mathematics", "Computer network", "Biology", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2409.14173" }, { "id": "https://openalex.org/W4408696821", "doi": "10.1109/icrisst59181.2024.10921789", "title": "A Review of Trust Models for Flying Ad-hoc Networks", "year": 2024, "publication_date": "2024-03-15", "type": "conference-paper", "language": "en", "authors": [ "Jerrin Joe Francis", "Murali Parmeswaran" ], "first_author": "Jerrin Joe Francis", "venue": null, "publisher": null, "abstract": "FANETs (Flying Ad-hoc networks) are a type of ad-hoc networks that consist of drones communicating with each other and forming a network. Such a network has multiple applications like providing last-mile connectivity for networks like VANETS when either nodes are too far apart or terrain is causing signals to be obstructed. Also, they can be used in areas where a natural disaster has destroyed the network infrastructure to provide connectivity by replacing damaged ground stations. A major issue identified in such networks is the trust of the FANET nodes as the network is highly dynamic and the chances of malicious nodes entering the network are high. As drones have limited processing capability trust models provide a lightweight solution to allow the network to function even with adversarial nodes. This paper describes current research in this field and classifies the different approaches used in the literature.", "keywords": [ "Wireless ad hoc network", "Computer science", "Mobile ad hoc network", "Vehicular ad hoc network", "Computer network", "Telecommunications" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icrisst59181.2024.10921789" }, { "id": "https://openalex.org/W4403987310", "doi": "10.32657/10356/180356", "title": "Fault-tolerant strategies for multi-rotor parcel delivery", "year": 2024, "publication_date": "2024-01-01", "type": "dissertation", "language": "en", "authors": [ "Jun Tan" ], "first_author": "Jun Tan", "venue": null, "publisher": null, "abstract": "Drones have revolutionized various industries, offering versatile applications from surveillance to agriculture and disaster response. Specifically in urban aerial delivery, their agility streamlines parcel transportation, reducing environmental impact. However, deploying drones in cities poses challenges regarding fault tolerance. Mechanical faults like actuator or sensor failures risk stability, while software issues can disrupt control and functionality. Dynamics faults alter flight capabilities and environmental factors like weather impact performance. To counter these, strategies involve mechanical solutions redistributing control inputs or facilitating emergency landings, software solutions ensuring stability despite faults, and introducing redundancy through backup components like actuators, sensors, and power systems, collectively fortifying drone reliability and safety in complex urban settings. With multi-rotors being vulnerable to a spectrum of faults, it presents a challenge to further strengthen them as pivotal assets in aerial delivery by enabling fault tolerance capabilities on top of their existing agility and adaptability. Therefore, the motivation of this thesis is to enhance drones' fault tolerance capabilities on 3 fronts. The first is to address environmental faults in the form of imagery noises that occur during aerial parcel delivery, specifically during landing operations. The second is to address hardware faults in the form of rotor failure during aerial parcel delivery, specifically when the drone is in flight. The last is to address dynamics faults in the form of change in dynamics such as the change in mass, size, and center of mass due to different parcel requirements. Following the motivation, the objective of this thesis is to devise and apply a range of methodologies to systematically tackle faults and failures within an aerial system. This overarching goal can be segmented into three specific sub-objectives. The first is to devise a software solution to address positional inaccuracy due to imagery noise produced by the external interaction during drone landing. The second is to devise a hardware solution to address in-flight rotor failure, a common mechanical fault in drones. And the last is to devise a hardware solution to address the changes in the center of mass, size, and, mass of a parcel, which is classified under dynamics faults. The first objective involves implementing a pixel-based path-planning algorithm to enhance an aerial system's resilience against image noise, allowing precision tasks like landing on moving platforms. This method integrates an extended Kalman filter and Nonlinear Model Predictive Control into the control architecture, proving more effective than existing approaches through experimental validations. The second objective addresses rotor failures on a hexacopter by introducing a geometric morphing mechanism that adjusts rotor arms to redefine thrust location, ensuring near-zero static hover post-failure. The system employs event-triggered sliding mode control and Multivariable Nonlinear Terminal Sliding Mode Control to maintain stability during single or adjacent rotor failures, validated experimentally. Finally, the third objective establishes a decentralized, modular aerial delivery system adaptable to various parcel sizes and masses. It utilizes a decentralized controller with a Graph Neural Network-based policy and a module placement planning workflow driven by neural network-trained measuring scales, showcasing effectiveness in flights with different parcel configurations in experimental tests. The proposed pixel-based path-planning algorithm enables the UAV to land on both static and moving platforms. For moving platforms, the UAV is capable of detecting and tracking the platform as it moves at a speed of 1.5 m/s, maintaining vertical separation, and successfully landing once the landing sequence is triggered. This method demonstrates higher tracking accuracy, achieving about a 75% reduction in RMS error compared to existing literature. Additionally, the morphing hexacopter, equipped with an MNTSMC strategy, can sustain flight even with two adjacent rotor failures - an event that would typically result in the crash of a conventional hexacopter. The event-triggered morphing sequence, powered by a trained feed-forward neural network, can adjust the arms via servos to reach 50% of the desired angle within 0.8 seconds. Further analysis reveals that a higher event-triggering gain leads to a faster morphing response. Moreover, in the decentralized modular aerial delivery system, a dynamic scale with neural network logic can accurately measure parcel parameters, including mass, size, and center of mass, and determine the optimal placement of the modules. Flight experiments show that a shift in the center of mass in a conventional multi-rotor layout significantly impacts the system's flight performance, particularly in terms of power efficiency when analyzing thrust variation. These experiments also demonstrate how modular systems can be reconfigured to recover power efficiency and maneuverability by adjusting rotor placements. Achieving fault tolerance in diverse aerial platforms poses a formidable challenge due to the absence of a universal solution for various scenarios. Nonetheless, prioritizing fault tolerance is crucial for enhancing drone safety and reliability, particularly in densely populated urban environments, and exploring diverse strategies in this regard expands the potential applications of drones across different domains.", "keywords": [ "Rotor (electric)", "Fault tolerance", "Computer science", "Engineering", "Distributed computing", "Reliability engineering", "Mechanical engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.32657/10356/180356" }, { "id": "https://openalex.org/W3138907901", "doi": null, "title": "City logistics and smart delivery", "year": 2021, "publication_date": "2021-03-17", "type": "conference-paper", "language": "en", "authors": [ "Sabine Limbourg" ], "first_author": "Sabine Limbourg", "venue": "Open Repository and Bibliography (University of Liège)", "publisher": "University of Liège", "abstract": "The current and expected growing number of people living and working in cities and the limited space available inside city centres implies a greater exchange of inbound and outbound freight flows between city centres and their surrounding regions. Urban freight transports provide economic benefits to society but are also responsible for negative externalities such as congestion, air and water pollution, climate change, accidents and noise. Access restrictions are one of the most applied measures to control urban traffics in the city's specific areas. A growing use of urban trucks based on electric, hydrogen and hybrid technologies or non-motorized transport such as bikes reduces pollutant emissions, noise and road congestion by making night deliveries and avoiding morning and afternoon peak periods. This seminar's objective is to discuss how to efficiently distribute shipments to customers from several cities by a freight transport operator. This delivery company has several vehicles to carry products to customers or small depots located at different cities' points. We consider the whole distribution network, allowing us to make decisions at firm, delivering companies and small depots level (Aguayo et al., 2020). Then we consider a platform bringing together several freight transport operators. Freight bundling is a central characteristic of the systems in focus. Besides, pricing decisions can be simultaneously integrated by extending a bilevel programming formulation (Tawfik and Limbourg, 2019). Finally, Unmanned Aerial Vehicle, which is also known as a drone, is examined as a possible way to facilitate biomedical transportation. We deal with the drone network design problem for biomedical material transportation. Four location models are developed and applied to Brussels and its periphery with respect to the associated market in terms of biomedical product flows. In the context of separate case studies of scenario-based analysis, the experiments show that charging stations is useful for extending the mission ranges and gaining market share. The results also show the possibility of gradually implementing the bases without requiring any significant changes, such as closing a base (Dhote and Limbourg, 2020).", "keywords": [ "Business", "Computer science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://orbi.uliege.be/handle/2268/258133" }, { "id": "https://openalex.org/W7208817857", "doi": "10.2991/978-94-6239-758-3_40", "title": "Urban Logistics UAV System Planning, Operation and Comprehensive Governance: A Review and Prospects", "year": 2026, "publication_date": "2026-01-01", "type": "book-chapter", "language": "en", "authors": [ "Jiang Lu", "Tao Li", "Mingzhu Shi", "Yachen Lu" ], "first_author": "Jiang Lu", "venue": "Advances in economics, business and management research/Advances in Economics, Business and Management Research", "publisher": "Atlantis Press", "abstract": null, "keywords": [ "Work (physics)", "Key (lock)", "Global Positioning System", "Field (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2991/978-94-6239-758-3_40" }, { "id": "https://openalex.org/W4411275081", "doi": "10.2139/ssrn.5292575", "title": "A Milp and a Genetic Algorithm for The Flying Sidekick Tsp with Variable Drone Speeds", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Federico Michelotto", "Roberto Roberti" ], "first_author": "Federico Michelotto", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Variable (mathematics)", "Computer science", "Genetic algorithm", "Algorithm", "Mathematical optimization", "Mathematics", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5292575" }, { "id": "https://openalex.org/W7166821108", "doi": "10.3390/drones10070502", "title": "A Study on Drone Logistics Delivery Based on Multi-Center Routing", "year": 2026, "publication_date": "2026-07-01", "type": "article", "language": "en", "authors": [ "Yi Yang", "Yujie Fu", "B Wang", "Kaijun Xu", "Weiqi Feng" ], "first_author": "Yi Yang", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid growth in e-commerce demand, increasing pressure on same-day delivery, and rising last-mile logistics costs, UAV-based logistics systems have emerged as a promising solution for efficient transportation in complex environments. In mountainous regions, however, irregular terrain, limited infrastructure accessibility, and strict flight constraints significantly increase the difficulty of logistics planning. To address these challenges, this study proposes a two-layer collaborative optimization framework for multi-center UAV logistics delivery systems. At the lower level, a multi-center site selection model was developed to determine the optimal distribution center locations and assign task areas. A trajectory cost matrix was constructed by comprehensively considering multiple constraints. The model was solved using a hybrid strategy that combines chaotic initialization and local enhancement based on the elite saDE method to improve the Starfish Optimization Algorithm, called the Mixed-Strategy Improved Starfish Optimization Algorithm (MISFOA), thereby generating feasible three-dimensional flight trajectories between local nodes. At the upper level, an improved Adaptive Large Neighborhood Search (IALNS) algorithm is applied to perform UAV mission assignment and route scheduling within each distribution center, based on the trajectory cost matrix pre-calculated at the lower level. The proposed framework achieves effective information exchange and hierarchical coupling between center selection and scheduling at the distribution level, thereby enabling unified optimization of the multi-center location and coordinated dispatch system. Simulation results demonstrate that the proposed method significantly improves delivery efficiency and solution quality in complex mountainous environments while ensuring trajectory feasibility and operational safety. This model provides a scalable and practical optimization framework for low-altitude logistics network planning under complex constraints.", "keywords": [ "Initialization", "Scheduling (production processes)", "Scalability", "Drone", "Distribution center", "Vehicle routing problem", "Trajectory", "Optimization problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/7/502/pdf?version=1782899498" }, { "id": "https://openalex.org/W4400777163", "doi": "10.4018/979-8-3693-3451-5.ch010", "title": "Supply Chain Security, Technological Advancements, and Future Trends", "year": 2024, "publication_date": "2024-06-30", "type": "book-chapter", "language": "en", "authors": [ "Shahad Altamimi", "Qasem S. Abu Al-Haija" ], "first_author": "Shahad Altamimi", "venue": "Advances in information security, privacy, and ethics book series", "publisher": "IGI Global", "abstract": "In the digital era, supply chain security (SCS) is paramount for companies, governments, and consumers. Technological advancements, notably IoT and blockchain, enhance supply chain safety and transparency. Blockchain ensures data integrity and traceability, reducing fraud. IoT devices enable real-time monitoring for proactive risk management. Networked sensors provide asset data for preventive maintenance and route optimization. ML and AI algorithms detect security issues, while UAVs and self-driving vehicles enhance logistics. Automated last-mile delivery mitigates human risk. The chapter explores blockchain's role in SCS, addresses challenges, and discusses future advancements to bolster global resilience.", "keywords": [ "Supply chain", "Business", "Marketing" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3693-3451-5.ch010" }, { "id": "https://openalex.org/W4393322878", "doi": "10.1016/b978-0-12-814462-6.00008-4", "title": "Reverse urban logistics", "year": 2024, "publication_date": "2024-01-01", "type": "book-chapter", "language": "en", "authors": [ "Rosàrio Macário", "Vasco Reis", "Juan Pablo Antún" ], "first_author": "Rosàrio Macário", "venue": "Elsevier eBooks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Business", "Computer science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/b978-0-12-814462-6.00008-4" }, { "id": "https://openalex.org/W4396219905", "doi": "10.4018/979-8-3693-1447-0", "title": "Theories and Practices for Sustainable Urban Logistics", "year": 2024, "publication_date": "2024-04-05", "type": "book", "language": "en", "authors": [ "Lingkon, HI", "Ali, SI" ], "first_author": "Lingkon, HI", "venue": "Advances in logistics, operations, and management science book series", "publisher": "Routledge", "abstract": "Sustainability has become more critical in logistics and transportation, especially in Last-Mile Delivery (LMD). Electronic Vehicles (EVs), Electronic(E)-bicycles, Cargo bikes, and Drones are alternative solutions to the existing Internal Combustion Engine (ICE) vehicles that enable low environmental and economic impact. However, they may require a higher investment and have constraints like battery autonomy, small loading capacity, and higher electricity production to meet the demand. This chapter aims to explore the environmental and economic impact of electronic vehicles, as well as the operational challenges and barriers to achieving sustainability. Hence, a thorough literature review is necessary to understand the scholarly work in the area. The study employs the Systematic Literature Review (SLR) to extract data from the Scopus database from 2019-2023. Thirty-two articles were reviewed. The descriptive and thematic analyses have identified the environmental, economic, and social components related to the distribution methods, delivery methods and perceived sustainability.", "keywords": [ "Business", "City logistics", "Environmental planning", "Process management", "Transport engineering", "Geography", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.4018/979-8-3693-1447-0" }, { "id": "https://openalex.org/W2903628540", "doi": null, "title": "Robotisation of urban freight transport", "year": 2018, "publication_date": "2018-01-01", "type": "book-chapter", "language": "en", "authors": [ "Gwynne van der Kaauwen", "Ron van Duin" ], "first_author": "Gwynne van der Kaauwen", "venue": "Research Repository (Delft University of Technology)", "publisher": "Delft University of Technology", "abstract": "This paper aims to research the developments and innovations in urban freight transport. Thereafter it focuses on the future and implications of robotisation in this field. The developments in urban freight transport from a logistics service provider perspective are based on the constraints space, legitimacy and resources. Governments are slowly starting to focus more on the sustainability of urban freight movement. Though, the progress differs greatly between cities and between European countries. The current innovations in urban freight transport can be divided into five primary categories; transport models, methods, electric vehicles, drones and robotics. Of these five, robotics has the most potential to drastically change the transport field. A combination of robotics with at least one of the other four categories remains possible. Robots can make urban freight movement more sustainable, safer, cheaper, faster and more satisfying for receivers. However, risks of robotics include the replacement of manpower, the lack of noise, additional congestion, and the discrepancy between technology, infrastructure, legislation and other road users. If robotics will be largely implemented, they will first be used for last mile delivery and package deliveries. The acceptance from receivers is pivotal here in order to make logistics service providers willing to apply robots. Governments can steer and stimulate the implementation by creating testing environments, the so-called living labs, and by establishing regulations. The lack of data makes this a tough task for most governments. In order to create a greater market share for robotics, traffic safety must be ensured together with acceptance by all involved stakeholders. Thus, more research is needed on the acceptance of receivers and the necessary government regulations to stimulate the implementation of robotics.", "keywords": [ "Robotics", "Service (business)", "Robot", "Service provider", "SAFER", "Legislation", "Order (exchange)", "Artificial intelligence", "Transport engineering", "Business", "Engineering", "Computer science" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "http://resolver.tudelft.nl/uuid:00624500-6ba4-4e43-a1b5-526d17942b52" }, { "id": "https://openalex.org/W7165631533", "doi": "10.3390/drones10070476", "title": "Data-Driven Optimization of Truck–Drone Collaborative Delivery with Shared Fleet Allocation", "year": 2026, "publication_date": "2026-06-23", "type": "article", "language": "en", "authors": [ "Didem Cicek", "Murat Şimşek", "Burak Kantarcı" ], "first_author": "Didem Cicek", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Truck–drone collaborative delivery (TDCD) refers to a coordinated logistics paradigm in which drones are deployed from delivery trucks to serve nearby customers, enabling parallelized last-mile operations. Much of the existing TDCD literature relies on synthetic datasets and manufacturer-declared drone specifications, which may overestimate performance in real-world operations. This study develops an empirically informed, route-based Mixed-Integer Linear Programming (MILP) framework that integrates empirically derived drone performance models with constrained fleet allocation decisions. Using delivery routes from the Amazon Last Mile Routing Dataset (2021), we consider three electric trucks departing from a common depot, each equipped with drones drawn from a shared fleet of 10 units. Drone flight time and energy consumption are modeled using regression functions calibrated with real flight test data from a DJI Matrice 100 platform, capturing observed variations due to payload and operational conditions. The optimization jointly determines truck stop selection, customer assignments, and drone allocation while minimizing a weighted combination of route makespan, total energy consumption, and fleet size under operational and energy constraints. The results indicate that coordinated truck–drone delivery can achieve substantial reductions in both delivery completion time and energy consumption relative to conventional truck-only delivery. These findings demonstrate the effectiveness of coordinated truck–drone operations under realistic constraints and highlight the importance of data-driven modeling and fleet-level resource allocation in improving last-mile delivery performance.", "keywords": [ "Drone", "Truck", "Payload (computing)", "Resource allocation", "Energy consumption", "Routing (electronic design automation)", "Linear programming", "Last mile (transportation)", "Fuel efficiency" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10070476" }, { "id": "https://openalex.org/W7122348100", "doi": null, "title": "Applications of Precision Flight Drones: From Infrastructure Inspection to Smart Agriculture", "year": 2025, "publication_date": "2025-12-31", "type": "article", "language": "en", "authors": [ "Agus Budiyono", "Masafumi Miwa" ], "first_author": "Agus Budiyono", "venue": null, "publisher": null, "abstract": "The rapid maturation of multi-rotor UAV technology has enabled a new generation of precision flight drones capable of centimeter-level positioning, stable station-keeping, and repeatable autonomous operations. These capabilities are transforming a diverse array of industries by enabling tasks that demand high spatial accuracy, temporal consistency, and operational reliability. This paper presents a comprehensive survey of precision flight drone applications across five major domains: infrastructure inspection, geospatial surveying and mapping, cinematography and media production, precision agriculture, and logistics and delivery. For each application domain, we identify the key performance requirements—including positional accuracy, attitude stability, trajectory repeatability, endurance, and payload capacity—and discuss how recent advances in sensing, navigation, and control technologies address these demands. We examine the role of Real-Time Kinematic (RTK) GNSS, visual-inertial odometry, LiDAR-based localization, and motion-capture systems in achieving the requisite precision. Furthermore, we analyze the trade-offs between accuracy, flight time, cost, and operational complexity that practitioners face when selecting or designing precision flight platforms. Case studies drawn from bridge and power-line inspection, photogrammetric reconstruction, autonomous crop monitoring, and last-mile parcel delivery illustrate the practical impact and economic value of precision flight drones. We also highlight emerging challenges, including regulatory compliance, safety assurance in beyond-visual-line-of-sight (BVLOS) operations, and the integration of precision drones into existing workflows and data pipelines. By synthesizing current best practices and identifying open research questions, this paper aims to guide both researchers and practitioners in leveraging precision flight drones to unlock new capabilities and efficiencies across diverse sectors.", "keywords": [ "Drone", "Payload (computing)", "Geospatial analysis", "Precision agriculture", "Photogrammetry", "Key (lock)", "Workflow", "Global Positioning System" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://ojs.unsysdigital.com/index.php/jias/article/view/1331" }, { "id": "https://openalex.org/W7105924554", "doi": "10.5281/zenodo.17625672", "title": "DRONE LOGISTICS IN PARAGUAY: Exploring the success of Zipline in Rwanda", "year": 2025, "publication_date": "2025-11-16", "type": "article", "language": "en", "authors": [ "Espinoza, Elías" ], "first_author": "Espinoza, Elías", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Abstract Purpose: This paper examines the operational model of Zipline’s drone logistics network in Rwanda to assess the technical feasibility, potential impact, and key requirements for adapting a similar system to enhance healthcare and logistics in Paraguay. Design/methodology/approach: The study employs a comparative case study methodology. It draws on operational data, policy analyses, government documents, and published academic studies to evaluate Zipline's hub-and-spoke architecture, regulatory enablers, and community impact. This framework is then used to analyze Paraguay's readiness, considering its geographic, infrastructural, and regulatory landscape. Findings: The analysis indicates that drone networks can significantly enhance the delivery of time-sensitive medical supplies (e.g., blood, vaccines) and bolster emergency response in areas with challenging road infrastructure, as demonstrated in Rwanda. Successful replication in Paraguay is feasible but requires a tailored regulatory framework for Beyond Visual Line Of Sight (BVLOS) operations, strategic investment in ground infrastructure, sustainable financing models, and robust local stakeholder engagement. Originality/value: This paper provides the first in-depth feasibility analysis of applying the successful Rwandan drone logistics model to Paraguay. It offers a prioritized set of evidence-based recommendations for policymakers, NGOs, and logistics stakeholders to develop a pilot project, addressing specific local challenges and opportunities. Keywords: Drone Logistics, Healthcare Supply Chain, Zipline, Paraguay, Rwanda, Last-Mile Delivery, Health Systems Strengthening Plain Language Summary Getting medical supplies like blood and vaccines to remote areas can be difficult because of mountains, poor roads, or bad weather. A company called Zipline solved this problem in Rwanda by using drones to fly medical products directly to hospitals and clinics, saving lives. This research paper explores how Zipline did it successfully. We then study whether the same idea could work in Paraguay, which also has hard-to-reach rural areas. The paper finds that Paraguay could greatly benefit from using drones for medical deliveries. However, to succeed, it would need to create clear rules for flying drones, build the right support systems on the ground, and work closely with local communities.", "keywords": [ "Drone", "Stakeholder", "Government (linguistics)", "Key (lock)", "Investment (military)", "Humanitarian Logistics", "Health care", "Supply chain" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.17625672" }, { "id": "https://openalex.org/W4412706988", "doi": "10.2139/ssrn.5366294", "title": "Investigating the Operational Feasibility for Drones Using Wind Simulations in Rotterdam", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Andres Morfin Veytia", "Akshay Patil", "Ivan Pađen", "Jacco M. Hoekstra", "Clara García‐Sánchez", "Joost Ellerbroek" ], "first_author": "Andres Morfin Veytia", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Aeronautics", "Environmental science", "Aerospace engineering", "Meteorology", "Rotterdam Study", "Computer science", "Systems engineering", "Engineering", "Physics", "Mathematics", "Statistics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5366294" }, { "id": "https://openalex.org/W3209179234", "doi": "10.1145/3476122.3484845", "title": "DroneStick: Flying Joystick as a Novel Type of Interface", "year": 2021, "publication_date": "2021-12-12", "type": "conference-paper", "language": "en", "authors": [ "Evgeny Tsykunov", "Aleksey Fedoseev", "Ekaterina Dorzhieva", "Ruslan Agishev", "Roman Ibrahimov", "Derek Vasquez", "Luiza Labazanova", "Dzmitry Tsetserukou" ], "first_author": "Evgeny Tsykunov", "venue": null, "publisher": null, "abstract": "DroneStick is a novel hands-free method for smooth interaction between a human and a robotic system via one of its agents, without training and any additional handheld or wearable device or infrastructure. A flying joystick (DroneStick), being a part of a multi-robot system, is composed of a flying drone and coiled wire with a vibration motor. By pulling on the coiled wire, the operator commands certain motions of the follower robotic system. The DroneStick system does not require the user to carry any equipment before or after performing the required interaction. DroneStick provides useful feedback to the operator in the form of force transferred through the wire, translation/rotation of the flying joystick, and motor vibrations at the fingertips. Feedback allows users to interact with different forms of robotic systems intuitively. A potential application can enhance an automated ‘last mile’ delivery when a recipient needs to guide a delivery drone/robot gently to a spot where a parcel has to be dropped.", "keywords": [ "Joystick", "Interface (matter)", "Simulation", "Robot", "Drone", "Wearable computer", "Computer science", "Engineering", "Human–computer interaction", "Artificial intelligence", "Embedded system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2110.09041" }, { "id": "https://openalex.org/W4378805849", "doi": "10.1109/csnt57126.2023.10134749", "title": "DPC: An amalgamated approach for drone package classification system", "year": 2023, "publication_date": "2023-04-08", "type": "conference-paper", "language": "en", "authors": [ "Niharika Niharika", "Raghav Jain", "Pulkit Singla", "Rishabh Sharma", "Vinay Kukreja", "Ramanjeet Singh" ], "first_author": "Niharika Niharika", "venue": null, "publisher": null, "abstract": "The logistics and e-commerce sectors have always incorporated parcel delivery and packing as their integral part. With the high amount of demand and supply in the domain of e-commerce, there is a high demand for drone-based delivery systems through which a certain amount of parcel packages can be delivered using drones. To assist the aforementioned domain, the proposed work aims to develop a drone package classification (DPC) system for the classification of packages according to their weight and the type of drone that can be employed to deliver the particular class of packages. The complete experiment has been executed using DL based hybrid approach consisting of convolutional neural network (CNN) and long short-term memory (LSTM) technique on a self-developed package5k dataset resulting in an overall accuracy of 93.82%. The entire investigation and suggested DL-based hybrid strategy have shown to be a successful, efficient, and sustainable method for the identification and classification of drone packages to create a successful and sustainable DPC system.", "keywords": [ "Drone", "Computer science", "Domain (mathematical analysis)", "Convolutional neural network", "Identification (biology)", "Class (philosophy)", "Artificial intelligence", "Embedded system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/csnt57126.2023.10134749" }, { "id": "https://openalex.org/W4297626324", "doi": "10.1061/9780784484265.290", "title": "An Exact Solution Method for Truck-Assisted Drone Routing Problem", "year": 2022, "publication_date": "2022-09-08", "type": "conference-paper", "language": "en", "authors": [ "Shuang Wang", "Sebastian Wandelt", "Xiaoqian Sun" ], "first_author": "Shuang Wang", "venue": "CICTP 2022", "publisher": null, "abstract": "Drone-truck combined delivery has huge potential for becoming an innovative last-mile solution for logistics. This paper presents a variation of drone-truck operation with the goal to minimize the completion time, where the truck provides a mobile recharging station for drones and drones serve all the customers. This problem can be decomposed into two elementary phases: (1) truck with a drone arriving at rendezvous nodes and (2) drone flying to the customer points. Our exact method is based on branch-and-bound, in which a tree node represents a customer subsequence and the lower bound is the objective value with optimal launch and retrieve locations for the fixed customer sequence. The best departing and collection sites for each tree node are obtained through custom cuts, which consider a customer-based pruning strategy and the interaction-based pruning between customers. Our experimental results show that the novel exact algorithm outperforms CPLEX in terms of computational time, while obtaining results on par regarding quality.", "keywords": [ "Drone", "Pruning", "Computer science", "Truck", "Subsequence", "Routing (electronic design automation)", "Tree (set theory)", "Node (physics)", "Mathematical optimization", "Operations research", "Real-time computing", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1061/9780784484265.290" }, { "id": "https://openalex.org/W4385622307", "doi": "10.1201/9781003261513", "title": "Urban Freight Analytics", "year": 2023, "publication_date": "2023-08-07", "type": "book", "language": "en", "authors": [ "Eiichi Taniguchi", "Russell George Thompson", "Ali Gul Qureshi" ], "first_author": "Eiichi Taniguchi", "venue": null, "publisher": null, "abstract": "Urban Freight Analytics examines the key concepts associated with the development and application of decision support tools for evaluating and implementing city logistics solutions. New analytical methods are required for effectively planning and operating emerging technologies including the Internet of Things (IoT), Information and Communication Technologies (ICT), and Intelligent Transport Systems (ITS). The book provides a comprehensive study of modelling and evaluation approaches to urban freight transport. It includes case studies from Japan, the US, Europe, and Australia that illustrate the experiences of cities that have already implemented city logistics, including analytical methods that address the complex issues associated with adopting advanced technologies such as autonomous vehicles and drones in urban freight transport. Also considered are future directions in urban freight analytics, including hyperconnected city logistics based on the Physical Internet (PI), digital twins, gamification, and emerging technologies such as connected and autonomous vehicles in urban areas. An integrated modelling platform is described that considers multiple stakeholders or agents, including emerging organisations such as PI companies and entities such as crowd-shippers as well as traditional stakeholders such as shippers, receivers, carriers, administrators, and residents. This book Presents procedures for evaluating city logistics technologies and policy measures Provides an overview of advanced modelling approaches, including agent-based model and machine learning Highlights the essential features of optimisation and simulation models applied to city logistics Discusses how models incorporating more uncertainty and dynamic data can be used to improve the sustainability and resilience of urban freight systems The book is ideal for graduate students in civil and environmental engineering and logistics management, urban planners, transport engineers, and logistics specialists.", "keywords": [ "Analytics", "Transport engineering", "Business", "Computer science", "Data science", "Engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1201/9781003261513" }, { "id": "https://openalex.org/W4415576532", "doi": "10.25144/14188", "title": "DRONE NOISE IN MY BACKYARD THE CHALLENGES FOR PUBLIC ACCEPTABILITY", "year": 2022, "publication_date": "2022-11-21", "type": "conference-paper", "language": null, "authors": [ "Roalt Aalmoes", "MÍRIAM GONÇALVES DE CASTRO", "Naomi Sieben", "Rui Roosien" ], "first_author": "Roalt Aalmoes", "venue": null, "publisher": null, "abstract": "The introduction of drones in urban areas for surveillance, parcel delivery services, air taxis, or other services have raised the issue of public acceptability.How can this concept known as Urban Air Mobility (UAM) be successfully introduced in an area without upsetting communities?And how can currently unknown benefits of these services be compared to anticipated drawbacks?Noise impact is already considered to be one of the main concerns for successful introduction of UAM, but focusing on noise levels exclusively may not be enough.Based on recent research on noise annoyance and how it affects individuals and communities, a holistic approach, including noise impact, as well as non-acoustical factors, is promoted to address the annoyance towards these disruptive air vehicles.Other subjective measures should be considered including demographic factors, as well as perceptional factors, such as the visual environment where these vehicles operate, and emotional factors such as attitude towards drones and air taxis.Using this approach, studies on noise impact of UAM will be able to evaluate the use cases in their intended setting and with the appropriate target communities to assess the true impact and define the real challenges to overcome for noise research in the coming decade.", "keywords": [ "Drone", "Noise (video)", "Key (lock)", "Background noise" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.ioa.org.uk/system/files/proceedings/r_aalmoes_mt_castro_n_sieben_r_roosien_drone_noise_in_my_backyard_the_challenges_for_public_acceptability.pdf" }, { "id": "https://openalex.org/W7125532418", "doi": "10.6084/m9.figshare.31136073", "title": "How assumptions shape sustainability – a scenario-based life cycle Assessment of delivery drones", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Hannah Aster", "Lukas Zeilerbauer", "Johannes Lindorfer" ], "first_author": "Hannah Aster", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "Unmanned aerial vehicles (UAVs) are gaining increasing interest, among others, in parcel delivery. As this market grows, thorough analyses of their environmental impacts are essential for sustainability-oriented decisions. Life Cycle Assessment (LCA) is a commonly used tool in such analyses. In this study, we used LCA to evaluate the environmental impacts of a delivery drone across its entire life cycle for 18 impact categories. A baseline scenario was defined and an extensive sensitivity analysis was conducted to investigate more parts replacement frequencies and electricity mixes. Results show that the climate change impact is 3.08 g CO2-eq/kg*km for the baseline scenario and 8.73 g CO2-eq/kg*km for a scenario with more frequent replacements. Lower, but still significant differences appear for different electricity mixes assumed for battery charging. Additionally, the choice of the LCA system model (cut-off or consequential) significantly alters the LCA results, particularly for end-of-life battery treatment. Batteries had a high environmental impact share in all analysed scenarios. Depending on the underlying assumptions, the charging electricity, frame, and rotor blades also exhibit significant environmental impacts. These findings highlight the importance of optimising battery use, implementing sustainable materials, and considering recyclable components to reduce the environmental impact of drone deliveries.", "keywords": [ "Life-cycle assessment", "Drone", "Baseline (sea)", "Sustainability", "Environmental impact assessment", "Electricity", "Battery (electricity)", "Impact assessment" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31136073" }, { "id": "https://openalex.org/W4413882306", "doi": "10.3390/math13172798", "title": "Neural