[ { "id": "https://openalex.org/W4285203817", "doi": "10.1109/tase.2022.3184324", "title": "Urban On-Demand Delivery via Autonomous Aerial Mobility: Formulation and Exact Algorithm", "year": 2022, "publication_date": "2022-06-23", "type": "article", "language": "en", "authors": [ "Zhi Pei", "Tao Fang", "Kebiao Weng", "Wenchao Yi" ], "first_author": "Zhi Pei", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The implementation of the autonomous unmanned aerial mobility is a game changer for the on-demand delivery service in the crowded urban setting. In this study, the first of its kind commercial unmanned aerial vehicle (UAV) urban delivery program in China is targeted. Different from the traditional ground pickup and delivery services, the aerial mode considers not only the time window constraints, but also the spatial conflicts incurred during the take-off and landing operations of UAVs. To obtain the optimal flying routes of the focused problem, a mixed integer programming model is formulated. Due to its inherent complexity, the optimal schedule cannot be attained within acceptable time via the off-the-shelf solvers. To help speed up the solving process, a branch-and-cut based exact algorithm is proposed, together with a series of customized valid inequalities. To further accelerate, a greedy insertion heuristic is designed to secure high-quality initial solutions. In the numerical section, it is observed that the algorithm proposed in this paper can help solve the real-life on-demand UAV delivery problem to near optimum (within 5% optimality gap) within reasonable computation time (in 5 minutes).Note to Practitioners—With the increase of labor cost, the distribution cost increases very rapidly. In the meantime, the employment of automated vehicles for logistics reshapes the landscape of the urban last-mile delivery. As an efficient courier carrier, the unmanned aerial vehicle (UAV) is trending the autonomous delivery endeavour. When integrating UAVs into the urban delivery program, practitioners need to pay special attention to the scheduling of UAVs at the operational level in addition to the hardware of the UAVs. To help solve the UAV dispatch problem, we propose an online scheduling scheme, considering the spatial conflict constraints in the actual UAV operations. And an exact algorithm is designed to accelerate the solving process. Numerical experiments demonstrate that the proposed algorithm can achieve near optimal dispatch plan with 5% optimality gap in 5 minutes. Furthermore, it is discovered that the demand pooling is an essential decision to make for UAV-based delivery. Longer pooling time can increase the UAV efficiency with more realized demand information, but too much pooling could lead to prolonged customer waiting and a low service level.", "keywords": [ "Computer science", "Schedule", "Drone", "Heuristic", "Integer programming", "Mathematical optimization", "Greedy algorithm", "Real-time computing", "Algorithm", "Operations research", "Simulation", "Engineering" ], "cited_by_count": 40, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tase.2022.3184324" }, { "id": "https://openalex.org/W2466428159", "doi": "10.7736/kspe.2016.33.6.499", "title": "Study on Performance Prediction of Electric Propulsion System for Multirotor UAVs", "year": 2016, "publication_date": "2016-06-03", "type": "article", "language": "en", "authors": [ "Jinseok Jeong", "Young-Seop Byun", "Woo-Jin Song", "Beom-Soo Kang" ], "first_author": "Jinseok Jeong", "venue": "Journal of the Korean Society for Precision Engineering", "publisher": "Korean Society of Precision Engineering", "abstract": "This paper describes a study of performance prediction of an electric propulsion system for multirotor UAVs. The electric propulsion system consists of motors, propellers, batteries and speed controllers, and significantly affects performance characteristics of the platform. The performance of the electric propulsion system for multirotor UAVs was predicted using an analytical model derived from the characteristics of each component, operation experiments and statistical analyses. Ground performance tests and endurance flights were performed to verify the reliability of the proposed performance prediction method. A quadrotor platform was designed to demonstrate the parcel delivery service used in the endurance flight. From the result of verification tests, it was confirmed that the proposed method has a good agreement.", "keywords": [ "Multirotor", "Propulsion", "Electrically powered spacecraft propulsion", "Performance prediction", "Automotive engineering", "Engineering", "Reliability (semiconductor)", "Simulation", "Aerospace engineering", "Computer science", "Control engineering", "Power (physics)" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7736/kspe.2016.33.6.499" }, { "id": "https://openalex.org/W4410770201", "doi": "10.23919/jsee.2025.000048", "title": "Aerial-Ground Collaborative Delivery Route Planning with UAV Energy Function and Multi-Delivery", "year": 2025, "publication_date": "2025-04-01", "type": "article", "language": "en", "authors": [ "Jingfeng Guo", "Rui Song", "Shiwei He" ], "first_author": "Jingfeng Guo", "venue": "Journal of Systems Engineering and Electronics", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the vehicle and its onboard UAVs are utilized efficiently. Vehicles not only provide delivery services to customers but also function as mobile warehouses and launch/recovery platforms for UAVs. This paper addresses the vehicle routing problem with UAVs considering time window and UAV multi-delivery (VRPU-TW&MD). A mixed integer linear programming (MILP) model is developed to minimize delivery costs while incorporating constraints related to UAV energy consumption. Subsequently, a micro-evolution augmented large neighborhood search (MEALNS) algorithm incorporating adaptive large neighborhood search (ALNS) and micro-evolution mechanism is proposed. Numerical experiments demonstrate the effectiveness of both the model and algorithm in solving the VRPU-TW&MD. The impact of key parameters on delivery performance is explored by sensitivity analysis.", "keywords": [ "Function (biology)", "Energy (signal processing)", "Delivery system", "Integrated project delivery", "Computer science", "Environmental science", "Engineering", "Civil engineering", "Physics", "Biomedical engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/jsee.2025.000048" }, { "id": "https://openalex.org/W4395064706", "doi": "10.48550/arxiv.2404.13068", "title": "SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle Routing Problem with Drones Using Reinforcement Learning", "year": 2024, "publication_date": "2024-04-13", "type": "preprint", "language": "en", "authors": [ "Navid Mohammad Imran", "Myounggyu Won" ], "first_author": "Navid Mohammad Imran", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The Vehicle Routing Problem with Drones (VRPD) seeks to optimize the routing paths for both trucks and drones, where the trucks are responsible for delivering parcels to customer locations, and the drones are dispatched from these trucks for parcel delivery, subsequently being retrieved by the trucks. Given the NP-Hard complexity of VRPD, numerous heuristic approaches have been introduced. However, improving solution quality and reducing computation time remain significant challenges. In this paper, we conduct a comprehensive examination of heuristic methods designed for solving VRPD, distilling and standardizing them into core elements. We then develop a novel reinforcement learning (RL) framework that is seamlessly integrated with the heuristic solution components, establishing a set of universal principles for incorporating the RL framework with heuristic strategies in an aim to improve both the solution quality and computation speed. This integration has been applied to a state-of-the-art heuristic solution for VRPD, showcasing the substantial benefits of incorporating the RL framework. Our evaluation results demonstrated that the heuristic solution incorporated with our RL framework not only elevated the quality of solutions but also achieved rapid computation speeds, especially when dealing with extensive customer locations.", "keywords": [ "Drone", "Reinforcement learning", "Heuristic", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Mathematical optimization", "Reinforcement", "Artificial intelligence", "Mathematics", "Engineering", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2404.13068" }, { "id": "https://openalex.org/W4280604383", "doi": "10.1109/icns54818.2022.9771497", "title": "Initial Reliability Assessment of a Commercial-Off-The-Shelf GPS Sensor for Generic UAVs", "year": 2022, "publication_date": "2022-04-05", "type": "conference-paper", "language": "en", "authors": [ "T. Thanaraj", "Kailun Tan", "Chung Hung J. Wang", "Ee Meng Ng", "Kin Huat Low" ], "first_author": "T. Thanaraj", "venue": null, "publisher": null, "abstract": "Unmanned aerial vehicles (UAVs) have gathered much attention commercially in recent times owing to the advancement in technology, such as the introduction of brushless-DC motors and micro-electromechanical systems (MEMS). As a result, these UAVs can undertake complex missions and pose potential benefits to a multitude of use cases, for instance, last-mile cargo delivery, infrastructure inspection, and even search-and-rescue missions. With the increase in the adoption of UAVs and the abundance of commercial-off-the-shelf (COTS) components, the airworthiness of the UAVs grows to be of concern as the assessment criteria for them are not as stringent as that of manned aircraft, largely due to the absence of an onboard pilot or passengers. In addition, UAVs with COTS components lack data to provide assurance of their reliability and determine if the UAVs are fit to fly. On-board navigational sensors are one of the critical subsystems in a UAV and any deterioration in the performance of these sensors can precipitate into a UAV system failure during flight missions, potentially leading to a crash. As such, the reliability of these sensors has to be ascertained before any flight mission to ensure their airworthiness. In this paper, we will assess a COTS GPS sensor that has been commonly used in self-assembled UAVs and predict its reliability based on the characteristics of the Surface Mounted Devices (SMD) and Integrated Circuits (IC) using MIL-HDBK-217F. Results from this assessment will provide a preliminary representation of the reliability of a typical COTS GPS sensor for the determination of its suitability for its intended operation.", "keywords": [ "Airworthiness", "Global Positioning System", "Reliability (semiconductor)", "Computer science", "Reliability engineering", "Crash", "Commercial off-the-shelf", "Systems engineering", "Aeronautics", "Engineering", "Embedded system", "Real-time