Network-Based Air–Ground Collaborative Logistics Delivery Path Planning with Dynamic Weather Adaptation", "year": 2025, "publication_date": "2025-08-31", "type": "article", "language": "en", "authors": [ "Linglin Feng", "Hongmei Cao" ], "first_author": "Linglin Feng", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty.", "keywords": [ "Adaptation (eye)", "Artificial neural network", "Motion planning", "Computer science", "Path (computing)", "Environmental science", "Meteorology", "Operations research", "Engineering", "Computer network", "Artificial intelligence", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2227-7390/13/17/2798/pdf?version=1756624249" }, { "id": "https://openalex.org/W7154575102", "doi": "10.22214/ijraset.2026.79985", "title": "Optimized Drone Based Routing System for Smart Cities", "year": 2026, "publication_date": "2026-04-16", "type": "article", "language": null, "authors": [ "Ajay B V" ], "first_author": "Ajay B V", "venue": "International Journal for Research in Applied Science and Engineering Technology", "publisher": "International Journal for Research in Applied Science and Engineering Technology (IJRASET)", "abstract": "This paper presents an AI-driven drone routing system for smart cities that optimizes delivery paths in real-time. Using Dijkstra’s Algorithm, A* Search, and Reinforcement Learning, the system considers distance, battery capacity, obstacles, weather, and no-fly zones. The proposed system reduces delivery time by 40–60% and energy consumption by 25–35% compared to traditional ground-based delivery. Simulation results demonstrate significant improvements in operational efficiency and cost reduction for urban logistics.", "keywords": [ "Drone", "Routing (electronic design automation)", "Energy consumption", "Battery (electricity)", "Delivery system", "Reduction (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.22214/ijraset.2026.79985" }, { "id": "https://openalex.org/W4406243733", "doi": "10.5038/cutr-nicr-y2-1-5", "title": "Multimodal Strategies for Mitigating Congestion from Urban Parcel Delivery", "year": 2024, "publication_date": "2024-10-01", "type": "report", "language": "en", "authors": [ "Hansen, Mark", "Lin, Pei-Sung", "Wang, Zhenyu", "Zhang, Yu", "Li, Ang" ], "first_author": "Hansen, Mark", "venue": null, "publisher": null, "abstract": "The explosive growth in e-commerce, the increasing urgency of de-carbonization, the rapid advances in UAV technology, and the continuous disruptive development of the gig economy create needs and opportunities for dramatic improvements in urban package delivery. E-commerce may greatly increase the demand for such deliveries, traditionally made by truck and van, as urban residents substitute purchases made over the Internet for those acquired in brick-and-mortar stores. To mitigate the congestion impact of truck and van traffic, as well as reduce costs and travel times, last-mile delivery should in many cases be shifted toward non-motorized modes (e.g., walkers and bicyclists) and UAVs. While there is literature on how to optimally configure urban delivery systems composed of these modes, most of the research in this area does not consider these problems in the context of urban congestion. In addition to the familiar issues of urban street congestion, in the future, we may also see congestion above the city from UAV traffic, as the use of these vehicles for urban package delivery and other purposes intensifies. In this project, we develop a suite of multimodal, congestion-sensitive strategies for urban delivery, by integrating traditional motorized vehicles, non-motorized modes, and UAVs. Building on prior research for modelling and managing urban road congestion as well as logistics studies for UAV route optimization and scheduling, we develop new models that combine UAVs with other modes for integrated and coordinated urban package delivery. The impacts of the new multimodal strategies on roadway operations and safety are evaluated.", "keywords": [ "Computer science", "Transport engineering", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5038/cutr-nicr-y2-1-5" }, { "id": "https://openalex.org/W7105884880", "doi": "10.5281/zenodo.17625673", "title": "DRONE LOGISTICS IN PARAGUAY: Exploring the success of Zipline in Rwanda", "year": 2025, "publication_date": "2025-11-16", "type": "article", "language": "en", "authors": [ "Espinoza, Elías" ], "first_author": "Espinoza, Elías", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Abstract Purpose: This paper examines the operational model of Zipline’s drone logistics network in Rwanda to assess the technical feasibility, potential impact, and key requirements for adapting a similar system to enhance healthcare and logistics in Paraguay. Design/methodology/approach: The study employs a comparative case study methodology. It draws on operational data, policy analyses, government documents, and published academic studies to evaluate Zipline's hub-and-spoke architecture, regulatory enablers, and community impact. This framework is then used to analyze Paraguay's readiness, considering its geographic, infrastructural, and regulatory landscape. Findings: The analysis indicates that drone networks can significantly enhance the delivery of time-sensitive medical supplies (e.g., blood, vaccines) and bolster emergency response in areas with challenging road infrastructure, as demonstrated in Rwanda. Successful replication in Paraguay is feasible but requires a tailored regulatory framework for Beyond Visual Line Of Sight (BVLOS) operations, strategic investment in ground infrastructure, sustainable financing models, and robust local stakeholder engagement. Originality/value: This paper provides the first in-depth feasibility analysis of applying the successful Rwandan drone logistics model to Paraguay. It offers a prioritized set of evidence-based recommendations for policymakers, NGOs, and logistics stakeholders to develop a pilot project, addressing specific local challenges and opportunities. Keywords: Drone Logistics, Healthcare Supply Chain, Zipline, Paraguay, Rwanda, Last-Mile Delivery, Health Systems Strengthening Plain Language Summary Getting medical supplies like blood and vaccines to remote areas can be difficult because of mountains, poor roads, or bad weather. A company called Zipline solved this problem in Rwanda by using drones to fly medical products directly to hospitals and clinics, saving lives. This research paper explores how Zipline did it successfully. We then study whether the same idea could work in Paraguay, which also has hard-to-reach rural areas. The paper finds that Paraguay could greatly benefit from using drones for medical deliveries. However, to succeed, it would need to create clear rules for flying drones, build the right support systems on the ground, and work closely with local communities.", "keywords": [ "Drone", "Stakeholder", "Government (linguistics)", "Key (lock)", "Investment (military)", "Humanitarian Logistics", "Health care", "Supply chain" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.17625673" }, { "id": "https://openalex.org/W4401441939", "doi": "10.1109/icacite60783.2024.10617414", "title": "The Development of Skyborne Kind of Base Stations for the Communication in 5G as well as 6G Networks", "year": 2024, "publication_date": "2024-05-14", "type": "conference-paper", "language": "en", "authors": [ "J Thenmozhi", "Md Naim Ansari", "A L. Arumugan", "Laith H. Jasim Alzubaidi", "J P Ranjusha", "P. Nagaveni", "S P V Subba Rao" ], "first_author": "J Thenmozhi", "venue": null, "publisher": null, "abstract": "In recent times, breakthroughs in collaboration and internet communications, combined with a rising demand for high-fidelity, a low-late last-mile connection, illustrate the pressing requirement for highly efficient sharing methods. This study dives into the exploration during utilizing wireless-enabled drones as flying base stations within a framework of the next-generation cellular networks. The rise of Flying Ad Hoc Communities (FANETs), acting as groups of movable relays to flexibly spread broadband connection, presents an enticing possibility in the world of next-generation high-availability telecommunications. The details of mobility, switching efficiency, energy administration, optimal placement, node location, and related multi-objective improvements are carefully examined, building the basic structure of this study. The focus stretches to improving the current wireless network core to support novel use cases and reduce the running costs that accompany diversity ad hoc deployments. Through an indepth review, this paper aims at bringing novel insights to the field, leaving light on avenues for enhancement for cellular infrastructure to foster seamless incorporation of cellular-enabled unmanned aerial vehicles as pivotal components in the rapidly changing landscape of the next-generation communication networks.", "keywords": [ "Base station", "Computer science", "Base (topology)", "Telecommunications", "Computer network", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/icacite60783.2024.10617414" }, { "id": "https://openalex.org/W4387389888", "doi": "10.48550/arxiv.2310.02542", "title": "Tightly Joining Positioning and Control for Trustworthy Unmanned Aerial Vehicles Based on Factor Graph Optimization in Urban Transportation", "year": 2023, "publication_date": "2023-10-04", "type": "preprint", "language": "en", "authors": [ "Peiwen Yang", "Weisong Wen" ], "first_author": "Peiwen Yang", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned aerial vehicles (UAV) showed great potential in improving the efficiency of parcel delivery applications in the coming smart cities era. Unfortunately, the trustworthy positioning and control algorithms of the UAV are significantly challenged in complex urban areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, for example, the system dynamics of the control can largely help with the positioning, this paper proposed a joint positioning and control method (JPCM) based on factor graph optimization (FGO), which combines sensors' measurements and control intention. In particular, the positioning measurements are formulated as the factors in the factor graph model, such as the positioning from the GNSS. The model predictive control (MPC) is also formulated as the additional factors in the factor graph model. By solving the factor graph contributed by both the positioning factor and the MPC-based factors, the complementariness of positioning and control can be fully explored. To guarantee reliable system dynamic parameters, we validate the effectiveness of the proposed method using a simulated quadrotor system which showed significantly improved trajectory following performance. To benefit the research community, we open-source our code and make it available at https://github.com/RoboticsPolyu/IPN_MPC.", "keywords": [ "GNSS applications", "Computer science", "Factor graph", "Positioning system", "Trajectory", "Graph", "Model predictive control", "Satellite system", "Real-time computing", "Global Positioning System", "Control (management)", "Simulation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2310.02542" }, { "id": "https://openalex.org/W3163444740", "doi": null, "title": "DLR Aviation and Space Psychology Findings on Drone Acceptance and Noise Concerns", "year": 2019, "publication_date": "2019-07-01", "type": "conference-paper", "language": "en", "authors": [ "Hinnerk Eißfeldt" ], "first_author": "Hinnerk Eißfeldt", "venue": "elib (German Aerospace Center)", "publisher": "Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)", "abstract": "Drones are becoming ever more present in public perception. Ranging from parcel delivery to wildlife protection, from precision farming to law enforcement, and from industrial inspection to digital fireworks, many applications are said to have market-changing potential. Against this background, nations and institutions around the world are trying to keep up with the dynamic development concerning rules and regulations. Since all of the parties involved anticipate a strong increase in both the number of drones and their range of uses, there is a rising interest in the acceptance of civil drones in the public. Widespread public acceptance can promote the dissemination of new technologies. Conversely, concerns among citizens about the use of drones in their daily environment could pose potential barriers to the further proliferation of civil drones, especially in urban areas. The psychoacoustic properties of drones have repeatedly been discussed as being one such limiting factor. This talk reports results of a representative national study on the social acceptance of civilian drones in Germany, taking a closer look at noise considerations. Therefore the results help improve understanding of the perception of civil unmanned aerial vehicles.", "keywords": [ "Drone", "Public space", "Enforcement", "Law enforcement", "Perception", "Engineering", "Civil aviation", "Public relations", "Computer security", "Business", "Political science", "Advertising" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://elib.dlr.de/128802/" }, { "id": "https://openalex.org/W4413954370", "doi": "10.1049/icp.2025.2021", "title": "Development of a model to assess the inclusion of supercapacitors in battery powered drones", "year": 2025, "publication_date": "2025-08-01", "type": "conference-paper", "language": "en", "authors": [ "Ben FitzPatrick", "Nick Wright", "Peter Malkin" ], "first_author": "Ben FitzPatrick", "venue": "IET conference proceedings.", "publisher": "Institution of Engineering and Technology", "abstract": "Last-mile delivery utilising all-electric unmanned vertical take-off drones is an expanding area of interest but is limited by the energy density of batteries. This is partly due to power required and energy spent during take-off, where a significant proportion of the energy consumed over multiple flights is expended, resulting in a substantial amount of the re-charge time being dedicated to re-supplying the energy to provide vertical take-off. Supercapacitors have been identified as a possible technology that could provide potential benefits in this area, providing the energy for the take-offs during a mission profile before being quickly re-charged at the main base while a new package is collected, thus reserving more of the battery for level flight.", "keywords": [ "Supercapacitor", "Drone", "Battery (electricity)", "Inclusion (mineral)", "Computer science", "Engineering", "Psychology", "Chemistry", "Power (physics)", "Biology", "Physics", "Capacitance" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1049/icp.2025.2021" }, { "id": "https://openalex.org/W7204784743", "doi": "10.3390/drones10090658", "title": "Joint Fleet Sizing and Routing for Multi-Truck–Multi-Drone Collaborative Delivery", "year": 2026, "publication_date": "2026-08-28", "type": "article", "language": "en", "authors": [ "Fengjie Xie", "Guojin Zhang", "Yuhua Jia" ], "first_author": "Fengjie Xie", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Truck–multi-drone collaborative delivery can reduce last-mile costs, but fleet sizing and routing are often optimized separately, making it difficult to match resources with demand under a delivery-period constraint. This study addresses the scenario of collaborative delivery involving multiple trucks and multiple drones by constructing a two-stage optimization framework that integrates fleet sizing and route planning. In the first stage, queueing models and continuous approximation are employed to determine the initial configuration of trucks and drones based on demand intensity and delivery cycle constraints. The second stage introduces continuous drone delivery and cross-vehicle retrieval to enhance the flexibility of truck–drone collaboration; while optimizing collaborative routes, the framework adjusts the allocation of trucks and drones—adding or reducing resources based on route feasibility and equipment utilization—thereby achieving the joint optimization of transport capacity and collaborative routes with the objective of minimizing total system costs. A node–resource–flow-separated three-chain encoding and an adaptive large neighborhood search–simulated annealing algorithm are designed to solve the model. Multi-scale numerical experiments show that, compared with four simplified fleet-sizing strategies, the proposed framework achieves average cost savings of 15.4–15.5% for medium- and large-scale instances. The results reveal an economic saturation point of the delivery period that shifts with node scale and a non-monotonic relationship between fleet size and coordination efficiency. The framework supports demand-driven fleet configuration and provides operational guidance for cost-effective truck–drone last-mile delivery.", "keywords": [ "Sizing", "Truck", "Flexibility (engineering)", "Simulated annealing", "Queueing theory", "Routing (electronic design automation)", "Drone", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10090658" }, { "id": "https://openalex.org/W4327614118", "doi": "10.14416/j.asep.2023.03.002", "title": "Integration of Three Vehicle Fleet Types for Delivering Relief Supplies During a Natural Disaster", "year": 2023, "publication_date": "2023-03-16", "type": "article", "language": "en", "authors": [ "Patchara Kitjacharoenchai", "Peerapol Sittivijan" ], "first_author": "Patchara Kitjacharoenchai", "venue": "Applied Science and Engineering Progress", "publisher": null, "abstract": "The combined use of trucks and drones in last-mile delivery offers a more efficient and faster way to make deliveries from an operational standpoint. In this paper, we propose a new routing model that combines different vehicle fleets, including hybrid trucks, traditional trucks, and large drones, to deliver packages from a depot to different destinations cooperatively. This research will give us a better understanding of this drone logistics application, particularly in routing optimization. It can be further implemented to mitigate the impacts of natural disasters, mainly earthquakes, flooding, and landslides. This research aims to study the possibility of using drones to deliver relief supplies such as food, water, and medicine for humanitarian purposes during natural disaster periods to find the best possible route to directly reach the destination and minimize the flying time in the air. We develop a Mixed Integer Programming (MIP) formulation to solve the I-VRPD optimally on a simulated small-scale problem and conduct a case study in one of the most affected regions by natural disasters. The numerical analysis demonstrates an improvement in the delivery time using three experiments that include testing the model on a set of benchmark problems and a case study based on the real scenario. The results show that the delivery time of the proposed model with the integration of three types of vehicle fleets can outperform the operation performed by a single-vehicle fleet by a significant percentage.", "keywords": [ "Truck", "Drone", "Vehicle routing problem", "Computer science", "Natural disaster", "Operations research", "Integer programming", "Benchmark (surveying)", "Transport engineering", "Flooding (psychology)", "Routing (electronic design automation)", "Humanitarian Logistics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.14416/j.asep.2023.03.002" }, { "id": "https://openalex.org/W7135612075", "doi": null, "title": "A Drone Fleet Optimization Model for Distribution of Relief Items Under Emergency Conditions in Developing Countries", "year": 2017, "publication_date": "2017-01-01", "type": "conference-paper", "language": "en", "authors": [ "Boualem Rabta", "Christian Wankmüller", "Gerald; id_orcid 0000-0001-7560-3410 Reiner" ], "first_author": "Boualem Rabta", "venue": "WU Research", "publisher": null, "abstract": "Humanitarian assistance operates under conditions characterized by the collapse of health facilities, the disruption of health systems and the breakdown of already on-going treatments in case of emergency. In addition to these circumstances, aid agencies in developing countries are often confronted with poor or non-existent infrastructure that is further disrupted in case of disasters, i.e., destroyed roads and debris-covered areas which hinder medical teams in reaching remote locations. As the supply via trucks and helicopters is not applicable in this situation, alternative means of transport have to be considered. Unmanned aerial vehicles (UAVs) are receiving increased attention by humanitarian organizations as they can help overcoming last-mile distribution problems, i.e., inaccessibility to cut-off regions. This paper considers drone applications in last-mile distribution in humanitarian logistics and presents an optimization model for the delivery of multiple packages of light-weight relief items (e.g. vaccine, water purification tablets, etc.) via drones to a certain number of remote locations within a disaster prone area. The objective of the model is to minimize the total travelling distance (or time/cost) of the drone under payload and energy constraints while recharging stations are installed to allow the extension of the operating distance of the drone. The implementation of different priority policies is discussed. The model is solved as a mixed integer linear program and illustrated numerically with different scenarios.", "keywords": [ "Drone", "Payload (computing)", "Truck", "Humanitarian aid", "Humanitarian Logistics", "Distribution (mathematics)", "Linear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://research.wu.ac.at/de/publications/712a1bb7-621a-43a9-adad-1aff23a8b548" }, { "id": "https://openalex.org/W4410975944", "doi": "10.1093/imaman/dpaf022", "title": "New saving-based formulae for the truck–drone routing problem", "year": 2025, "publication_date": "2025-06-03", "type": "article", "language": "en", "authors": [ "Batool Madani", "Malick Ndiaye", "Saı̈d Salhi" ], "first_author": "Batool Madani", "venue": "IMA Journal of Management Mathematics", "publisher": "Oxford University Press", "abstract": "Abstract Accepted by: M. Zied Babai This study investigates the hybrid truck–drone routing problem with an emphasis on last-mile delivery. To address this challenge, novel savings rules are developed by extending the classical Clarke and Wright savings formula, and these are incorporated into an adapted savings-based heuristic. In contrast to the traditional Clarke and Wright algorithm, the drone launch and retrieval points are not predetermined but instead depend on the truck’s route, resulting in greater complexity and flexibility. Consequently, nine distinct configurations are identified, each associated with a specific savings formulation. These contribute to the development of an enhanced Clarke and Wright-based heuristic, further augmented with a computational acceleration mechanism. For comparative evaluation, a sweep-based algorithm is also implemented to handle larger problem instances. The proposed savings-based heuristic (MSH) demonstrates superior performance over the sweep-based method, effectively capturing the operational structure of a moving depot. While the new savings equations remain relatively simple, they offer analytically rigorous and computationally efficient solutions that can serve as a strong foundation for future hybrid or metaheuristic approaches.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Operations research", "Aeronautics", "Automotive engineering", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1093/imaman/dpaf022" }, { "id": "https://openalex.org/W7167706259", "doi": "10.1007/978-3-032-11306-1_16", "title": "A Sustainable PV Drone Design Based Delivery Systems", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Mais Alzgool", "Musab B. Alzghoul" ], "first_author": "Mais Alzgool", "venue": "Advances in Science, Technology & Innovation/Advances in science, technology & innovation", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Photovoltaic system", "Renewable energy", "Efficient energy use", "Environmentally friendly", "Key (lock)", "Sustainability", "Sustainable development" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-11306-1_16" }, { "id": "https://openalex.org/W4416874775", "doi": "10.1109/qce65121.2025.10478", "title": "Quantum Optimization for Solving Coordinated Truck–Drone Routing Problem", "year": 2025, "publication_date": "2025-08-30", "type": "conference-paper", "language": "en", "authors": [ "Ahmad Bassaleh", "Syed Farhan Ahmad", "Ramkumar Harikrishnakumar", "Gregory T. Byrd", "Bayram Yildirim" ], "first_author": "Ahmad Bassaleh", "venue": null, "publisher": null, "abstract": "The integration of drones into last-mile delivery operations has the potential to significantly enhance responsiveness and reduce delivery costs. In this paper, we study the Coordinated Truck-Drone Routing Problem (CTDRP), where a truck serves as mobile base station that launches and retrieves a drone to deliver to geographically dispersed customers. Some customers are served by the truck, while others are served by the drone. The objective is to optimize the truck route and drone assignments in order to minimize the total delivery distance, including both ground and aerial travel. We model the CTDRP as a combinatorial optimization problem and solve it using the Quantum Approximate Optimization Algorithm (QAOA), a variational hybrid quantum-classical algorithm that offers promising capabilities for discrete optimization. The proposed method is benchmarked against classical solutions obtained using the Gurobi optimizer, and the results show that QAOA delivers competitive performance. These findings demonstrate the applicability of QAOA in solving integrated aerial-ground logistics problems and support its potential for advancing quantum-enhanced transportation systems.", "keywords": [ "Drone", "Routing (electronic design automation)", "Truck", "Optimization problem", "Order (exchange)", "Vehicle routing problem", "Quantum" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/qce65121.2025.10478" }, { "id": "https://openalex.org/W7125530218", "doi": "10.6084/m9.figshare.31136073.v1", "title": "How assumptions shape sustainability – a scenario-based life cycle Assessment of delivery drones", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Hannah Aster", "Lukas Zeilerbauer", "Johannes Lindorfer" ], "first_author": "Hannah Aster", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "Unmanned aerial vehicles (UAVs) are gaining increasing interest, among others, in parcel delivery. As this market grows, thorough analyses of their environmental impacts are essential for sustainability-oriented decisions. Life Cycle Assessment (LCA) is a commonly used tool in such analyses. In this study, we used LCA to evaluate the environmental impacts of a delivery drone across its entire life cycle for 18 impact categories. A baseline scenario was defined and an extensive sensitivity analysis was conducted to investigate more parts replacement frequencies and electricity mixes. Results show that the climate change impact is 3.08 g CO2-eq/kg*km for the baseline scenario and 8.73 g CO2-eq/kg*km for a scenario with more frequent replacements. Lower, but still significant differences appear for different electricity mixes assumed for battery charging. Additionally, the choice of the LCA system model (cut-off or consequential) significantly alters the LCA results, particularly for end-of-life battery treatment. Batteries had a high environmental impact share in all analysed scenarios. Depending on the underlying assumptions, the charging electricity, frame, and rotor blades also exhibit significant environmental impacts. These findings highlight the importance of optimising battery use, implementing sustainable materials, and considering recyclable components to reduce the environmental impact of drone deliveries.", "keywords": [ "Life-cycle assessment", "Drone", "Baseline (sea)", "Sustainability", "Environmental impact assessment", "Electricity", "Battery (electricity)", "Impact assessment" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31136073.v1" }, { "id": "https://openalex.org/W2750076254", "doi": null, "title": "Strategic Network Design for Delivery by Drones under Service-based Competition", "year": 2017, "publication_date": "2017-08-14", "type": "dissertation", "language": "en", "authors": [ "Gohram" ], "first_author": "Gohram", "venue": "UWSpace (University of Waterloo)", "publisher": "University of Waterloo", "abstract": "In today’s world, E-commerce is a fast growing industry and e-retailers are looking for innovative ways to deliver customer orders within short delivery times at a low cost. Currently, the use of drone technology for last-mile delivery is being developed by such companies as Amazon, FedEx, and UPS. Drones are relatively cheaper and faster than trucks but are limited in range and may be restricted in landing and takeoff. Most of the work in the Operations Research literature focusses on the operational challenges of integrating drones with truck delivery. The more strategic questions of whether it is economically feasible to use drones and the effects on distribution network design are rarely addressed. These questions are the focus of this work. We consider an e-retailer offering multiple same day services using both existing vehicles and drones, and develop a facility location problem under service-based competition where the services offered by the e-retailer not only compete with the stores (convenience, grocery, etc.), but also with each other. The competition in the market is incorporated using the Multinomial Logit (MNL) market share model. To solve the resulting nonlinear mathematical formulation we develop a novel logic-based Benders decomposition approach. We also show that the nonlinear model can be transformed into a linear mixed integer formulation. Computational experiments show that our algorithm outperforms direct solution of the linear formulation. We carry out extensive numerical testing of the model and perform sensitivity analyses over pricing, delivery time, government regulations, technological limitations, customer behavior, and market size. The results show that government regulations play a vital role in determining the future of drone delivery.", "keywords": [ "Drone", "Competition (biology)", "Business", "Service (business)", "Service delivery framework", "Engineering", "Marketing", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10012/12140" }, { "id": "https://openalex.org/W7161929073", "doi": null, "title": "Research on the Synergistic Development Model of Unmanned Aerial Vehicle Logistics and Traditional Air Logistics under the Background of Low-altitude Economy", "year": 2026, "publication_date": "2026-01-01", "type": "dissertation", "language": "en", "authors": [ "Yurou Huang" ], "first_author": "Yurou Huang", "venue": "Theseus (Ammattikorkeakoulujen)", "publisher": null, "abstract": "Against the backdrop of the low-altitude economy and rising demand for time-sensitive logistics, traditional air freight still faces the structural problem of “fast trunk lines, congested last mile”. This thesis takes SF Express as a single case and, through literature review, case analysis, and comparative analysis, examines how its trunk aircraft, FH-98 branch UAVs, and Arkseries terminal UAVs form a three-level logistics network. The scope is bounded to SF's publicly disclosed operations in the Guangdong-Hong Kong-Macao Greater Bay Area and selected mountainous and island routes from 2022 to 2025. The analysis shows that the model performs well in scenarios that are difficult for ground transport. On the Shenzhen Dapeng Bay—Zhuhai Doumen route, UAV relay cuts transit time by about 61% compared with a truck detour; on the Hong Kong Cyberport—Cheung Chau route, delivery time falls from 68 minutes to 24 minutes. The main constraints are managerial rather than technological: transshipment SOPs are not standardised, information systems are not fully linked, emergency switching under extreme weather remains weak, and airspace approval is uneven. These factors create handoff waiting times of 48 to 110 minutes and limit UAV competitiveness in short urban routes where ground transport still has a cost advantage. To address these issues, the thesis proposes a standardised transshipment SOP, dynamic time-slot matching, an air-ground backup mechanism, and scenario-based capacity allocation. It argues that UAV logistics should be treated as a complementary capacity in specific scenarios rather than as a universal replacement for ground transport, with the goal of improving efficiency, resilience, and sustainable resource use.", "keywords": [ "Transshipment (information security)", "Backup", "Truck", "Scope (computer science)", "Resource (disambiguation)", "Air traffic control", "Track (disk drive)", "Traffic management", "Plan (archaeology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.theseus.fi/bitstream/10024/919083/2/Huang_Yurou.pdf" }, { "id": "https://openalex.org/W4401071423", "doi": "10.1109/iccea62105.2024.10603892", "title": "A Variable Neighborhood Search Algorithm for Drone-Truck Joint Distribution Problem", "year": 2024, "publication_date": "2024-04-12", "type": "conference-paper", "language": "en", "authors": [ "Hexin Li" ], "first_author": "Hexin Li", "venue": null, "publisher": null, "abstract": "Delivery with drones combines the advantages of short delivery time, low cost and reduced road congestion. The joint delivery of trucks and drones to serve the customers in a community is the breakthrough to solve the problem of the last-mile delivery. We propose an arc-based mixed integer programming model with the aim of the lowest total distribution cost. We verify the effectiveness of Variable Neighborhood Search(VNS) algorithm in solving the model.", "keywords": [ "Drone", "Computer science", "Variable (mathematics)", "Truck", "Joint (building)", "Variable neighborhood search", "Algorithm", "Mathematical optimization", "Mathematics", "Engineering", "Metaheuristic", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/iccea62105.2024.10603892" }, { "id": "https://openalex.org/W4415999491", "doi": "10.48550/arxiv.2509.18162", "title": "A Simple and Reproducible Hybrid Solver for a Truck-Drone VRP with Recharge", "year": 2025, "publication_date": "2025-09-17", "type": "preprint", "language": "en", "authors": [ "Meraryslan Meraliyev", "Cemil Turan", "Shirali Kadyrov" ], "first_author": "Meraryslan Meraliyev", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We study last-mile delivery with one truck and one drone under explicit battery management: the drone flies at twice the truck speed; each sortie must satisfy an endurance budget; after every delivery the drone recharges on the truck before the next launch. We introduce a hybrid reinforcement learning (RL) solver that couples an ALNS-based truck tour (with 2/3-opt and Or-opt) with a small pointer/attention policy that schedules drone sorties. The policy decodes launch-serve-rendezvous triplets with hard feasibility masks for endurance and post-delivery recharge; a fast, exact timeline simulator enforces launch/recovery handling and computes the true makespan used by masked greedy/beam decoding. On Euclidean instances with $N{=}50$, $E{=}0.7$, and $R{=}0.1$, the method achieves an average makespan of \\textbf{5.203}$\\pm$0.093, versus \\textbf{5.349}$\\pm$0.038 for ALNS and \\textbf{5.208}$\\pm$0.124 for NN -- i.e., \\textbf{2.73\\%} better than ALNS on average and within \\textbf{0.10\\%} of NN. Per-seed, the RL scheduler never underperforms ALNS on the same instance and ties or beats NN on two of three seeds. A decomposition of the makespan shows the expected truck-wait trade-off across heuristics; the learned scheduler balances both to minimize the total completion time. We provide a config-first implementation with plotting and significance-test utilities to support replication.", "keywords": [ "Job shop scheduling", "Solver", "Truck", "Drone", "Scheduling (production processes)", "Simple (philosophy)", "Vehicle routing problem", "Decomposition" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2509.18162" }, { "id": "https://openalex.org/W7165434158", "doi": null, "title": "Optimization of drone-truck coordinated scheduling for urban end-of-city delivery", "year": 2026, "publication_date": "2026-01-01", "type": "dissertation", "language": "en", "authors": [ "Yuting Lu" ], "first_author": "Yuting Lu", "venue": "DR-NTU (Nanyang Technological University)", "publisher": "Nanyang Technological University", "abstract": "Truck–drone collaborative delivery has emerged as a promising solution for improving the efficiency and flexibility of last-mile logistics.However,the strong coupling among truck routing,drone operations,and synchronization decisions makes the problem difficult to model and solve exactly.To address this challenge,this dissertation studies a truck–drone collaborative delivery problem under a flexible synchronization setting. An integrated optimization model is developed to capture the key operational features of the system,including truck routing,drone task assignment,endurance constraints,and synchronization feasibility.To solve the problem,an exact solution framework is proposed by combining decomposition ideas with dynamic route generation and feasibility-enhancing mechanisms. The framework improves computational tractability while preserving the logical integrity of the original problem. Computational experiments show that the proposed method can effectively solve the target problem and generate operationally meaningful collaborative delivery plans.The results further indicate that trucks and drones are not merely substitute resources,but complementary components of a coordinated delivery system. In particular,the effectiveness of drone deployment depends strongly on the spatial and temporal support provided by truck operations. Overall,this dissertation provides a systematic study of the modelling,exact optimization, and mechanism analysis of truck–drone collaborative delivery under flexible synchronization. The research contributes both methodological support and practical insights for the design of intelligent and efficient last-mile logistics systems.", "keywords": [ "Truck", "Flexibility (engineering)", "Synchronization (alternating current)", "Software deployment", "Drone", "Scheduling (production processes)", "Key (lock)", "Job shop scheduling" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/10356/216874" }, { "id": "https://openalex.org/W4410915894", "doi": "10.32628/ijsrset241487", "title": "Supporting AI in Logistics Optimization through Data Integration, Real-Time Analytics, and Autonomous Systems", "year": 2024, "publication_date": "2024-06-26", "type": "article", "language": "en", "authors": [ "Toluwanimi Adenuga", "Noah Ayanbode", "Tolulope Ayobami", "Francess Chinyere Okolo" ], "first_author": "Toluwanimi Adenuga", "venue": "International Journal of Scientific Research in Science Engineering and Technology", "publisher": "Technoscience Academy", "abstract": "The integration of artificial intelligence (AI) into logistics systems is reshaping the efficiency and agility of global supply chains. This paper explores the transformative role of AI in optimizing logistics operations through advanced data integration, real-time analytics, and autonomous systems. AI technologies are increasingly applied to enhance core logistics functions such as dynamic routing, intelligent scheduling, and capacity planning, enabling organizations to meet rising customer expectations while minimizing operational costs. The fusion of big data and IoT-enabled supply chains allows for continuous data flow across interconnected logistics networks, providing the foundation for real-time, data-driven decision-making. Key to this evolution is the deployment of digital twins, which create virtual replicas of physical logistics systems to simulate, monitor, and predict performance outcomes under varying conditions. These systems leverage predictive analytics and machine learning algorithms including reinforcement learning to improve resource allocation, identify anomalies, and adapt routing and inventory decisions in real-time. Demand sensing models, informed by structured and unstructured data, further support proactive forecasting and inventory balancing, thereby reducing lead times and avoiding stockouts or overstock situations. Moreover, the integration of predictive maintenance tools within logistics fleets ensures that asset health is continuously monitored, preventing unplanned downtimes and extending vehicle lifespan. Autonomous mobile robots and AI-powered drones are also emerging as vital components in last-mile delivery and warehouse management, offering enhanced speed, accuracy, and scalability. The study presents use cases from multinational logistics providers that have successfully implemented AI-powered platforms, resulting in significant gains in fuel efficiency, delivery accuracy, and supply chain resilience. It also addresses the technical and organizational challenges associated with adopting AI, including data interoperability, cybersecurity, workforce adaptation, and ethical governance. By synthesizing advancements in AI, IoT, and real-time analytics, this paper underscores how intelligent logistics systems are not only enhancing operational performance but also setting new standards for sustainability and customer-centricity in global trade. The findings advocate for continued investment in integrated AI infrastructures to ensure logistics networks are agile, responsive, and future-ready in the face of evolving market demands and global disruptions.", "keywords": [ "Analytics", "Computer science", "Real-time data", "Data analysis", "Big data", "Data science", "Data mining", "Artificial intelligence", "Operating system" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.ijsrset.com/index.php/home/article/download/IJSRSET241487/IJSRSET241487" }, { "id": "https://openalex.org/W4320883181", "doi": "10.1016/j.xinn.2023.100392", "title": "Urban aerial mobility: Reshaping the future of urban transportation", "year": 2023, "publication_date": "2023-02-15", "type": "article", "language": "en", "authors": [ "Kai Wang", "Xiaobo Qu" ], "first_author": "Kai Wang", "venue": "The Innovation", "publisher": "Elsevier BV", "abstract": "Urbanization causes concentrated economic and social activities in urban areas, leading to high living standards, e.g., better economy, food, education, housing, healthcare, and entertainment. However, such a concentration comes at some costs, e.g., traffic congestion, pollution, overcrowding, and fast spread of disease, and among these, traffic congestion is a growing problem that is neutralizing the efficiency of urbanization. Indeed, traffic congestion leads to tremendous costs for large cities. For instance, INRIX estimated that the annual economic losses from traffic congestion in New York City and Los Angeles were $11.0 billion and $8.2 billion in 2019, respectively, and on average for each traveler, the annual wasted hours are 140 and 103, respectively.1INRIX2019 global traffic scorecard.https://inrix.com/scorecard/Date: 2019Google Scholar Situations are much more serious for cities in China, e.g., Beijing and Shanghai.2Yang J. Purevjav A.O. Li S. The marginal cost of traffic congestion and road pricing: evidence from a natural experiment in Beijing.Am. Econ. J. Econ. Pol. 2020; 12: 418-453Crossref Scopus (19) Google Scholar Plenty of attempts have been made to alleviate traffic congestion, spanning congestion pricing, public transportation promotion, traffic signal control, high-occupancy driving lanes, road space rationing, recent vehicle-sharing and ride-sharing models, etc.3Santi P. Resta G. Szell M. Sobolevsky S. Strogatz S.H. Ratti C. Quantifying the benefits of vehicle pooling with shareability networks.Proc. Natl. Acad. Sci. USA. 2014; 111: 13290-13294Crossref PubMed Scopus (456) Google Scholar,4Hanna R. Kreindler G. Olken B.A. Citywide effects of high-occupancy vehicle restrictions: evidence from “three-in-one” in Jakarta.Science. 2017; 357: 89-93Crossref PubMed Scopus (32) Google Scholar,5Eliasson J. Efficient transport pricing–why, what, and when?.Commun. Trans. Res. 2021; 1: 100006Crossref Scopus (24) Google Scholar However, all these cannot essentially solve the problem because the root cause is the imbalance between ever-growing travel demand and fixed traffic capacity. Any strategic planning of the transport infrastructure cannot be based on the worst-case demand, and the traffic capacity of any road network is always capped by its bottlenecks, e.g., intersections of roads. Travel demand has increased steadily over the years because of urbanization, but large cities in the world have limited space to expand road capacity. Thus, traffic congestion cannot be solved on the ground, on the traditional two-dimensional surface. Since the road capacity on the ground cannot accommodate the travel demand, we turn to explore unused traffic capacity from the air, i.e., leveraging the three-dimensional cubic space. Such a solution has been recently revealed due to the rise of electric vertical-takeoff-and-landing (eVTOL) vehicles. Up to now, hundreds of eVTOL designs have been proposed. An eVTOL vehicle is an aircraft that uses electric power to hover, and it leverages designated helipads, called vertiports, to take off and land, exhibiting the following advantages compared with helicopters: less noise, a higher level of safety, more economical, and more environmentally friendly. These features suggest that eVTOLs shall become a key player in future urban mobility. EVTOLs drive the era of urban aerial mobility (UAM), a more convenient, efficient, and sustainable urban commuting option, moving future mobility off the road and into the air. Although there are some uncertainties, UAM is moving much faster than most imagine. The FAA has developed the UAM Concept of Operations version 1. NASA has proposed several initiatives to help emerging UAM players. European Union Aviation Safety Agency (EASA) is also working on several pilot projects to make UAM a reality in Europe within 3–5 years. Joby Aviation is expecting to launch its commercial aerial ride-sharing service in the United States by 2024. Japan’s government targeted 2023 as the starting date for commercial flying taxi services. EHang is close to eventual certification in China and will move to commercial operations in 2025. All these players endeavor to take the leading position in this emerging market. For UAM, vehicle technologies are close to being mature, and all the deployment plans expect it in 3–5 years. Morgan Stanley predicted that UAM will ultimately grow to a $9 trillion market globally. For such a giant market, and a close timeline for the deployment, we should be well prepared. Below, we highlight some urgently needed developments or challenges for the early stage of UAM and finally point out the roadmap to the future path of UAM. Although eVTOL vehicle technologies are ready for deployment, energy and power technologies are still the main bottlenecks that significantly affect the efficiency of UAM systems. Based on the current technologies, most energy will be consumed during the climbing and descending processes, so flying distances are relatively short, and batteries will need to be frequently recharged, so the utilization of eVTOL fleets are relatively low. Thus, for eVTOLs, high-energy-density batteries are always the direction, and keeping the batteries small and light is also critical for flying. Meanwhile, the design of the power systems should follow the principle of creating as little noise as possible, since flying aircraft will create noise pollution in urban environments. Future urban mobility systems rely heavily on advanced wireless communications to enable high data rates and low latency transmissions for being “smart.” For instance, the recent deployment of the fifth-generation (5G) mobile network led to a monumental application in connected and autonomous vehicles, so vehicles can efficiently communicate with control centers and nearby vehicles. Such a similar technology should also be in place for UAM. Traditional radar and radio communications, used for civil aviation, cannot be applied to low", "keywords": [ "Transport engineering", "Geography", "Urban planning", "Environmental planning", "Environmental science", "Civil engineering", "Engineering" ], "cited_by_count": 44, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.xinn.2023.100392" }, { "id": "https://openalex.org/W4287374018", "doi": "10.48550/arxiv.2101.07877", "title": "Flying Robots for Safe and Efficient Parcel Delivery Within the COVID-19\\n Pandemic", "year": 2021, "publication_date": "2021-01-19", "type": "preprint", "language": "en", "authors": [ "Manuel Patchou", "Benjamin Sliwa", "Christian Wietfeld" ], "first_author": "Manuel Patchou", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The integration of small-scale Unmanned Aerial Vehicles (UAVs) into\\nIntelligent Transportation Systems (ITSs) will empower novel smart-city\\napplications and services. After the unforeseen outbreak of the COVID-19\\npandemic, the public demand for delivery services has multiplied. Mobile\\nrobotic systems inherently offer the potential for minimizing the amount of\\ndirect human-to-human interactions with the parcel delivery process. The\\nproposed system-of-systems consists of various complex aspects such as\\nassigning and distributing delivery jobs, establishing and maintaining reliable\\ncommunication links between the vehicles, as well as path planning and mobility\\ncontrol. In this paper, we apply a system-level perspective for identifying key\\nchallenges and promising solution approaches for modeling, analysis, and\\noptimization of UAV-aided parcel delivery. We present a system-of-systems model\\nfor UAV-assisted parcel delivery to cope with higher capacity requirements\\ninduced by the COVID-19. To demonstrate the benefits of hybrid vehicular\\ndelivery, we present a case study focusing on the prioritization of\\ntime-critical deliveries such as medical goods. The results further confirm\\nthat the capacity of traditional delivery fleets can be upgraded with drone\\nusage. Furthermore, we observe that the delay incurred by prioritizing\\ntime-critical deliveries can be compensated with drone deployment. Finally,\\ncentralized and decentralized communication approaches for data transmission\\ninside hybrid delivery fleets are compared.\\n", "keywords": [ "Drone", "Software deployment", "Computer science", "Process (computing)", "Prioritization", "Coronavirus disease 2019 (COVID-19)", "Engineering", "Process management" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2101.07877" }, { "id": "https://openalex.org/W4214749388", "doi": "10.2139/ssrn.4040528", "title": "The Latency Location Drone Routing Problem Under Travel Time Uncertainty", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti" ], "first_author": "Maria Elena Bruni", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Latency (audio)", "Computer science", "Travel time", "Time travel", "Real-time computing", "Operations research", "Transport engineering", "Engineering", "Artificial intelligence", "Telecommunications" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4040528" }, { "id": "https://openalex.org/W4384208192", "doi": "10.59287/idtscm.67", "title": "Sustainable Agricultural Supply Chains: The Role of Drones in Improving Efficiency and Reducing Environmental Impact", "year": 2023, "publication_date": "2023-06-30", "type": "book-chapter", "language": "en", "authors": [ "Naz TÜRKSOY" ], "first_author": "Naz TÜRKSOY", "venue": null, "publisher": null, "abstract": "The logistics and supply chain management industry faces increasing complexity in managing various components and stakeholders. Unmanned aerial vehicles (UAVs), commonly known as drones, have gained popularity in logistics and supply chain management due to their ability to deliver goods, collect data, and perform various tasks. This research examines the potential of drones in enhancing the sustainability of agricultural supply chains and aims to explore how drones can improve efficiency and reduce the environmental impact of supply chain operations in agriculture. It focuses on analyzing the benefits and challenges associated with using drones in agricultural logistics, inventory management, last-mile delivery, monitoring, and inspection. The study highlights the significant contributions of the Lean approach in utilizing drones for supply chain management in agriculture. The findings indicate that drones optimize lastmile delivery, providing timely and efficient transportation while reducing costs and overcoming challenges faced by traditional vehicles. They also enhance inventory management by providing real-time information, leading to cost reduction and improved supply chain operations. Furthermore, drones play a crucial role in crop monitoring, facility inspections, and precision agriculture, improving overall productivity, reducing environmental impact, and promoting sustainability in the agricultural sector. This research provides valuable insights into the potential applications of drones in agricultural supply chains. By understanding the benefits and challenges associated with their use, stakeholders in the logistics and supply chain management industry can make informed decisions and develop strategies to incorporate drones into their operations. Ultimately, the integration of drones in agricultural supply chains can contribute to a more sustainable and efficient industry.", "keywords": [ "Drone", "Supply chain", "Business", "Sustainability", "Agriculture", "Supply chain management", "Environmental economics", "Popularity", "Marketing", "Economics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://as-books.com/index.php/idtscm/article/download/67/61" }, { "id": "https://openalex.org/W3003265285", "doi": null, "title": "DESAIN KONSEPTUAL DRONE PENGIRIMAN BARANG DENGAN QUALITY FUNCTION DEPLOYMENT", "year": 2020, "publication_date": "2020-01-13", "type": "dissertation", "language": "id", "authors": [ "Noer Said Anjar" ], "first_author": "Noer Said Anjar", "venue": "Andalas University eThesis (Andalas University)", "publisher": "Andalas University", "abstract": "Indonesia merupakan pasar terbesar e-commerce di Asia Tenggara. Gross Merchandise Value (GMV) di Indonesia pada tahun 2018 mencapai US$ 12,2 miliar. E-commerce di Indonesia menyumbang US$ 1 dari setiap US$ 2 yang dibelanjakan di Asia Tenggara. E-commerce di Indonesia memiliki peminat yang tinggi namun belum tersebar merata ke seluruh pulau di Indonesia, hanya terpusat di pulau Jawa. Hal ini disebabkan oleh faktor meratanya jangkauan internet di wilayah tersebut. Sumatera Barat merupakan salah satu provinsi di Sumatera yang berpeluang untuk meningkatkan pasar e-commerce. Keberhasilan e-commerce tidak hanya dipengaruhi oleh kemerataan jangkauan internet, namun faktor infrastruktur pun sangat mempengaruhi. Salah satu masalah infrastruktur yang sering terjadi yaitu kemacetan pada pusat kota. Padang merupakan salah satu pusat kota dari Sumatera Barat yang mengalami peningkatan kemacetan dalam beberapa tahun terakhir. Untuk dapat bersaing, perusahaan jasa pengiriman dituntut untuk menekan biaya keluar namun tetap menjaga kualitas pelayanan. Salah satu cara agar hal tersebut dapat tercapai adalah dengan menggunakan teknologi baru dalam operasional perusahaan. Perusahaan dapat menerapkan inovasi teknologi dalam proses operasional seperti menggunakan unmanned aerial vehicles (UAV) atau sering dikenal dengan istilah drone. Namun drone yang sudah ada di pasaran tidak dapat digunakan oleh perusahaan pengiriman barang karena karakteristiknya yang belum sesuai. Maka perlu adanya penelitian untuk mengetahui bagaimana karakteristik drone yang sesuai untuk perusahaan pengiriman barang di Kota Padang dengan metode Quality Function Deployment. Pengambilan data dilakukan terhadap perusahaan jasa pengiriman yang melakukan pengiriman last-mile, membawa barang maksimal 10 Kg per piece, dan telah menerapkan teknologi dalam proses pengiriman. Penelitian menggunakan wawancara dan kuisioner dengan skala likert untuk pengambilan data. Wawancara dan kuisioner dilakukan kepada 15 orang responden yang ahli dalam operasional di beberapa perusahaan pengiriman barang di Kota Padang. Penelitian dilakukan menggunakan metode Quality Function Deployment sampai fase dua. Hasil pengolahan data didapatkan 10 prioritas komponen karakteristik teknis yang akan dilanjutkan untuk pengembangan drone yaitu, baterai LiPo 22000 mAh 12 S, motor brushless P80-S 120KV, urutan ketiga box dengan material fiber carbon, kamera DJI Osmo +, busa polyuterna, box ukuran 40 x 30 cm, proppeler guard, material frame dengan fiber carbon agar lebih ringan dan kuat, membuat stiker petunjuk, long range telemetry. \\n \\n \\nKata Kunci: Drone, Jasa Pengiriman, QFD", "keywords": [ "Business" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://scholar.unand.ac.id/56030/2/2.%20BAB%20I.pdf" }, { "id": "https://openalex.org/W2026589647", "doi": "10.1137/1006005", "title": "Heuristic Methods for Location-Allocation Problems", "year": 1964, "publication_date": "1964-01-01", "type": "article", "language": "en", "authors": [ "Leon N. Cooper" ], "first_author": "Leon N. Cooper", "venue": "SIAM Review", "publisher": "Society for Industrial and Applied Mathematics", "abstract": "Previous article Next article Heuristic Methods for Location-Allocation ProblemsLeon CooperLeon Cooperhttps://doi.org/10.1137/1006005PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] Leon Cooper, Location-allocation problems, Operations Res., 11 (1963), 331–343, No. 3 MR0152365 0113.14201 CrossrefISIGoogle Scholar[2] Google Scholar[3] D. H. Lehmer, Teaching combinatorial tricks to a computerProc. Sympos. Appl. Math., Vol. 10, American Mathematical Society, Providence, R.I., 1960, 179–193, Combinatorial Analysis MR0113289 0096.00504 CrossrefGoogle Scholar[4] J. Heller, Some numerical experiments for an $M\\times J$ flow shop and its decision-theoretical aspects, Operations Res., 8 (1960), 178–184, No. 2 MR0111626 0092.27910 CrossrefISIGoogle Scholar[5] J. Moshman, The Application of Sequential Estimation To Computer Simulation and Monte Carlo Procedures, J. Association for Computing Machinery, 5 (1958), 0086.11603 CrossrefISIGoogle Scholar[6] A. Wald, Sequential Analysis, John Wiley and Sons, New York, 1952 Google Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails An elliptical cover problem in drone delivery network design and its solution algorithmsEuropean Journal of Operational Research, Vol. 304, No. 3 | 1 Feb 2023 Cross Ref Two lower-bounding algorithms for the p-center problem in an areaComputational Urban Science, Vol. 2, No. 1 | 24 January 2022 Cross Ref Optimal planar facility location with dense demands along a curveJournal of the Operational Research Society, Vol. 73, No. 8 | 8 May 2021 Cross Ref A continuous location and maintenance routing problem for offshore wind farms: Mathematical models and hybrid methodsComputers & Operations Research, Vol. 144 | 1 Aug 2022 Cross Ref Spatial autocorrelation informed approaches to solving location–allocation problemsSpatial Statistics, Vol. 50 | 1 Aug 2022 Cross Ref Electric vehicle charging stations emplacement using genetic algorithms and 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Environment and Urban Systems, Vol. 88 | 1 Jul 2021 Cross Ref Self-Configuring (1 + 1)-Evolutionary Algorithm for the Continuous p-Median Problem with Agglomerative MutationAlgorithms, Vol. 14, No. 5 | 22 April 2021 Cross Ref Complex Energy Networks Optimization: Part I—Development and Validation of a Software for Optimal Load AllocationJournal of Engineering for Gas Turbines and Power, Vol. 143, No. 4 | 22 February 2021 Cross Ref Fiber to the Home (FTTH) Automation Planning, Its Impact on Customer Satisfaction & Cost-EffectivenessWireless Personal Communications, Vol. 117, No. 2 | 24 November 2020 Cross Ref Heuristic-Based Location Allocation of Single Frequency Network StationsIEEE Transactions on Broadcasting, Vol. 67, No. 1 | 1 Mar 2021 Cross Ref Multi-objective solid transportation-location problem with variable carbon emission in inventory management: a hybrid approachAnnals of Operations Research, Vol. 41 | 5 February 2021 Cross Ref Optimized Supply Chain Management of Rice in 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gradual cover: th", "keywords": [ "Computer science", "Heuristic", "Location-allocation", "Mathematical optimization", "Operations research", "Mathematics", "Artificial intelligence" ], "cited_by_count": 537, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1137/1006005" }, { "id": "https://openalex.org/W4410110648", "doi": "10.2139/ssrn.5214885", "title": "Beyond Boundaries: Leveraging Autonomous Drones for Sustainable Development in Emerging Economies", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Ed Koellner" ], "first_author": "Ed Koellner", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Emerging markets", "Business", "Sustainable development", "Economic geography", "Economics", "Political science", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5214885" }, { "id": "https://openalex.org/W4287235541", "doi": "10.48550/arxiv.2104.04664", "title": "Congestion-aware Bi-modal Delivery Systems Utilizing Drones", "year": 2021, "publication_date": "2021-04-10", "type": "preprint", "language": "en", "authors": [ "Mark Beliaev", "Negar Mehr", "Ramtin Pedarsani" ], "first_author": "Mark Beliaev", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Bi-modal delivery systems are a promising solution to the challenges posed by the increasing demand of e-commerce. Due to the potential benefit drones can have on logistics networks such as delivery systems, some countries have taken steps towards integrating drones into their airspace. In this paper we aim to quantify this potential by developing a mathematical model for a Bi-modal delivery system composed of trucks and drones. We propose an optimization formulation that can be efficiently solved in order to design socially-optimal routing and allocation policies. We incorporate both societal cost in terms of road congestion and parcel delivery latency in our formulation. Our model is able to quantify the effect drones have on mitigating road congestion, and can solve for the path routing needed to minimize the chosen objective. To accurately capture the effect of stopping trucks on road latency, we model it using SUMO by simulating roads shared by trucks and cars. Based on this, we show that the proposed framework is computationally feasible to scale due to its reliance on convex quadratic optimization techniques.", "keywords": [ "Drone", "Truck", "Computer science", "Modal", "Routing (electronic design automation)", "Traffic congestion", "Latency (audio)", "Transport engineering", "Mathematical optimization", "Operations research", "Computer network", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2104.04664" }, { "id": "https://openalex.org/W4401201021", "doi": "10.48550/arxiv.2407.12286", "title": "Bayesian Optimization for Fast Radio Mapping and Localization with an Autonomous Aerial Drone", "year": 2024, "publication_date": "2024-07-17", "type": "preprint", "language": "en", "authors": [ "Paul S. Kudyba", "Qin Lu", "Haijian Sun" ], "first_author": "Paul S. Kudyba", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This paper explores how a flying drone can autonomously navigate while constructing a narrowband radio map for signal localization. As flying drones become more ubiquitous, their wireless signals will necessitate new wireless technologies and algorithms to provide robust radio infrastructure while preserving radio spectrum usage. A potential solution for this spectrum-sharing localization challenge is to limit the bandwidth of any transmitter beacon. However, location signaling with a narrow bandwidth necessitates improving a wireless aerial system's ability to filter a noisy signal, estimate the transmitter's location, and self-pilot to improve the location estimate. By showing results through simulation, emulation, and a final drone flight experiment, this work provides an algorithm using a Gaussian process for radio signal estimation and Bayesian optimization for drone automatic guidance. This research supports advanced radio and aerial robotics applications in critical areas such as search-and-rescue, last-mile delivery, and large-scale platform digital twin development.", "keywords": [ "Drone", "Bayesian optimization", "Bayesian probability", "Computer science", "Artificial intelligence", "Bayesian network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2407.12286" }, { "id": "https://openalex.org/W3154394456", "doi": null, "title": "Congestion-aware Multi-modal Delivery Systems Utilizing Drones", "year": 2021, "publication_date": "2021-04-10", "type": "preprint", "language": "en", "authors": [ "Mark Beliaev", "Negar Mehr", "Ramtin Pedarsani" ], "first_author": "Mark Beliaev", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Multi-modal delivery systems are a promising solution to the challenges posed by the increasing demand of e-commerce. Due to the potential benefit drones can have on logistics networks such as delivery systems, some countries have taken steps towards integrating drones into their airspace. In this paper we aim to quantify this potential by developing a mathematical model for a multi-modal delivery system composed of trucks and drones. We propose an optimization formulation that can be efficiently solved in order to design socially-optimal routing and allocation policies. We incorporate both societal cost in terms of road congestion and parcel delivery latency in our formulation. Our model is able to quantify the effect drones have on mitigating road congestion, and can solve for the path routing needed to minimize the chosen objective. To accurately capture the effect of stopping trucks on road latency, we use SUMO simulations and derive a mathematical latency function for roads shared by trucks and cars. Based on this, we show that the proposed framework is computationally feasible to scale due to its reliance on convex quadratic optimization techniques.", "keywords": [ "Drone", "Computer science", "Truck", "Modal", "Routing (electronic design automation)", "Traffic congestion", "Latency (audio)", "Mathematical optimization", "Vehicle routing problem", "Transport engineering", "Computer network", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2104.04664.pdf" }, { "id": "https://openalex.org/W4403014495", "doi": "10.1007/978-981-97-2282-2_59", "title": "FSO and Optical Networks", "year": 2024, "publication_date": "2024-01-01", "type": "book-chapter", "language": "en", "authors": [ "Yibeltal Chanie Manie", "Cheng-Kai Yao", "Peng‐Chun Peng" ], "first_author": "Yibeltal Chanie Manie", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Computer science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-97-2282-2_59" }, { "id": "https://openalex.org/W7152507733", "doi": "10.1007/s42496-026-00314-7", "title": "Optimizing Unmanned Aerial Vehicle Path finding in Complex Urban Airspaces: A Hybrid Genetic Algorithm with Bidirectional and Greedy Initialization", "year": 2026, "publication_date": "2026-04-09", "type": "article", "language": "en", "authors": [ "Hari Balakrishnan", "Abhilash Suryan", "Anu Plavara Alex", "V R Jisha", "Sonia S. Raj", "Guang Chun Zhang" ], "first_author": "Hari Balakrishnan", "venue": "Aerotecnica Missili & Spazio", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Initialization", "Motion planning", "Heuristics", "Genetic algorithm", "Path (computing)", "Population", "Convergence (economics)", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s42496-026-00314-7" }, { "id": "https://openalex.org/W7112469100", "doi": null, "title": "Joint optimisation of drone routing and battery wear for sustainable supply chain development", "year": null, "publication_date": null, "type": "preprint", "language": null, "authors": [ "Yang Xia", "Wenjia Zeng", "Xinjie Xing", "Yuanzhu Zhan", "Kim Hua Tan", "Ajay Kumar" ], "first_author": "Yang Xia", "venue": "RePEc: Research Papers in Economics", "publisher": "Federal Reserve Bank of St. Louis", "abstract": "Alongside the rise of ‘last-mile' delivery in contemporary urban logistics, drones have demonstrate commercial potential, given their outstanding triple-bottom-line performance. However, as a lithium-ion battery-powered device, drones' social and environmental merits can be overturned by battery recycling and disposal. To maintain economic performance, yet minimise environmental negatives, fleet sharing is widely applied in the transportation field, with the aim of creating synergies within industry and increasing overall fleet use. However, if a sharing platform's transparency is doubted, the sharing ability of the platform will be discounted. Known for its transparent and secure merits, blockchain technology provides new opportunities to improve existing sharing solutions. In particular, the decentralised structure and data encryption algorithm offered by blockchain allow every participant equal access to shared resources without undermining security issues. Therefore, this study explores the implementation of a blockchain-enabled fleet sharing solution to optimise drone operations, with consideration of battery wear and disposal effects. Unlike classical vehicle routing with fleet sharing problems, this research is more challenging, with multiple objectives (i.e., shortest path and fewest charging times), and considers different levels of sharing abilities. In this study, we propose a mixed-integer programming model to formulate the intended problem and solve the problem with a tailored branch-and-price algorithm. Through extensive experiments, the computational performance of our proposed solution is first articulated, and then the effectiveness of using blockchain to improve overall optimisation is reflected, and a series of critical influential factors with managerial significance are demonstrated.", "keywords": [ "Drone", "Supply chain", "Transparency (behavior)", "Sustainable development", "Routing (electronic design automation)", "Joint (building)", "Battery (electricity)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-04381308v1/document" }, { "id": "https://openalex.org/W7162621943", "doi": null, "title": "Estimating WTP for drone-based delivery of medicines and pharmaceuticals with social acceptability as a latent factor", "year": 2025, "publication_date": "2025-09-01", "type": "dissertation", "language": "en", "authors": [ "James Lindsay Afonso de Brito Mcleod" ], "first_author": "James Lindsay Afonso de Brito Mcleod", "venue": "Repositório do ISCTE-IUL", "publisher": null, "abstract": "Despite advances in drone-based last-mile delivery and a growing number of commercial pilots, large-scale adoption remains limited. While technical and economic feasibility is well documented, societal acceptability emerges as one of the least understood barriers to implementation. Concerns about noise, privacy, and airspace disruption have fuelled regulatory hesitation and public resistance, stalling projects even where technology is viable. Existing predictive models of drone delivery demand focus mainly on functional attributes - cost, and time - while neglecting acceptability as a latent factor shaping adoption. Although willingness to pay (WTP) has been examined in contexts such as ridesharing and passenger eVTOLs, no established framework links WTP in drone delivery to societal concerns. Addressing this gap is critical for European Innovative Air Mobility, where opposition risks delaying deployment regardless of technical readiness. This thesis develops a framework for modelling WTP in drone delivery by integrating social acceptability as a latent explanatory variable within a choice modelling context. Methodological inspiration is drawn from Berke et al. (2023), who modelled WTP for drone delivery in the United States. This study extends their work by embedding acceptability as a latent construct and focusing on Portuguese (and European) consumers. Based on stated-preference data collected in Portugal and estimated through an Integrated Choice and Latent Variable (ICLV) model with ordered probit measurement of acceptability, results show that higher acceptability reduces disutility from cost and time. This constitutes the first European application of ICLV modelling to drone delivery, extending WTP literature by quantifying the economic value of societal acceptance.", "keywords": [ "Willingness to pay", "Probit model", "Latent variable", "Software deployment", "Construct (python library)", "Latent variable model", "Drone", "Probit" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10071/37291" }, { "id": "https://openalex.org/W7166804027", "doi": "10.48550/arxiv.2606.30680", "title": "Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones", "year": 2026, "publication_date": "2026-06-26", "type": "preprint", "language": null, "authors": [ "Xuanyu Liu", "Hui Hu", "Jiao Zhao", "Ziliang Wang", "Zhengbing He" ], "first_author": "Xuanyu Liu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Truck-drone delivery is an emerging last-mile logistics mode combining the long-haul capacity of trucks with the flexible service capability of drones. In locker-based operations, smart lockers serve not only as temporary parcel storage facilities but also as automated drone docking and service nodes. These automated nodes support drone takeoff, landing, parcel handover, and battery replacement, thereby significantly extending the service range and operational flexibility of drone-assisted delivery networks. However, practical locker-based delivery systems face complex real-world challenges, requiring the integrated coordination of not only parcel delivery, return pickup, battery-constrained and load-dependent drone flights, but also necessary detours around restricted airspace. To address this practical and multifaceted challenge, this paper introduces a locker-based truck-drone routing problem with integrated considerations of pickups, deliveries, and no-fly zones (LTDRP-PDNF), with the objective of minimizing the total operational cost of a fleet of drone-equipped trucks. We formulate the route construction process as a Markov Decision Process and develop a two-stage deep reinforcement learning-based neural heuristic. The first stage utilizes an attention-based encoder and a Bidirectional Gated Recurrent Unit decoder to solve the truck-only routing problem, formulated as a capacitated vehicle routing problem. The second stage combines a policy-transfer strategy with a hybrid dispatch assignment heuristic to construct fully coordinated truck and drone routes for LTDRP-PDNF. Experiments on instances of different scales demonstrate that the proposed method outperforms metaheuristic and neural heuristic baselines in most cases while maintaining exceptionally short computation times, offering an effective, scalable solution framework under practical operational constraints.", "keywords": [ "Drone", "Markov decision process", "Routing (electronic design automation)", "Flexibility (engineering)", "Scalability", "Truck", "Heuristic", "Service (business)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2606.30680" }, { "id": "https://openalex.org/W7171155317", "doi": "10.1007/978-3-032-30228-1_11", "title": "A Multi-granularity Collaborative Genetic Algorithm for Truck-Drone Routing Problem", "year": 2026, "publication_date": "2026-07-25", "type": "conference-paper", "language": "en", "authors": [ "Li HU", "G Wang", "Q F Liu" ], "first_author": "Li HU", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Crossover", "Scalability", "Routing (electronic design automation)", "Flexibility (engineering)", "Genetic algorithm", "Truck", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-30228-1_11" }, { "id": "https://openalex.org/W4409475660", "doi": "10.1109/soli63266.2024.10955985", "title": "Modeling and Evaluation of Coordinated Delivery of Truck and Drones in Depopulated Areas of Japan", "year": 2024, "publication_date": "2024-06-22", "type": "conference-paper", "language": "en", "authors": [ "Anxiang Chen", "Yoshiyasu Takahashi", "Youichi Nonaka" ], "first_author": "Anxiang Chen", "venue": null, "publisher": null, "abstract": "The logistics industry in Japan faces significant challenges due to its complex geographical landscape, characterized by deep mountains and isolated islands, making last-mile delivery particularly difficult. Japanese government reports highlight stark differences in delivery between urban and depopulated areas, with higher unit delivery costs in depopulated areas. To address these issues, this study proposes coordinated delivery using truck and multiple drones. By integrating truck and drones, this approach seeks to overcome geographical barriers and enhance delivery efficiency, thus tackling the unique logistical challenges of Japan's depopulated areas. Despite numerous studies on the coordinated delivery problem of trucks and drones, research on terrain constraints and drone no-fly zones remains sparse. This study aims to fill this gap by establishing complex scenarios based on specific conditions in Japan. Compared to similar studies, our research is novel in its focus on complex delivery scenarios, validation of the performance and advantages of coordinated delivery, and the establishment of energy consumption models for traditional and electric trucks to compare their respective energy cost performances.", "keywords": [ "Drone", "Truck", "Aeronautics", "Computer science", "Transport engineering", "Aerospace engineering", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/soli63266.2024.10955985" }, { "id": "https://openalex.org/W4417298728", "doi": "10.48550/arxiv.2505.13836", "title": "Duawlfin: A Drone with Unified Actuation for Wheeled Locomotion and Flight Operation", "year": 2025, "publication_date": "2025-05-20", "type": "preprint", "language": "en", "authors": [ "Jerry Tang", "Zhang, Ruiqi", "Kaan Beyduz", "Jiang, Yiwei", "Cody Wiebe", "Haoyu Zhang", "Osaruese Asoro", "Mark W. Mueller" ], "first_author": "Jerry Tang", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This paper presents Duawlfin, a drone with unified actuation for wheeled locomotion and flight operation that achieves efficient, bidirectional ground mobility. Unlike existing hybrid designs, Duawlfin eliminates the need for additional actuators or propeller-driven ground propulsion by leveraging only its standard quadrotor motors and introducing a differential drivetrain with one-way bearings. This innovation simplifies the mechanical system, significantly reduces energy usage, and prevents the disturbance caused by propellers spinning near the ground, such as dust interference with sensors. Besides, the one-way bearings minimize the power transfer from motors to propellers in the ground mode, which enables the vehicle to operate safely near humans. We provide a detailed mechanical design, present control strategies for rapid and smooth mode transitions, and validate the concept through extensive experimental testing. Flight-mode tests confirm stable aerial performance comparable to conventional quadcopters, while ground-mode experiments demonstrate efficient slope climbing (up to 30°) and agile turning maneuvers approaching 1g lateral acceleration. The seamless transitions between aerial and ground modes further underscore the practicality and effectiveness of our approach for applications like urban logistics and indoor navigation. All the materials including 3-D model files, demonstration video and other assets are open-sourced at https://sites.google.com/view/Duawlfin.", "keywords": [ "Propulsion", "Drivetrain", "Actuator", "Drone", "Power (physics)", "Robot", "Mode (computer interface)", "Torque", "Spinning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2505.13836" }, { "id": "https://openalex.org/W4313203001", "doi": "10.5281/zenodo.7488489", "title": "Time Dependent Drone-aided Vehicle Routing Problem with Time Windows and Multi-visits", "year": 2022, "publication_date": "2022-10-12", "type": "article", "language": "en", "authors": [ "Yiliang Yuan", "Jiahang Xie" ], "first_author": "Yiliang Yuan", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The continuous development of e-commerce and ongoing pandemic in recent years leads to a large increase in the demand for last-mile delivery with higher standards of service quality. The usage of unmanned aerial vehicles, also known as drones, is an ideal solution for such complex delivery problems. When designing the delivery system with the drone, we cannot omit the following features of drones: drones have the unique ability to avoid traffic congestion compared to truck traveling; and the dispatched drones need customer assistance to retrieve goods in parcel releasing procedures. In this paper, the time-dependent vehicle routing problem with drones and time windows (TDVRPDTW) is introduced. For practical application scenarios, the parcel releasing operation of drones needs customer assistance. Thus, the drone visits are fulfilled in given time windows for each demand point. Besides, the time-dependent traveling routes of trucks are also considered to capture road congestion. The objective is to minimize total route makespan, and vehicles’ dispatch times at the depot are crucial to avoid urban congestion. We present a branch-and-price algorithm to solve the optimization. The master problem is solved using a customized column generation algorithm and the pricing problem is solved by a tailored labeling algorithm with a novel dominance rule that allows more label dominance. For our numerous experimental results, we modified Solomon’s data sets by adding time dependency. Our algorithm can solve all the small-scale and medium-scale instances optimally. In terms of large-size instances, more than 90\\% are solved optimally within 2 hours.", "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://zenodo.org/record/7488489" }, { "id": "https://openalex.org/W7126348154", "doi": null, "title": "Hybrid matheuristics for mobile parcel locker delivery with robots and