computing" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dr.ntu.edu.sg/bitstream/10356/160572/2/ICNS%202022_ThanarajT_KLTan_CHJWang_EMNg_KHLow_v2.pdf" }, { "id": "https://openalex.org/W4391967687", "doi": "10.3997/2214-4609.2023510032", "title": "Prototype of Eco-Sustainable Cargo Drone: National and International Regulations and Cost-Benefit Evaluation for Potential Operational Startups", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "E. Genovese", "G. Barrile", "Sonia Calluso" ], "first_author": "E. Genovese", "venue": null, "publisher": null, "abstract": "Summary Cargo drone technology has disrupted traditional logistics, eliminating human-operated vehicles. Advancements in unmanned aerial systems have led to sophisticated drones equipped with advanced sensors, navigation capabilities, and obstacle avoidance mechanisms. These drones are employed in different sectors, from last-mile deliveries to critical medical supply transportation, showcasing their versatility and potential impact. However, regulatory frameworks pose challenges, particularly in urban environments. Complexities such as battery endurance and adverse weather performance remain significant hurdles. Despite these obstacles, substantial investments are being directed toward the research and development of cargo drone solutions. This study centres on an experimental eco-sustainable cargo drone prototype, highlighting its technical attributes. The investigation extends to dissecting regulatory intricacies, both domestically and internationally. Furthermore, the feasibility of establishing a startup based on this technology is evaluated. This research encapsulates the converging domains of drone technology, regulatory dynamics, and entrepreneurial opportunities.", "keywords": [ "Drone", "Obstacle", "Business", "Risk analysis (engineering)", "Transport engineering", "Aeronautics", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.3997/2214-4609.2023510032" }, { "id": "https://openalex.org/W7160154228", "doi": "10.3390/systems14050491", "title": "Optimization of Transportation and Delivery Routes Under Regional Constraints: A Two-Stage Solution Model Based on SDVRP and Truck-Drone Collaboration", "year": 2026, "publication_date": "2026-04-30", "type": "article", "language": "en", "authors": [ "Weiquan Kong", "Senlai Zhu", "Gaoming Yu" ], "first_author": "Weiquan Kong", "venue": "Systems", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid development of e-commerce and the increasing complexity of urban logistics, traditional delivery methods face significant challenges due to regional traffic restrictions and congestion. This paper presents a two-stage optimization approach for urban delivery routing, integrating the Split Delivery Vehicle Routing Problem (SDVRP) and truck-drone collaboration to address these challenges. In the first stage, a transportation route optimization model based on SDVRP is proposed, which accounts for regional constraints and vehicle capacity limitations. The model allows for demand splitting, reducing the number of vehicles required and minimizing transportation costs. In the second stage, a truck-drone collaborative delivery model is introduced to handle the “last mile” distribution, where drones complement trucks by delivering to areas with restricted vehicle access. The optimization model aims to minimize overall delivery costs while ensuring timely service. An enhanced genetic algorithm is further developed to solve this complex, multi-constrained model. Experimental results show that the proposed collaborative strategy reduces delivery costs by over 10% compared to truck-only delivery, and the improved algorithm achieves a 4.77% average cost reduction over traditional approaches. This study provides valuable insights for optimizing urban logistics systems under regional constraints, offering both theoretical and practical contributions to smart logistics development.", "keywords": [ "Truck", "Vehicle routing problem", "Genetic algorithm", "Routing (electronic design automation)", "Traffic congestion", "City logistics", "Complement (music)", "Drone" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-8954/14/5/491/pdf?version=1779066150" }, { "id": "https://openalex.org/W7112399389", "doi": null, "title": "Last Mile Delivery by Drones", "year": 2025, "publication_date": "2025-01-01", "type": "dissertation", "language": "en", "authors": [ "Alawala Dewage, Hishani Indunil" ], "first_author": "Alawala Dewage, Hishani Indunil", "venue": "Theseus (Ammattikorkeakoulujen)", "publisher": null, "abstract": "Last-Mile Delivery is a critical and costly part of the distribution chain, providing the final connection to customers. Modern drone technology is viewed as a promising solution, enabling faster delivery, lower costs, and enhanced environmental protection. Research suggests that drones can alleviate the limitations of traditional truck delivery – including traffic congestion, gas and electricity consumption, and greenhouse gas emissions – and enable faster delivery of goods to rural and remote areas. However, there are technical challenges that need to be addressed, including routing optimization, cargo distribution, battery management, data communication, and environmental protection. Companies such as Amazon Prime Air and UPS Flight Forward are also introducing new ordering systems and “flying warehouse” concepts, which are new areas of focus for drone use. In business, the use of drones is expected to increase the efficiency of the entire supply chain, enhance overall customer satisfaction, and promote safe and sustainable operations.", "keywords": [ "Drone", "Last mile (transportation)", "Truck", "Electricity", "Greenhouse gas", "Sustainable development", "Supply chain" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.theseus.fi/bitstream/10024/901731/2/AlawalaDewage_HishaniIndunil.pdf" }, { "id": "https://openalex.org/W3030900412", "doi": null, "title": "Last Mile Delivery by Drone", "year": 2020, "publication_date": "2020-05-02", "type": "preprint", "language": "en", "authors": [ "Senathipathi Velmurugan", "K. Gayathri", "P. Praseetha", "D. Priyadarshini", "M. Chithra" ], "first_author": "Senathipathi Velmurugan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Mile", "Drone", "Last mile (transportation)", "Aeronautics", "Computer science", "Engineering", "Geography", "Geodesy", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID3597152_code3560658.pdf?abstractid=3597152&mirid=1" }, { "id": "https://openalex.org/W7171050063", "doi": "10.6084/m9.figshare.33086759", "title": "Last Mile Delivery with Drone", "year": 2026, "publication_date": "2026-07-25", "type": "dataset", "language": null, "authors": [ "Yavuz Toraman" ], "first_author": "Yavuz Toraman", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "Consumer Acceptance, Last mile delivery process.", "keywords": [], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.33086759" }, { "id": "https://openalex.org/W7128732494", "doi": "10.1109/tits.2026.3658745", "title": "A Constraint Relaxation Co-Evolutionary Algorithm for the Two-Echelon Vehicle Routing Problem With Load-Dependent Drones", "year": 2026, "publication_date": "2026-02-12", "type": "article", "language": null, "authors": [ "Fan Yu", "Jinlong Zhou", "Qun Chen", "Yange Li", "Chao Li", "Sheng Qi", "Weixiong Huang" ], "first_author": "Fan Yu", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The growing adoption of drones in last-mile logistics has raised urgent demands for controlling energy consumption costs while ensuring delivery efficiency. To address this, we formulate the Two-Echelon Vehicle Routing Problem with Load-dependent Drones (2E-VRPLD) within a depot-satellite-customer framework, which reflects realistic logistics conditions where drones experience continuously changing payloads as they pick up and deliver parcels. These load fluctuations cause discrete shifts in power consumption, making their actual flight endurance highly uncertain under a fixed energy budget and thereby significantly increasing the complexity of planning feasible routes and minimizing overall transportation costs. To solve this complex VRP variant, we develop a novel Constraint Relaxation Co-evolutionary Algorithm (CRCEA) that integrates a customer-satellite matching mechanism to minimize drone endurance redundancy and a minimal impact assessment strategy for route segment merging. Experiments show that CRCEA attains nearly negligible optimality gaps relative to Gurobi on small-scale instances, significantly outperforms seven representative heuristics on large-scale instances, and consistently delivers superior solutions in real-world delivery scenarios from leading express carriers JD and SF Express in Changsha, China. Furthermore, ablation studies identify key factors affecting algorithmic performance, and sensitivity analyses illustrate how drone performance parameters influence the efficiency of solving the 2E-VRPLD. This work provides a robust theoretical and practical approach for solving the two-echelon pickup-and-delivery routing problem with load-dependent drones, offering important insights for advancing smart logistics and improving enterprise operational efficiency.", "keywords": [ "Drone", "Heuristics", "Routing (electronic design automation)", "Relaxation (psychology)", "Redundancy (engineering)", "Key (lock)", "Lagrangian relaxation", "Constraint (computer-aided design)", "Vehicle routing problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2026.3658745" }, { "id": "https://openalex.org/W7171126525", "doi": "10.6084/m9.figshare.33086759.v1", "title": "Last Mile Delivery with Drone", "year": 2026, "publication_date": "2026-07-25", "type": "dataset", "language": null, "authors": [ "Yavuz Toraman" ], "first_author": "Yavuz Toraman", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "Consumer Acceptance, Last mile delivery process.", "keywords": [], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.33086759.v1" }, { "id": "https://openalex.org/W7171626731", "doi": "10.2139/ssrn.6814722", "title": "The Bus-Assisted Drone Delivery Problem: Integrating Public Transport and Drones for Last-Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Bipan Zou", "Yuqian Shi", "Jiliu Li", "Rui Zhang" ], "first_author": "Bipan Zou", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Public transport", "Dominance (genetics)", "Representation (politics)", "Routing (electronic design automation)", "Integer programming", "Set (abstract data type)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6814722" }, { "id": "https://openalex.org/W4317515518", "doi": "10.1109/ccis57298.2022.10016378", "title": "Edge4FR: A Novel Device-Edge Collaborative Framework for Facial Recognition in Smart UAV Delivery Systems", "year": 2022, "publication_date": "2022-11-26", "type": "conference-paper", "language": "en", "authors": [ "Yi Xu", "Fengguang Luan", "Xiao Liu", "Xuejun Li" ], "first_author": "Yi Xu", "venue": "2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)", "publisher": null, "abstract": "In recent years, smart UAV (unmanned aerial vehicle) delivery has become a promising solution to solve the last-mile delivery problem in smart logistics. In a smart UAV delivery system, the accurate identification of the goods receiver is a critical task. At present, using smart lockers with quick response (QR) codes is one of the most widely used solutions. However, this solution is very expensive and limited by the space available to deploy smart lockers. In contrast, using facial recognition technology for identification is a promising solution which does not need any extra equipment besides the UAV itself. However, due to the instability and the unusual shooting angle of the UAV from the air, existing facial recognition technologies often suffer the issue of low accuracy in practice. Therefore, to improve the accuracy of UAV based facial recognition, we propose Edge4FR, a Device-Edge Collaborative Framework based on face frontalization and facial recognition. Specifically, first, the facial detection algorithm based on deep learning deployed in the UAV can detect facial images frame by frame, and extract detected faces and transmit them to the nearby edge server. Afterwards, the face frontalization model trained by the generative adversarial network (GAN) deployed in the edge server can frontalize facial images. Finally, the facial recognition algorithm based on deep learning deployed in the edge server can confirm the identity by checking if the frontal facial image matches the goods receiver’s facial image registered in the delivery system. Experimental results in a real-world smart UAV delivery system demonstrate the effectiveness of the proposed framework.", "keywords": [ "Computer science", "Facial recognition system", "Artificial intelligence", "Computer vision", "Enhanced Data Rates for GSM Evolution", "Frame (networking)", "Identification (biology)", "Feature extraction", "Telecommunications" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/conference_contribution/Edge4FR_A_Novel_Device-Edge_Collaborative_Framework_for_Facial_Recognition_in_Smart_UAV_Delivery_Systems/22197559" }, { "id": "https://openalex.org/W7171642582", "doi": "10.1108/bfj-12-2025-1645", "title": "Determinants of consumers' intention to use drone food delivery services: a hybrid approach using PLS-SEM, ANN and NCA", "year": 2026, "publication_date": "2026-07-29", "type": "article", "language": "en", "authors": [ "Behzad Foroughi", "Hanh Thi My Vu", "Kian Yeik Koay", "Elaheh Yadegaridehkordi" ], "first_author": "Behzad Foroughi", "venue": "British Food Journal", "publisher": "Emerald Publishing Limited", "abstract": "Purpose Although drone-based food delivery has gained attention as a sustainable last-mile solution, limited research has examined consumer adoption using an integrated theoretical and methodological lens. To address this gap, this study aims to investigate the key factors influencing consumers' intention to adopt “drone food delivery services” (DFDS) by integrating the “Unified Theory of Acceptance and Use of Technology 2” and the “theory of consumption values”, while incorporating psychological traits such as environmental innovativeness and risk aversion. Design/methodology/approach Using survey data from 353 respondents, this study applies a hybrid analytical approach that combines “partial least squares structural equation modeling”, “artificial neural networks”, and “necessary condition analysis” to capture linear, non-linear, and necessary conditions influencing adoption behavior. Findings The results reveal that functional, emotional, social, epistemic and environmental values significantly influence consumers' intention to use DFDS. Environmental value emerged as the strongest predictor across all three analytical methods and was also identified as a necessary condition. Moreover, environmental innovativeness positively moderated the link between functional value and intention, whereas risk aversion did not significantly alter any of the value-intention relationships. Originality/value This study contributes to the food service and sustainable food delivery literature by offering an integrative and context-specific theoretical model for DFDS adoption, while also extending understanding of technology-enabled last-mile delivery. Grounded in both technology acceptance and value-based theories, the study advances the field methodologically through a hybrid analytical framework that captures linear, non-linear, and necessary relationships. The findings provide actionable insights for service providers, platform designers, and policymakers seeking to support the adoption of sustainable and consumer-aligned food delivery innovations.", "keywords": [ "Food delivery", "Value (mathematics)", "Structural equation modeling", "Service (business)", "Risk perception", "Service delivery framework", "Drone", "Theory of planned behavior", "Field (mathematics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1108/bfj-12-2025-1645" }, { "id": "https://openalex.org/W4281692248", "doi": "10.54097/fbem.v4i1.410", "title": "Cold Chain Logistics UAV Path Optimization Enlightenment", "year": 2022, "publication_date": "2022-05-31", "type": "article", "language": "en", "authors": [ "Hongxia Miao" ], "first_author": "Hongxia Miao", "venue": "Frontiers in Business Economics and Management", "publisher": null, "abstract": "As the market size of cold chain logistics in China grows year by year, consumers have significantly improved their requirements for the quality of cold chain items. Meanwhile, national laws and policies supporting the development of cold chain logistics have been introduced successively, which makes the field related to cold chain logistics develop rapidly. However, there are some problems in the development of the cold chains, such as waste of logistics data supervision and high cost of logistics distribution. Especially in the critical situation of the epidemic, the \"last mile\" delivery without contact is advocated, and the transportation time of cold-chain vehicles is long and the cost is high, which causes great resistance to solving the \"last mile\". This paper puts forward the corresponding enlightenment according to the relevant literature.", "keywords": [ "Cold chain", "Enlightenment", "Business", "Mile", "Chain (unit)", "Path (computing)", "China", "Supply chain", "Quality (philosophy)", "Operations management", "Transport engineering", "Computer science" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://drpress.org/ojs/index.php/fbem/article/download/410/352" }, { "id": "https://openalex.org/W4285813977", "doi": "10.1109/iwcmc55113.2022.9824768", "title": "WPT-enabled UAV Trajectory Design for Healthcare Delivery Using Reinforcement Learning", "year": 2022, "publication_date": "2022-05-30", "type": "conference-paper", "language": "en", "authors": [ "Adel Merabet", "Abderrahmane Lakas", "Abdelkader Nasreddine Belkacem" ], "first_author": "Adel Merabet", "venue": "2022 International Wireless Communications and Mobile Computing (IWCMC)", "publisher": null, "abstract": "Over the last few years, the use of unmanned aerial vehicles (UAVs) has grown, with the goal of being widely deployed in sectors such as deliveries, rescue operations, mining fields, patrolling, and monitoring. However, the limitations of the onboard battery capacity and the flying range pose a problem to most applications while performing daily tasks such as parcel delivery or aerial communications in large areas. This paper proposes a reinforcement learning method to compute optimal trajectories for a UAV, considering both visiting delivery locations and recharging stations. The use of wireless power transfer (WPT) technology allows UAV s to wirelessly recharge their batteries on the fly and therefore to extend their flying range further. In this scenario, we consider several WPT-enabled charging stations placed around the serviced area. The proposed approach leverages a reinforcement learning strategy, and the performance results obtained show its effectiveness in finding an optimal trajectory by minimizing the UAV's travel and service time.", "keywords": [ "Reinforcement learning", "Patrolling", "Trajectory", "Computer science", "Wireless", "Range (aeronautics)", "Real-time computing", "Wireless power transfer", "Simulation", "Artificial intelligence", "Engineering", "Telecommunications" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iwcmc55113.2022.9824768" }, { "id": "https://openalex.org/W7123242867", "doi": null, "title": "Drone For Urban Last-mile Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "dissertation", "language": "en", "authors": [ "Hussein Sharif" ], "first_author": "Hussein Sharif", "venue": "KTH Publication Database DiVA (KTH Royal Institute of Technology)", "publisher": "KTH Royal Institute of Technology", "abstract": "Introduction This thesis explores how a modular conceptual framework can support UAV-based last-mile delivery for logistics SMEs operating in regulated urban environments. As urban traffic and environmental demands increase, drones are emerging as a viable alternative for small-package logistics, yet technical, regulatory, and operational barriers hinder their widespread adoption, particularly among small and mid-sized operators. Research Question How can a cost-effective, regulation-compliant, and operationally feasible drone delivery model be designed to support small package last- mile delivery in urban environments? Method To investigate this, the study applied a Design Science Research (DSR) methodology. The artifact was developed through iterative synthesis of academic literature,regulatory protocols (notably EASAs SORA) and industry benchmarks (Zipline, Wing,Amazon).Evaluation involved benchmarking, regulatory fit assessment, and a GIS-based scenario simulation using real urban data from Stockholm. Results The results show that the proposed framework meets core functional and regulatory requirements for SME deployment. With a 5 kg payload capacity, a 10–20 km range, and integration capabilities with ERP and routing systems, the system was shown to cover much of Stockholm’s delivery needs from just three hubs, while maintaining compliance with BVLOS and no-fly–zone restrictions. Discussion These findings suggest that modular, regulation ready UAV systems could be realistically implemented by SMEs under EU drone policy. While the framework has not been field-tested, its theoretical validation indicates strong potential for scalable, sustainable urban drone delivery. Future work should focus on empirical trials, system integration with live logistics networks, and public acceptance modeling.", "keywords": [ "Drone", "Payload (computing)", "Modular design", "Artifact (error)", "Work (physics)", "Conceptual framework", "Conceptual model", "Last mile (transportation)", "Cover (algebra)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://su.diva-portal.org/smash/get/diva2:2026769/FULLTEXT01" }, { "id": "https://openalex.org/W7204180322", "doi": "10.2139/ssrn.7349465", "title": "Facility Location and Fleet Optimization for Last-Mile Drone Delivery with Charging Infrastructure", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Xudong Wang", "Mustafa Can Camur", "Xueping Li" ], "first_author": "Xudong Wang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Reachability", "Facility location problem", "Integer programming", "Fraction (chemistry)", "Lagrangian relaxation", "Route planning", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7349465" }, { "id": "https://openalex.org/W4324031737", "doi": "10.1145/3579375.3579383", "title": "Multi-Featured Anomaly Detection for Mobile Edge Computing Based UAV Delivery Systems", "year": 2023, "publication_date": "2023-01-30", "type": "conference-paper", "language": "en", "authors": [ "Leilei Xu", "Xiao Liu", "Frank Jiang", "Yi Xu", "Aiting Yao", "Xu Jia", "Xuejun Li" ], "first_author": "Leilei Xu", "venue": null, "publisher": null, "abstract": "With the rapid development of communication infrastructure and the widespread of IoT computing facilities such as Mobile Edge Computing (MEC), the application of Unmanned Aerial Vehicles (UAVs) are increasingly growing especially in last-mile deliveries in smart logistics. However, the anomaly detection in the MEC environment is still one yet unresolved issue. In recent years, the methods of UAV anomaly detection mainly focus on detections of specific types of UAVs or a certain feature of UAVs, and it is not as certain to achieve efficient and reliable results, in other words, there is currently a lack of better ways to address security issues in UAV networks. In this paper, we propose a Multi-Featured Anomaly Detection (MFAD) method for MEC-based UAV delivery systems to detect malicious attacks in the network through the abnormal state of UAV. Firstly, we develop deep learning model to recognize the correct UAV behaviors using several common UAV data in normal states, including altitude and speed. Secondly, we apply attacks in the data to simulate anomalous UAV behaviors under cyberattacks. Thirdly, the abnormal data is merged into the selected normal behavior model to detect the Normalized Root Mean Square Error (NRMSE) between the input data and the data provided by the normal behavior model. Finally, experiments with UAV sensor data demonstrate that our method is effective in detecting anomalies, but also reduce the vital energy consumption in UAV delivery systems.", "keywords": [ "Computer science", "Anomaly detection", "Focus (optics)", "Real-time computing", "Enhanced Data Rates for GSM Evolution", "Anomaly (physics)", "Feature (linguistics)", "Deep learning", "Mobile edge computing", "Artificial intelligence" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/conference_contribution/Multi-Featured_Anomaly_Detection_for_Mobile_Edge_Computing_Based_UAV_Delivery_Systems/22647667" }, { "id": "https://openalex.org/W4292719508", "doi": "10.48550/arxiv.2208.09067", "title": "Dynamic Order Fulfillment in Last-Mile Drone Delivery under Demand Uncertainty", "year": 2022, "publication_date": "2022-08-18", "type": "preprint", "language": "en", "authors": [ "Linxuan Shi", "Zhengtian Xu", "Lejeune, Miguel", "Luo, Qi" ], "first_author": "Linxuan Shi", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Drones have attracted growing interest in last-mile delivery due to their potential to significantly reduce costs and enhance operational flexibility, particularly in areas of sparse and uncertain demand where traditional truck delivery proves inefficient. This paper addresses the dynamic order fulfillment problem faced by a retailer operating a fleet of drones to service delivery requests that arrive stochastically. These delivery requests may vary in package profiles, delivery locations, and urgency. We adopt a rolling-horizon framework for order fulfillment and devise a two-stage stochastic program aimed at strategically managing existing orders while considering incoming requests that are subject to various uncertainties. A significant challenge in deploying the envisioned two-stage model lies in its incorporation of vehicle routing constraints, on which exact or brute-force methods are computationally inefficient and unsuitable for real-time operational decisions. To address this, we propose an accelerated L-shaped algorithm that (i) reduces the branching tree size, (ii) replaces exact second-stage solutions with heuristic estimates, and (iii) adapts an alternating strategy for adding optimality cuts. The proposed heuristic demonstrates remarkable performance superiority over the exact method, achieving a 20-fold reduction in average runtime while maintaining an average optimality gap of less than 1\\%. We apply the algorithm to a wide range of instances to evaluate the benefits of postponing orders for batch service using the stochastic model. Our results show potential long-term cost savings of up to 20\\% when demand uncertainty is explicitly considered in order fulfillment decisions. Meanwhile, the derived savings tend to diminish as the uncertainty increases in order arrivals.", "keywords": [ "Computer science", "Mathematical optimization", "Stochastic programming", "Integer programming", "Context (archaeology)", "Last mile (transportation)", "Order (exchange)", "Dynamic programming", "Integer (computer science)", "Mile", "Real-time computing", "Operations research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2208.09067" }, { "id": "https://openalex.org/W7156850340", "doi": "10.48550/arxiv.2604.23015", "title": "Approximating Energy-Constrained Drone Delivery Packing Problem for Last-Mile Logistics", "year": 2026, "publication_date": "2026-04-24", "type": "preprint", "language": null, "authors": [ "Saswata Jana", "Partha Sarathi Mandal" ], "first_author": "Saswata Jana", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Collaboration between drones and trucks in a last-mile delivery system offers numerous benefits and reduces many challenges of the traditional delivery system. Here, we introduce Drone-Delivery Packing Problem, where a set of parcels, associated with delivery intervals and cost, should be delivered to customer locations. The system comprises a set of identical drones and battery stations along truck's route, where drones swap depleted batteries or recharge them. The objective is to find assignment for all parcels by using the minimum number of drones, subject to the battery budget and compatibility of each drone's assignment. We consider three variants of the problem, based on conflicting characteristics and existence of battery service stations. All are NP-hard, and we have proposed approximation algorithms for each. When there are no battery stations, we propose a constant factor approximation algorithm using first fit decreasing bin packing algorithm. When the intervals are non-conflicting, we design a $(2+ ψ)$-approximation algorithm. In the presence of both battery stations and conflicting intervals, we present a $(4+ψ)$-approximation algorithm. The algorithm is later modified into a $(3+ψ)$-approximation algorithm when the battery service stations act as swapping stations. Here $ψ$ is a function of battery budget, minimum and maximum cost of the deliveries. Finally, we validate our results and compare the performance with the optimum on different instances.", "keywords": [ "Swap (finance)", "Drone", "Approximation algorithm", "Battery (electricity)", "Bin packing problem", "Truck", "Battery capacity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2604.23015" }, { "id": "https://openalex.org/W4410248705", "doi": "10.1007/978-3-031-82334-3_10", "title": "Drones, Last Mile Delivery, and Europe", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "David Sánchez-Wells", "L. González-R. Pedro", "L. Andrade-Pineda José" ], "first_author": "David Sánchez-Wells", "venue": "Lecture notes on data engineering and communications technologies", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Mile", "Drone", "Last mile (transportation)", "Aeronautics", "Geography", "Engineering", "Biology", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-82334-3_10" }, { "id": "https://openalex.org/W7164296424", "doi": "10.1016/j.asoc.2026.115738", "title": "Robust optimization for two-echelon truck and drone collaborative delivery in emergency relief: Mathematical formulation and heuristic algorithm", "year": 2026, "publication_date": "2026-06-12", "type": "article", "language": "en", "authors": [ "Yong Shi", "Qin Guo", "Peng Pan", "Miao Chen", "Yang Wu", "Haixiang Guo" ], "first_author": "Yong Shi", "venue": "Applied Soft Computing", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Robust optimization", "Heuristic", "Optimization algorithm", "Robustness (evolution)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.asoc.2026.115738" }, { "id": "https://openalex.org/W4409273357", "doi": "10.2139/ssrn.5209336", "title": "Dynamic Order Fulfillment in Last-Mile Drone Delivery Under Demand Uncertainty", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Linxuan Shi", "Zhengtian Xu", "Miguel A. Lejeune", "Qi Luo" ], "first_author": "Linxuan Shi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Order (exchange)", "Computer science", "Business", "Geography", "Geodesy", "Finance", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5209336" }, { "id": "https://openalex.org/W4211052883", "doi": "10.1109/ccnc49033.2022.9700681", "title": "Multi-Operator Intelligent UAV Delivery Networks in Beyond Visual Line of Sight Operations", "year": 2022, "publication_date": "2022-01-08", "type": "conference-paper", "language": "en", "authors": [ "Anindo Mahmood", "Kevin Ramirez Reynoso", "Mostafizur Rahman", "Sheikh Ariful Islam" ], "first_author": "Anindo Mahmood", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicle (UAV) based delivery networks have emerged as a promising mode of parcel delivery owing to their efficient and cost-effective operations. In this paper, we propose a novel techno-economic framework to facilitate multi-operator UAV delivery networks in Beyond Visual Line of Sight (BVLoS) communications. The proposed model aims at partitioning a large delivery zone into several small cells, each receiving separate licensing to carry out UAV operations. A controlling entity called UAV Traffic Controller (UTC) governs each cell and manages all UAV movements within the cell while adhering to the Federal Aviation Authority’s regulations. A UTC also authenticates all UAVs running within its airspace and secures delivery service providers’ and customers’ privacy.", "keywords": [ "Computer science", "Aviation", "Sight", "Operator (biology)", "Air traffic control", "Controller (irrigation)", "Drone", "Service (business)", "Computer security", "Computer network", "Engineering", "Aerospace engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccnc49033.2022.9700681" }, { "id": "https://openalex.org/W4396699030", "doi": "10.1016/j.adhoc.2024.103532", "title": "A privacy-preserving location data collection framework for intelligent systems in edge computing", "year": 2024, "publication_date": "2024-05-07", "type": "article", "language": "en", "authors": [ "Aiting Yao", "Shantanu Pal", "Xuejun Li", "Zheng Zhang", "Chengzu Dong", "Frank Jiang", "Xiao Liu" ], "first_author": "Aiting Yao", "venue": "Ad Hoc Networks", "publisher": "Elsevier BV", "abstract": "With the rise of smart city applications, the accessibility of users’ location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis.", "keywords": [ "Computer science", "Edge computing", "Enhanced Data Rates for GSM Evolution", "Information privacy", "Location data", "Distributed computing", "Computer security", "Artificial intelligence" ], "cited_by_count": 49, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.adhoc.2024.103532" }, { "id": "https://openalex.org/W3003532379", "doi": "10.1016/j.ejor.2020.01.053", "title": "A transformation technique for the clustered generalized traveling salesman problem with applications to logistics", "year": 2020, "publication_date": "2020-01-29", "type": "article", "language": "en", "authors": [ "Pouya Baniasadi", "Mehdi Foumani", "Kate Smith‐Miles", "Vladimir Ejov" ], "first_author": "Pouya Baniasadi", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Travelling salesman problem", "Computer science", "Flexibility (engineering)", "Transformation (genetics)", "Mathematical optimization", "Set (abstract data type)", "Node (physics)", "Extension (predicate logic)", "Service (business)", "Operations research", "Mathematics", "Algorithm" ], "cited_by_count": 142, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2020.01.053" }, { "id": "https://openalex.org/W4406267187", "doi": "10.1109/vtc2024-fall63153.2024.10757864", "title": "Evaluation of Radio Interference Areas by Flight Path and Antenna Directivity of UAV in Urban Area", "year": 2024, "publication_date": "2024-10-07", "type": "conference-paper", "language": "en", "authors": [ "Hideya So" ], "first_author": "Hideya So", "venue": null, "publisher": null, "abstract": "The market for unmanned aerial vehicle (UAV) services is increasing yearly. A law revision in 2022 in Japan will lift the ban on blind flights in urban areas, and parcel delivery services are expected to begin in the future. When UAVs fly automatically, they must report their location and other information periodically, and wireless communication is essential. However, when a UAV in the sky communicates, it is more likely to propagate than when communicating on the ground, and radio interference to various devices on the ground is a problem. This report evaluates the amount of radio interference caused by the antenna directivity and altitude of UAVs during the operation of a delivery service in an urban area. I combined ray-trace propagation simulations with a 3D urban model provided by PLATEAU to simulate a real environment. A 1 km square Nishi-Shinjuku area in Tokyo, Japan, was used, and UAVs were assumed to move along the main street. The simulation revealed that the radio interference area can be narrowed by using a directional antenna on the UAV and setting the flight path at a low altitude.", "keywords": [ "Interference (communication)", "Directivity", "Antenna (radio)", "Electromagnetic interference", "Directional antenna", "Omnidirectional antenna", "Computer science", "Path loss", "Mobile radio", "Radio frequency", "Telecommunications", "Electronic engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/vtc2024-fall63153.2024.10757864" }, { "id": "https://openalex.org/W4413904488", "doi": "10.1109/meditcom64437.2025.11104427", "title": "Integrating Charging Station Location and Delivery UAV Route by a Cascade Opitimization Framework", "year": 2025, "publication_date": "2025-07-07", "type": "conference-paper", "language": "en", "authors": [ "Jinbo Li", "Xinyuan Ni", "Mingzhuang Hua", "Qin Wang", "Miaokun Xu", "Feng Zhu" ], "first_author": "Jinbo Li", "venue": null, "publisher": null, "abstract": "Against the backdrop of accelerating global urbanization and surging instant delivery demands, the application of Unmanned Aerial Vehicles (UAVs) in last-mile logistics faces critical challenges: limited battery endurance, heterogeneous demand, and infrastructure coordination. This study proposes a K-means-VNS hybrid framework, integrating a three-level “location-routing-charging” linkage model with heuristic algorithms. It constructs a delivery network topology, optimizes charging station placement via an improved K-means algorithm, dynamically adjusts routes through Variable Neighborhood Search (VNS) operations, and uses a Mixed Integer Linear Programming (MILP) model to integrate spatio-temporal constraints. Experimental results show that K-means reduces total flight distance compared to spectral clustering and hypergraph partitioning under the same station counts. The VNS algorithm improves computational efficiency by over 40 times compared to A* and RRT. Leveraging dynamic weighting and parallel computing, the framework reduces solving time and breaking through the limitations of traditional stepwise optimization.", "keywords": [ "Cascade", "Computer science", "Systems engineering", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/meditcom64437.2025.11104427" }, { "id": "https://openalex.org/W4416040408", "doi": "10.1016/j.ast.2025.111242", "title": "Air route network planning for urban logistics with vertihub: An airspace model", "year": 2025, "publication_date": "2025-11-08", "type": "article", "language": "en", "authors": [ "Zhong‐Ming Li", "Yifei Zhao", "Xinhui Ren", "Ruijia Gu" ], "first_author": "Zhong‐Ming Li", "venue": "Aerospace Science and Technology", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Energy consumption", "Route planning", "Visibility", "Key (lock)", "Motion planning", "Time horizon", "Network planning and design", "Air traffic control", "Flow network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ast.2025.111242" }, { "id": "https://openalex.org/W4389398323", "doi": "10.1016/j.eswa.2023.122809", "title": "A bi-criteria approach to the truck-multidrone routing problem", "year": 2023, "publication_date": "2023-12-06", "type": "article", "language": "en", "authors": [ "Pedro Luis González Rodríguez", "David Sánchez-Wells", "José Luis Andrade-Pineda" ], "first_author": "Pedro Luis González Rodríguez", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": "In the rapidly expanding field of e-commerce logistics, the optimisation of last-mile delivery solutions is paramount. This paper introduces a novel methodology that addresses this challenge by generating approximate Pareto fronts in a hybrid truck-drone delivery system. Specifically, we examine a generalized single truck multi-drone problem that allows multi-visit flight missions and rendezvous points distinct from launch locations. Our goal is providing decision-makers with a portfolio of optimal routing solutions that balance service time and environmental impact, criteria that are increasingly shaping decision-making in this domain. To achieve this, we introduce a bivector coding scheme inspired by flow-shop scheduling problems and implement a Simulated Annealing algorithm. This algorithm features an advanced stopping mechanism, negating the need for manual adjustments by utilizing a distinctive blend of a domination rate and a Kalman filter. Importantly, our framework employs an iterated greedy search algorithm to evolve from initial solutions towards identifying non-dominated solutions sets, which are then ranked using a hypervolume coefficient. To validate our methodology, we conduct a sensitivity analysis on two different size instances using a full factorial design of experiments. Our analysis reveals crucial insights into the impact of the number of drones, their autonomy, and their flight speed settings. From it, we conclude that it is a robust and adaptable framework for its practical application for obtaining Pareto fronts solutions among which picking the ultimate routing to be implemented.", "keywords": [ "Computer science", "Drone", "Mathematical optimization", "Pareto principle", "Iterated local search", "Truck", "Operations research", "Vehicle routing problem", "Routing (electronic design automation)", "Metaheuristic", "Algorithm", "Mathematics" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2023.122809" }, { "id": "https://openalex.org/W7163395172", "doi": "10.5281/zenodo.20491776", "title": "Dataset for The use of UAVs in Last-Mile Logistics: opportunities, constraints and potential benefits of Autonomous Drone Delivery Systems (ADDS)", "year": 2026, "publication_date": "2026-06-01", "type": "dataset", "language": "en", "authors": [ "Michał Kłodawski" ], "first_author": "Michał Kłodawski", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The purpose of this dataset is to enable the replication and interpretation of the simulation research results presented in the articleMichał Kłodawski, The use of UAVs in Last-Mile Logistics: opportunities, constraints and potential benefits of Autonomous Drone Delivery Systems (ADDS). The dataset supports the analysis of last-mile parcel delivery systems with the use of unmanned aerial vehicles (UAVs), with particular attention to demand distribution, parcel-locker locations, wind conditions, UAV speed changes, energy consumption and CO2 emissions. The dataset includes:- spatial data describing network nodes and links,- residential building locations,- parcel-locker locations,- parcel demand assigned to buildings and parcel lockers,- model parameters used in the simulation study,- wind data used to analyse the influence of wind conditions on UAV operations,- simulation results for UAV speed under no-wind and wind conditions,- aggregated comparison of energy consumption and CO2 emissions for different delivery modes. The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022. E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).- The Chinese part of the project is funded by National Natural Science Foundation of China.- The Swedish part of the project is funded by Swedish Energy Agency.- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).", "keywords": [ "Drone", "Energy consumption", "Wind power", "China", "Consumption (sociology)", "Time horizon" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20491776" }, { "id": "https://openalex.org/W3199104677", "doi": "10.3390/electronics10182218", "title": "Design, Simulation, Analysis and Optimization of PID and Fuzzy Based Control Systems for a Quadcopter", "year": 2021, "publication_date": "2021-09-10", "type": "article", "language": "en", "authors": [ "Isaac Sánchez Leal", "Chamil Abeykoon", "Yasith S. Perera" ], "first_author": "Isaac Sánchez Leal", "venue": "Electronics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicles or drones are becoming one of the key machines/tools of the modern world, particularly in military applications. Numerous research works are underway to explore the possibility of using these machines in other applications such as parcel delivery, construction work, hurricane hunting, 3D mapping, protecting wildlife, agricultural activities, search and rescue, etc. Since these machines are unmanned vehicles, their functionality is completely dependent upon the performance of their control system. This paper presents a comprehensive approach for dynamic modeling, control system design, simulation and optimization of a quadcopter. The main objective is to study the behavior of different controllers when the model is working under linear and/or non-linear conditions, and therefore, to define the possible limitations of the controllers. Five different control systems are proposed to improve the control performance, mainly the stability of the system. Additionally, a path simulator was also developed with the intention of describing the vehicle’s movements and hence to detect faults intuitively. The proposed PID and Fuzzy-PD control systems showed promising responses to the tests carried out. The results indicated the limits of the PID controller over non-linear conditions and the effectiveness of the controllers was enhanced by the implementation of a genetic algorithm to autotune the controllers in order to adapt to changing conditions.", "keywords": [ "Quadcopter", "PID controller", "Fuzzy control system", "Computer science", "Fuzzy logic", "Control engineering", "Control system", "Control theory (sociology)", "Control (management)", "Engineering", "Artificial intelligence", "Temperature control" ], "cited_by_count": 39, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-9292/10/18/2218/pdf?version=1631520313" }, { "id": "https://openalex.org/W4323848813", "doi": "10.3390/en16062617", "title": "Sustainable Urban Freight for Energy-Efficient Smart Cities—Systematic Literature Review", "year": 2023, "publication_date": "2023-03-10", "type": "review", "language": "en", "authors": [ "Paulina Golińska-Dawson", "Kanchana Sethanan" ], "first_author": "Paulina Golińska-Dawson", "venue": "Energies", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Smart cities need energy-efficient and low-emission transportation for people and goods. Most studies focus on sustainable urban-transportation systems for passengers. Freight transportation in cities has increased significantly during the COVID-19 pandemic, leading to greenhouse gases emissions and negative externalities, such as traffic congestion. The purpose of this paper is to identify through a systematic literature review which innovations (hardware and software) applied by logistics service providers (LSPs) in sustainable urban freight (SUF) are suitable to support the transition to energy-efficient smart cities. We propose to classify the existing innovations in last-mile delivery for SUF into categories: (1) urban freight consolidation and/or trans-shipment; (2) the Consumer as a Service Provider (CaaSP); (3) choice of transportation modes. We introduce the concept of CaaSP as an innovative solution in last-mile delivery (LMD), where customers take over some transport operations with the use of smart technologies, and thus reduce the energy demand. We consider the modes of transportation, such as: drones, autonomous delivery robots, autonomous vehicles, cargo bikes (including e-cargo bikes, e-tricycles), electric vehicles (mainly vans), and combined passenger-and-cargo transportation rapid-transit systems. From the analyzed dataset, we find that energy-efficiency in smart cities can be improved by the consolidation of parcels in micro-depots, parcel lockers, and mobile depots. We analyze smart technologies (the Internet of things, big data, artificial intelligence, and digital twins), which enable energy efficiency by reducing the energy demand (fuel) of SUF, due to better operational planning and infrastructure sharing by logistics service providers. We propose a new IEE matrix as an actionable tool for the classification of innovations applied by LSPs in SUF, according to the level of their interconnectivity and energy efficiency. Additionally, this paper contributes to the theory by exploring possible future research directions for SUF in energy-efficient smart cities.", "keywords": [ "Sustainable transport", "Transport engineering", "Last mile (transportation)", "Efficient energy use", "Smart city", "City logistics", "Traffic congestion", "Service provider", "Service (business)", "Business", "Environmental economics", "Computer science" ], "cited_by_count": 65, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1996-1073/16/6/2617/pdf?version=1678442722" }, { "id": "https://openalex.org/W7169789253", "doi": "10.1016/j.eswa.2026.133716", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating multi-factor energy-consumption inputs and a partial recharging strategy", "year": 2026, "publication_date": "2026-07-20", "type": "article", "language": "en", "authors": [ "Shuai Zhang", "Yue Wu", "Wanting Chen", "Yong Chen" ], "first_author": "Shuai Zhang", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Key (lock)", "Electric vehicle", "Multipath routing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2026.133716" }, { "id": "https://openalex.org/W7170132001", "doi": "10.1016/j.rtbm.2026.101780", "title": "Drivers and barriers to drone delivery adoption in Canada: Insights for sustainable last-mile logistics", "year": 2026, "publication_date": "2026-07-23", "type": "article", "language": "en", "authors": [ "Didem Cicek", "Burak Kantarcı" ], "first_author": "Didem Cicek", "venue": "Research in Transportation Business & Management", "publisher": "Elsevier BV", "abstract": "The continued growth of e-commerce has placed increasing pressure on last-mile delivery systems, which contributes to congestion and environmental challenges in urban areas. Autonomous delivery technologies, particularly drone-based solutions, have been proposed as a potential response to these issues. However, their large-scale implementation depends not only on technological feasibility but also on user acceptance. Although interest in drone delivery is expanding globally, empirical evidence on consumer adoption in the Canadian context remains limited. This study investigates the factors influencing Canadian consumers’ intentions to adopt drone delivery services by applying an extended UTAUT2 framework. A quantitative survey was conducted with 156 Canadian online shoppers, primarily representing technology-oriented and innovation-focused communities across Canada, through an online questionnaire. As such, the findings largely reflect the perspectives of early adopters of emerging delivery technologies. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that five of the seven hypothesized relationships are supported. Performance Expectancy, Social Influence, and Environmental Friendliness positively influence behavioral intention, whereas Perceived Risk and Price Sensitivity have significant negative effects. In contrast, Effort Expectancy and Facilitating Conditions do not appear to significantly affect users’ adoption intentions. This study contributes to the literature by validating an extended UTAUT2 framework within the context of drone delivery and by offering empirically grounded insights into user acceptance in Canada, which can inform both future research and implementation efforts.", "keywords": [ "Expectancy theory", "Drone", "Context (archaeology)", "Unified theory of acceptance and use of technology", "Structural equation modeling", "Social influence", "Affect (linguistics)", "Survey data collection" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.rtbm.2026.101780" }, { "id": "https://openalex.org/W7135291522", "doi": null, "title": "Intelligent routing for smart last-mile drone delivery with mobile wireless charging stations", "year": 2024, "publication_date": "2024-12-01", "type": "dissertation", "language": "en", "authors": [ "Hossein Eskandaripour" ], "first_author": "Hossein Eskandaripour", "venue": "Holmes Museum Of Anthropology (Wichita State University)", "publisher": "Wichita State University", "abstract": "Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of of Industrial, Systems and Manufacturing Engineering", "keywords": [ "Drone", "Wireless", "State (computer science)", "Key (lock)", "Routing (electronic design automation)", "Internet of Things" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/10057/29165" }, { "id": "https://openalex.org/W7163430823", "doi": "10.5281/zenodo.20491777", "title": "Dataset for The use of UAVs in Last-Mile Logistics: opportunities, constraints and potential benefits of Autonomous Drone Delivery Systems (ADDS)", "year": 2026, "publication_date": "2026-06-01", "type": "dataset", "language": "en", "authors": [ "Michał Kłodawski" ], "first_author": "Michał Kłodawski", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The purpose of this dataset is to enable the replication and interpretation of the simulation research results presented in the articleMichał Kłodawski, The use of UAVs in Last-Mile Logistics: opportunities, constraints and potential benefits of Autonomous Drone Delivery Systems (ADDS). The dataset supports the analysis of last-mile parcel delivery systems with the use of unmanned aerial vehicles (UAVs), with particular attention to demand distribution, parcel-locker locations, wind conditions, UAV speed changes, energy consumption and CO2 emissions. The dataset includes:- spatial data describing network nodes and links,- residential building locations,- parcel-locker locations,- parcel demand assigned to buildings and parcel lockers,- model parameters used in the simulation study,- wind data used to analyse the influence of wind conditions on UAV operations,- simulation results for UAV speed under no-wind and wind conditions,- aggregated comparison of energy consumption and CO2 emissions for different delivery modes. The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022. E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).- The Chinese part of the project is funded by National Natural Science Foundation of China.- The Swedish part of the project is funded by Swedish Energy Agency.- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).", "keywords": [ "Drone", "Energy consumption", "Wind power", "China", "Consumption (sociology)", "Time horizon" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20491777" }, { "id": "https://openalex.org/W4295021238", "doi": "10.1016/j.commtr.2022.100080", "title": "Replacing urban trucks via ground–air cooperation", "year": 2022, "publication_date": "2022-09-09", "type": "article", "language": "en", "authors": [ "Xiaobo Qu", "Ziling Zeng", "Kai Wang", "Shuaian Wang" ], "first_author": "Xiaobo Qu", "venue": "Communications in Transportation Research", "publisher": "Elsevier BV", "abstract": "The advent of drones is leading to a paradigm shift in courier services, while their large-scale deployment is confined by a limited range. Here, we design a low-cost product that allows drones to drop parcels onto and pick them up from the roofs of moving passenger vehicles. With this, we propose a ground-air cooperation (GAC) based business model for parcel delivery in an urban environment. As per our case study using real-world data in Beijing, the new business model will not only shorten the parcel delivery time by 86.5% with a comparable cost, but also reduce road traffic by 8.6%, leading to an annual social benefit of 6.67 billion USD for Beijing. The proposed model utilizes the currently “wasted or unused” rooftops of passenger vehicles and has the potential to replace most parcel trucks and trailers, thus fundamentally addressing the congestion, noise, pollution, and road wear and tear problems caused by trucks, and bringing in immense social benefit.", "keywords": [ "Truck", "Beijing", "Software deployment", "Transport engineering", "Drone", "Business model", "Traffic congestion", "Computer science", "Environmental science", "Business", "Engineering", "Automotive engineering" ], "cited_by_count": 45, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S2772424722000300/pdf" }, { "id": "https://openalex.org/W7132023476", "doi": null, "title": "Last mile delivery with drones : Zipline drones case study", "year": 2024, "publication_date": "2024-01-01", "type": "dissertation", "language": "en", "authors": [ "Tochukwu Jaireh Njoku" ], "first_author": "Tochukwu Jaireh Njoku", "venue": "FH JOANNEUM ePUB", "publisher": null, "abstract": "Das Konzept des Handels ist so alt wie die Geschichte der Menschheit und der Kommunikation. Der Handel wurde schon immer von der unaufhörlichen Nachfrage der Menschen nach verschiedenen Arten von Produkten angetrieben. Dies hat zu einem kontinuierlichen Anstieg der globalen Lieferketten- und Logistikaktivitäten geführt. Diese Aktivitäten zielen immer auf eine effiziente und schnelle Lieferung von Waren und Dienstleistungen an Endverbraucher zur Zufriedenheit der Verbraucher ab. Allerdings wächst das Bewusstsein für die Auswirkungen von Treibhausgasemissionen auf das Klima. Dies hat zu einer Neubewertung der Parameter für den Erfolg im Handel geführt, von der Profitorientierung hin zur Nachhaltigkeit der Produktionsprozesse und der Innovation nachhaltiger Produkte. Es ist auch eine Tatsache, dass globale Lieferketten- und Logistikaktivitäten zu 80 % die Hauptverursacher indirekter Scope-3-Emissionen sind. Diese Bedenken haben kleine, mittlere und große Geschäftsinhaber sowie Regierungs- und Unternehmensorganisationen dazu veranlasst, innovative erneuerbare Technologien wie Drohnen zu erkunden, um ein gewisses Maß an Nachhaltigkeit in den Lieferketten-Liefervorgängen auf der letzten Meile zu erreichen. Drohnen gewinnen als zuverlässiges und nachhaltiges Mittel zur effizienten Zustellung von Waren und Dienstleistungen auf der letzten Meile zunehmend an Bedeutung. Diese Arbeit untersucht anhand der Literatur verschiedene Studien zur Logistik der Zustellung auf der letzten Meile mit Drohnen und legt dabei den Schwerpunkt auf Energie, Kosten, Entfernung und Effizienz. Anschließend zeichnet sich diese Arbeit durch eine reale Fallstudie der Zipline Company aus, einem in den USA ansässigen Unternehmen, das Drohnen zur nachhaltigen Zustellung von Produkten auf der letzten Meile einsetzt. Es wurde eine Analyse durchgeführt, um den Energieverbrauch und die CO2-Emissionen bei einer 80 km langen Hin- und Rückfahrt mit der Quadrocopter-Drohne Zipline Platform-2 zu ermitteln. Der Energieverbrauch und die entsprechenden Emissionen wurden ermittelt, wenn ein Elektrofahrrad, ein Elektroauto, Benzin- und Dieseltransporter für dieselbe Zustellung eingesetzt würden. Für die Bodenfahrzeuge wurden Annahmen hinsichtlich eines Nullverkehrseffekts und einer optimalen geraden, optimierten Route getroffen. Anschließend wurde die Effizienz des Quadrocopters und der Bodenfahrzeuge in Bezug auf die CO2-Emissionen grafisch ermittelt. Die Drohne wurde im Nachhaltigkeitskontext mit anderen Bodenzustellfahrzeugen verglichen. Die Effizienz der Drohne erwies sich im Vergleich zu anderen Bodenfahrzeugen mit einer Reichweite von 71 – 99 % als überwältigend beeindruckend. Abschließend bot das Papier eine Lösung an, um die hohe Effizienz der Drohne weiter zu festigen und durch erneuerbare Energie aus Sonnenlicht eine Netto-Null-Kohlenstoffemission zu erreichen: ein vollständig netzunabhängiges Solarsystem zur Stromversorgung der Dockingstationen der Zipline-Drohne. Dies wurde als ideale, kostengünstige und nachhaltige Lösung für den Zipline-Flug angesehen. Einsätze in afrikanischen Ländern wie Nigeria, Ghana, der Elfenbeinküste und Kenia, wo die Stromversorgung unzureichend und unzuverlässig ist.", "keywords": [ "Drone", "Aircraft industry" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://resolver.obvsg.at/urn:nbn:at:at-fhj:1-44219" }, { "id": "https://openalex.org/W3166155537", "doi": "10.1109/icns52807.2021.9441549", "title": "Communication in Challenging Environments: Application of LEO/MEO Satellite Constellation to Emerging Aviation Networks", "year": 2021, "publication_date": "2021-04-20", "type": "conference-paper", "language": "en", "authors": [ "L. D. Earley" ], "first_author": "L. D. Earley", "venue": null, "publisher": null, "abstract": "The next generation of aviation will operate in a data-driven environment. Aviation's destiny is now coupled with the Internet of Things (IoT)- wherein devices and autonomous systems have an inherent need for continuous network connectivity. For next generation technology to reach its full potential, a network connection should be available anywhere, anytime. Unmanned Aerial Vehicles (UAVs or drones) are increasing in number and large companies are designing drone delivery systems. A next generation Unmanned Aerial System (UAS) will require a more flexible network that can deliver data in non-traditional environments. However, looking beyond UAS flight operations, the aviation industry at large needs to consider integrating new services into the Air Navigation Service Provider's (ANSP's) enterprise communications architectures-both to get low-cost communication to remote areas for UAS operations and as a cost-effective last mile alternative for high-availability, low latency services.These developments will require a communication network that is scalable to the needs of the future. Low and Medium Earth Orbit (LEO/MEO) satellite constellations can address the challenges of a rapidly evolving future by being able to provide communications beyond the existing commercial and public infrastructure, to include a global broadband, low-latency footprint. Remote regions typically require costly site-specific infrastructure solutions to provide last-mile connectivity. By utilizing lower orbits, the constellation can provide high data rates and low latency on the links by bringing the edge of broadcasting closer to the Earth versus a Geosynchronous Orbit (GEO). The telecommunications industry is at a crossroads for determining the network design for tomorrow's aviation requirements. The introduction of UAVs into the National Airspace System (NAS) will present the challenge of locating and surveilling these small and elusive aircraft. The time is now to build a new infrastructure; a Satellite Communication (SatCom) network, that achieves rapid global broadband coverage, versus localized terrestrial networks.", "keywords": [ "Drone", "Constellation", "Computer science", "Telecommunications", "Aviation", "Communications satellite", "Computer network", "Satellite", "Engineering" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icns52807.2021.9441549" }, { "id": "https://openalex.org/W7159559548", "doi": null, "title": "Drone Delivery in Urban Last-Mile Logis tics: Challenges, Significance, Barriers, and Future Prospects", "year": 2026, "publication_date": "2026-01-01", "type": "dissertation", "language": "en", "authors": [ "Caroline shan Xu" ], "first_author": "Caroline shan Xu", "venue": "Theseus (Ammattikorkeakoulujen)", "publisher": null, "abstract": "In recent years, increasing pressure has been placed on urban last-mile delivery systems due to rising e-commerce demand, higher customer expectations, and growing concerns related to congestion and sustainability. As a result, alternative delivery solutions, including drone delivery, have attracted significant attention. The objective of the study was to examine the role of drone delivery in urban last-mile logistics by analysing three key aspects: the challenges faced by current delivery systems, the reasons drone delivery is considered important and innovative, and the barriers that limit its practical implementation. The study was conducted using a qualitative research approach based on a literature review. Academic articles, industry reports, and applied research publications were collected from databases such as Google Scholar, ScienceDirect, and Theseus. The data were analysed using a thematic analysis method, which enabled the identification of recurring patterns and key themes across the selected sources. The results indicate that urban last-mile delivery systems face structural challenges related to high operational costs, congestion, complex delivery conditions, and increasing service expectations. Drone delivery is identified as a potentially valuable complementary solution, particularly in situations requiring speed, direct routing, and time-sensitive delivery. However, significant barriers remain, including technical limitations, regulatory constraints, operational complexity, and issues related to public acceptance. It was concluded that drone delivery should not be regarded as a universal replacement for conventional delivery systems, but rather as a context-dependent and supplementary solution. Its future role in urban logistics depends on the coordinated development of technology, regulation, infrastructure, and social acceptance.", "keywords": [ "Drone", "Identification (biology)", "Thematic analysis", "Service delivery framework", "Key (lock)", "Delivery system", "Service (business)", "Public transport" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.theseus.fi/bitstream/10024/915402/2/Xu_Caroline%20shan%20.pdf" }, { "id": "https://openalex.org/W4254369816", "doi": "10.36871/ek.up.p.r.2021.04.01.012", "title": "LAST MILE DELIVERY BY DRONES: CHALLENGES AND PROSPECTS", "year": 2021, "publication_date": "2021-01-01", "type": "article", "language": "en", "authors": [ "И.