drone resupply [Abstract]", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Cheng Chen", "Emrah Demir" ], "first_author": "Cheng Chen", "venue": "ORCA Online Research @Cardiff (Cardiff University)", "publisher": "Cardiff University", "abstract": "This research introduces a novel vehicle routing problem with time windows in an autonomous last mile delivery system featuring mobile parcel lockers (MPLs), delivery robots, and drone resupply. Customers can choose between home delivery via robots or self-pickup from MPLs stationed at designated parking areas. Delivery robots operate from MPLs, while drones replenish these lockers as needed. We define this problem as the Mobile Parcel Locker Problem with Delivery Robot and Drone Resupply (MPLPDR-DR). To address this problem, we propose a mixed-integer linear programming (MILP) model and develop a hybrid matheuristic approach. Our method integrates a metaheuristic for optimising MPL and robot routing, while a MILP model determines optimal drone resupply operations. The metaheuristic is based on the artificial bee colony framework, incorporating large neighbourhood search, variable neighbourhood descent, and mutation mechanisms to enhance solution quality. Extensive computational experiments demonstrate the efficiency of the proposed approach in solving large-scale MPLPDR-DR instances. The findings provide key managerial insights into optimising autonomous delivery operations, improving efficiency, and reducing last mile logistics costs.", "keywords": [ "Drone", "Robot", "Metaheuristic", "Mobile robot", "Key (lock)", "Vehicle routing problem", "Linear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://orca.cardiff.ac.uk/view/cardiffauthors/A2271339I.html>" }, { "id": "https://openalex.org/W4360764806", "doi": "10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00055", "title": "A Novel Adaptive Computation Offloading Strategy for Collaborative DNN Inference over Edge Devices", "year": 2022, "publication_date": "2022-12-01", "type": "conference-paper", "language": "en", "authors": [ "Yansong Zhang", "Xiao Liu", "Xu Jia", "Dong Yuan", "Xuejun Li" ], "first_author": "Yansong Zhang", "venue": null, "publisher": null, "abstract": "With the breakthrough of deep learning technology and the rapid growth of Internet of Things (IoT'), a fast-increasing number of artificial intelligence (AI) applications are being widely used in many smart IoT systems. Taking the UAV last mile delivery system as an example, UAVs require the assistance of various AI applications such as object detection and route planning during the delivery process. However, provisioning AI services over the cloud computing environment are facing many challenges such as network congestion and high latency. To tackle these challenges, edge computing has emerged as a new computing paradigm where computing resources are provisioned closer to the end devices from the network edge. In this paper, combined with the edge computing-based UAV last mile delivery scenario, we focus on the computation offloading problem for deep learning-based applications at the inference stage. A novel computation offloading strategy for multi-device collaborative pipelining processing of deep neural networks (DNNs) tasks called CoDNN is proposed. First, when the DNN inference task is generated, CoDNN will predict the execution time for each layer of the DNN and model the process of DNN inference. Second, CoDNN will use the offloading partitioning algorithm based on Particle Swarm Optimization (PSO) to determine the target computing resources and assign them to appropriate workloads. Specifically, we explore the impact of different computing environments on the performance of CoDNN and show its advantages over other representative computation offloading strategies.", "keywords": [ "Computer science", "Computation offloading", "Edge computing", "Edge device", "Cloud computing", "Distributed computing", "Provisioning", "Inference", "Deep learning", "Artificial intelligence", "Enhanced Data Rates for GSM Evolution", "Artificial neural network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.26187/deakin.32739231" }, { "id": "https://openalex.org/W7203739177", "doi": "10.2139/ssrn.7317759", "title": "The Drone-Autonomous Delivery Vehicle Cooperative Pickup and Delivery Problem with Transshipment", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Shuang Zeng", "Haiyan Yu", "Tengyu Wu" ], "first_author": "Shuang Zeng", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Transshipment (information security)", "Scheduling (production processes)", "Pickup", "Schedule", "Job shop scheduling" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7317759" }, { "id": "https://openalex.org/W4410034855", "doi": "10.3390/futuretransp5020049", "title": "Flight Scheduling for Transportation of Packages Between Logistics Bases Using Drones", "year": 2025, "publication_date": "2025-05-01", "type": "article", "language": "en", "authors": [ "Ryo Nakagawa", "Tomotaka Kimura", "Kouji Hirata", "Jun Cheng" ], "first_author": "Ryo Nakagawa", "venue": "Future Transportation", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In recent years, interest in drone-based logistics has grown due to the increasing demand for efficient and sustainable package transportation, driven by the expansion of e-commerce and rising environmental awareness. In this study, we focus on flight scheduling for the efficient transportation of packages between logistics bases, rather than on last-mile delivery. In scenarios where the number of packages handled at each base varies, efficient transportation can be achieved by having drones visit high-demand bases more frequently. To this end, we consider a system with two types of drones: local drones that visit all bases, and express drones that visit only selected high-demand bases. We formulate this problem as a mixed integer linear programming (MILP) model that minimizes the total transportation time. This model simultaneously determines which bases should be visited frequently and computes flight schedules that enable efficient package delivery. Unlike existing transportation models that assume fixed linear routes, our model allows for flexible routing, including direct flights and loop-based paths between bases. To ensure scalability, we also propose an approximation method that significantly reduces the computational cost. As the number of logistics bases increases, the exact solution of the MILP becomes intractable. Therefore, we pre-select candidate high-demand bases based on package volume and spatial layout, thereby reducing the number of decision variables. This makes it possible to compute high-quality solutions even in large-scale environments. Through numerical experiments, we show the effectiveness of our proposed methods for the transportation of packages between logistics bases.", "keywords": [ "Drone", "Scheduling (production processes)", "Computer science", "Aeronautics", "Transport engineering", "Operations research", "Business", "Engineering", "Operations management", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2673-7590/5/2/49/pdf?version=1746064620" }, { "id": "https://openalex.org/W7138875886", "doi": "10.1109/globecom59602.2025.11432691", "title": "Online Integrated Localization and Communication Service Provisioning for UAV-guided Low-altitude Urban Logistics", "year": 2025, "publication_date": "2025-12-08", "type": "conference-paper", "language": null, "authors": [ "Cheng Hu", "Suzhi Bi", "Yong Zeng", "Xiao-Hui Lin" ], "first_author": "Cheng Hu", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Service (business)", "Provisioning", "Information and Communications Technology", "Service provider", "Key (lock)", "Mobile telephony" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/globecom59602.2025.11432691" }, { "id": "https://openalex.org/W4405433126", "doi": "10.54097/0st6q879", "title": "Ribbit Autonomous Drone Network in E-commerce: Statistical Modeling and Optimization", "year": 2024, "publication_date": "2024-12-15", "type": "article", "language": "en", "authors": [ "Zhihao Liu" ], "first_author": "Zhihao Liu", "venue": "Highlights in Science Engineering and Technology", "publisher": null, "abstract": "This study delves into the integration of the Ribbit Autonomous Drone Network within the e-commerce sector, aiming to assess its potential through statistical modeling and optimization. The Ribbit network, with its fleet of drones, promises to revolutionize last-mile delivery by offering faster, more efficient, and environmentally friendly solutions compared to traditional ground transportation. The abstract summarizes the study's objectives, challenges, and findings, emphasizing the significance of addressing operational constraints and optimizing drone operations for e-commerce logistics. The study identifies key challenges such as the variability in package dimensions and weights, the need for real-time tracking, and the dynamic nature of e-commerce demand, which includes seasonal fluctuations. It also highlights the importance of regulatory compliance, energy efficiency, and public acceptance in the successful deployment of autonomous drone networks. Data collection and preprocessing are crucial steps in the model development process, ensuring that the dataset accurately reflects the operational realities and can be effectively used for analysis. The study employs statistical modeling to predict delivery times, energy consumption, and cost efficiency, providing actionable insights for strategic decision-making. It also evaluates the economic and environmental impact of the Ribbit network, suggesting that with the right technological and operational strategies, it can meet the high demands of e-commerce while reducing carbon emissions and operational costs. The analysis reveals that the Ribbit network can handle peak demand periods and adapt to changing consumer behaviors, offering a scalable and sustainable solution for e-commerce logistics.", "keywords": [ "Drone", "E-commerce", "Computer science", "World Wide Web" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://drpress.org/ojs/index.php/HSET/article/download/28221/27714" }, { "id": "https://openalex.org/W7133978656", "doi": "10.32469/10355/111051", "title": "Optimization frameworks for collaborative truck-drone logistics under environmental and operational considerations", "year": 2025, "publication_date": "2025-12-01", "type": "dissertation", "language": "en", "authors": [ "Arash Alizadeh" ], "first_author": "Arash Alizadeh", "venue": null, "publisher": null, "abstract": "The integration of unmanned vehicles (UVs) into last-mile logistics enables costefficient, rapid, and environmentally sustainable deliveries. Yet, most prior work treats operational and environmental factors separately and simplifies key dependencies such as speed regulation, roadway grade, and coordination among heterogeneous vehicles. This dissertation develops a unified suite of optimization frameworks that address these gaps through three progressively enriched variants, each supported by mixed-integer programming (MIP) and advanced metaheuristics. The first variant introduces the Truck Multi-Drone Pollution Routing Problem with Pickup and Delivery under Variable Truck Speeds (TMD-PRP-PDVS). The formulation integrates multi-drone coordination, segment-specific truck speed control, and weight-dependent fuel and energy models. A MIP minimizes truck fuel, drone energy, and labor costs, while an adaptive large neighborhood search (ALNS) efficiently solves larger instances. Experiments show that collaboration with drones reduces total cost by about 40 percent relative to truck-only delivery, and speed optimization yields an additional 7–13 percent reduction across contrasting cost regimes. The second variant extends the framework to the Gradient-Aware Truck–Drone Pollution Routing Problem with Adaptive Drone Deployment Sites and Soft Time Windows (GA-TDPRP-AS-STW), explicitly modeling roadway slopes, variable truck speeds, and flexible drone deployment sites under penalty-based time windows. Both a MIP and an enhanced ALNS are developed. On small benchmarks, the MIP proves optimality and the ALNS attains near-optimal solutions; on larger sets (up to 100 customers), the ALNS scales effectively. Collaboration delivers 33–40 percent savings over truck-only, and speed optimization contributes a further 6–12 percent, reaching up to 62 percent total savings. Elevation data and adaptive sites expand synchronization options and lower fuel consumption even when route length increases modestly. Lastly, the third variant, A Synergistic Truck-Drone-Robot System for Last-Mile Delivery with Hand-Off Points and Microstations for Dynamic Repositioning, unifies tri-vehicle operations with a network of micro-/docking stations that enable parallel service, recovery without truck waiting, and dynamic drone and ADR repositioning. For this variant, an ALNS developed to coordinate facility usage, routing, and allocation decisions. Computational studies indicate 20–38 percent reductions in completion time versus truck-only baselines, with additional gains when hybrid trucks carry UVs between stations. Across variants, extensive experiments demonstrate that integrating environmental characteristics, speed selection, adaptive deployment sites, and facility-aided repositioning materially improves both cost and completion time performance. The resulting models and algorithms provide actionable guidance for designing sustainable, multi-agent last-mile networks.", "keywords": [ "Truck", "Software deployment", "Drone", "Variable (mathematics)", "Routing (electronic design automation)", "Key (lock)", "Vehicle routing problem", "Throughput", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.32469/10355/111051" }, { "id": "https://openalex.org/W4401280202", "doi": "10.2139/ssrn.4913582", "title": "Reliable Design of Hub-Based Truck-Drone Cooperative Delivery System Under Uncertainty", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Bo Zhang", "Meng Zhao", "Jiateng Yin", "Lixing Yang", "Xiangpei Hu" ], "first_author": "Bo Zhang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Computer science", "Engineering", "Systems engineering", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.2139/ssrn.4913582" }, { "id": "https://openalex.org/W4392982396", "doi": "10.1109/icaiccit60255.2023.10465955", "title": "Advanced Aerial Network Nodes for Next-Generation Wireless Infrastructure", "year": 2023, "publication_date": "2023-11-23", "type": "conference-paper", "language": "en", "authors": [ "Abhirup Bhawal", "M K Ranganathaswamy", "Trapty Agrawal" ], "first_author": "Abhirup Bhawal", "venue": null, "publisher": null, "abstract": "This study explores the use of drones with mobile capacity as airborne hubs for connection to the last mile, highlighting the requirement of high-fidelity, low-latency communications. The main goal is to improve on-demand connection to the internet by using Future Aerial Networks (FANETs), which group drone as handheld intermediaries. The research thoroughly examines important factors such energy availability, flexibility, communication effectiveness while ideal node placement, and adaptable optimizations. This study work’s basic core is deeply shaped by these components. The study also improves the fundamental ideas of mobile network architecture to accommodate novel app possibilities and reduce operating expenses associated with various ad hoc installs. The thoroughness of this research opens the door for new transmission concepts by illuminating the advantages and difficulties of using drone-based overhead intermediaries.", "keywords": [ "Computer network", "Computer science", "Wireless network", "Wireless", "Telecommunications" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icaiccit60255.2023.10465955" }, { "id": "https://openalex.org/W4414615223", "doi": "10.59573/emsj.9(5).2025.49", "title": "Revolutionizing Healthcare Logistics: Implementation of AI-Powered Drones for Medication Distribution", "year": 2025, "publication_date": "2025-09-16", "type": "article", "language": "en", "authors": [ "Harish Balakrishnan Vimala" ], "first_author": "Harish Balakrishnan Vimala", "venue": "European Modern Studies Journal", "publisher": null, "abstract": "The use of artificial intelligence-driven drones in healthcare logistics is a revolutionary solution to age-old problems in medical supply chain management, especially for remote and infrastructure-lacking areas around the world. This article discusses the extent of AI-driven drone applications in healthcare delivery systems, reviewing their technical architecture, practical applications, and potential to transform global healthcare accessibility. By synthesizing analysis of deployments in a variety of geographical settings, ranging from early-stage programs in Rwanda and Ghana to pilot studies in developed countries, this article illustrates the ways in which drone technology solves key last-mile delivery issues while significantly enhancing health outcomes. The article examines the intricate technical architecture integrating autonomous flight, machine learning algorithms, and sophisticated sensor suites that power these systems to function in high-complexity environments. Although recognizing regulatory hurdles, privacy issues, and infrastructure needs, the article points to sizable advantages such as unprecedented decreases in delivery times, enhanced cost savings, and enormous environmental benefits through carbon emissions reduction. The article shows that AI-powered healthcare drones not only enhance real-time healthcare delivery capabilities but also set the basis for sustainable, equitable healthcare systems that can deliver efficiently to underserved groups everywhere in the world, with ongoing evolution in 5G technology and incorporation of green energy providing even more potential for transforming global healthcare logistics.", "keywords": [ "Drone", "Health care", "Supply chain", "Healthcare delivery", "Function (biology)", "Variety (cybernetics)", "Architecture", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://lorojournals.com/index.php/emsj/article/download/1621/1576" }, { "id": "https://openalex.org/W7160275102", "doi": "10.1109/wacv61042.2026.00831", "title": "FlyPose: Towards Robust Human Pose Estimation From Aerial Views", "year": 2026, "publication_date": "2026-03-06", "type": "conference-paper", "language": null, "authors": [ "Hassaan Farooq", "Marvin Brenner", "Peter Stütz" ], "first_author": "Hassaan Farooq", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) are increasingly deployed in close proximity to humans for applications such as parcel delivery, traffic monitoring, disaster response and infrastructure inspections. Ensuring safe and reliable operation in these human-populated environments demands accurate perception of human poses and actions from an aerial viewpoint. This perspective challenges existing methods with low resolution, steep viewing angles and (self-)occlusion, especially if the application demands realtime feasibile models. We train and deploy FlyPose, a lightweight top-down human pose estimation pipeline for aerial imagery. Through multi-dataset training, we achieve an average improvement of 6.8 mAP in person detection across the test-sets of Manipal-UAV, VisDrone, HIT-UAV as well as our custom dataset. For 2D human pose estimation we report an improvement of 16.3 mAP on the challenging UAV-Human dataset. FlyPose runs with an inference latency of ~20 milliseconds including preprocessing on a Jetson Orin AGX Developer Kit and is deployed onboard a quadrotor UAV during flight experiments. We also publish FlyPose-104, a small but challenging aerial human pose estimation dataset, that includes manual annotations from difficult aerial perspectives: https://github.com/farooqhassaan/FlyPose.", "keywords": [ "Pose", "3D pose estimation", "Articulated body pose estimation", "Feature (linguistics)", "Pattern recognition (psychology)", "Estimation" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wacv61042.2026.00831" }, { "id": "https://openalex.org/W7204473241", "doi": "10.3390/drones10090654", "title": "A Four-Dimensional Planning Framework for Drone-Enabled Mobility Systems: Integrating Goods, Information, Sensing, and Human Mobility", "year": 2026, "publication_date": "2026-08-27", "type": "article", "language": "en", "authors": [ "Lorenzo Brocchini", "Chenxi Wang", "Antonio Pratelli", "Dorothée Conte", "Alessandro Farina" ], "first_author": "Lorenzo Brocchini", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems.", "keywords": [ "Drone", "Conceptual framework", "Mobility model", "Emergency management", "Public transport", "Conceptual model", "Component (thermodynamics)", "Mobile device" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10090654" }, { "id": "https://openalex.org/W2735173165", "doi": null, "title": "Smartphones, Drones and IoT: Security and Privacy in Heterogeneous Smart Devices", "year": 2017, "publication_date": "2017-01-01", "type": "dissertation", "language": "en", "authors": [ "Daluwathumulla Gamage Kanishka Ariyapala" ], "first_author": "Daluwathumulla Gamage Kanishka Ariyapala", "venue": "Florence Research (University of Florence)", "publisher": "University of Florence", "abstract": "Heterogeneous computing devices are surrounding us in our day-to-day life at an unprecedented rate and they are showing promising capabilities. For example, the drone Loon Copter is one such device providing an unrestricted mobility in air, surface and underwater. Similarly, smartphones and IoT are also enabling to sense our environment. Many of these devices are embedded with processing, sensing, software and communication capabilities, allowing many services to be built on top. In the near future these connected devices will be everywhere from smart cities, factories to our homes and even on our bodies. In order to reach the full potential of these emerging devices, a prominent requirement, “security by design\", must be fulfilled to make the technology ready for mass adaptation. Following this direction, we focus on security and privacy issues of three heterogeneous devices: (i) Smartphone related security and privacy issues; (ii) Application of drones for secure localization; and (iii) IoT related security issues. In the first part of this dissertation, we look into security and privacy challenges in smartphones. Smartphones are taking a leading role in binding most of the heterogeneous computing devices and also getting cluttered with a lot of personal data. We investigated three security related issues in smartphones: i) malware detection, ii) preserving anonymity in mobile cloud communications, and iii) analyzing the energy consumption of cryptographic protocols to improve user experiences. As for point i), in most attack scenarios an adversary takes local or remote control of a mobile device (by leveraging system vulnerabilities via malicious apps), and sends the collected information from the smartphone to a remote web server. This undermines the users security and privacy, and we propose a new approach for detecting malware by focusing on network communications. As for point ii), Smartphone applications are increasingly relying on cloud services such as online banking, instant messaging and file exchange. For an external observer, this communication side channel may reveal a lot of information. Strong adversaries like government agencies are also proposing these channels as a means to monitor their surveillance targets. Similarly, if the mobile network providers and cloud service providers collude together, they can violate the privacy of the users. We propose an end-to-end anonymous communications protocol for delay-tolerant applications (similar to Whatsapp or Email), to protect user privacy and prove the security properties of the protocol under this strong attack model. Finally, as for point iii), we analyze the energy consumption of cryptographic protocols running on smartphones. The number of web services accessed over encrypted traffic is rapidly growing, especially via SSL/TLS. In our investigation, we focused on TLS and show how TLS session resume can greatly save energy by avoiding asymmetric cryptographic operations. We further propose Cloud aided TLS (CaT). In the second part of this dissertation, we explore the possibility of using the emerging drone technology to solve the secure location verification problem. Many innovations are emerging using drones such as last mile delivery and emergency response. Many dependable distributed systems are vulnerable to node displacement attacks. For example, a hostile actor physically moving few sensors in a pollution monitoring system can easily disrupt the monitoring. This displacement attack is simple, but difficult to detect. Current solutions require several fixed anchor nodes with trusted positions. We propose VerifierBee, which replace all the fixed anchors with a single drone that flies through a sequence of waypoints. VerifierBee, finds a good approximation of the shortest path, and at the same time it respects a set of requirements about drone controllability, localization precision and communication range. The third part of this dissertation focuses on IoT related security issues. In many scenarios, IoT systems comprise of widely deployed sensors and actuators with connectivity. Many of these sensors are battery operated, with low processing power and left unattended after deployment. Therefore, lightweight low-power security protocols are needed. In this part of the dissertation we propose a framework to detect IoT sensor node actions by observing the encrypted communication traffic. In particular, IETF standardized DTLS encrypted traffic. There are many recent incidents about DDoS attacks using compromised IoT devices and our work steps in this direction to detect any compromised nodes.", "keywords": [ "Drone", "Computer security", "Internet privacy", "Internet of Things", "Computer science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/2158/1079068" }, { "id": "https://openalex.org/W4408697408", "doi": "10.1109/tase.2025.3553200", "title": "A Branch-and-Price Algorithm for the Urban Aerial Delivery Problem With Energy Constraints", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Zhi Pei", "Zhaohui Liang", "Jiayan Huang", "Tao Fang", "Na Li" ], "first_author": "Zhi Pei", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In this paper, an urban aerial delivery problem (UADP) is investigated, where the parcel transportation service is accomplished by drones in an urban setting. The aim of the problem is to minimize the total service completion time, by taking into account of the flow balance, the energy consumption, and the response time window. To fully explore the structure of the UADP, a mixed integer linear programming (MILP) model is constructed based on an arc-flow scheme. However, directly handling the UADP with commercial solvers is time consuming. In order to enhance the responsiveness of urban courier services and speed up the solving process, a set-covering model (UADP-SC) is proposed with a linear programming based relaxation. Then a branch-and-price algorithm is designed with pricing accelerating strategies based on heuristics. The computational experiments show that the proposed branch-and-price algorithm outperforms the off-the-shelf commercial solvers in terms of computation efficiency. In the mean time, the proposed algorithm can also serve to obtain optimal battery swapping and path planing decisions in face of the large-scale urban aerial delivery problem with energy constraints.Note to Practitioners—With the intensification of the aging population issue, the manual labor costs in logistics have sharply increased. Simultaneously, advancements in drone technology enable uncrewed aerial vehicles to participate in logistics distribution systems, addressing the last-mile delivery challenge. In the planning of drone delivery routes, the constraint of drone batteries cannot be ignored. This constraint not only affects delivery safety but also impacts delivery efficiency—both crucial considerations for decision-makers. Consequently, we propose a model considering the energy constraints. We introduce a branch-and-pricing algorithm to expedite the problem solving. The results show that the proposed algorithm performs well across various problem scales. Moreover, adopting a strategy of replacing batteries only when necessary can save approximately 17% to 23% of the total completion time. We also conducted performance comparisons under different ratios of orders to drones, providing decision-makers with a useful benchmark.", "keywords": [ "Mathematical optimization", "Energy (signal processing)", "Computer science", "Algorithm", "Engineering", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tase.2025.3553200" }, { "id": "https://openalex.org/W4288772330", "doi": "10.2139/ssrn.4176344", "title": "The Drone Location-Routing Problem with Homogeneous Demand and Load-Dependent Energy Consumption", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti" ], "first_author": "Maria Elena Bruni", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Homogeneous", "Energy consumption", "Consumption (sociology)", "Routing (electronic design automation)", "Computer science", "Economics", "Computer network", "Engineering", "Electrical engineering", "Mathematics", "Sociology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4176344" }, { "id": "https://openalex.org/W4416477987", "doi": "10.1145/3680207.3765594", "title": "Demo: HawkEye: Practical In-Flight Obstacle Avoidance with Event Camera and LiDAR Fusion", "year": 2025, "publication_date": "2025-11-03", "type": "conference-paper", "language": null, "authors": [ "Wenhua Ding", "Zhengli Zhang", "Xin Xu", "Haoyang Wang", "Yinan Zhu", "Shilong Ji", "Xin Zhou", "Jingao Xu", "Dongyue Huang", "Xinlei Chen" ], "first_author": "Wenhua Ding", "venue": null, "publisher": null, "abstract": "Drones are increasingly used in applications such as last-mile delivery and infrastructure inspection, but their safe operation, especially in high-speed scenarios, remains a critical challenge. Existing vision- and LiDAR-based obstacle localization methods suffer from motion blur, latency, and low spatio-temporal resolution, making them inadequate for detecting and tracking fast-moving objects. In this work, we present HawkEye, a drone obstacle avoidance system that fuses event cameras and LiDAR to achieve high-frequency, accurate 3D tracking of dynamic objects. By leveraging the complementary strengths of both sensors, Hawkeye enables robust real-time sensing and safe evasive maneuvers, addressing a key requirement for the large-scale deployment of autonomous drones. Demo: https://wenhua00.github.io/HawkEye/.", "keywords": [ "Drone", "Obstacle avoidance", "Obstacle", "Software deployment", "Event (particle physics)", "Key (lock)", "Lidar" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dl.acm.org/doi/pdf/10.1145/3680207.3765594" }, { "id": "https://openalex.org/W7140210966", "doi": "10.1016/j.cie.2026.111992", "title": "A regulatory-no-fly-zone-aware drone-rider takeouts routing optimization", "year": 2026, "publication_date": "2026-03-24", "type": "article", "language": "en", "authors": [ "Fuqiang Lu", "Mathias Mankoe", "Hualing Bi" ], "first_author": "Fuqiang Lu", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Cluster analysis", "Tabu search", "Heuristic", "Key (lock)", "Vehicle routing problem", "Variable neighborhood search", "Routing (electronic design automation)", "Drone", "Range (aeronautics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2026.111992" }, { "id": "https://openalex.org/W4417094462", "doi": "10.1080/13504509.2025.2595056", "title": "Effectiveness of early-warning systems and disaster risk reduction for rural food security under hydro-meteorological hazards in Sub-Saharan Africa: systematic review", "year": 2025, "publication_date": "2025-12-07", "type": "review", "language": "en", "authors": [ "Degfie Teku", "Getasew Daru Tariku" ], "first_author": "Degfie Teku", "venue": "International Journal of Sustainable Development & World Ecology", "publisher": "Taylor & Francis", "abstract": "Sub-Saharan Africa (SSA) faces escalating hydro-meteorological hazards that disrupt rural food systems, intensify hunger, and undermine livelihoods. This systematic review synthesizes evidence from 2010 to August 2025 on the effectiveness of Early-Warning Systems (EWS) and Disaster Risk Reduction (DRR) interventions in protecting rural food security. A PRISMA-style screening identified 320 records, with 29 duplicates removed, and 212 full-text articles assessed for eligibility, with data extracted independently by two reviewers. Sources include peer-reviewed studies, program evaluations, regional assessments, and institutional reports from FEWS-NET, ICPAC, IPCC, FAO, WFP, WHO, and CREWS. The review addresses three questions: (i) How effective are EWS in hazard detection, dissemination, and anticipatory action?; (ii) What are the impacts of DRR measures including social protection, risk financing, nature-based solutions, water management, and community-based approaches on food security?; and (iii) Which governance, institutional, and socio-economic factors facilitate or constrain adoption? Findings indicate EWS and DRR reduce vulnerability to droughts and floods when systems are reliable, people-centered, and linked to pre-arranged finance and delivery mechanisms. Integrated approaches combining early warnings with anticipatory cash, climate-smart land and water management, and inclusive social protection are most effective. Constraints include fragmented governance, underfunded hydromet-infrastructure, weak last-mile communication, insurance basis risk, and inequities excluding pastoralists, women, and remote communities. Emerging technologies-satellite monitoring, AI, IoT, drones, and digital advisories enhance forecasting if co-produced, ethically governed, and sustainably financed. Strengthening legal-frameworks, regional interoperability, shock-responsive social protection, and harmonized monitoring aligned with Sendai Target C and SDG-2 is essential for resilient, equitable, adaptive food systems in SSA.", "keywords": [ "Food security", "Disaster risk reduction", "Reduction (mathematics)", "Vulnerability (computing)", "Hazard", "Natural disaster", "Food supply" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/13504509.2025.2595056" }, { "id": "https://openalex.org/W4390903311", "doi": "10.33619/2414-2948/98/35", "title": "Optimizing Logistic Processes and Transport Challenges in the Dynamic Landscape of Online Commerce", "year": 2024, "publication_date": "2024-01-15", "type": "article", "language": "en", "authors": [ "Нуркасым Кылычбекович Аркабаев", "G. Doolotbek-kyzy", "S. Amanbaev" ], "first_author": "Нуркасым Кылычбекович Аркабаев", "venue": "Bulletin of Science and Practice", "publisher": "Publishing Center Science and Practice", "abstract": "The article discusses modern challenges and trends in logistics associated with dynamic online commerce. It explores the optimization of logistical processes and solutions to transport problems in the context of evolving needs in the contemporary market. The article presents key innovative approaches, such as using Internet of Things technologies, artificial intelligence, and big data analytics to enhance route management, reduce excess inventory, and ensure precise delivery to endpoints. It also highlights the importance of environmental sustainability in logistics and adaptability of logistical networks to quickly respond to changes in demand. Overall, the article presents modern logistics management strategies necessary for effective operation in the online commerce sphere. The article provides a detailed analysis of the challenges faced by logistics in the dynamic online market and offers efficient solutions for optimizing transport tasks. Principles like Just-in-Time and Lean Logistics are considered pivotal methods for reducing excess inventory and improving inventory management, contributing to a more efficient supply chain. Additionally, it covers modern approaches to last-mile logistics, including the use of drones, robots, and other innovations for delivering goods to endpoints. This revolutionizes delivery methods and enhances speed and efficiency in customer service. In conclusion, it emphasizes the importance of sustainability and environmental responsibility in logistical schemes, encouraging the use of eco-friendly transportation and packaging to reduce adverse effects on the environment. The strategies discussed in the article represent vital directions for the development of contemporary logistics, aimed at efficiency, sustainability, and adaptability in the dynamic landscape of online commerce.", "keywords": [ "Adaptability", "Supply chain", "Sustainability", "Context (archaeology)", "Process management", "Analytics", "Business", "Supply chain management", "Humanitarian Logistics", "Big data", "Computer science", "Risk analysis (engineering)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.33619/2414-2948/98/35" }, { "id": "https://openalex.org/W4237964428", "doi": "10.1553/ita-doss-034en", "title": "A sky full of delivery drones? (ITA dossier no. 34en, April 2018)", "year": 2018, "publication_date": "2018-01-01", "type": "report", "language": "en", "authors": [], "first_author": null, "venue": null, "publisher": null, "abstract": "-> Worldwide, large online retailers, postal operators and numerous start-ups are developing and testing drone-operated parcel deliveries. -> Despite technology not having been fully developed, feasibility has been demonstrated. However, in most countries, the law does not yet allow autonomous drones; specific air traffic rules would have to be developed. -> Omnipresent drone flights would have a series of environmental, safety, ethical, and societal implications that stipulate a need for public debate before implementation and regulation. Authors: Michael Nentwich, Delila Horváth", "keywords": [ "Drone", "Sky", "Geography", "Meteorology", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://epub.oeaw.ac.at/0xc1aa5576_0x00388989.pdf" }, { "id": "https://openalex.org/W7213665632", "doi": "10.1038/s41598-026-68290-3", "title": "Research on route optimization for bus–drone collaborative delivery under time-dependent road networks", "year": 2026, "publication_date": "2026-09-19", "type": "article", "language": "en", "authors": [ "Leiming Hou", "Zejian Zhang", "Mingxu Chen", "Jing Liu", "Chao Ma", "Xun Wu" ], "first_author": "Leiming Hou", "venue": "Scientific Reports", "publisher": "Nature Portfolio", "abstract": "With the increasing demand for time-sensitive urban distribution services, traditional ground-based last-mile delivery faces challenges such as traffic congestion, rising costs, and unstable timeliness. This study investigates the bus–drone collaborative delivery route optimization problem under time-dependent road network conditions and mixed-cargo demand. Different from most truck–drone delivery studies, fixed-route buses are introduced as mobile transfer platforms for drones, and the coordination between bus stop sequences and drone launch/recovery decisions is considered. A cost-minimization model is developed by incorporating time-dependent bus travel times, drone payload and endurance constraints, customer time windows, and different delivery time requirements of ordinary and fresh products.An improved adaptive large neighborhood search algorithm (IALNS) is proposed, which integrates greedy initialization, multiple destruction and repair operators, adaptive operator weight updating, improved genetic operations, and a simulated annealing acceptance criterion. Computational results show that IALNS reduces delivery costs by 2.69–6.22% compared with ALNS and by 2.79–7.76% compared with GA. The average convergence curve over 10 independent runs indicates that the algorithm converges stably after about 200 iterations. Ablation experiments show that removing key improvement strategies increases the average delivery cost by 2.89–8.82%. Sensitivity analyses further indicate that increasing the proportion of fresh cold-chain customers raises total cost, while improving drone payload capacity helps reduce total cost. This study provides a reference for integrating public transit resources into urban last-mile delivery systems.", "keywords": [ "Payload (computing)", "Drone", "Simulated annealing", "Convergence (economics)", "Food delivery", "Public transport", "Key (lock)", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.nature.com/articles/s41598-026-68290-3_reference.pdf" }, { "id": "https://openalex.org/W7214520495", "doi": "10.1016/j.cor.2026.107689", "title": "The traveling salesman problem with release dates and drone resupply: A new formulation and a matheuristic approach", "year": 2026, "publication_date": "2026-09-01", "type": "article", "language": "en", "authors": [ "Claudia Archetti", "Maurizio Boccia", "Adriano Masone", "Claudio Sterle" ], "first_author": "Claudia Archetti", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Benchmark (surveying)", "Truck", "Schedule", "Exploit", "Matching (statistics)", "Order (exchange)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2026.107689" }, { "id": "https://openalex.org/W7171326958", "doi": "10.3724/sp.j.1249.2026.04377", "title": "Research progress in truck-drone collaborative route planning: a systematic review", "year": 2026, "publication_date": "2026-07-01", "type": "article", "language": "en", "authors": [ "Cui Haipeng", "Li Huan", "Zhang Xuxin", "Chen Xiangsheng", "Hu Mingwei" ], "first_author": "Cui Haipeng", "venue": "JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING", "publisher": "Science Press", "abstract": "To systematically review the research progress in truck-drone collaborative routing, 386 relevant publications retrieved from the Web of Science Core Collection were analyzed using VOSviewer for bibliometric and scientific knowledge mapping, complemented by a qualitative review of representative studies. The development trajectory and knowledge base of this research field were examined from five perspectives: annual publication trends, country and regional distributions, source journals, document co-citation networks, and keyword co-occurrence. Based on the keyword co-occurrence analysis, a research framework encompassing problem types, model development, solution algorithms, and application scenarios was established. The results show that truck-drone collaborative route planning has evolved from the traveling salesman problem (TSP) and the vehicle routing problem (VRP), with research settings gradually expanding from early single-truck-single-drone collaborative delivery to more complex scenarios involving multiple trucks and drones, flexible launch and recovery, multiple customer visits, integrated pickup and delivery, and dynamic operations. Model development have formed a comprehensive constraint framework centered on objective functions, network constraints, resource constraints, service constraints, truck-drone coordination and synchronization constraints, and uncertainty constraints. Solution approaches have evolved through the parallel development of exact algorithms, heuristic and metaheuristic methods, and learning-based methods. In terms of applications, relevant studies have extended from urban last-mile delivery to remote-area logistics, medical logistics, humanitarian logistics, agricultural and forestry operations, and infrastructure inspection. With the rapid advancement of air-ground collaborative transportation technologies, this systematic review not only clarifies the technical evolution of truck-drone collaborative route planning but also provides references for future model development and algorithm design in real-world applications, thereby facilitating the translation of theoretical research into engineering practice.", "keywords": [ "Vehicle routing problem", "Field (mathematics)", "Heuristic", "Resource (disambiguation)", "Memetics", "Knowledge base", "Service (business)", "Constraint (computer-aided design)", "Synchronization (alternating current)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3724/sp.j.1249.2026.04377" }, { "id": "https://openalex.org/W7164905220", "doi": "10.34190/eccws.25.1.4855", "title": "Artificial Intelligence, Drones and Cyber Warfare: Ethical and Anticipated Ethical Issues", "year": 2026, "publication_date": "2026-06-15", "type": "conference-paper", "language": null, "authors": [ "Richard Wilson", "Noah Donnelly" ], "first_author": "Richard Wilson", "venue": "European Conference on Cyber Warfare and Security", "publisher": null, "abstract": "Artificial Intelligence plays an active role when both drones and Cyber Warfare are introduced into kinetic wars. The importance of drones for kinetic warfare points to the importance of cyber warfare for countering drone warfare. A drone that flies, reroutes, and avoids threats without human interaction no longer operates as a remote-controlled vehicle. Instead, it acts autonomously in combat situations. Small quadcopters in Ukraine were equipped with AI navigation packages, enabling them to complete their missions even after Russian troops had jammed their GPS signals. Programs in the US, such as Darpa Evade, are deploying Sikorsky's autonomy MATRIX software in vertical takeoff drones, which nominally have the capabilities of flight and fight without communication with an operator, while they have situational awareness, and continue to achieve their objectives. The most contentious application of AI in targeting military drones is the recognition of targets. Using deep learning models trained on massive amounts of image data, drones can recognize vehicles, artillery, or even people. The AI models utilize visual patterns and infrared signatures to help identify and distinguish between an enemy armored vehicle on a battlefield, hidden in a field, or a civilian tractor, also hidden in the same spot. Russia's KUB loitering munition features an AI visual identification module designed to enhance its search range and accuracy. The Bayraktar TB2 features onboard targeting systems that provide unified target designation and tracking. The TB2's follow-on munition, the Kamankes loitering munition drone, is advertised to have AI-guided optics that enable it to loiter, identify, and autonomously kill any targets (Aviacionline, 2023; Baykar Technology, 2026). A drone that could perform all of these activities could loiter for hours, detect a missile launcher or armor target, and kill without verification by human input at any point. Ukraine's advances in AI are even more unprecedented when factoring in that they are using modular AI kits to sever the \"last mile\" of the target kill chain, reducing the time from hours to minutes. AI proponents argue that accuracy enables speed and precision with minimal risk of unintended consequences. Critics caution that algorithmic-driven lethal decisions can go wrong if an object is incorrectly identified, leaving the laws of armed conflict with challenging and fuzzy questions of moral, legal, and professional liability. This analysis is concerned with identifying how AI can be applied in the context of cyberwarfare to counter the capabilities of AI being employed to enhance the capabilities of Drones as they are currently employed in military operations. In addition, this analysis will focus on ethical and anticipated ethical issues related to how AI can be applied in the context of cyberwarfare operations to counter the capabilities of AI being employed to enhance the capabilities of Drones as they are currently employed in military operations.", "keywords": [ "Drone", "Adversary", "Situation awareness", "Identification (biology)", "Ammunition" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://papers.academic-conferences.org/index.php/eccws/article/download/4855/4383" }, { "id": "https://openalex.org/W4412455077", "doi": "10.1016/j.future.2025.108004", "title": "Digital-twins and machine learning-assisted stable, energy-aware unmanned aerial and ground vehicles delivery in blockchain-enabled crowdsourcing framework", "year": 2025, "publication_date": "2025-07-15", "type": "article", "language": "en", "authors": [ "Feruz K. Elmay", "Maha Kadadha", "Shakti Singh", "Rabeb Mizouni", "Hadi Otrok", "Azzam Mourad" ], "first_author": "Feruz K. Elmay", "venue": "Future Generation Computer Systems", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Crowdsourcing", "Computer science", "Blockchain", "Unmanned ground vehicle", "Artificial intelligence", "Drone", "Energy (signal processing)", "Human–computer interaction", "Computer security", "World Wide Web" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.future.2025.108004" }, { "id": "https://openalex.org/W7126463367", "doi": "10.2139/ssrn.6161432", "title": "Public acceptance as a constraint on drone logistics deployment: a socio-technical extension of technology acceptance model", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Anika Tahsin", "Daniela Paddeu" ], "first_author": "Anika Tahsin", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Extension (predicate logic)", "Technology acceptance model", "Drone", "Constraint (computer-aided design)", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6161432" }, { "id": "https://openalex.org/W7137178326", "doi": "10.48175/ijarsct-31611", "title": "Smart Energy Optimization for Autonomous Delivery Drones : Insights for E-Commerce Applications", "year": 2026, "publication_date": "2026-03-16", "type": "article", "language": null, "authors": [ "Mr. Sagar Deo Mr. Harshal Shelar", "Mr. Bhimrao Dabhade Mr. Sushant Sananse" ], "first_author": "Mr. Sagar Deo Mr. Harshal Shelar", "venue": "International Journal of Advanced Research in Science Communication and Technology", "publisher": "Shivkrupa Publication's", "abstract": "Last-mile delivery is expensive, slow, and getting harder to scale. E-commerce volumes keep climbing, and the logistics infrastructure underneath them is struggling to keep up. Autonomous delivery drones offer a way out of that bottleneck, but battery range keeps getting in the way. Most commercial drones fly for 20 to 45 minutes before they need to land and recharge, which makes anything beyond a short urban hop logistically awkward. This paper reports on a series of controlled flight experiments designed to find out how much energy can be saved through smarter operational choices without changing the hardware at all. We varied payload weight, cruise speed, flight altitude, and route distance across 120 flight trials and tracked battery discharge, effective delivery range, and energy consumed per kilogram of cargo. The short version: flying at the right speed and altitude, with loads consolidated where possible, cuts energy use by up to 34% compared to default operations. We also propose a three-module Smart Energy Management Framework (SEMF) that puts these findings into a practical structure e-commerce operators can actually implement..", "keywords": [ "Drone", "Payload (computing)", "Energy (signal processing)", "Battery (electricity)", "Range (aeronautics)", "Cruise" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.48175/ijarsct-31611" }, { "id": "https://openalex.org/W7214218602", "doi": "10.71548/22626", "title": "Optimizing Drone-Enabled Logistics: Mathematical Models for Regulatory-Constrained Delivery in Urban and Rural Environments", "year": 2025, "publication_date": "2025-01-01", "type": "dissertation", "language": "en", "authors": [ "Shamik Pushkar" ], "first_author": "Shamik Pushkar", "venue": "MacSphere (McMaster University)", "publisher": "McMaster University", "abstract": "The integration of drones into last-mile delivery systems has garnered significant attention across sectors such as e-commerce, healthcare, and emergency logistics. Despite technological advancements and industry investment, widespread adoption remains limited due to regulatory, financial, and operational challenges. This thesis investigates the feasibility and efficiency of drone-assisted delivery under real-world constraints, with a particular focus on regulatory frameworks. Three mathematical models are developed. The first evaluates the impact of no-fly zones and flight distance restrictions on coordinated truck-drone delivery. The second introduces a hybrid logistics model incorporating droneports to facilitate delivery in urban areas with zoning laws. The third addresses a rural healthcare application, proposing a synchronized delivery and scheduling model for livestock vaccine administration, balancing workload among veterinarians. Each model is formulated as a Mixed Integer Linear Program (MILP), with computational experiments validating their performance. Heuristic and matheuristic algorithms are proposed to solve large-scale instances. Results demonstrate that while drones can enhance delivery efficiency and equity, their benefits are significantly influenced by regulatory severity and operational design.", "keywords": [ "Scheduling (production processes)", "Drone", "Heuristic", "Workload", "Mathematical model", "Integer programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.71548/22626" }, { "id": "https://openalex.org/W2993570899", "doi": "10.5281/zenodo.21591753", "title": "GPS Based Fully Ardupilot Drone Using APM Flight Controller and Sensors", "year": 2018, "publication_date": "2018-03-30", "type": "article", "language": "en", "authors": [ "Kundeti Muralidhar", "Mayank Agarwal", "K. Akash Reddy", "M. Monica", "E. Thirumaleshwari" ], "first_author": "Kundeti Muralidhar", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The project goal was to design a semi-autonomous Quad copter capable of self-sustained flight via wireless communications. The Quad copter was designed to be small enough so that costs would be minimized. Drones can significantly accelerate delivery times and reduce the human cost associated with the delivery. This report examines the value chain and opportunities in the delivery drones market. It also discusses the barriers for adoption. It concludes with our case for drones to handle the last mile of delivery of most lightweight packages. The drone cameras are operated above 500 meters and covered a radius of three kilometers. The cameras were also operated in the lanes and by lanes of the town to check the movement of people. This project also used to detect obstructs and alerts form collision occurrence. It also used to detect the fire, temperature, humidity and sends an alert message to our mobile.", "keywords": [ "Drone", "Global Positioning System", "Computer science", "Real-time computing", "Aerospace engineering", "Aeronautics", "Remote sensing", "Engineering", "Geology", "Operating system", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.21591753" }, { "id": "https://openalex.org/W4391302768", "doi": "10.2514/6.2024-0232", "title": "Risk Assessment for sUAS in Urban Environments: A Comprehensive Analysis, Modelling and Validation for Safe Operations", "year": 2024, "publication_date": "2024-01-04", "type": "conference-paper", "language": "en", "authors": [ "Victor Celdran Martinez", "Hyo‐Sang Shin", "Antonios Tsourdos" ], "first_author": "Victor Celdran Martinez", "venue": null, "publisher": null, "abstract": "The rapidly growing number of applications for small Unmanned Aerial Systems (sUAS) in last-mile applications within metropolitan environments creates a complex airspace and ground safety scenario, where numerous risks must be considered to accomplish safe operations. The built-up and heterogeneously shaped geometry, together with the densely populated and transited nature of urban scenes, define a challenging scene for piloted and autonomous missions, where operators need to consider performance-based and third-party risks. In response to the increasing requirements inherent from urban scenarios, this paper proposes an integrated and comprehensive risk model for urban sUAS operations, composed by different risk layers designed for real-world scenarios, and validated through simulated drone flights and 3D risk-based navigation. By identifying the different risk requirements for sUAS operations, first party risks – including navigation performance, data link monitoring and collision avoidance – are computed within a photorealistic simulation environment for a discretized airspace representation. On top of this, trajectory based third party risks are modelled to identify potential routes subject to drone failure and consequent fatality and third party damage, as well as societal impact in terms of noise and privacy. Risk-based navigation techniques are implemented to validate the resulting model, including classical path planning and reinforcement learning. The results enable the perception of urban scenes associated risks through the lenses of risk modelling, providing a valuable methodology for sUAS urban operations and contributing to safer drone flights.", "keywords": [ "Computer science", "Systems engineering", "Risk analysis (engineering)", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dspace.lib.cranfield.ac.uk/bitstreams/f8761d15-8d81-4f55-88a8-9429dc39fbff/download" }, { "id": "https://openalex.org/W4416009004", "doi": "10.1117/12.3089283", "title": "Research on collaborative subway + drone low-altitude logistics distribution mode", "year": 2025, "publication_date": "2025-11-07", "type": "conference-abstract", "language": null, "authors": [ "Longhai Li", "Ye He" ], "first_author": "Longhai Li", "venue": null, "publisher": null, "abstract": "With the development of e-commerce and high-tech manufacturing, the traditional ground distribution model is facing prominent dilemmas. This study proposes a \"metro + drone\" low-altitude collaborative distribution model, integrating underground and low-altitude transportation resources to build a three-dimensional logistics system. Based on the genetic algorithm, a mathematical model considering transportation costs, transfer costs, and startup costs is constructed to plan distribution routes. The case study of Luoyang proves that this model can reduce distribution costs, improve efficiency, alleviate traffic pressure, and provide a new solution for urban logistics distribution with both theoretical and practical values.", "keywords": [ "Drone", "Distribution (mathematics)", "Mode (computer interface)", "Plan (archaeology)", "Genetic algorithm", "Trip distribution" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3089283" }, { "id": "https://openalex.org/W7147471444", "doi": "10.1007/978-3-032-21628-1_35", "title": "Balancing Sustainability and Service in Drone-Assisted Logistics via a Hybrid NSGA-II-VNS Approach", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Nguyen Thi My Binh", "Do Thi Ngoc Huyen", "Ho Viet Duc Luong", "Nguyen Thi Kim Son", "Trinh Van Chien", "Ban Ha Bang", "Nguyen Van Truong", "Dang Trong Hop", "Nguyen Quang Dai", "Tran Hung Cuong", "Duong Thi Hien Thanh" ], "first_author": "Nguyen Thi My Binh", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Sustainability", "Service (business)", "Thesaurus", "Supply chain", "Chain (unit)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-21628-1_35" }, { "id": "https://openalex.org/W4411572777", "doi": "10.2139/ssrn.5317463", "title": "A Shared Framework of Unpairing Truck-Drone Coordination with Cross-Fleet Reallocation", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Yuhang Han", "Yuxuan Sun", "Wang Li", "Dong Sasha" ], "first_author": "Yuhang Han", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Computer science", "Transport engineering", "Aeronautics", "Engineering", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5317463" }, { "id": "https://openalex.org/W4405949288", "doi": "10.58915/ijact.v4.2024.1579", "title": "Optimizing Drone-Based Delivery Using the Pity Beetle Algorithm A Novel Approach.", "year": 2024, "publication_date": "2024-12-31", "type": "article", "language": "en", "authors": [ "Yugeswary Kanesen" ], "first_author": "Yugeswary Kanesen", "venue": "International Journal of Advanced Communication Technology (IJACT)", "publisher": null, "abstract": "The Travelling Salesman Problem with Drones (TSP-D) has emerged as a significant optimization challenge in last-mile delivery systems. Addressing scalability issues with datasets exceeding 250 nodes, this study introduces the Pity Beetle Algorithm (PBA), a novel metaheuristic. The PBA demonstrates superior performance in balancing exploration and exploitation to optimize delivery routes effectively. Results from new simulations conducted for this journal show delivery time reductions in a broader range of scenarios, with improvements up to 60% over standard benchmarks. Statistical analyses confirm the algorithm’s capability to enhance computational efficiency and scalability. Additionally, the PBA’s dynamic tuning mechanisms enable it to adapt effectively to varying dataset sizes and configurations. Beyond its computational benefits, this study underscores the real-world applicability of the PBA in logistics, providing industries with a robust tool to optimize delivery times and reduce operational costs. This research opens avenues for integrating PBA with hybrid models and real-time optimization techniques, further enhancing its potential to tackle complex logistical challenges.", "keywords": [ "Drone", "Pity", "Computer science", "Algorithm", "Artificial intelligence", "Biology", "Art", "Literature", "Botany" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ejournal.unimap.edu.my/index.php/ijact/article/download/1579/1030" }, { "id": "https://openalex.org/W4403085988", "doi": "10.36676/irt.v8.i4.1515", "title": "AI-Enhanced Supply Chain Optimization: A Study of Indian E-Commerce Sector", "year": 2022, "publication_date": "2022-12-27", "type": "article", "language": "en", "authors": [ "Pooja Verma" ], "first_author": "Pooja Verma", "venue": "Innovative Research Thoughts", "publisher": null, "abstract": "This paper investigates the role of Artificial Intelligence (AI) in optimizing supply chains in India's rapidly growing e-commerce sector. AI-powered algorithms such as Machine Learning (ML) and Reinforcement Learning (RL) are increasingly being deployed to enhance supply chain efficiency by predicting demand, optimizing inventory, and improving last-mile delivery. The study analyzes the performance of AI models like Decision Trees, Random Forest, and Deep Q-Networks (DQN) in supply chain management, focusing on real-time decision-making capabilities and cost reduction. Case studies from leading Indian e-commerce companies, including Flipkart and Amazon India, are presented to demonstrate the impact of AI on reducing operational inefficiencies and improving customer satisfaction. The research also explores the challenges of implementing AI in supply chain operations in India, including data integration issues, infrastructure limitations, and workforce adaptation to AI technologies. Finally, the paper discusses future trends in AI-driven supply chain management, such as autonomous vehicles and drone deliveries, which hold immense potential in the Indian context.", "keywords": [ "Supply chain", "Supply chain optimization", "Business", "Chain (unit)", "Industrial organization", "E-commerce", "Computer science", "Supply chain management", "Commerce", "Marketing", "World Wide Web" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.36676/irt.v8.i4.1515" }, { "id": "https://openalex.org/W4387390066", "doi": "10.48550/arxiv.2310.02726", "title": "Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms", "year": 2023, "publication_date": "2023-10-04", "type": "preprint", "language": "en", "authors": [ "Akash Kopparam Sreedhara", "Deepesh Padala", "Shashank Mahesh", "Kai Cui", "Mengguang Li", "Heinz Koeppl" ], "first_author": "Akash Kopparam Sreedhara", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Swarms of aerial drones have recently been considered for last-mile deliveries in urban logistics or automated construction. At the same time, collaborative transportation of payloads by multiple drones is another important area of recent research. However, efficient coordination algorithms for collaborative transportation of many payloads by many drones remain to be considered. In this work, we formulate the collaborative transportation of payloads by a swarm of drones as a novel, under-capacitated generalization of vehicle routing problems (VRP), which may also be of separate interest. In contrast to standard VRP and capacitated VRP, we must additionally consider waiting times for payloads lifted cooperatively by multiple drones, and the corresponding coordination. Algorithmically, we provide a solution encoding that avoids deadlocks and formulate an appropriate alternating minimization scheme to solve the problem. On the hardware side, we integrate our algorithms with collision avoidance and drone controllers. The approach and the impact of the system integration are successfully verified empirically, both on a swarm of real nano-quadcopters and for large swarms in simulation. Overall, we provide a framework for collaborative transportation with aerial drone swarms, that uses only as many drones as necessary for the transportation of any single payload.", "keywords": [ "Drone", "Swarm behaviour", "Computer science", "Vehicle routing problem", "Payload (computing)", "Generalization", "Routing (electronic design automation)", "Distributed computing", "Computer network", "Artificial intelligence", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2310.02726" }, { "id": "https://openalex.org/W4402799224", "doi": "10.2139/ssrn.4966127", "title": "Optimizing the Efficiency of Logistics Using Hybrid Truck-Drone Collaborative Delivery", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Shih‐Hao Lu", "R. Thundil Karuppa Raj", "Yun-Hsin Lin" ], "first_author": "Shih‐Hao Lu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Computer science", "Transport engineering", "Business", "Aeronautics", "Engineering", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.2139/ssrn.4966127" }, { "id": "https://openalex.org/W7133311929", "doi": "10.3390/aerospace13030234", "title": "Centralized Landing Flow Merging for Drones Using Deep Reinforcement Learning", "year": 2026, "publication_date": "2026-03-03", "type": "article", "language": "en", "authors": [ "Sasha Vlaskin", "Dieudonne Groot", "Emmanuel Sunil", "Joost Ellerbroek", "Jacco M. Hoekstra", "Dennis Nieuwenhuisen" ], "first_author": "Sasha Vlaskin", "venue": "Aerospace", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Drones are expected to support applications such as emergency response, parcel delivery, and infrastructure monitoring in dense urban airspaces, creating traffic levels that are unmanageable for human operators. Autonomous separation management is therefore essential, combining strategic and tactical control to prevent conflicts. This paper addresses the tactical landing phase by introducing a centralized landing flow manager—a reinforcement learning (RL) agent that adjusts drone speed and heading to merge landing flows safely and efficiently prior to a final approach fix. The objective of the work was to demonstrate the potential of reinforcement learning in this novel context, by implementing and evaluating it in simulation and testing its capabilities with 10 concurrent landing drones. The RL agent learns to successfully separate traffic, thereby lowering intrusion counts compared to the baseline autopilot, but is outperformed in safety by the decentralized Modified Voltage Potential (MVP) method due to outlier scenarios. Nevertheless, the RL-based system achieves faster scenario completion and thus a higher overall throughput, by speeding up the vehicles towards the final approach fix. Future work will explore improved network architectures, transfer learning across varied scenarios, and algorithmic fine-tuning to further enhance safety performance.", "keywords": [ "Reinforcement learning", "Drone", "Merge (version control)", "Intrusion detection system", "Baseline (sea)", "Intrusion", "Outlier", "Heading (navigation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2226-4310/13/3/234/pdf?version=1772520089" }, { "id": "https://openalex.org/W7128617265", "doi": "10.1109/icicis65613.2025.11371216", "title": "Advancing Last Meal Delivery: Developing a Reliable and Efficient YOLO Algorithm for Autonomous Food Delivery Drones", "year": 2025, "publication_date": "2025-09-09", "type": "conference-paper", "language": null, "authors": [ "P. A. Vikhar", "A. V. Kalaskar", "Kanhaiya Prakash Patil", "Siddhant Shinde" ], "first_author": "P. A. Vikhar", "venue": null, "publisher": null, "abstract": "FOODCOPTER Innovation in Food Delivery Combining Cleaner Sources of Energy with Fully Autonomous Flying Drones By solving last-mile delivery problem in congested Tier 1 cities, FOODCOPTER is also the place traditional ways of delivery are nowadays lagging behind due to increasing traffic and security issues. The YOLO (You Only Look Once) algorithm was used for real-time object detection so that drones in FOODCOPTER can navigate their way on precisely agile maneuvers around dynamic urban environments. The project has high standards of safety, and the focus is on the maximum energy efficiency of the drone design. FOODCOPTER does its best job in the construction of the delivery system through properly equipped drones and using an optimized flight path for minimal possible effects on the environment. This actually becomes really important as sustainability is part of the global effort toward greener urban infrastructures. With this, the application built will be mobile responsive. Which will assure the customer that he can order and track his delivery easily in real time. Effortless platform, allowing consumers to seamlessly enjoy fast and reliable food deliveries with less hassle in comparison to traditional approaches. Through the highest testing and iteration, FOODCOPTER will strive to break the barrier in efficiency and sustainability in city logistics regarding food. It will represent a new norm in a smarter system of final mile delivery in the industry. Foodcopter is an example of innovation technology use and responsibility towards the environment. It's going to be the ultimate game-changer for the future of urban logistics.", "keywords": [ "Drone", "Agile software development", "Lagging", "Efficient energy use", "Sustainability", "Food delivery", "Last mile (transportation)", "Cloud computing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icicis65613.2025.11371216" }, { "id": "https://openalex.org/W7166647364", "doi": "10.1016/j.trc.2026.105831", "title": "Metro-drone collaborative delivery: Integrated hub activation and multi-trip scheduling under time windows", "year": 2026, "publication_date": "2026-06-30", "type": "article", "language": "en", "authors": [ "Shuo Sun", "Laijun Zhao", "Changzhi Wu", "Huiyong Li", "Yifei Wang" ], "first_author": "Shuo Sun", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Scheduling (production processes)", "Benchmark (surveying)", "Total cost", "Drone", "Job shop scheduling", "Customer service", "Public transport", "Traffic congestion", "Operating cost" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2026.105831" }, { "id": "https://openalex.org/W4415125765", "doi": "10.22541/au.176038131.17106095/v1", "title": "Collaborative Truck-Drone Routing with Multi-visit via Surrogate-Assisted Bi-level Optimization", "year": 2025, "publication_date": "2025-10-13", "type": "preprint", "language": "en", "authors": [ "Ruonan Zhai", "Xuejun Zhang", "Yi Mei", "Wenbo Du", "Tong Guo", "Tao Song" ], "first_author": "Ruonan Zhai", "venue": null, "publisher": null, "abstract": "The development of drone techniques has extensive applications to fields like last-mile parcel delivery. For economic and efficiency advantages, collaborative truck-drone systems have attracted great attention and formulated as truck-drone routing problems. In this paper, a multi-visit traveling salesman problem with drones is investigated. We decompose it into a two-layer structure and formulate it as a new bi-level model (Bi-MTSPD). The major advantage of the bi-level model is its ability to represent the complex interactions between truck and drone routes by dispatching the drones under the premise of truck routes. To solve the bi-level model, we propose a novel surrogate-assisted bi-level optimization method. The upper-level subproblem can be treated as a traveling salesman problem with customer assignment, and high-quality truck routes are identified by the K-nearest neighbor-based surrogate model with newly proposed features in order to allocate more computational resources to promising upper-level solutions. For the lower-level drone location routing problem (DLR), a customized memetic algorithm is developed to optimize the drone routes for the corresponding truck route. Comprehensive experimental results show that the proposed algorithm significantly outperforms the existing state-of-the-art algorithms across most benchmark instances. Further analysis verifies the congruence of the bi-level optimization for MTSP-D and the effectiveness of upper-level and lower-level solvers.", "keywords": [ "Routing (electronic design automation)", "Drone", "Process (computing)", "Field (mathematics)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.authorea.com/doi/pdf/10.22541/au.176038131.17106095/v1" }, { "id": "https://openalex.org/W7127226167", "doi": null, "title": "Developing efficient order and split heuristics for coordinated covering tour problems with drones", "year": 2022, "publication_date": "2022-05-01", "type": "dissertation", "language": "en", "authors": [ "Abhishake Kundu" ], "first_author": "Abhishake Kundu", "venue": "ThinkTech (Texas Tech University)", "publisher": "Texas Tech University", "abstract": "Coordinated Covering Tour Problems (CCTP) represents a class of routing problems that involve a primary vessel that is equipped with one or more secondary vessels who undergo a launch and recovery process from the primary vessel. The secondary vessels are constrained to meet up with the primary vessel at various nodes in the network and possibly many times, presumably due to the coverage limitations of the secondary vessels. The problem is thus one of simultaneously determining which nodes should be visited by which vessel, in what order each vessel must visit its assigned nodes, and at which nodes the secondary vessels should be launched and recovered by the primary vessel. Last-mile delivery of parcels using trucks (as the primary vessel) and drone (as the secondary vessel) is an example of Coordinated Covering Tour Problems with application in logistics. This problem has received a lot of attention in the last seven years since its seminal definition in 2015. Various authors have extended this problem by including multiple primary/ secondary vehicles, defining new network configurations for the vehicles, and by incorporating environmental and drone dynamics within the model. However, it being an NP-hard problem, there is no way we can solve a problem of modest size in polynomial time. The key lies in developing heuristics that are both efficient and fast, and it has been observed that there is space for considerable improvement in this research area. I propose developing a novel ‘order and split’ heuristic which is an improvement to the best heuristic to date. Furthermore, the algorithm is extended to several variants of the CTTP, and likewise, its accuracy and efficiency are evaluated.", "keywords": [ "Heuristics", "Drone", "Heuristic", "Routing (electronic design automation)", "Truck", "Class (philosophy)", "Process (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/2346/89264" }, { "id": "https://openalex.org/W4408622425", "doi": "10.3390/aerospace12030257", "title": "Optimized Airspace Structures and Sequencing Method for Urban Logistics Droneport", "year": 2025, "publication_date": "2025-03-19", "type": "article", "language": "en", "authors": [ "Yuan Guang Zheng", "Dongjia Li", "Zhou Shen", "Chenglong Li", "Z. -H. Zhang" ], "first_author": "Yuan Guang Zheng", "venue": "Aerospace", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "As an emerging strategic industry, drone delivery operation has demonstrated significant potential in urban environments due to its efficiency and adaptability to complex scenarios. However, critical bottlenecks persist during the take-off and landing phases, where accident rates account for over 52% of total flight risks, severely limiting operational safety and throughput. While existing droneport designs and sequencing strategies draw inspiration from traditional aviation methods, they inadequately address the separation of take-off/landing flows and lack tailored solutions for logistics drones’ unique characteristics. To overcome these limitations, this paper presents an integrated framework combining innovative airspace design with dynamic sequencing optimization. First, a novel terminal airspace structure is proposed to enable simultaneous multi-drone operations through spatially segregated routes and dedicated zones, fundamentally resolving collision risks between ascending and descending drones. Second, a real-time sequencing model based on the Hungarian algorithm is developed, incorporating drone-specific factors such as battery levels and task priorities to formulate a cost matrix for optimal scheduling. Experimental results demonstrate that the proposed airspace design reduces take-off/landing time by 34.8% compared to conventional funnel-shaped configurations. The sequencing algorithm prioritizes high-value missions while reducing the average waiting time for low-battery drones by 47.3%, effectively alleviating endurance pressures. Notably, the sequencing algorithm prevents low-battery drones from crashing in the experiments. In comparison, under the sequencing of the comparison method, numerous drones crash due to low battery levels.", "keywords": [ "Aeronautics", "Computer science", "Engineering", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/aerospace12030257" }, { "id": "https://openalex.org/W7204276744", "doi": "10.2139/ssrn.7354378", "title": "An Exact Approach for Coordinated Drone-Courier Delivery with Alternative Lockers and Order Consolidation", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Xizi Qiao", "Jixuan Feng", "Ying Yang", "Qinghe Sun", "Shuaian Wang" ], "first_author": "Xizi Qiao", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Solver", "Flexibility (engineering)", "Drone", "Exploit", "Consolidation (business)", "Limiting", "Vehicle routing problem", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7354378" }, { "id": "https://openalex.org/W7214172379", "doi": "10.1016/j.cie.2026.112408", "title": "Energy-aware routing for EV-drone collaborative delivery with battery charging and swapping", "year": 2026, "publication_date": "2026-09-24", "type": "article", "language": "en", "authors": [ "Hongmei Shan", "Kaixin Du", "Jialu Shi", "Wenwei Zhang", "Jing Shi" ], "first_author": "Hongmei Shan", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Battery (electricity)", "Drone", "Electrification", "Automation", "Routing (electronic design automation)", "Battery capacity", "Electric vehicle", "Sensitivity (control systems)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2026.112408" }, { "id": "https://openalex.org/W3215957345", "doi": "10.48550/arxiv.2111.11463", "title": "Drone flight data reveal energy and greenhouse gas emissions savings for small package delivery", "year": 2021, "publication_date": "2021-11-22", "type": "preprint", "language": "en", "authors": [ "Thiago A. Rodrigues", "Jay Patrikar", "Natalia Lombardi Oliveira", "H. Scott Matthews", "Sebastian Scherer", "Constantine Samaras" ], "first_author": "Thiago A. Rodrigues", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will affect the energy productivity of package delivery and require new methods to understand the associated energy consumption and greenhouse gas (GHG) emissions. Here we combine empirical testing of 187 quadcopter flights with first principles analysis to develop a usable energy model for drone package delivery. We develop a machine-learning algorithm to assess energy use across three different flight regimes: takeoff, cruise, and landing. Our model shows that, in the US, a small electric quadcopter drone with a payload of 1 kg would consume approximately 0.05 MJ/km and result in 41 g of CO$_{2}$e per package. The energy per package delivered by drones (0.19 MJ/package) can be up to 96\\% lower than conventional transportation modes. Our open model and generalizable coefficients can assist stakeholders in understanding and improving the energy use of drone package delivery.", "keywords": [ "Quadcopter", "Drone", "Takeoff", "Greenhouse gas", "Payload (computing)", "Energy consumption", "Automotive engineering", "Environmental science", "Computer science", "Simulation", "Engineering", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2111.11463" }, { "id": "https://openalex.org/W4412059203", "doi": "10.1007/978-3-031-97638-4_10", "title": "Investigating the Choice of Receiving On-Line Purchases Through Discrete Choice Modelling Approach", "year": 2025, "publication_date": "2025-06-28", "type": "conference-paper", "language": "en", "authors": [ "Antonio Comi", "Ippolita Idone", "Marco Cortese", "Olesia Hriekova" ], "first_author": "Antonio Comi", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Line (geometry)", "Operations research", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-97638-4_10" }, { "id": "https://openalex.org/W4292814075", "doi": "10.1117/12.2645488", "title": "A two-layer heuristic optimization algorithm for large-scale vehicle routing problem with drones", "year": 2022, "publication_date": "2022-08-23", "type": "conference-paper", "language": "en", "authors": [ "Jiajing Tang", "Shuaipeng Jia", "Zhongyu Ma" ], "first_author": "Jiajing Tang", "venue": null, "publisher": null, "abstract": "With the increasing application in real life, drones have received more and more attention in recent years. In particular, drones show great potential when it comes to solving the last-mile delivery problems. Large-scale vehicle routing problems with drones (LSVRPDs) are challenging because most real-world problems typically involve thousands of delivery nodes. In this article, we proposed a two-layer heuristic optimization algorithm called THOA to address them. In the upper layer, three local search operators are used to generate different routes, each of which consists of several nodes. In the lower layer, a Branch-and-Bound method with special constraint is suggested to optimize the routes obtained from the upper layer. The proposed THOA is compared with a simulated annealing algorithm in six scenarios with different number of nodes. The experimental results demonstrate the effectiveness and efficiency of THOA, especially in VRPDs with a large number of nodes.", "keywords": [ "Drone", "Simulated annealing", "Computer science", "Routing (electronic design automation)", "Heuristic", "Mathematical optimization", "Upper and lower bounds", "Layer (electronics)", "Vehicle routing problem", "Branch and bound", "Algorithm", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2645488" }, { "id": "https://openalex.org/W7138125021", "doi": "10.1016/j.trpro.2026.02.069", "title": "Autonomous electric vehicle routing problem with battery recharging by drones using public transit", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Giusy Macrina", "Luigi Di Puglia Pugliese", "Francesca Guerriero" ], "first_author": "Giusy Macrina", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "The autonomous vehicle routing problem with battery recharging by drones, using public transit consists of routing a fleet of autonomous electric vehicles (AEVs) with limited battery capacity to deliver orders to customers in the last mile. In order to prevent service disruptions, resulting from the limited battery capacity of the AEVs, it is possible to recharge them during the trip when they are at customer locations for the delivery tasks. This can be achieved through drones equipped with wireless energy transmission technology, which can be viewed as mobile chargers. Furthermore, drones can be launched from the depot as well as from some dedicated public stations. Each drone will then complete part of its journey on the roof of the public lines. We mathematically represent the problem as a Mixed-Integer Linear Programming (MILP) model, considering capacity, time windows, and synchronization constraints. We solve the MILP for studying the framework and analyzing the potential of the new paradigm.", "keywords": [ "Drone", "Battery (electricity)", "Transit (satellite)", "Public transport", "Electric vehicle", "Routing (electronic design automation)", "Urban transit" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2026.02.069" }, { "id": "https://openalex.org/W7127883248", "doi": "10.6084/m9.figshare.31268628", "title": "Dynamic medical logistics with drone-truck collaboration and pheromone decay in ant colony optimization", "year": 2026, "publication_date": "2026-02-05", "type": "article", "language": null, "authors": [ "Tejinder Singh Lakhwani", "Yerasani Sinjana", "Anuj Pal Kapoor" ], "first_author": "Tejinder Singh Lakhwani", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The timely and reliable delivery of medical supplies is vital in emergencies, where even minor delays can endanger lives. This study proposes a real-time optimisation framework for healthcare logistics that incorporates an urgency-weighted pheromone decay mechanism into an Ant Colony Optimization (ACO) algorithm. The method dynamically adjusts routing priorities according to medical urgency, enabling adaptive path selection under fluctuating traffic, battery, and weather conditions. A hybrid drone–truck collaboration system is developed, combining the endurance of trucks for long-range transport with the agility of drones for last-mile, time-critical deliveries. Simulation experiments using operational data from 40 healthcare facilities in Delhi demonstrate an approximately 30% reduction in average delivery time, a 15% decrease in fuel consumption, and an approximately 30% improvement in overall energy efficiency compared to conventional static routing models. These findings indicate that the proposed framework offers a scalable, energy-aware, and urgency-responsive solution for dynamic healthcare supply chains, with potential applicability to other critical logistics domains.", "keywords": [ "Ant colony optimization algorithms", "Truck", "Drone", "Vehicle routing problem", "Path (computing)", "Ant colony", "Routing (electronic design automation)", "Selection (genetic algorithm)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31268628" }, { "id": "https://openalex.org/W7164551829", "doi": "10.5281/zenodo.20667509", "title": "HED-CVRP: A Hybrid Evolutionary Diffusion Framework for 4D Asymmetric Capacitated Vehicle Routing in Drone Logistics", "year": 2026, "publication_date": "2026-06-12", "type": "preprint", "language": null, "authors": [ "Yi Min Lin" ], "first_author": "Yi Min Lin", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The Capacitated Vehicle Routing Problem (CVRP) is one of the most extensively studied problems in combinatorial optimization and logistics. In recent years, the emergence of Unmanned Aerial Vehicles (UAVs) as a viable last-mile delivery platform has introduced new dimensions of complexity to routing problems. Unlike ground-based vehicles that operate on a two-dimensional road network, drones navigate through three-dimensional airspace where altitude changes and atmospheric wind vectors create highly asymmetric energy costs. Traditional Operations Research (OR) solvers, most notably the Lin-Kernighan Heuristic (LKH-3), achieve near-optimal solutions for symmetric CVRP instances. However, their reliance on symmetric edge-swap operations causes severe performance degradation when confronted with asymmetric cost matrices arising from real-world 4D drone routing environments. In this paper, we propose HED-CVRP (Hybrid Evolutionary Diffusion for Capacitated Vehicle Routing Problem), a novel hybrid architecture that bridges the gap between Neural Combinatorial Optimization (NCO) and evolutionary heuristic search. The proposed framework consists of two tightly integrated components. First, we design a Graph Neural Network (GNN) trained via a Denoising Diffusion Probabilistic Model (DDPM) on ground-truth solutions generated by exact or near-exact solvers. This neural component is compressed into a single-step inference via Denoising Diffusion Implicit Models (DDIM) consistency distillation, generating a global edge-probability heatmap that effectively prunes over 90% of the combinatorial search space in approximately 0.48 seconds. Second, we introduce an Asynchronous Shared-Memory Consensus Mechanism implemented within a multi-threaded C++ backend, where multiple Simulated Annealing agents exchange elite genetic material in real-time to rapidly converge on globally optimal solutions. We evaluate HED-CVRP on simulated 4D asymmetric CVRP instances ranging from N=50 to N=1000 nodes. Experimental results demonstrate that HED-CVRP achieves double-dominance over LKH-3: on N=50 instances, our method reduces execution time from 1.70 seconds to 0.88 seconds (a 48% reduction) while simultaneously reducing total energy consumption from 3714.77 to 2340.84 (a 37% improvement). For large-scale instances up to N=1000, we employ a zone-parallel clustering strategy that maintains near-linear time scaling, solving city-wide logistics networks in under 4 seconds compared to over 20 minutes for LKH-3. Comprehensive ablation studies confirm that both the neural prior and the evolutionary backend are essential for achieving state-of-the-art performance. Keywords: Capacitated Vehicle Routing Problem, Unmanned Aerial Vehicles, Diffusion Models, Graph Neural Networks, Simulated Annealing, Evolutionary Algorithms, Combinatorial Optimization", "keywords": [ "Vehicle routing problem", "Drone", "Simulated annealing", "Routing (electronic design automation)", "Combinatorial optimization", "Heuristic", "Swarm behaviour", "Evolutionary algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20667509" }, { "id": "https://openalex.org/W7164572731", "doi": "10.5281/zenodo.20665208", "title": "HED-CVRP: A Hybrid Evolutionary Diffusion Framework for 4D Asymmetric Capacitated Vehicle Routing in Drone Logistics", "year": 2026, "publication_date": "2026-06-12", "type": "preprint", "language": null, "authors": [ "Yi Min Lin" ], "first_author": "Yi Min Lin", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The Capacitated Vehicle Routing Problem (CVRP) is one of the most extensively studied problems in combinatorial optimization and logistics. In recent years, the emergence of Unmanned Aerial Vehicles (UAVs) as a viable last-mile delivery platform has introduced new dimensions of complexity to routing problems. Unlike ground-based vehicles that operate on a two-dimensional road network, drones navigate through three-dimensional airspace where altitude changes and atmospheric wind vectors create highly asymmetric energy costs. Traditional Operations Research (OR) solvers, most notably the Lin-Kernighan Heuristic (LKH-3), achieve near-optimal solutions for symmetric CVRP instances. However, their reliance on symmetric edge-swap operations causes severe performance degradation when confronted with asymmetric cost matrices arising from real-world 4D drone routing environments. In this paper, we propose HED-CVRP (Hybrid Evolutionary Diffusion for Capacitated Vehicle Routing Problem), a novel hybrid architecture that bridges the gap between Neural Combinatorial Optimization (NCO) and evolutionary heuristic search. The proposed framework consists of two tightly integrated components. First, we design a Graph Neural Network (GNN) trained via a Denoising Diffusion Probabilistic Model (DDPM) on ground-truth solutions generated by exact or near-exact solvers. This neural component is compressed into a single-step inference via Denoising Diffusion Implicit Models (DDIM) consistency distillation, generating a global edge-probability heatmap that effectively prunes over 90% of the combinatorial search space in approximately 0.48 seconds. Second, we introduce an Asynchronous Shared-Memory Consensus Mechanism implemented within a multi-threaded C++ backend, where multiple Simulated Annealing agents exchange elite genetic material in real-time to rapidly converge on globally optimal solutions. We evaluate HED-CVRP on simulated 4D asymmetric CVRP instances ranging from N=50 to N=1000 nodes. Experimental results demonstrate that HED-CVRP achieves double-dominance over LKH-3: on N=50 instances, our method reduces execution time from 1.70 seconds to 0.88 seconds (a 48% reduction) while simultaneously reducing total energy consumption from 3714.77 to 2340.84 (a 37% improvement). For large-scale instances up to N=1000, we employ a zone-parallel clustering strategy that maintains near-linear time scaling, solving city-wide logistics networks in under 4 seconds compared to over 20 minutes for LKH-3. Comprehensive ablation studies confirm that both the neural prior and the evolutionary backend are essential for achieving state-of-the-art performance. Keywords: Capacitated Vehicle Routing Problem, Unmanned Aerial Vehicles, Diffusion Models, Graph Neural Networks, Simulated Annealing, Evolutionary Algorithms, Combinatorial Optimization", "keywords": [ "Vehicle routing problem", "Drone", "Simulated annealing", "Routing (electronic design automation)", "Combinatorial optimization", "Heuristic", "Swarm behaviour", "Evolutionary algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20665208" }, { "id": "https://openalex.org/W4401387718", "doi": "10.1117/12.3034872", "title": "Adaptive large neighborhood search algorithm for vehicle routing problem with drones with delivery options", "year": 2024, "publication_date": "2024-08-07", "type": "conference-paper", "language": "en", "authors": [ "Jiapu Sheng", "Lifeng Mu" ], "first_author": "Jiapu Sheng", "venue": null, "publisher": null, "abstract": "Compared to traditional urban delivery systems, the last-mile delivery has become a complex issue due to lower daily customer demands, significant delivery time variations, and a widespread distribution of locations. The delivery time of customer orders is associated with the choice of delivery locations, where considerations of capacity and operating hours arise, especially when utilizing shared delivery spots such as parcel lockers. To address these issues and emphasize the advantages of shared parcel lockers, we propose a delivery problem with delivery options and preference levels. We define a routing scheme for the coordinated delivery of vehicles and drones, integrating various types of delivery locations. We solve this problem using an adaptive large neighborhood search algorithm with simulated annealing, introducing specific ruin and repair operators. These operators, combined with various operators from the literature, undergo numerical experiments. In the specific context of this problem, our proposed method outperforms conventional algorithms, such as large neighborhood search and a single-parent genetic algorithm.", "keywords": [ "Vehicle routing problem", "Simulated annealing", "Computer science", "Routing (electronic design automation)", "Drone", "Scheme (mathematics)", "Context (archaeology)", "Mathematical optimization", "Local search (optimization)", "Delivery Performance", "Food delivery", "Algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3034872" }, { "id": "https://openalex.org/W4408432428", "doi": "10.2139/ssrn.5179422", "title": "A Constraint Relaxation Co-Evolutionary Algorithm for the Two-Echelon Vehicle Routing Problem with Load-Dependent Drones", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Fan Yu", "Qun Chen", "Jinlong Zhou", "Chao Li", "Sheng Qi", "Weixiong Huang" ], "first_author": "Fan Yu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Vehicle routing problem", "Drone", "Constraint (computer-aided design)", "Mathematical optimization", "Relaxation (psychology)", "Computer science", "Evolutionary algorithm", "Algorithm", "Routing (electronic design automation)", "Mathematics", "Biology", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5179422" }, { "id": "https://openalex.org/W7164046643", "doi": "10.6084/m9.figshare.31268628.v2", "title": "Dynamic medical logistics with drone-truck collaboration and pheromone decay in ant colony optimization", "year": 2026, "publication_date": "2026-02-05", "type": "article", "language": null, "authors": [ "Tejinder Singh Lakhwani", "Yerasani Sinjana", "Anuj Pal Kapoor" ], "first_author": "Tejinder Singh Lakhwani", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The timely and reliable delivery of medical supplies is vital in emergencies, where even minor delays can endanger lives. This study proposes a real-time optimisation framework for healthcare logistics that incorporates an urgency-weighted pheromone decay mechanism into an Ant Colony Optimization (ACO) algorithm. The method dynamically adjusts routing priorities according to medical urgency, enabling adaptive path selection under fluctuating traffic, battery, and weather conditions. A hybrid drone–truck collaboration system is developed, combining the endurance of trucks for long-range transport with the agility of drones for last-mile, time-critical deliveries. Simulation experiments using operational data from 40 healthcare facilities in Delhi demonstrate an approximately 30% reduction in average delivery time, a 15% decrease in fuel consumption, and an approximately 30% improvement in overall energy efficiency compared to conventional static routing models. These findings indicate that the proposed framework offers a scalable, energy-aware, and urgency-responsive solution for dynamic healthcare supply chains, with potential applicability to other critical logistics domains.", "keywords": [ "Ant colony optimization algorithms", "Truck", "Drone", "Vehicle routing problem", "Path (computing)", "Ant colony", "Routing (electronic design automation)", "Selection (genetic algorithm)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31268628.v2" }, { "id": "https://openalex.org/W7127967613", "doi": "10.6084/m9.figshare.31268628.v1", "title": "Dynamic medical logistics with drone-truck collaboration and pheromone decay in ant colony optimization", "year": 2026, "publication_date": "2026-02-05", "type": "article", "language": null, "authors": [ "Tejinder Singh Lakhwani", "Yerasani Sinjana", "Anuj Pal Kapoor" ], "first_author": "Tejinder Singh Lakhwani", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The timely and reliable delivery of medical supplies is vital in emergencies, where even minor delays can endanger lives. This study proposes a real-time optimisation framework for healthcare logistics that incorporates an urgency-weighted pheromone decay mechanism into an Ant Colony Optimization (ACO) algorithm. The method dynamically adjusts routing priorities according to medical urgency, enabling adaptive path selection under fluctuating traffic, battery, and weather conditions. A hybrid drone–truck collaboration system is developed, combining the endurance of trucks for long-range transport with the agility of drones for last-mile, time-critical deliveries. Simulation experiments using operational data from 40 healthcare facilities in Delhi demonstrate an approximately 30% reduction in average delivery time, a 15% decrease in fuel consumption, and an approximately 30% improvement in overall energy efficiency compared to conventional static routing models. These findings indicate that the proposed framework offers a scalable, energy-aware, and urgency-responsive solution for dynamic healthcare supply chains, with potential applicability to other critical logistics domains.", "keywords": [ "Ant colony optimization algorithms", "Truck", "Drone", "Vehicle routing problem", "Path (computing)", "Ant colony", "Routing (electronic design automation)", "Selection (genetic algorithm)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31268628.v1" }, { "id": "https://openalex.org/W7166843401", "doi": "10.1007/978-3-032-30494-0_34", "title": "Evaluating Alternative Continuous Metaheuristic Solution Encodings for Routing Problems with Multiple Drones with Interceptions", "year": 2026, "publication_date": "2026-07-01", "type": "conference-paper", "language": "en", "authors": [ "Sarah Dillon", "Jacomine Grobler" ], "first_author": "Sarah Dillon", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Metaheuristic", "Routing (electronic design automation)", "Network routing", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-30494-0_34" }, { "id": "https://openalex.org/W7164526619", "doi": "10.5281/zenodo.20665209", "title": "HED-CVRP: A Hybrid Evolutionary Diffusion Framework for 4D Asymmetric Capacitated Vehicle Routing in Drone Logistics", "year": 2026, "publication_date": "2026-06-12", "type": "preprint", "language": null, "authors": [ "Yi Min Lin" ], "first_author": "Yi Min Lin", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The Capacitated Vehicle Routing Problem (CVRP) is one of the most extensively studied problems in combinatorial optimization and logistics. In recent years, the emergence of Unmanned Aerial Vehicles (UAVs) as a viable last-mile delivery platform has introduced new dimensions of complexity to routing problems. Unlike ground-based vehicles that operate on a two-dimensional road network, drones navigate through three-dimensional airspace where altitude changes and atmospheric wind vectors create highly asymmetric energy costs. Traditional Operations Research (OR) solvers, most notably the Lin-Kernighan Heuristic (LKH-3), achieve near-optimal solutions for symmetric CVRP instances. However, their reliance on symmetric edge-swap operations causes severe performance degradation when confronted with asymmetric cost matrices arising from real-world 4D drone routing environments. In this paper, we propose HED-CVRP (Hybrid Evolutionary Diffusion for Capacitated Vehicle Routing Problem), a novel hybrid architecture that bridges the gap between Neural Combinatorial Optimization (NCO) and evolutionary heuristic search. The proposed framework consists of two tightly integrated components. First, we design a Graph Neural Network (GNN) trained via a Denoising Diffusion Probabilistic Model (DDPM) on ground-truth solutions generated by exact or near-exact solvers. This neural component is compressed into a single-step inference via Denoising Diffusion Implicit Models (DDIM) consistency distillation, generating a global edge-probability heatmap that effectively prunes over 90% of the combinatorial search space in approximately 0.48 seconds. Second, we introduce an Asynchronous Shared-Memory Consensus Mechanism implemented within a multi-threaded C++ backend, where multiple Simulated Annealing agents exchange elite genetic material in real-time to rapidly converge on globally optimal solutions. We evaluate HED-CVRP on simulated 4D asymmetric CVRP instances ranging from N=50 to N=1000 nodes. Experimental results demonstrate that HED-CVRP achieves double-dominance over LKH-3: on N=50 instances, our method reduces execution time from 1.70 seconds to 0.88 seconds (a 48% reduction) while simultaneously reducing total energy consumption from 3714.77 to 2340.84 (a 37% improvement). For large-scale instances up to N=1000, we employ a zone-parallel clustering strategy that maintains near-linear time scaling, solving city-wide logistics networks in under 4 seconds compared to over 20 minutes for LKH-3. Comprehensive ablation studies confirm that both the neural prior and the evolutionary backend are essential for achieving state-of-the-art performance. Keywords: Capacitated Vehicle Routing Problem, Unmanned Aerial Vehicles, Diffusion Models, Graph Neural Networks, Simulated Annealing, Evolutionary Algorithms, Combinatorial Optimization", "keywords": [ "Vehicle routing problem", "Drone", "Simulated annealing", "Routing (electronic design automation)", "Combinatorial optimization", "Heuristic", "Swarm behaviour", "Evolutionary algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20665209" }, { "id": "https://openalex.org/W4285196401", "doi": "10.2139/ssrn.4111273", "title": "The Vehicle Routing Problem with Heterogeneous Autonomous Assistants", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Cheng Chen", "Emrah Demir", "Xisheng Hu", "Shuaijv Du" ], "first_author": "Cheng Chen", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Computer network", "Business" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4111273" }, { "id": "https://openalex.org/W7135087749", "doi": "10.1177/29776481261427592", "title": "Sustainable Design of Smart Multimodal Urban Logistics in the Era of AI and Low-Altitude Economy", "year": 2026, "publication_date": "2026-03-01", "type": "article", "language": "en", "authors": [ "Hui Fu", "Hongpeng Li", "Youbin Chen", "Jiahong Zhao", "Zhixuan Wu", "Zhenqi Liang", "Nan Zheng" ], "first_author": "Hui Fu", "venue": "Design for Augmented Humanity", "publisher": null, "abstract": "Conventionally, urban logistics operate in various independent transport modes, for example, trucks, vans, and motorcycles. Such operation will not be optimal when facing large-scale demand because isolated operation can be inefficient and environmental-unfriendly. To enhance the efficiency and sustainability of the next-generation urban logistics system, a comprehensive framework is proposed to identify the critical operation scenarios, its main system components, and the key technologies for applications of smart multimodal urban logistics (SMUL). There are four innovative characteristics in the proposed SMUL system: (1) co-modality for passenger and freight transportation through public transit; (2) integration of drone or unmanned aerial vehicles, autonomous delivery robots, automated delivery vehicles, and multimodal parcel lockers for last-mile delivery; (3) deployment and operation of robotic smart warehouses for storage; and (4) joint scheduling of various vehicles using large language model (LLM)-based agents. This paper focuses on two application cases, a last-mile home delivery service and a freight-integrated public transit service. A cyber-physical SMUL system is designed as the digital twin for monitoring the operations. A novel simulation platform is designed, using LLM-agents, for predicting system dynamics and thus supporting the economic evaluation of an SMUL system. Moreover, stakeholder benefit allocation and operation resilience are discussed for designing a sustainable SMUL system. Finally, insights are provided for policy and practice, for example, the logistics service providers are recommended to integrate aerial, road, and underground transport modes in consideration of local regulation, land use, and public trust in technology to achieve synergistic economic, social, and ecological efficiency.", "keywords": [ "Multimodal transport", "Drone", "Software deployment", "Sustainability", "Smart city", "Public transport", "City logistics", "Key (lock)", "Supply chain" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/29776481261427592" }, { "id": "https://openalex.org/W7168339238", "doi": "10.48550/arxiv.2607.12198", "title": "Designing Hierarchical Hub-and-Spoke Drone-Based Networks for Delivering Time-Sensitive Healthcare Items", "year": 2026, "publication_date": "2026-07-13", "type": "preprint", "language": null, "authors": [ "Sayed Hamid Hosseini Dolatabadi", "Tanveer Hossain Bhuiyan", "Bo Zeng", "J. Tyler Kennedy", "Brian O Neill" ], "first_author": "Sayed Hamid Hosseini Dolatabadi", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Timely distribution of medical items (e.g., whole blood and vaccines) across regional healthcare networks often requires high-volume delivery operations from central facilities (e.g., blood banks) to intermediate regional hospitals, followed by rapid last-mile deliveries to points of injury. Traditional ground-based delivery systems often suffer from limited responsiveness (e.g., traffic congestion) and operational inefficiencies (e.g., blood waste). Leveraging aerial-drone-based delivery systems offers a promising solution for the fast and efficient delivery of time-sensitive medical items across regional healthcare networks. Therefore, we study a hierarchical hub-and-spoke drone-based network for delivering time-sensitive medical items with distinct release and due times to fixed and mobile delivery destinations. We consider a heterogeneous fleet of drones with distinct characteristics (e.g., cost, battery capacity, and speed) and different multi-trip delivery modes. We propose an efficient mixed-integer programming model for location/allocation of mobile delivery destinations, as well as routing and scheduling drones to minimize the total investment and operational costs of the drone delivery network while maintaining the delivery due times. We develop a customized exact solution method integrating problem-specific reformulations and dynamic cutting planes, as well as a fast heuristic algorithm by leveraging a simplified problem variant. Results based on a real-life case study of whole blood delivery data from Pendleton, Oregon, United States, and actual drone flight test data demonstrate that our exact and heuristic solution methods are 31 and 3,375 times faster, respectively, than the Gurobi solver. Results also show that allowing drones to perform multiple trips is 142.8% more cost-efficient than single trips.", "keywords": [ "Drone", "TRIPS architecture", "Scheduling (production processes)", "Heuristic", "Healthcare delivery", "Routing (electronic design automation)", "Health care" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2607.12198" }, { "id": "https://openalex.org/W7162291522", "doi": "10.56975/tijer.v13i5.162763", "title": "Drone Governance in Rural Punjab: Administration, Security, and Technological Transformation in Contemporary India.", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Sarbjit Singh" ], "first_author": "Sarbjit Singh", "venue": "Technix International Journal for Engineering Research", "publisher": null, "abstract": "The rapid expansion of Unmanned Aerial Systems (UAS) or Remote Piloted Aircraft Systems (RPAS) has given an exponential boom to the industry of drones in the 21st century. From their use in military operations like border security and surveillance, drones are now, extensively, being used in civil applications like agriculture, mining, parcel delivery, media, mining, and insurance – to mention a few. Owing to their revolutionary benefits and affordability, drones have now become an ideal choice for various sectors. In India, particularly in rural Punjab, drones occupy a dual role as instruments of economic modernization and mechanisms of national security. Drone governance has now emerged as one of the most significant interdisciplinary policy concerns of the twenty-first century India. The governance structures have transformed dramatically across agriculture, public administration, disaster management, border security, surveillance, and rural development. The present research paper examines drone governance through different perspectives and argues that drone governance is gradually evolving into a specialized sub-type of governance akin to e-governance and digital governance. The paper further explores the role of drones in land mapping, crop monitoring, anti-drug operations, militancy combatting, and rural administration. Drawing upon the works of various scholars and security experts, the paper demonstrates that drone governance reflects broader transformations in State Power, Technological Governance, and Data-driven Administration. It is envisioned that Punjab as a state - may emerge as a major laboratory for rural drone governance in India, on the condition that governance frameworks remain transparent, accountable, and democratically regulated.", "keywords": [ "Drone", "Corporate governance", "Modernization theory", "State (computer science)", "Dual (grammatical number)", "Civil society" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://tijer.org/tijer/papers/TIJER2605159.pdf" }, { "id": "https://openalex.org/W7213314020", "doi": "10.2139/ssrn.7471017", "title": "Routing Optimization for Heterogeneous Drone-truck toward Sustainable Delivery: Considering Rendezvous Waiting Time", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Ke Chen", "Fei Ma", "Ningning Yuan", "Zhijie Yang", "Qipeng Sun", "Kum Fai Yuen" ], "first_author": "Ke Chen", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Payload (computing)", "Carbon footprint", "Rendezvous", "Routing (electronic design automation)", "Linear programming", "Carbon tax" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7471017" }, { "id": "https://openalex.org/W4411573207", "doi": "10.1007/978-3-031-95489-4_12", "title": "Reinforcement Learning Application for Controlling a Quadcopter Carrying a Slung Payload", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Nourah Al Saud", "Eric Lanteigne" ], "first_author": "Nourah Al Saud", "venue": "Mechanisms and machine science", "publisher": "Springer Nature (Netherlands)", "abstract": null, "keywords": [ "Quadcopter", "Payload (computing)", "Reinforcement learning", "Reinforcement", "Computer science", "Engineering", "Artificial intelligence", "Aerospace engineering", "Structural engineering", "Computer security", "Network packet" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-95489-4_12" }, { "id": "https://openalex.org/W4387976945", "doi": "10.3390/s23218757", "title": "A Study on Generative Models for Visual Recognition of Unknown Scenes Using a Textual Description", "year": 2023, "publication_date": "2023-10-27", "type": "article", "language": "en", "authors": [ "José Martínez-Carranza", "Delia Irazú Hernández-Farías", "Victoria Eugenia Vazquez-Meza", "Leticia Oyuki Rojas-Perez", "Aldrich A. Cabrera-Ponce" ], "first_author": "José Martínez-Carranza", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In this study, we investigate the application of generative models to assist artificial agents, such as delivery drones or service robots, in visualising unfamiliar destinations solely based on textual descriptions. We explore the use of generative models, such as Stable Diffusion, and embedding representations, such as CLIP and VisualBERT, to compare generated images obtained from textual descriptions of target scenes with images of those scenes. Our research encompasses three key strategies: image generation, text generation, and text enhancement, the latter involving tools such as ChatGPT to create concise textual descriptions for evaluation. The findings of this study contribute to an understanding of the impact of combining generative tools with multi-modal embedding representations to enhance the artificial agent's ability to recognise unknown scenes. Consequently, we assert that this research holds broad applications, particularly in drone parcel delivery, where an aerial robot can employ text descriptions to identify a destination. Furthermore, this concept can also be applied to other service robots tasked with delivering to unfamiliar locations, relying exclusively on user-provided textual descriptions.", "keywords": [ "Generative grammar", "Artificial intelligence", "Generative model", "Computer science", "Natural language processing", "Pattern recognition (psychology)", "Computer vision" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/23/21/8757/pdf?version=1698389547" }, { "id": "https://openalex.org/W7115907804", "doi": "10.5281/zenodo.17960283", "title": "Global Drone Market Industry Outlook 2025–2030: Growth, Technology Trends, and Regional Insights", "year": 2025, "publication_date": "2025-12-17", "type": "report", "language": null, "authors": [ "Next Move Strategy Consulting" ], "first_author": "Next Move Strategy Consulting", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The global drone market is witnessing rapid expansion driven by advancements in AI, automation, and connectivity. Valued at USD 54.83 billion in 2024, the market is projected to reach USD 117.62 billion by 2030, supported by strong demand across commercial, industrial, defense, and public safety sectors. Increasing adoption of autonomous drones, integration of 5G and IoT, growth in e-commerce and last-mile delivery, and supportive government regulations are reshaping the industry landscape. This industry outlook provides in-depth insights into market growth, key technological trends, regional dynamics, competitive landscape, and future opportunities shaping the global drone ecosystem.To access the full report, visit - https://www.nextmsc.com/report/drone-market", "keywords": [ "Drone", "Government (linguistics)", "Key (lock)", "Market analysis", "Public policy", "Market research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.17960283" }, { "id": "https://openalex.org/W3190698543", "doi": null, "title": "Carbon emissions in city logistics: a literature review", "year": 2021, "publication_date": "2021-04-08", "type": "review", "language": "en", "authors": [ "Aurelio Christopher Dichio" ], "first_author": "Aurelio Christopher Dichio", "venue": null, "publisher": null, "abstract": "Carbon emissions reduction in city logistics is a central theme for the future sustainability and liveability of cities. With the increasing number of people living in cities, the growing trend of e-commerce deliveries and the increasing environmental awareness, the academic interest in this theme is consequently increasing. This thesis represents a literature review on the theme of carbon emissions in city logistics. Fifty papers have been analysed and classified in ten categories: drones, cargo bikes, electric vans, urban consolidation centres, delivery robots, crowdshipping, parcel lockers, collaboration, online shopping, and other minor solutions. Main research results and future research perspectives are highlighted.", "keywords": [ "Theme (computing)", "Sustainability", "City logistics", "Consolidation (business)", "Business", "Environmental planning", "Engineering", "Transport engineering", "Geography", "Computer science", "Finance", "World Wide Web" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://webthesis.biblio.polito.it/18401/" }, { "id": "https://openalex.org/W4399593967", "doi": "10.2139/ssrn.4862260", "title": "The Performance Evaluation of Digital Technologies in Warehouse Management: A Systematic Literature Review", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Yusuf Ayokunle", "Nagesh Shukla", "Towfique Rahman", "Subrata Chakraborty", "Sushma Kumari" ], "first_author": "Yusuf Ayokunle", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Systematic review", "Warehouse", "Computer science", "Data warehouse", "Business", "Process management", "Knowledge management", "Database", "Political science", "Marketing", "MEDLINE" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4862260" }, { "id": "https://openalex.org/W7115916851", "doi": "10.5281/zenodo.17960282", "title": "Global Drone Market Industry Outlook 2025–2030: Growth, Technology Trends, and Regional Insights", "year": 2025, "publication_date": "2025-12-17", "type": "report", "language": null, "authors": [ "Next Move Strategy Consulting" ], "first_author": "Next Move Strategy Consulting", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The global drone market is witnessing rapid expansion driven by advancements in AI, automation, and connectivity. Valued at USD 54.83 billion in 2024, the market is projected to reach USD 117.62 billion by 2030, supported by strong demand across commercial, industrial, defense, and public safety sectors. Increasing adoption of autonomous drones, integration of 5G and IoT, growth in e-commerce and last-mile delivery, and supportive government regulations are reshaping the industry landscape. This industry outlook provides in-depth insights into market growth, key technological trends, regional dynamics, competitive landscape, and future opportunities shaping the global drone ecosystem.To access the full report, visit - https://www.nextmsc.com/report/drone-market", "keywords": [ "Drone", "Government (linguistics)", "Key (lock)", "Market analysis", "Public policy", "Market research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.17960282" }, { "id": "https://openalex.org/W4415003074", "doi": "10.1109/tits.2025.3616580", "title": "Learning Safe, Optimal, and Real-Time Flight Interaction With Deep Confidence-Enhanced Reachability Guarantee", "year": 2025, "publication_date": "2025-10-09", "type": "article", "language": "en", "authors": [ "Yuanyuan Zhang", "Yingying Wang", "Penggao Yan", "Weisong Wen" ], "first_author": "Yuanyuan Zhang", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In the low-altitude economy, ensuring the safe and agile flight of unmanned aerial vehicles (UAVs) in dynamic obstacle environments is essential for expanding interactive applications like parcel delivery. While deep reinforcement learning (DRL) shows promise for UAV motion planning and control, its trial-and-error exploration often struggles to ensure both agility and safety, especially under uncertain observational noise. Therefore, this paper proposes a deep confidence-enhanced reachability policy optimization (DCRPO) framework. By integrating safe DRL with nonlinear model predictive control (NMPC), DCRPO achieves high-level safety decisions, complex real-time joint planning and control for UAVs. Furthermore, we develop a deep confidence-enhanced reachability guarantee that constructs a set of stochastically forward-reachable planned trajectories under uncertainty, enabling robust safety collision probability certifications. This safe reachability mechanism adaptively selects belief space actions from planned actions to interact with the environment, further enhancing safety and reducing training time. In extensive experiments of UAVs traversing a fast-moving rectangular gate, the proposed method outperforms other state-of-the-art baseline methods under varying environments in terms of operational robustness. Furthermore, the proposed method significantly reduces overall collision violations and training time, greatly improving both training safety and efficiency. The demonstration video (https://youtu.be/7xkp9U7FSJg) and the source code (https://github.com/ZyyFLY/DCRPO) are also provided.", "keywords": [ "Reachability", "Traverse", "Collision avoidance", "Reinforcement learning", "Agile software development", "Motion planning", "Collision", "Robot", "Set (abstract data type)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2025.3616580" }, { "id": "https://openalex.org/W4402978636", "doi": "10.1109/access.2024.3471179", "title": "Interchangeable Visual Inertial LiDAR Odometry and Mapping Payload Unit for Aerial Vehicles", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Ravindu G. Thalagala", "Sahan M. Gunawardana", "Oscar De Silva", "George K. I. Mann", "Awantha Jayasiri", "Arthur W. Gubbels", "Raymond G. Gosine" ], "first_author": "Ravindu G. Thalagala", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper presents an aeronautical-grade payload unit designed for real-time execution of visual-inertial-LiDAR odometry and mapping (VILOAM) algorithms. The payload offers platform interchangeability between full-scale aircraft (e.g., Bell 412 helicopter), small-scale drones (e.g., DJI M600), and ground platforms. The use of small-scale drones renders a convenient option for the research and development of VILOAM algorithms due to the reduced resource demand and simplified pilot training, while full-scale aircraft experiments provide important operationally relevant datasets to test navigation algorithm performance for field deployment. The payload unit consists of two monocular cameras, an inertial measurement unit (IMU), a light detection and ranging (LiDAR) sensor, and a real-time kinematic (RTK) enabled global navigation satellite system (GNSS) receiver. A portable GPU interfaces with these sensors to capture hardware time-synchronized sensing data and perform real-time VILOAM, including support for AI modules for obstacle detection, emergency landing zone detection that typically occurs in field robotic applications such as last-mile goods delivery, surveillance and search and rescue flights. Field validation results for the payload unit are provided by running the developed VILOAM algorithm, as well as state-of-the-art VILOAM algorithms, and evaluating their performance in real-time localization and mapping on both platforms.", "keywords": [ "Payload (computing)", "Odometry", "Artificial intelligence", "Computer science", "Computer vision", "Lidar", "Inertial measurement unit", "Remote sensing", "Robot", "Mobile robot", "Geology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2024.3471179" }, { "id": "https://openalex.org/W7172208599", "doi": "10.2139/ssrn.7217550", "title": "Untact Delivery Service: Optimization Approach for Collaboration of Autonomous Vehicles, Delivery Robots and Drones", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Byung Duk Song", "Hyeonbin Jeong", "Ju-Yong Lee", "Kyungsu Park" ], "first_author": "Byung Duk Song", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Scalability", "Robot", "Robustness (evolution)", "Drone", "Vehicle routing problem", "Service (business)", "Mobile robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7217550" }, { "id": "https://openalex.org/W7130780572", "doi": null, "title": "Leading an emerging technology towards purposeful transitions: A socio-technical exploration of civilian drones", "year": 2026, "publication_date": "2026-01-01", "type": "dissertation", "language": "en", "authors": [ "Aishwarya Raghunatha" ], "first_author": "Aishwarya Raghunatha", "venue": "DiVA (University of Gävle)", "publisher": "University of Gävle", "abstract": "Rapid technological change is driving complex sustainability transitions in unpredictable ways, bringing both opportunities and systemic uncertainty. One such technology are civilian drones, emerging as a mode of transport within broader agendas for resilient, electrified, and sustainable transport which promises to improve accessibility, congestion, and services. Its growing technological readiness and evolving regulatory frameworks brings forth complex systems dynamics to transport transition which largely remains unexplored. This thesis develops a foundational understanding of drone transport as an emerging socio-technical system within broader sustainable transport transitions. This research focuses on its impacts, systems interactions, governance challenges, and implications for low-carbon and energy-efficient mobility. A combination of methods such as literature reviews, semi-structured interviews, narrative analysis, energy and emission assessments, life-cycle costs, are employed along with a co-creative approach to explore this new field. Potential barriers and drivers for drone transport implementation are identified in the thesis based on their impacts and systems interactions. Results show how its impacts, particularly for last-mile goods delivery, are strongly influenced by the type of drone and how it is implemented such as the spatial context, operational parameters, and what vehicles are being complemented or substituted. Further, regulatory uncertainty, competing discourses, and complex planning challenges that interact with public acceptance are revealed and addressed to align their development towards local transport needs. Moreover, results provide insights for drones and other emerging technologies to be understood critically, with nuanced discourses on technology, coordinated policy and planning approaches, engagements in transparent dialogue, to actively steer their development in early stages toward purposeful transitions.", "keywords": [ "Drone", "Corporate governance", "Sustainability", "Emerging technologies", "Sustainable transport", "Sustainable development", "Public transport", "Narrative" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-49055" }, { "id": "https://openalex.org/W7169768119", "doi": "10.1007/s00607-026-01712-2", "title": "Sustaining collaboration and mitigating conflicts in IoT-based drone systems using recommendation of things", "year": 2026, "publication_date": "2026-07-20", "type": "article", "language": "en", "authors": [ "Ayesha Qamar", "Muhammad Asim", "Zakaria Maamar", "Ali Ismail Awad", "Qaisar M. Shafi" ], "first_author": "Ayesha Qamar", "venue": "Computing", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Task (project management)", "Identification (biology)", "Baseline (sea)", "Focus (optics)", "Internet of Things", "Resource (disambiguation)", "Resource allocation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s00607-026-01712-2" }, { "id": "https://openalex.org/W4416271941", "doi": "10.48550/arxiv.2511.05265", "title": "An End-to-End Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drones", "year": 2025, "publication_date": "2025-11-07", "type": "preprint", "language": null, "authors": [ "Zeng, Taihelong", "Yun Lin", "Shi, Yuhe", "Li, Yan", "Zhiqing Wei", "Ji, Xuanru" ], "first_author": "Zeng, Taihelong", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The emergence of truck-drone collaborative systems in last-mile logistics has positioned the Traveling Salesman Problem with Drones (TSP-D) as a pivotal extension of classical routing optimization, where synchronized vehicle coordination promises substantial operational efficiency and reduced environmental impact, yet introduces NP-hard combinatorial complexity beyond the reach of conventional optimization paradigms. Deep reinforcement learning offers a theoretically grounded framework to address TSP-D's inherent challenges through self-supervised policy learning and adaptive decision-making. This study proposes a hierarchical Actor-Critic deep reinforcement learning framework for solving the TSP-D problem. The architecture consists of two primary components: a Transformer-inspired encoder and an efficient Minimal Gated Unit decoder. The encoder incorporates a novel, optimized k-nearest neighbors sparse attention mechanism specifically for focusing on relevant spatial relationships, further enhanced by the integration of global node features. The Minimal Gated Unit decoder processes these encoded representations to efficiently generate solution sequences. The entire framework operates within an asynchronous advantage actor-critic paradigm. Experimental results show that, on benchmark TSP-D instances of various scales (N=10 to 100), the proposed model can obtain competitive or even superior solutions in shorter average computation times compared to high-performance heuristic algorithms and existing reinforcement learning methods. Moreover, compared to advanced reinforcement learning algorithm benchmarks, the proposed framework significantly reduces the total training time required while achieving superior final performance, highlighting its notable advantage in training efficiency.", "keywords": [ "Reinforcement learning", "Benchmark (surveying)", "Travelling salesman problem", "Drone", "Heuristic", "Node (physics)", "Asynchronous communication", "Encoder", "Temporal difference learning", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2511.05265" }, { "id": "https://openalex.org/W7202172614", "doi": "10.14254/jsdtl.2026.11-1.7", "title": "Cargo drones vs trucks in the mid-mile: An integrated cost, reliability, and emissions analysis for sustainable logistics", "year": 2026, "publication_date": "2026-06-30", "type": "article", "language": null, "authors": [ "Li Luo", "Zhou Mingyang", "Lu Yonghua" ], "first_author": "Li Luo", "venue": "Journal of Sustainable Development of Transport and Logistics", "publisher": "Scientific Publishing House \"CSR\" and Scientific Publishing House \"SciView\"", "abstract": "Purpose. This paper investigates whether cargo drones can serve as a viable, sustainable alternative to diesel trucks in mid-mile freight logistics, an operational segment linking regional hubs and fulfilment points that remains dependent on fossil-fuel trucking despite mounting cost, emissions, and regulatory pressures. Methodology. An integrated, scenario-based analytical framework compares drones and trucks across cost, carbon emissions, and operational reliability. The framework combines modular cost decompositions, first-principles emissions accounting, and a simplified additive reliability model, parameterised with consolidated secondary data on energy prices, grid carbon intensity, and weather-driven unmanned aerial vehicle performance across five representative operational scenarios. Results. Drones deliver lower per-kilometre operating costs than trucks in most high-demand and high-fuel-price scenarios and consistently achieve 80-90 percent lower emissions across all scenarios. Reliability is the binding constraint: drone availability drops sharply under adverse weather, while trucks maintain stable performance across nearly all conditions. Theoretical contribution. The study extends the drone-logistics literature, previously concentrated on last-mile, single-dimension assessments, by offering one of the first integrated, multi-criteria comparative frameworks for mid-mile operations and by showing that climatic and grid-decarbonisation factors, not technology maturity alone, determine modal competitiveness. Practical implications. The findings support hybrid truck-drone network design, weather-adaptive routing, and prioritised regulatory pathways for Beyond Visual Line of Sight corridors in climatically stable, decarbonising regions, providing operators and policymakers with a decision-support basis for sustainable freight modal choice. SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action", "keywords": [ "Truck", "Drone", "Modular design", "Sustainable development", "Greenhouse gas", "Reliability (semiconductor)", "Energy consumption" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://jsdtl.sciview.net/index.php/jsdtl/article/download/268/202" }, { "id": "https://openalex.org/W4382644466", "doi": "10.54941/ahfe1003607", "title": "Logistics future trends and their transformative impact", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Armindo Frias", "Mário J. Simões-Marques", "Pedro B. Água", "Anacleto C. Correia" ], "first_author": "Armindo Frias", "venue": "AHFE international", "publisher": null, "abstract": "Autonomous vehicles, Artificial Intelligence (AI), IoT, drones, 3D Printing, green fuels or sustainable practices are presented as the Holy Grail for the logistics of the future. But, what are the implications of their adoption in people’s lives and in the society?The dynamism of knowledge creation and the adoption of new methodologies and technologies are increasing. Contrary to what happened in the past, there are no longer periods of stability after the emergence of new technologies, preventing societies to smoothly adapt to the new reality. Periods of change and uncertainty usually end up affecting people’s living conditions - e.g. the early days of the industrial revolution. Thus, it will be relevant to identify the new trends in logistics and their impacts on people. Anticipating potential problems, allows for mitigating measures.More agile organizations represents a competitive advantage in responding to environments of uncertainty and greater customer customization requirement, but may imply less job stability, making people nomadic. The globalization of markets and the existence of even longer supply chains, dependent on effective and efficient transportation and communication technologies, increase the risk of supply disruption being exposed both to natural and anthropogenic causes (such as ideological, cultural or resource conflicts), with economic and social consequences. Recent examples are the COVID-19 Pandemics, the cyberattack to Maersk shipping company, the grounding of the Evergreen container, in the Suez Canal, in 2021, or the current war in Ukraine. On the other hand, technological evolution has increased the automation and autonomy of systems and boosted the reduction of human involvement. The development of AI and IoT allows, among other impacts, the use of drones to perform last mile deliveries or the existence of autonomous vehicles, being ships, planes or trucks, to carry out long-haul transport. Their adoption allows cost reduction, but also anticipates the reduction of jobs. It is recognized that new technologies themselves create new jobs; but the pertinent doubt is if they will compensate for the lost jobs? The existence of more sustainable activities (e.g., environmentally friendly), while respecting ethical and social principles, brings better living conditions for current and future generations. However, it can be conflicting with the existing long supply chains or imply lower process efficiency, leading to increased costs for customers.The aim of this work is to systematize the main future trends in logistics, assessing possible impacts that these trends can have on the daily life of individuals and society. To achieve this objective, a literature review will be carried out and the opinion of professionals will be surveyed. The expected contributions of this work are to systemize of the main logistics’ future trends and helping to anticipate the human factor consequences arising from its adoption. As a result of this work, subsequent mitigating measures to avoid negative impacts on people’s lives and society can be identify, leading to a smoother and more sustainable change process, in line with the principles of green and circular economy, and the targets of UN SDG.", "keywords": [ "Dynamism", "Business", "Agile software development", "Autonomy", "Emerging technologies", "Industrial organization", "Computer science", "Economics", "Political science", "Management" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.54941/ahfe1003607" }, { "id": "https://openalex.org/W4414229458", "doi": "10.1109/tits.2025.3606420", "title": "Q-Learning-Based Multi-Objective Path Planning for AAV Parcel Delivery Through Public Transportation Vehicles", "year": 2025, "publication_date": "2025-09-16", "type": "article", "language": "en", "authors": [ "Mohammed Rahmani", "Sidi‐Mohammed Senouci", "Florian Delavernhe", "Marion Berbineau" ], "first_author": "Mohammed Rahmani", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Combining Autonomous Aerial Vehicles (AAVs) with public transportation systems offers a novel approach to enhancing urban last-mile delivery efficiency. However, current research lacks comprehensive solutions for optimizing AAV path planning that can adapt to real-time changes in public transportation schedules while avoiding dynamically changing high-interference zones, ensuring consistent drone connectivity, and accounting for limited drone battery life. This paper addresses these gaps by optimizing and balancing multiple objectives, such as ensuring early arrival, minimizing energy consumption, and reducing signal interference. To achieve these goals, we propose a Q-learning-based algorithm for real-time path optimization, ensuring path adaptability to random changes in interference zones and public transportation schedules, and compare its performance against multi-objective A* algorithm variants. Results show that Q-learning achieves superior early arrival times, energy efficiency, and reduced interference, with a nearly 100% success rate in package deliveries, compared to 60-80% for A*. These findings demonstrate the robustness and reliability of Q-learning in AAV-Aided Public Transportation systems, making it a valuable solution for real-world urban delivery challenges.", "keywords": [ "Public transport", "Adaptability", "Drone", "Motion planning", "Robustness (evolution)", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2025.3606420" }, { "id": "https://openalex.org/W4410293761", "doi": "10.1016/j.ress.2025.111233", "title": "Corrigendum to “Dynamic UAV trajectory optimisation for parcel delivery with integrated third-party risk mitigation” [Reliability Engineering & System Safety (2025) 111178]", "year": 2025, "publication_date": "2025-05-12", "type": "erratum", "language": "en", "authors": [ "Enrique Aldao", "Fernando Veiga-López", "Caroline Ponzoni Carvalho Chanel", "Yoko Watanabe", "H. González-Jorge" ], "first_author": "Enrique Aldao", "venue": "Reliability Engineering & System Safety", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Reliability (semiconductor)", "Trajectory", "Reliability engineering", "Computer science", "Risk analysis (engineering)", "Engineering", "Business", "Physics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ress.2025.111233" }, { "id": "https://openalex.org/W3118635654", "doi": "10.2514/6.2021-1321", "title": "Achieving Equilibrium for Dense, Integrated Vehicle Navigation", "year": 2021, "publication_date": "2021-01-04", "type": "conference-paper", "language": "en", "authors": [ "Mahyar R. Malekpour" ], "first_author": "Mahyar R. Malekpour", "venue": "AIAA Scitech 2021 Forum", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2021-1321.vid Drone usage has proliferated in recent years with many applications that have market-changing potential. Applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. In this paper, the term drone is used to mean both Unmanned Aerial Vehicle and small Unmanned Aircraft System vehicles. Flight infrastructure can currently only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport sends ripple effects through the system, causing more delays and missed connections. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the NAS is expected to skyrocket to millions, potentially congesting the airspace resulting in possible separation violations. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is a safety critical property for drones in the airspace. This paper addresses separation in time and in distance for high volume corridors (en-route) and lanes (on ground). The requirements and necessary conditions for maintaining proper separation and reaching maximum throughput for a given corridor/lane are addressed assuming unidirectional corridors/lanes where an aircraft arrives from one side and departs from the opposite side. Simulation results are presented that show the presented solution guarantees a set of drones to reach the equilibrium state by adjusting their speed based on their distance to the aircraft in front of them. The equilibrium state is defined as a state when a set of n aircraft moving at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as a state when the aircraft cannot move at their maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach using a large number of drones and evaluate the scalability of the proposed solution.", "keywords": [ "Computer science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2021-1321" }, { "id": "https://openalex.org/W7165055570", "doi": "10.2139/ssrn.6956675", "title": "Optimizing E-commerce Same-Day Fulfillment: Truck-Drone Routing with Forward Warehouses and Order Splitting", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Tianze Chi", "Bing Han", "Jie Zhang" ], "first_author": "Tianze Chi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Bidding", "Routing (electronic design automation)", "Service (business)", "Trailer", "Payload (computing)", "Order (exchange)", "Order fulfillment" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6956675" }, { "id": "https://openalex.org/W7159958927", "doi": "10.3389/frsc.2026.1798928", "title": "Hybrid metaheuristic optimization for multi-criteria truck–drone collaborative routing in sustainable supply chains", "year": 2026, "publication_date": "2026-05-01", "type": "article", "language": "en", "authors": [ "K. N. Mayuri", "M. Gowsalya" ], "first_author": "K. N. Mayuri", "venue": "Frontiers in Sustainable Cities", "publisher": "Frontiers Media", "abstract": "Background Last-mile logistics accounts for approximately 28% of total logistics costs and is a major contributor to urban carbon emissions. The integration of drones with electric trucks offers a path toward more efficient and sustainable delivery, but the resulting Truck–Drone Collaborative Routing Problem (TDCRP) is NP-hard, combining binary customer-to-mode assignment, route sequencing, and drone sortie scheduling into a single combinatorial challenge that exact methods cannot solve at realistic instance sizes. Objective This paper proposes a novel Hybrid ACO+BOA algorithm—the first to couple Ant Colony Optimisation (ACO) with the Butterfly Optimisation Algorithm (BOA) for any routing problem—to simultaneously minimize truck distance, delivery makespan, and carbon emissions in the TDCRP. Methods ACO constructs 20 route sets per iteration using pheromone-heuristic guided selection. BOA refines the entire population using fragrance-scaled guided Or-opt relocation—a discrete analog of the continuous BOA position update—with greedy acceptance. Pheromone deposits are drawn from BOA-refined solutions, creating a reinforcing feedback loop. Five benchmark CVRP instances were evaluated against Pure ACO, BOA, PSO, and ABC over 20 independent runs each. A 33-configuration sensitivity analysis identified the optimal parameter set. Full non-parametric statistical validation was performed (Friedman, Nemenyi, Wilcoxon with Cohen's d ). Results The Hybrid achieved the lowest or statistically equivalent normalized combined objective on all five instances, with significant superiority on P-series instances. On P-n101-k4, it recorded a mean normalized objective of 0.2267 ± 0.0046—reductions of 5.2%, 63.6%, 72.4%, and 69.3% relative to ACO, BOA, PSO, and ABC, respectively. On A-series instances, the Hybrid was statistically equivalent to Pure ACO ( p >0.19), reflecting structural characteristics where short per-vehicle routes leave limited scope for BOA refinement. The Hybrid was significantly superior to BOA, PSO, and ABC on all instances ( p < 0.001, | d | = 13.7–47.8). Drone sorties were 1.9–3.1 × higher than non-ACO baselines, directly reducing fleet-level emission. Conclusion Tightly coupling pheromone-guided construction with fragrance-scaled exploitation yields consistent, statistically validated improvements over all single-algorithm baselines. The algorithm provides a practical, scalable framework for sustainable last-mile logistics planning with computation times of 30–49 seconds remaining within daily scheduling horizons.", "keywords": [ "Metaheuristic", "Vehicle routing problem", "Scheduling (production processes)", "Benchmark (surveying)", "Population", "Routing (electronic design automation)", "Drone", "Greedy algorithm", "Truck" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3389/frsc.2026.1798928" }, { "id": "https://openalex.org/W7213878564", "doi": "10.1117/12.3125576", "title": "Truck-drone collaborative delivery method based on three-stage adaptive ant colony optimization", "year": 2026, "publication_date": "2026-09-21", "type": "conference-abstract", "language": null, "authors": [ "Zhenghan Li" ], "first_author": "Zhenghan Li", "venue": null, "publisher": null, "abstract": "To address the inefficiency of “last-mile” delivery in urban and rural logistics caused by road condition constraints, this paper proposes a collaborative truck-drone routing optimization scheme. First, a mixed-integer programming model is constructed with the objective of minimizing the total delivery time, comprehensively considering physical constraints such as drone endurance, payload capacity, and spatial-temporal synchronization of both vehicles at rendezvous points. Second, to overcome the defect of the traditional ant colony algorithm falling into local optima when solving complex routes, an improved ant colony algorithm based on a three-stage adaptive mechanism is designed. This algorithm divides the iteration process into exploration, balance, and convergence stages, and coordinates with dynamic parameter adjustment and a dedicated route decoder to achieve efficient task allocation between the truck and drone. Finally, simulation experiments based on the modified Solomon benchmark show that at a 50-node scale, the total delivery time of the proposed TS-ACO algorithm is reduced by 13.9% compared to the traditional ant colony algorithm. Compared with the truck-only delivery mode, the collaborative delivery mode can save 28.4% of the total time and reduce the truck driving distance by 38.1%. This study provides a practical theoretical model and solution tool for the optimization of terminal logistics networks in complex terrains.", "keywords": [ "Ant colony optimization algorithms", "Payload (computing)", "Convergence (economics)", "Benchmark (surveying)", "Truck", "Rendezvous", "Ant colony", "Process (computing)", "Reservation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3125576" }, { "id": "https://openalex.org/W4409295615", "doi": "10.2139/ssrn.5210954", "title": "Truck-Drone Tandem Delivery with Time Window: Its Efficiency, Cost Structure, and Carbon Emission", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "NA WU", "J. Z.", "Yuandong Liu" ], "first_author": "NA WU", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Truck", "Window (computing)", "Tandem", "Drone", "Carbon fibers", "Environmental science", "Business", "Automotive engineering", "Computer science", "Engineering", "Aerospace engineering", "Operating system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5210954" }, { "id": "https://openalex.org/W4297996976", "doi": "10.1007/978-981-19-2635-8_71", "title": "Multi-agent Reinforcement Learning-Based UAS Control for Logistics Environments", "year": 2022, "publication_date": "2022-09-29", "type": "conference-paper", "language": "en", "authors": [ "Hyungeun Jo", "Hoeun Lee", "Sangwoo Jeon", "Vishnu Kumar Kaliappan", "Tuấn Anh Nguyễn", "Dugki Min", "Jae Wook Lee" ], "first_author": "Hyungeun Jo", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Reinforcement learning", "Drone", "Computer science", "Landmark", "Multi-agent system", "Control (management)", "Artificial intelligence", "Human–computer interaction" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-19-2635-8_71" }, { "id": "https://openalex.org/W4410830332", "doi": "10.52783/jisem.v10i48s.10105", "title": "\"Solving the Traveling Salesman Problem with Drones Using Proximal Policy Optimization and Deep Reinforcement Learning\"", "year": 2025, "publication_date": "2025-05-19", "type": "article", "language": "en", "authors": [ "Ali Abdul Razzaq Taresh" ], "first_author": "Ali Abdul Razzaq Taresh", "venue": "Journal of Information Systems Engineering & Management", "publisher": "Lectito Journals", "abstract": "The increasing demand for scalable and efficient last-mile delivery has prompted the integration of drones with trucks in hybrid logistics systems. While reinforcement learning (RL) methods have shown promise in addressing the Traveling Salesman Problem with Drones (TSP-D), most approaches focus on single truck-drone coordination, limiting their real-world applicability. This paper introduces a novel multi-agent reinforcement learning (MARL) framework based on Proximal Policy Optimization (PPO) to address a multi-truck multi-drone TSP-D scenario. Each agent (truck or drone) learns a decentralized policy with a shared global reward, enabling real-time, cooperative route planning. An enhanced state representation captures vehicle positions, battery constraints, and inter-agent interactions. The actor-critic network employs deep residual layers and agent identity encoding to support dynamic adaptation and coordination. A Dijkstra-based module ensures drone reachability under energy constraints, while a task allocation mechanism balances delivery loads and prevents conflicts. Experiments on synthetic and real-world-inspired datasets demonstrate the proposed model’s superiority over single-agent PPO and classical metaheuristics in terms of delivery time, energy efficiency, and scalability. As the number of agents and delivery nodes grows, the system maintains high performance, demonstrating strong potential for real-time autonomous logistics in urban environments.", "keywords": [ "Travelling salesman problem", "Reinforcement learning", "Drone", "Artificial intelligence", "Computer science", "Mathematical optimization", "Reinforcement", "2-opt", "Mathematics", "Psychology", "Biology", "Social psychology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://jisem-journal.com/index.php/journal/article/download/10105/4645" }, { "id": "https://openalex.org/W7116712979", "doi": "10.1109/issc67739.2025.11291475", "title": "Shortest Path Planning for Fleets of Delivery Drones: Evaluating the Benefit of Including a Temporal Dimension in the Routing Network", "year": 2025, "publication_date": "2025-06-09", "type": "conference-paper", "language": null, "authors": [ "Conor Nolan", "Paul Cuffe" ], "first_author": "Conor Nolan", "venue": null, "publisher": null, "abstract": "As the commercial deployment of drone delivery services accelerates worldwide, efficient airspace utilisation has emerged as a key challenge for urban logistics. This paper examines the limitations of purely spatial two-dimensional networks for drone routing purposes and evaluates the efficacy of adding a temporal dimension to enhance available airspace capacity. With multiple operators seeking to deploy drone fleets in crowded airspace, simplistic spatial routing approaches risk congestion and reduced service reliability. Our simulations compare spatial and spatio-temporal approaches using sequential drone delivery routing scenarios, demonstrating that temporal networks significantly reduce path deviations and eliminate logjamming effects. Additional simulations evaluate how varying edge lengths affect spatio-temporal network performance, revealing tradeoffs between granularity, capacity, and computational requirements. Results indicate that selecting between spatial and spatiotemporal networks involves balancing computational cost against achievable network capacity. Spatial networks offer simplicity and low computation complexity, while spatio-temporal networks provide superior capacity at greater computational expense. These findings provide valuable guidance for the development of scalable drone traffic management systems as aerial delivery services expand in urban environments.", "keywords": [ "Drone", "Scalability", "Key (lock)", "Routing (electronic design automation)", "Software deployment", "Dimension (graph theory)", "Service (business)", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/issc67739.2025.11291475" }, { "id": "https://openalex.org/W3123218837", "doi": "10.2139/ssrn.3766229", "title": "Which States Are Prepared for the Drone Industry? A 50-State Report Card, Release 2.0", "year": 2021, "publication_date": "2021-01-01", "type": "preprint", "language": "en", "authors": [ "Brent Skorup", "Connor Haaland" ], "first_author": "Brent Skorup", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "State (computer science)", "Smart card", "Report card", "Business", "Card security code", "Aeronautics", "Computer security", "Commerce", "Engineering", "Credit card", "Computer science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.3766229" }, { "id": "https://openalex.org/W4389370867", "doi": "10.1109/ojcsys.2023.3340034", "title": "A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario", "year": 2023, "publication_date": "2023-12-06", "type": "article", "language": "en", "authors": [ "Sooyung Byeon", "Joonwon Choi", "Inseok Hwang" ], "first_author": "Sooyung Byeon", "venue": "IEEE Open Journal of Control Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper proposes a quantitative framework for optimally allocating task functions in human-autonomy teaming (HAT). HAT involves cooperation between humans and autonomous agents to achieve common goals. As humans and autonomous agents possess different capabilities, function allocation plays a crucial role in ensuring effective HAT. However, designing the best adaptive function allocation remains a challenge, as existing methods often rely on qualitative rules and intensive human-subject studies. To address this limitation, we propose a computational function allocation approach that leverages cognitive engineering, computational work models, and optimization techniques. The proposed optimal adaptive function allocation method is composed of three main elements: 1) analyze the teamwork to identify a set of all possible function allocations within a team construction, 2) numerically simulate the teamwork in temporal semantics to explore the interaction of the team with complex environments using the identified function allocations in a trial-and-error manner, and 3) optimize the adaptive function allocation with respect to a given situation such as physical conditions, available information resources, and human mental workload. For the optimization, we utilize performance metrics such as task performance, human mental workload, and coherency in function allocations. To illustrate the effectiveness of the proposed framework, we present a simulated HAT scenario involving a human work model and drone fleet for last-mile delivery in disaster relief operations.", "keywords": [ "Computer science", "Workload", "Task (project management)", "Function (biology)", "Teamwork", "Set (abstract data type)", "Operations research", "Distributed computing", "Management science", "Systems engineering", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/9552933/9712344/10345767.pdf" }, { "id": "https://openalex.org/W4384912270", "doi": "10.35266/1999-7604-2023-2-21-29", "title": "A MODEL OF COOPERATED GROUP PURSUIT WITH DISTRIBUTION BY TARGETS", "year": 2023, "publication_date": "2023-01-01", "type": "article", "language": "en", "authors": [ "Alexander A. Dubanov" ], "first_author": "Alexander A. Dubanov", "venue": "Vestnik kibernetiki", "publisher": null, "abstract": "The article considers the problems of modeling a table with a set of competing parties and a group pursuit of multiple targets. Each table cell represents a structural variable containing data about the predicted time required for an ith pursuer to reach a jth target. For each finite set of pursuer’s distribution by targets, the minimum time value of the pursuer’s reaching the target is selected out of all permissible max-imum ones. Upon analyzing the problems based on the models of real-world objects movements, the author suggests using them for simulation modeling, virtual modeling of game processes, and transport logistics, as well as for developing virtual reality models for parcel delivery performed by drones using a hub-network.", "keywords": [ "Pursuer", "Table (database)", "Computer science", "Set (abstract data type)", "Round table", "Group (periodic table)", "Drone", "Mathematical optimization", "Operations research", "Simulation", "Theoretical computer science", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.35266/1999-7604-2023-2-21-29" }, { "id": "https://openalex.org/W7165792252", "doi": "10.1109/ccdc69976.2026.11561795", "title": "A Hybrid Method for Multi-UAV Cooperative Task Allocation in Urban Logistics based on Sparrow Search and Ant Colony Optimization", "year": 2026, "publication_date": "2026-05-15", "type": "conference-paper", "language": null, "authors": [ "Jingjie Li", "Sun H", "X B Zheng", "Yanshou Luo", "Xutong Zhang", "Jun AN" ], "first_author": "Jingjie Li", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Ant colony optimization algorithms", "Task (project management)", "Sparrow", "Ant colony", "Metaheuristic", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccdc69976.2026.11561795" }, { "id": "https://openalex.org/W4406281817", "doi": "10.2139/ssrn.5094252", "title": "Physical Internet Hub-Integrated Drone Logistics Model: Optimizing Cost and Energy Efficiency for Urban Delivery Systems", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Murugaiyan Pachayappan", "Tanmoy Kundu", "Rohit Kapoor", "Sundar Rengasamy", "Jiuh‐Biing Sheu" ], "first_author": "Murugaiyan Pachayappan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "The Internet", "Efficient energy use", "Business", "City logistics", "Transport engineering", "Environmental economics", "Computer science", "Engineering", "Economics", "World Wide Web" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5094252" }, { "id": "https://openalex.org/W4408242636", "doi": "10.52783/jisem.v10i16s.2647", "title": "The Effects of Agile Marketing Capabilities on Drone Technology Adoption among Courier Service Companies in Malaysia", "year": 2025, "publication_date": "2025-03-06", "type": "article", "language": "en", "authors": [ "Wan Marhaini Wan Omar" ], "first_author": "Wan Marhaini Wan Omar", "venue": "Journal of Information Systems Engineering & Management", "publisher": "Lectito Journals", "abstract": "Drone technology has emerged as a transformative solution for addressing last-mile delivery challenges within the rapidly evolving logistics landscape. In Malaysia, the adoption of drone technology for delivery services remains in its early stages. Despite its potential benefits, concerns remain regarding cost implications and practicality. Addressing these challenges, this study aims to examine the influence of agile marketing capabilities dimensions on drone technology adoption among Malaysian courier service companies. To provide a robust theoretical foundation, the study integrates the Dynamic Capability (DC) theory and Diffusion of Innovation (DOI) theory into a unified framework. Using a simple random sampling technique, data were collected from 244 courier service companies operating in Wilayah Persekutuan Kuala Lumpur and Selangor, two states recognized for their well-developed courier infrastructure. The proposed relationships were analyzed using regression analysis. The findings highlight customer-oriented responsiveness, high flexibility and absorptive capacity capabilities significantly influence drone technology adoption among Malaysian courier service companies. However, human collaboration was found to have no significant impact on adoption. This study contributes to the existing body of knowledge by identifying critical dimensions of agile marketing capabilities that drive drone technology adoption in the courier service industry. The insights gained provide practical implications for courier service companies seeking to enhance their capabilities and integrate emerging technologies in this rapidly evolving sector. Future research should explore the impact of external factors, such as regulations and public perception, as well as technological advancements on adoption rates and operational outcomes to provide a more nuanced understanding of the variables shaping the long-term integration of drone technology in the logistics industry.", "keywords": [ "Drone", "Agile software development", "Business", "Marketing", "Service (business)", "Process management", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.52783/jisem.v10i16s.2647" }, { "id": "https://openalex.org/W7154850917", "doi": "10.2139/ssrn.6604467", "title": "A Diffusion-Driven Two-Stage Optimization Approach for Large-Scale Traveling Salesman Problems with a Drone", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Peixin Zuo", "Yuhang Zhou", "Ting Huang", "Yu Kang", "Shanlin Yang" ], "first_author": "Peixin Zuo", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Heuristic", "Stability (learning theory)", "Embedding", "Benchmark (surveying)", "2-opt", "Quality (philosophy)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6604467" }, { "id": "https://openalex.org/W4410421545", "doi": "10.1016/j.ijtst.2025.05.002", "title": "Erosion-enhanced rain optimization algorithm for efficient blood bag transport in multimodal healthcare logistics", "year": 2025, "publication_date": "2025-05-16", "type": "article", "language": "en", "authors": [ "Tejinder Singh Lakhwani", "Yerasani Sinjana", "Anuj Pal Kapoor" ], "first_author": "Tejinder Singh Lakhwani", "venue": "International Journal of Transportation Science and Technology", "publisher": "Elsevier BV", "abstract": "The Erosion-Enhanced Rain Optimization Algorithm (ROA) is introduced as an advanced framework to address pressing challenges in multimodal healthcare logistics, focusing on the efficient and timely delivery of critical supplies such as blood bags. This study integrates dynamic erosion penalties and predictive machine learning models into the ROA, enabling real-time adaptation to route degradation, traffic congestion, and environmental variability. The algorithm balances operational efficiency, sustainability, and resilience by utilizing trucks for long-distance transport and drones for last-mile delivery. The predictive component, with metrics such as MSE (0.0301), RMSE (0.1736), and R2 (0.8393), forecasts route degradation trends, supporting preemptive maintenance and rerouting strategies to ensure consistent performance. Validated using real-world datasets from Delhi, India, under conditions like no-fly zones, adverse weather, and high traffic density, the ROA demonstrated remarkable performance. It reduced delivery times by 9%, improved drone energy efficiency by 15%, and achieved a 12%–18% reduction in carbon emissions compared to static models. Maintenance-based erosion recovery reduced overall route degradation by 8%, reinforcing the system’s adaptability and long-term sustainability. These results underscore the model’s capacity to handle complex and dynamic healthcare logistics networks effectively. This study highlights the ROA’s unique contributions, including its dynamic adaptation mechanisms and predictive insights, and it focuses on balancing efficiency with environmental sustainability. Its scalability and versatility extend its application potential beyond healthcare logistics to other industries facing real-time logistical challenges.", "keywords": [ "Business", "Computer science", "Health care", "Multimodal transport", "Erosion", "Algorithm", "Transport engineering", "Engineering", "Marketing", "Economics", "Geology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ijtst.2025.05.002" }, { "id": "https://openalex.org/W7138155779", "doi": "10.1080/23249935.2026.2643437", "title": "Route optimisation of truck-and-drone systems for multi-type rescue tasks: a heuristic approach", "year": 2026, "publication_date": "2026-03-16", "type": "article", "language": "en", "authors": [ "W. D. Sun", "Fangni Zhang", "Lingxiao Wu", "Yong‐Hong Kuo" ], "first_author": "W. D. Sun", "venue": "Transportmetrica A Transport Science", "publisher": "Taylor & Francis", "abstract": "This study investigates a Vehicle Routing Problem with Drones (VRPD) for multi-type rescue tasks in post-disaster response. Specifically, we consider a truck-and-drone collaborative system that can simultaneously deliver essential relief supplies and assess network conditions with drones in disaster scenarios. To enhance the rescue efficiency, drones can collaborate with different trucks and perform multi-type tasks (delivery and surveillance), making the proposed problem challenging to solve. We first provide a Mixed Integer Linear Programming (MILP) model for the proposed problem. The objective is to minimise the priority cost, which is calculated as the weighted delay cost of serving all rescue tasks and is different from most existing studies on VRPD for parcel deliveries. Then, we develop an Adaptive Simulated Annealing (ASA) algorithm. Since the priority cost depends on the intertwined influence of truck-and-drone routes, the ASA algorithm is integrated with a dynamic programming method to calculate the priority cost. The ASA algorithm can always get the (near) optimal solution on instances with fewer than 30 nodes, and solves larger instances efficiently. Numerical experiments demonstrate that enabling drones to perform multi-type tasks and allowing flexible truck-and-drone collaboration reduces the priority cost by an average of 15.7% across all tested instances, compared with the single-type task and dedicated truck-and-drone collaboration. Sensitivity analysis shows that introducing more drones can reduce the number of trucks involved in the disaster response while achieving similar performance. Moreover, this study reveals a trade-off between minimising priority cost and other objectives, such as minimising travel cost or makespan.", "keywords": [ "Heuristic", "Work (physics)", "Key (lock)", "Process (computing)", "Minification" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.tandfonline.com/doi/pdf/10.1080/23249935.2026.2643437?needAccess=true" }, { "id": "https://openalex.org/W7124712300", "doi": "10.17632/8v2mg7p5hm.1", "title": "Distributionally robust truck-drone collaborative delivery network design for urban-rural warehouses: A normal-emergency perspective", "year": 2026, "publication_date": "2026-01-19", "type": "dataset", "language": null, "authors": [ "cheng Cheng" ], "first_author": "cheng Cheng", "venue": "Mendeley Data", "publisher": null, "abstract": "In this case study, based on preliminary market research conducted by the company, 6 large-scale integrated logistics parks in the region are selected as urban-rural warehouses. From these, 56 potential locations for front warehouses within the Sixth Ring Road are chosen. Additionally, 70 last-mile nodes with relatively high population density are selected at the city street level", "keywords": [ "Perspective (graphical)", "Population", "Network planning and design", "Work (physics)", "Flow network", "Front (military)", "Facility location problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/8v2mg7p5hm.1" }, { "id": "https://openalex.org/W7155177658", "doi": "10.2139/ssrn.6626357", "title": "A Diffusion-Driven Two-Stage Optimization Approach for Large-Scale Traveling Salesman Problems with a Drone", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Peixin Zuo", "Yuhang Zhou", "Ting Huang", "Yu Kang", "Shanlin Yang" ], "first_author": "Peixin Zuo", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Heuristic", "Stability (learning theory)", "Embedding", "Benchmark (surveying)", "2-opt", "Quality (philosophy)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6626357" }, { "id": "https://openalex.org/W4409895171", "doi": "10.52783/jisem.v10i38s.7021", "title": "Transforming Supply Chain Management with Next-Generation Technology (AI, Drones and Robots)- A Bibliographic Review", "year": 2025, "publication_date": "2025-04-22", "type": "article", "language": "en", "authors": [ "Rangappa Yaraddi" ], "first_author": "Rangappa Yaraddi", "venue": "Journal of Information Systems Engineering & Management", "publisher": "Lectito Journals", "abstract": "The global supply chain is undergoing a radical transformation driven by emerging technologies such as robots, drones, and artificial intelligence (AI). These (modern technologies) are redefining supply chain management by enhancing efficiency, reducing operational costs, and improving resilience against disruptions such as natural disasters, geopolitical tensions, and pandemics. The integration of robots in warehouse automation has streamlined logistics processes, while drones are revolutionizing last-mile delivery, particularly in remote and hard-to-reach locations. AI-driven predictive analytics have significantly optimized demand forecasting, inventory management, and decision-making processes, leading to a more responsive and agile supply chain ecosystem. This paper explores the profound impact of these next-generation technologies, analysing their contributions to supply chain optimization, logistics enhancement, and real-time monitoring as well examine the critical challenges associated with their implementation, including cybersecurity risks, regulatory constraints, data privacy concerns, and ethical considerations related to workforce displacement. The study highlights the growing need for businesses to adapt to these advancements and the importance of policy frameworks to ensure a sustainable and ethical transition. The findings suggest that the adoption of robots, drones, and AI is not just a competitive advantage but a necessity for organizations striving for long-term sustainability and market leadership. While challenges remain, strategic implementation and regulatory alignment can help organizations maximize the benefits of these technologies, creating a more resilient and efficient global supply chain. This paper concludes with recommendations for businesses, policymakers, and supply chain professionals on how to navigate this technological evolution successfully.", "keywords": [ "Drone", "Supply chain", "Robot", "Supply chain management", "Business", "Computer science", "Engineering management", "Engineering", "Artificial intelligence", "Marketing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://jisem-journal.com/index.php/journal/article/download/7021/3245" }, { "id": "https://openalex.org/W7203735986", "doi": "10.2139/ssrn.7312524", "title": "Charging Timing Matters: Green Electric-Truck–Drone Collaborative Delivery under Time-Varying Grid Carbon Intensity", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "chao li", "Yanchun Chen" ], "first_author": "chao li", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Grid", "Greenhouse gas", "Carbon fibers", "Intensity (physics)", "Certification", "Electricity generation", "Linear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7312524" }, { "id": "https://openalex.org/W7135191271", "doi": "10.3389/frai.2026.1759688", "title": "An improved adaptive large neighborhood search algorithm for the flexible two-tier vehicle routing problem with drone stations", "year": 2026, "publication_date": "2026-03-13", "type": "article", "language": "en", "authors": [ "Qi Li", "Wenhao Yan", "Tad Gonsalves" ], "first_author": "Qi Li", "venue": "Frontiers in Artificial Intelligence", "publisher": "Frontiers Media", "abstract": "The two-tier vehicle routing problem (2T-VRP) represents a novel variant differing from the traditional VRPs. It can be applied to urban logistics operations system, offering significant potential for mitigating last-mile traffic congestion and reducing delivering costs. Distinct from traditional VRPs, this scenario contains a hierarchical two-tier structure, with trucks operating on the first tier and drones at stations on the second tier. In this study, we first extend the 2T-VRP framework by relaxing the hierarchical constraints, enabling trucks to transport goods not only to robot stations but also directly to customers. This new variant is referred to as the flexible two-tier vehicle routing problem with drone stations (F2T-VRP-DS). Then we formulate the problem as a mixed-integer linear programming (MILP) model. Finally, given the complexity of this problem, an improved adaptive large neighborhood search heuristic algorithm (IALNS) is proposed. The algorithm incorporates an adapted Clark and Wright saving algorithm as the initial solution, and a simulated annealing scheme is employed as the acceptance criterion. The experimental results show that our algorithm can provide high-quality solutions. In particular, compared with the MILP method, our algorithm demonstrates strong competitiveness on large-scale instances, offering smaller time consumption and better solution quality compared to commercial solver like Gurobi. In addition, based on the results of the sensitivity analysis, we further assessed the influence of several key parameters within the F2T-VRP-DS framework.", "keywords": [ "Simulated annealing", "Vehicle routing problem", "Solver", "Key (lock)", "Routing (electronic design automation)", "Drone", "Variable neighborhood search", "Traffic congestion" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://public-pages-files-2025.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1759688/pdf" }, { "id": "https://openalex.org/W7124693466", "doi": "10.17632/8v2mg7p5hm", "title": "Distributionally robust truck-drone collaborative delivery network design for urban-rural warehouses: A normal-emergency perspective", "year": 2026, "publication_date": "2026-01-19", "type": "dataset", "language": null, "authors": [ "cheng Cheng" ], "first_author": "cheng Cheng", "venue": "Mendeley Data", "publisher": null, "abstract": "In this case study, based on preliminary market research conducted by the company, 6 large-scale integrated logistics parks in the region are selected as urban-rural warehouses. From these, 56 potential locations for front warehouses within the Sixth Ring Road are chosen. Additionally, 70 last-mile nodes with relatively high population density are selected at the city street level", "keywords": [ "Perspective (graphical)", "Population", "Network planning and design", "Work (physics)", "Flow network", "Front (military)", "Facility location problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/8v2mg7p5hm" }, { "id": "https://openalex.org/W7203610926", "doi": "10.1016/j.cie.2026.112302", "title": "Multi-objective green location-routing problem for drone-assisted delivery under uncertain demand", "year": 2026, "publication_date": "2026-08-17", "type": "article", "language": "en", "authors": [ "Yuanrui Li", "Qiuhong Zhao", "Qi Lin" ], "first_author": "Yuanrui Li", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Decomposition", "Heuristic", "Realization (probability)", "Routing (electronic design automation)", "Vehicle routing problem", "Stochastic programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2026.112302" }, { "id": "https://openalex.org/W7135047858", "doi": "10.2139/ssrn.6362569", "title": "Enhancing Healthcare Access in Rural Appalachia: A Drone-truck System Powered by Deep Reinforcement Learning", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Sabrullah Deniz", "Zhenbo Wang", "Courtney C. Cronley", "Jennifer Tourville", "Daniel Wang" ], "first_author": "Sabrullah Deniz", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Payload (computing)", "Process (computing)", "Reinforcement learning", "Markov decision process", "Resource (disambiguation)", "Resource allocation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6362569" }, { "id": "https://openalex.org/W4411888990", "doi": "10.3390/modelling6030060", "title": "Quasi-LPV Approach for the Stabilization of an Innovative Quadrotor", "year": 2025, "publication_date": "2025-07-01", "type": "article", "language": "en", "authors": [ "Saïd Chaabani", "Naoufel Azouz" ], "first_author": "Saïd Chaabani", "venue": "Modelling—International Open Access Journal of Modelling in Engineering Science", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In recent decades, the deployment of quadcopters has significantly expanded, particularly in outdoor applications such as parcel delivery. These missions require highly stable aerial platforms capable of maintaining balance under diverse environmental conditions, ensuring the safe operation of both the drone and its payload. This paper focuses on the stabilization of a quadcopter designed for outdoor use. A detailed dynamic model of a compact vertical takeoff and landing (VTOL) drone forms the basis for a non-linear control strategy targeting stability during the critical takeoff phase. The control law is designed using a quasi-linear parameter-varying (quasi-LPV) model that captures the system’s non-linear dynamics. Lyapunov theory and linear matrix inequalities (LMIs) are employed to validate the stability and design the controller. Numerical simulations demonstrate the controller’s effectiveness, and a comparative study is conducted to benchmark its performance against a reference quadrotor model.", "keywords": [ "Computer science", "Aeronautics", "Control theory (sociology)", "Engineering", "Control (management)", "Artificial intelligence" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2673-3951/6/3/60/pdf?version=1751364934" }, { "id": "https://openalex.org/W7164566131", "doi": "10.1117/12.3118839", "title": "Research on vehicle-drone collaborative path planning for emergency power supplies based on hybrid genetic algorithm", "year": 2026, "publication_date": "2026-06-12", "type": "conference-abstract", "language": null, "authors": [ "Gen Li", "Shu Ren", "Longfei Li", "Zilu Wang", "Yuheng Jia" ], "first_author": "Gen Li", "venue": null, "publisher": null, "abstract": "Aiming at the critical issues of stringent timeliness requirements for material distribution and vehicle access impediments in complex terrains such as mountainous areas during power emergency repair scenarios, this paper proposes a hybrid genetic algorithm-based vehicle-drone collaborative logistics route planning method. Integrating the long-distance transportation capacity of vehicles with the flexible cross-obstacle response characteristics of drones, a multiobjective optimization model is constructed for minimizing delivery time, reducing transportation cost, and optimizing service level. Simulation experiments based on the power emergency logistics network in the mountainous area of Wuhua County, Meizhou City, Guangdong Province, show that in a scenario involving 47 repair stations, this method reduces total delivery time by 9.10% and comprehensive transportation cost by 22.73% compared to the traditional genetic algorithm. It effectively addresses the \"last-mile\" delivery challenge of power emergency materials in mountainous areas, providing technical support for the construction of smart power emergency logistics systems.", "keywords": [ "Genetic algorithm", "Path (computing)", "Power (physics)", "Key (lock)", "Motion planning", "Context (archaeology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3118839" }, { "id": "https://openalex.org/W4399338788", "doi": "10.34293/commerce.v12i2.7466", "title": "Evolution of E-Commerce Logistics: Global Trends and Implementations", "year": 2024, "publication_date": "2024-04-01", "type": "article", "language": "en", "authors": [ "G Deepak Raj", "S Thandayudhapani" ], "first_author": "G Deepak Raj", "venue": "ComFin Research", "publisher": null, "abstract": "E-commerce has revolutionized the way businesses operate and consumers shop, leading to a surge in online shopping and placing immense pressure on logistics providers and retailers to optimize their operations. The evolution of e-commerce logistics has become a critical factor in determining the success or failure of businesses in the digital age. The impact of e-commerce on logistics is multifaceted, encompassing various aspects such as supply chain management, transportation, warehousing, last-mile delivery, and reverse logistics. Traditional logistics models have proven inadequate in addressing the unique challenges posed by the business-to-consumer (B2C) e-commerce landscape, necessitating the development of sophisticated order fulfilment systems and the optimization of transportation networks to ensure timely and cost-effective delivery. One of the significant challenges faced by e-commerce logistics is the complexity of managing a vast number of individual orders and delivering them promptly to consumers. Unlike traditional retail, where bulk shipments are sent to stores, e-commerce requires the processing and delivery of countless parcels directly to customers’ doorsteps. This has necessitated the development of sophisticated order fulfillment systems and the optimization of transportation networks to ensure timely and cost-effective delivery. Another key aspect of e-commerce logistics is the need for seamless integration between various stakeholders, including retailers, suppliers, logistics providers, and technology platforms. Effective collaboration and data sharing among these entities are crucial for ensuring end-to-end visibility, enabling real-time tracking, and facilitating efficient decision-making throughout the supply chain. The rise of e-commerce has also led to the emergence of new business models and innovative logistics solutions, such as the proliferation of third-party logistics providers (3PLs) specializing in e-commerce fulfilment, the adoption of advanced technologies such as automation and robotics in warehousing operations, and the exploration of alternative last-mile delivery methods like drones and autonomous vehicles.", "keywords": [ "Implementation", "E-commerce", "Computer science", "Business", "Data science", "Industrial organization", "World Wide Web", "Software engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.34293/commerce.v12i2.7466" }, { "id": "https://openalex.org/W4391790633", "doi": "10.2139/ssrn.4725041", "title": "Distribution Route Optimization Under Joint Distribution Mode of Drones and Vehicles Based on the Meta-Heuristic Algorithm", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Wusheng Liu", "Wang Li", "Yiping Xiao", "Qian Tan" ], "first_author": "Wusheng Liu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Meta heuristic", "Drone", "Mode (computer interface)", "Distribution (mathematics)", "Heuristic", "Joint (building)", "Computer science", "Mathematical optimization", "Algorithm", "Engineering", "Mathematics", "Structural engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4725041" }, { "id": "https://openalex.org/W7119485187", "doi": "10.57760/sciencedb.35070", "title": "A Dataset of Distributed Operational States and Resource Allocation for Urban Low-Altitude Logistics Drone Swarms", "year": 2026, "publication_date": "2026-01-07", "type": "dataset", "language": "en", "authors": [ "Yuetan Zhang" ], "first_author": "Yuetan Zhang", "venue": "ScienceDB", "publisher": null, "abstract": "This dataset is a high-precision real-world operational dataset for the distributed operation of a logistics drone swarm. It comprehensively covers complete flight state records of multiple drones over continuous time periods, involving 10 logistics drones and 100 logistics tasks, cumulatively generating 1.0486 million high-precision operational state records. The dataset fully records multi-dimensional dynamic information during the distributed operation of the drone swarm, providing a complete data foundation for research on drone swarm operational state analysis, resource allocation optimization, and task scheduling strategies.The dataset covers the complete task cycle of drones from takeoff, cruise, and maneuvering to landing, and includes various typical operational scenarios such as formation keeping, dynamic obstacle avoidance, path re-planning, and cooperative resource gaming phases. Data were collected at millisecond-level frequency, preserving typical disturbance characteristics of the urban low-altitude environment (such as wind shear, signal attenuation caused by building occlusion), providing a realistic, fine-grained data foundation for the validation and optimization of distributed control algorithms, resource allocation models, and their coupling mechanisms.Stored in structured tabular format accompanied by detailed metadata descriptions and usage examples, providing an open and reliable data resource for interdisciplinary research in drone swarms, distributed systems, urban logistics, etc.This dataset is stored in a structured tabular format, accompanied by detailed metadata descriptions and usage examples, aiming to provide an open and reliable data resource for interdisciplinary research in drone swarms, distributed systems, urban logistics, and related fields.", "keywords": [ "Drone", "Metadata", "Scheduling (production processes)", "Resource allocation", "Swarm behaviour", "Resource (disambiguation)", "Task (project management)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.57760/sciencedb.35070" }, { "id": "https://openalex.org/W7133226472", "doi": "10.1109/icctit68197.2025.11406228", "title": "Real-time Conflict Avoidance for Multiple Drones in Low-Altitude Logistics Based on Deep Reinforcement Learning", "year": 2025, "publication_date": "2025-12-26", "type": "conference-paper", "language": null, "authors": [ "Zerui Shen" ], "first_author": "Zerui Shen", "venue": null, "publisher": null, "abstract": "This paper proposes a DAW-TD3 conflict avoidance algorithm to address the conflict avoidance problem in multidrone urban last-mile delivery. Traditional algorithms suffer from state redundancy, Q-value overestimation, and safety-efficiency imbalances caused by single-objective optimization. First, a state space containing self-motion, neighbor interactions, and environmental constraints was defined through problem modeling, and a dynamic safety distance (introducing a velocity correction factor) and a physically compliant action space were designed. Next, a dynamic attention mechanism is constructed to select 3-5 key neighbors based on the relative distance and velocity direction, reducing the state dimension by 60%-70%. Finally, the TD3 network was improved (the number of Actor/Critic neurons was reduced from 128 to 64), and a multi-objective reward function was designed (balancing conflict avoidance, path tracking, and energy consumption control). Based on the AirSim+ROS+PyTorch platform, comparative experiments with TD3, DDPG, and ORCA show that with 30 drones, the DAW-TD3 collision rate is only 0.8% (lower than TD3's 3.8%), with 20 drones, the average response time is 8.2ms (nearly half that of TD3), energy consumption per unit distance is 8.5Wh/km (11.5% lower than DDPG), path deviation rate is 1.2% (better than ORCA's 1.8%), and GPU utilization is 65% (lower than TD3's 82%), making it suitable for edge computing. This algorithm provides technical support for the safe and efficient operation of multidrone collaborative delivery.", "keywords": [ "Reinforcement learning", "Collision avoidance", "Energy consumption", "Dimension (graph theory)", "Drone", "Path (computing)", "State space", "Enhanced Data Rates for GSM Evolution", "Motion planning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icctit68197.2025.11406228" }, { "id": "https://openalex.org/W4416949231", "doi": "10.1504/ijvsmt.2025.150170", "title": "Optimisation cost and carbon emission in vehicle-drone collaborative delivery under dynamic traffic conditions", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Kai Fa Wu", "Zhijiang Lu", "E. Bai" ], "first_author": "Kai Fa Wu", "venue": "International Journal of Vehicle Systems Modelling and Testing", "publisher": "Inderscience Publishers", "abstract": "The rising number of vehicles in urban areas has caused severe congestion in logistics, increasing delivery and carbon costs. To address this, the vehicle and drone co-delivery model (VDCDM) has become a research focus, yet existing studies lack a strong connection to urban traffic conditions. This paper develops a multi-objective path optimisation model for vehicle-drone collaborative delivery, incorporating traffic congestion to minimise carbon emissions and total delivery costs. We introduce an improved BBO algorithm (IBBO) that enhances global search capability while reducing complexity. Testing reveals stable optimisation across various traffic scenarios. Our findings on the collaborative delivery index (CDI) show that lower CDIs lead to more drone-served customers, increasing overall costs but decreasing emissions. This highlights the need for companies to assess their strategies and choose suitable CDIs, offering valuable insights for urban logistics and emergency transport applications.", "keywords": [ "Traffic congestion", "Drone", "Path (computing)", "Carbon fibers", "Index (typography)", "Traffic optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijvsmt.2025.150170" }, { "id": "https://openalex.org/W7202069755", "doi": "10.3390/a19080666", "title": "Grid-Aware Bi-Level Optimization for Truck–Drone Routing: Integrating Grid Feasibility and Shadow Pricing", "year": 2026, "publication_date": "2026-08-10", "type": "article", "language": "en", "authors": [ "Heictor Alves de Oliveira Costa", "Fernando José Von Zuben" ], "first_author": "Heictor Alves de Oliveira Costa", "venue": "Algorithms", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Electric trucks operating as mobile depots for delivery drones are promising for last-mile logistics, yet fleet electrification makes depot charging a critical issue governed by distribution-grid limits. Existing truck–drone routing formulations omit the electrical network, treating energy as exogenous, while grid-aware routing models overlook the combinatorial structure of mobile-depot drone synchronization. This paper introduces an energy-aware bi-level framework for the truck–drone routing problem that closes this gap. A distribution-grid leader solves slot-wise alternating current (AC) optimal power flow (OPF) under time-varying base loads and line deratings, returning a grid-feasible energy headroom and shadow prices. A logistics follower then co-optimizes truck routes, drone sorties, and ramp-constrained charging against this effective price, within a multi-objective cost structure. A damped fixed-point iteration couples the two levels, communicating grid scarcity through a single price signal without the logistics layer solving power-flow equations. On a Tokyo-inspired 100-customer instance with a stressed IEEE 33-bus feeder, the framework confines charging to slots with genuine headroom, reaching at most 81% loading and returning the fleet fully charged, whereas a grid-blind baseline reaches 109% loading. This comparison validates shadow pricing as an effective coordination mechanism.", "keywords": [ "Drone", "Grid", "Truck", "Electrification", "Routing (electronic design automation)", "Electricity pricing", "Electricity", "Power flow", "Demand response" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/a19080666" }, { "id": "https://openalex.org/W7211952327", "doi": "10.1007/978-3-032-26919-5_24", "title": "Collaborative Energy-Aware Indoor Drone Navigation using BLE Beacons and Multi-agent Reinforcement Learning", "year": 2026, "publication_date": "2026-08-02", "type": "conference-paper", "language": "en", "authors": [ "Budesh Kanwer", "Rekha Jain" ], "first_author": "Budesh Kanwer", "venue": "Algorithms for intelligent systems", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Beacon", "Reinforcement learning", "Control (management)", "Reinforcement" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-26919-5_24" }, { "id": "https://openalex.org/W7202094624", "doi": "10.1007/s40032-026-01430-w", "title": "Experimental and Numerical Analysis of Payload Capacity and Structural Integrity in a Smart Autonomous Delivery Drone (SADDUL)", "year": 2026, "publication_date": "2026-08-10", "type": "article", "language": "en", "authors": [ "Kaustav Dey", "Raj Kumar", "Suman Debnath" ], "first_author": "Kaustav Dey", "venue": "Journal of The Institution of Engineers (India) Series C", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Payload (computing)", "Aerodynamics", "Airframe", "Rotor (electric)", "Controller (irrigation)", "Computational fluid dynamics", "Airfoil", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s40032-026-01430-w" }, { "id": "https://openalex.org/W7167206649", "doi": "10.37609/akya.3956.c2654", "title": "Lojistikte Stratejik Bir Açılım Olarak İnsansız Hava Araçları (İha) ve Drone Lojistiği: Kullanım Alanları, Sınırlılıklar ve IOD Entegrasyon", "year": 2026, "publication_date": "2026-07-03", "type": "book-chapter", "language": "tr", "authors": [ "İsmail Bakan", "Burcu Erşahan", "Yağmur Matyar Tanır" ], "first_author": "İsmail Bakan", "venue": null, "publisher": null, "abstract": "İnsansız hava araçları (İHA), daha yaygın adıyla drone’lar, lojistik ve tedarik zincirlerinde giderek önemli bir rol üstlenmektedir. Özellikle son kilometre teslimat, afet ve acil yardım operasyonları ile sağlık lojistiği gibi kritik alanlarda kullanımları artmaktadır. Drone’lar, yüksek hareket kabiliyeti, insan erişiminin zor olduğu bölgelere ulaşabilme ve kablosuz ağ entegrasyonu sayesinde operasyonel esnekliği arttırırken, teslimat sürelerini kısaltmakta ve maliyetleri düşürmektedir. Bununla birlikte, batarya ömrü, sınırlı taşıma kapasitesi ve siber güvenlik riskleri gibi sınırlılıkları da mevcuttur. Teslimat süreçlerinde drone’lar ve kamyonlar, birbirini tamamlayıcı şekilde kullanılabilir; kamyonlar ana taşımayı gerçekleştirirken, drone’lar son kilometre teslimatlarını hızlı ve etkin biçimde tamamlayarak hibrit bir lojistik modeli oluşturur. Bu bağlamda, İnsansız Hava Araçları İnterneti (IoD), drone’ların birbirleriyle ve yer altyapılarıyla kesintisiz ve koordineli bir şekilde etkileşim kurmasını sağlayan merkezi bir paradigma olarak öne çıkmaktadır. IoD, operasyonel koordinasyonu, veri paylaşımını ve görev yönetimini optimize ederken, güvenlik, gizlilik ve sistem güvenilirliği konularında titiz önlemler alınmasını gerektirmektedir. Bu kapsamda bu çalışma, İHA’ların lojistik amaçlı kullanım potansiyelini, avantajlarını ve sınırlılıklarını değerlendirirken, drone ve kamyon entegrasyonunun lojistik uygulamalardaki önemini vurgulamaktadır. Unmanned aerial vehicles (UAVs), more commonly known as drones, are playing an increasingly important role in logistics and supply chains. Their use is expanding particularly in critical areas such as last-mile delivery, disaster and emergency response operations, and healthcare logistics. Drones enhance operational flexibility by offering high mobility, the ability to access hard-to-reach areas, and integration with wireless networks, while also reducing delivery times and lowering costs. Nevertheless, they face several limitations, including limited battery life, restricted payload capacity, and cybersecurity risks. In delivery processes, drones and trucks can be used in a complementary manner: while trucks perform the main transportation, drones complete last-mile deliveries quickly and efficiently, thereby creating a hybrid logistics model. In this context, the Internet of Drones (IoD) emerges as a central paradigm that enables drones to interact seamlessly and in a coordinated manner with each other and with ground infrastructures. While IoD optimizes operational coordination, data sharing, and mission management, it also necessitates stringent measures in terms of security, privacy, and system reliability. Within this scope, this study evaluates the potential, advantages, and limitations of UAVs for logistics purposes, while emphasizing the importance of drone–truck integration in logistics applications.", "keywords": [ "Drone", "Payload (computing)", "On board", "Flexibility (engineering)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.37609/akya.3956.c2654" }, { "id": "https://openalex.org/W7144017568", "doi": "10.1080/12265934.2026.2648837", "title": "Urban air mobility and drone safety in cities: accident rate prediction and smart city integration", "year": 2026, "publication_date": "2026-03-30", "type": "article", "language": "en", "authors": [ "HanYeol Beak", "JungHoon Kim" ], "first_author": "HanYeol Beak", "venue": "International Journal of Urban Sciences", "publisher": "Taylor & Francis", "abstract": "In densely built-up cities, physical constraints and intensive land use increasingly limit the expansion of road networks and underground rail systems, leading to growing interest in alternative urban transportation modes such as urban air mobility (UAM) and drones. As these aerial systems are expected to operate within complex urban environments, ensuring their safety has become a critical planning challenge. However, quantitative safety assessment is constrained by the lack of empirical accident data due to their limited commercialization. This study estimates accident frequencies and potential human casualties associated with UAM and drone operations by adapting established aviation safety proxies, including the International Civil Aviation Organization’s Target Level of Safety (TLS), Boeing’s Statistical Summary of Commercial Jet Airplane Accidents (SSCJA), and the UK Risk of Travel (ROT) statistics. Using a projected UAM flight network for Seoul in 2035 and scenario-based assumptions for drone-based urban logistics, the expected numbers of crashes, takeoff/landing accidents, and fatalities are calculated. To address uncertainty arising from the application of conventional aviation metrics to emerging urban air systems, a one-way sensitivity analysis (±10–30%) is conducted. The results show that predicted accident and fatality levels for UAM remain low even under conservative assumptions, whereas drone logistics scenarios produce higher accident counts due to high operational frequency. Sensitivity analysis confirms bounded risk ranges rather than single-point estimates. The findings highlight the importance of managing takeoff and landing risks and support integrating UAM and drone operations into smart city platforms for urban safety management.", "keywords": [ "Smart city", "Drone", "Accident (philosophy)", "Internet of Things", "Public transport" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/12265934.2026.2648837" }, { "id": "https://openalex.org/W4401441818", "doi": "10.1109/mits.2024.3430938", "title": "Autonomous Airborne Transportation: Field Trials in Urban Water Landscapes", "year": 2024, "publication_date": "2024-08-09", "type": "article", "language": "en", "authors": [ "Rajveer Singh Brar", "Walter Mérida" ], "first_author": "Rajveer Singh Brar", "venue": "IEEE Intelligent Transportation Systems Magazine", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Unmanned aerial vehicles (UAVs) operating on unlicensed spectra have short operational range. To perform complex beyond visual line-of-sight UAV operations, licensed-spectrum cellular systems can be leveraged for intelligent transportation applications, including parcel delivery via UAVs. This work investigates the long-term evolution (LTE) network-enabled UAV performance at different altitudes under real propagation conditions over a water body in an urban area. In this work, connectivity characterization relies on real data from commercial cellular networks. The performance metrics in this investigation include reference signal received power (RSRP), reference signal received quality, and signal-to-interference-and-noise ratio (SINR). This research discusses the impact of UAV altitude, location of base stations (BSs), and cellular radio frequency on UAV connectivity. Our preliminary data show that handover to higher frequency bands limits the RSRP at UAV terminals, although higher frequency bands have increased bandwidth for better data throughput. The RSRP and SINR are poor when the serving BSs are comparatively distant and obstructed by nearby BSs, respectively. This work also compares the over-the-water propagation environment to other published propagation environments to identify cellular coverage challenges over water surfaces.", "keywords": [ "Field trial", "Environmental science", "Field (mathematics)", "Remote sensing", "Transport engineering", "Engineering", "Marine engineering", "Aeronautics", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mits.2024.3430938" }, { "id": "https://openalex.org/W7138838614", "doi": "10.1109/jiot.2026.3675581", "title": "Multitruck Multidrone Collaborative Delivery via EG-GAT Embedding Multiagent DRL in Rural Areas", "year": 2026, "publication_date": "2026-03-19", "type": "article", "language": null, "authors": [ "Huamei Qi", "Jiaxing Liu", "Xiaoheng Deng", "Hairong Lin", "Jinsong Gui", "Jingjing Zhang", "Shaohua Wan" ], "first_author": "Huamei Qi", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The vast geographic coverage and sparse customer distribution in rural areas lead to inefficiency in traditional last-mile delivery. Truck-drone collaborative delivery systems have emerged as a promising solution to these rural logistics challenges. Accordingly, we introduce a multi-truck multi-drone collaborative delivery framework. Within this framework, we propose a novel graph embedding module—the edge-gated graph attention network (EG-GAT)—which incorporates multi-dimensional edge features into the attention mechanism and introduces a learnable gating module for adaptive multi-head fusion. We further propose a graded flexible time window mechanism, which permits limited service advancement or deferral while applying graded incentive-penalty structures. This approach better captures the temporal flexibility inherent in rural customer service requirements. The resulting multi-objective truck-drone routing problem is modeled as a rewardmaximization task and solved using multi-agent proximal policy optimization (MAPPO) under a centralized-training with decentralized-execution framework. Extensive experimental results demonstrate that the proposed method outperforms other approaches. Furthermore, studies assess the individual effects of graded flexible time window settings and objective function weight coefficients on the optimization performance of collaborative delivery. Finally, we evaluate the practical advantages of our proposed model using real-world rural road cases.", "keywords": [ "Embedding", "Flexibility (engineering)", "Probabilistic logic", "Adaptability", "Enhanced Data Rates for GSM Evolution", "Graph", "Mobile telephony", "Service (business)", "Minification" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2026.3675581" }, { "id": "https://openalex.org/W4416017217", "doi": "10.2139/ssrn.5719551", "title": "Real-time pickup and delivery with electric autonomous vending vehicles and drones in large urban leisure spaces: A hybrid routing approach", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": null, "authors": [ "Bing Han", "Tianze Chi", "Tianren Yang" ], "first_author": "Bing Han", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Pickup", "Heuristic", "Expansive", "Routing (electronic design automation)", "Vehicle routing problem", "Order (exchange)", "Electricity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5719551" } ]