В. Пустохина", "L. R. MARDANSHINA" ], "first_author": "И.В. Пустохина", "venue": "EKONOMIKA I UPRAVLENIE PROBLEMY RESHENIYA", "publisher": "Publishing House Scientific Library", "abstract": "The article analyzes Russian and foreign legislation in the field of regulating flights using drones, which showed that the delivery of goods by drones is a promising, and in some cases, a necessary technology. However, legal restrictions related to the safety of the use of airspace prevent the use of drones on a regular basis for commercial purposes. Also, points of growth of their use in the delivery of goods \"to the door\" were found.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Legislation", "Business", "Aeronautics", "Advertising", "Engineering", "Political science", "Law", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.36871/ek.up.p.r.2021.04.01.012" }, { "id": "https://openalex.org/W7136409034", "doi": "10.1109/itsc60802.2025.11423269", "title": "LogisticsVLN: Vision-Language Navigation for Low-Altitude Terminal Delivery Based on Agentic UAVs", "year": 2025, "publication_date": "2025-11-18", "type": "conference-paper", "language": null, "authors": [ "Xinyuan Zhang", "Yonglin Tian", "Fei Lin", "Yue Liu", "Jing Ma", "Xiao Wang", "Kornélia Sára Szatmáry", "Fei-Yue Wang" ], "first_author": "Xinyuan Zhang", "venue": null, "publisher": null, "abstract": "The growing demand for intelligent logistics, particularly fine-grained terminal delivery, underscores the need for autonomous UAV (Unmanned Aerial Vehicle)-based delivery systems. However, most existing last-mile delivery studies rely on ground robots, while current UAV-based Vision-Language Navigation (VLN) tasks primarily focus on coarse-grained, long-range goals, making them unsuitable for precise terminal delivery. To bridge this gap, we propose LogisticsVLN, a scalable aerial delivery system built on multimodal large language models (MLLMs) for autonomous terminal delivery. LogisticsVLN integrates lightweight Large Language Models (LLMs) and Visual-Language Models (VLMs) in a modular pipeline for request understanding, floor localization, object detection, and decision-making. To support research and evaluation in this new setting, we construct the Vision-Language Delivery (VLD) dataset within the CARLA simulator. Experimental results on the VLD dataset showcase the feasibility of the LogisticsVLN system. In addition, we conduct subtask-level evaluations of each module of our system, offering valuable insights for improving the robustness and real-world deployment of foundation model-based vision-language delivery systems.", "keywords": [ "Terminal (telecommunication)", "Global Positioning System", "Terminal guidance", "Action (physics)", "Field (mathematics)" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itsc60802.2025.11423269" }, { "id": "https://openalex.org/W3108761254", "doi": "10.4230/oasics.atmos.2020.17", "title": "Crowdsourced Delivery with Drones in Last Mile Logistics", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Mehdi Behroozi", "Dinghao Ma" ], "first_author": "Mehdi Behroozi", "venue": "DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)", "publisher": "Schloss Dagstuhl – Leibniz Center for Informatics", "abstract": "We consider a combined system of regular delivery trucks and crowdsourced drones to provide a technology-assisted crowd-based last-mile delivery experience. We develop analytical models and methods for a system in which package delivery is performed by a big truck carrying a large number of packages to a neighborhood or a town in a metropolitan area and then assign the packages to crowdsourced drone operators to deliver them to their final destinations. A combination of heuristic algorithms is used to solve this NP-hard problem, computational results are presented, and an exhaustive sensitivity analysis is done to check the influence of different parameters and assumptions.", "keywords": [ "Drone", "Computer science", "Truck", "Last mile (transportation)", "Heuristic", "Metropolitan area", "Crowdsourcing", "Mile", "Operations research", "Transport engineering", "Engineering", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://drops.dagstuhl.de/storage/01oasics/oasics-vol085-atmos2020/OASIcs.ATMOS.2020.17/OASIcs.ATMOS.2020.17.pdf" }, { "id": "https://openalex.org/W7125815668", "doi": "10.5281/zenodo.18384972", "title": "Drones as Means to Revolutionize Last Mile Logistics", "year": 2026, "publication_date": "2026-01-27", "type": "article", "language": null, "authors": [ "Federica Bosco", "Francesco de Bosio", "Alberto Giacomel", "Renato Redondi" ], "first_author": "Federica Bosco", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The paper focuses on the role of drones in revolutionizing last mile logistics, the most expensive and complex segment of the logistics chain due to urban congestion and growing consumer expectations for fast, low-cost deliveries.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Humanitarian Logistics", "City logistics", "Chain (unit)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18384972" }, { "id": "https://openalex.org/W4386395528", "doi": "10.48550/arxiv.2308.16408", "title": "Last Mile Delivery with Drones and Sharing Economy", "year": 2023, "publication_date": "2023-08-31", "type": "preprint", "language": "en", "authors": [ "Mehdi Behroozi", "Dinghao Ma" ], "first_author": "Mehdi Behroozi", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We consider a combined system of regular delivery trucks and crowdsourced drones, available via a sharing economy platform, to provide a technology-assisted crowd-based last-mile delivery experience. We develop analytical models and methods for a system in which package delivery is performed by a big truck carrying many packages to a neighborhood or a town in a metropolitan area and then the packages are assigned to crowdsourced drone operators to deliver them to their final destinations. We develop several optimization models for various cases of the problem and use a combination of heuristic algorithms to solve this NP-hard problem. Finally, we present computational results for the models and algorithms, and conduct an exhaustive sensitivity analysis to check the influence of different parameters and assumptions on the final results. We also provide extensive managerial insights on the benefits of our proposed model if implemented in practice.", "keywords": [ "Drone", "Computer science", "Truck", "Heuristic", "Last mile (transportation)", "Metropolitan area", "Crowdsourcing", "Sharing economy", "Operations research", "Mile", "Engineering", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2308.16408" }, { "id": "https://openalex.org/W4280492112", "doi": "10.1109/icns54818.2022.9771495", "title": "Preliminary Environmental Risk Consideration for Small UAV Ground Risk Mapping", "year": 2022, "publication_date": "2022-04-05", "type": "conference-paper", "language": "en", "authors": [ "Chung Han Jie", "Mohd Hasrizam Che Man", "Anush Kumar Sivakumar", "Kin Huat Low" ], "first_author": "Chung Han Jie", "venue": null, "publisher": null, "abstract": "Small unmanned aerial vehicles (sUAV) or 'drones' are projected to rapidly develop in the future and be employed for a multitude of applications such as parcel delivery, search and rescue, surveillance monitoring, and farming. Consequently, an increase in sUAV traffic may lead to a higher likelihood of failure, and crash occur primarily due to environmental factors. Hence, the objective of this study was to investigate the influence of environmental factors on sUAV operations.The study identifies three environmental risk categories relevant to Singapore’s context, namely weather, obstacle, and signal. Subsequently, Python code was developed to perform data analysis on environmental risk data gathered from open source, relevant agencies, and literature surveys. Three environmental risk factors were considered for proof of concept, namely rainfall, windspeed, bird population, and cell tower signal strength.From the data analysis, the average daily rainfall across weather stations in Singapore was observed to be 8.58 mm. In August, weather stations in West of Singapore, such as Choa Chu Kang and Tengah, reported the highest rainfall, approximately 22.65 mm. In contrast, weather stations in the East such as Changi and Pasir Ris reported the lowest rainfall near 0.036 mm in February. Data of the three most invasive bird species in Singapore was examined. The site of Chinese Garden in West Singapore reported the highest bird population of 432 birds per site. The study of bird population data potentially provides an insight into the possibility of sUAV bird strike events. Similarly, signal strength data from cell towers in Singapore was studied.Overall environmental risk-tiering can be visualized using all the analyzed environmental factors data on a risk map. The regions with high rainfall, increased bird population per site and strong signal interference can be extracted.These high-risk locations can be expected to be more hazardous for sUAVs to fly in. Findings for environmental risk-tiering and risk-mapping are preliminary and based on the available environmental data as identified in this study. Further research will be done to include other environmental risk factors and to generate better accuracy of the environmental risk-tier map. Insights from this study will facilitate the demarcation of low, medium, and high-risk areas for risk assessment of sUAVs operation in Singapore.", "keywords": [ "Population", "Geography", "Context (archaeology)", "Python (programming language)", "Environmental science", "Environmental data", "Extreme weather", "Climate change", "Ecology", "Computer science" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dr.ntu.edu.sg/bitstream/10356/160547/2/ICNS_Submission_Mar7.pdf" }, { "id": "https://openalex.org/W7125831481", "doi": "10.5281/zenodo.18384973", "title": "Drones as Means to Revolutionize Last Mile Logistics", "year": 2026, "publication_date": "2026-01-27", "type": "article", "language": null, "authors": [ "Federica Bosco", "Francesco de Bosio", "Alberto Giacomel", "Renato Redondi" ], "first_author": "Federica Bosco", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The paper focuses on the role of drones in revolutionizing last mile logistics, the most expensive and complex segment of the logistics chain due to urban congestion and growing consumer expectations for fast, low-cost deliveries.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Humanitarian Logistics", "City logistics", "Chain (unit)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18384973" }, { "id": "https://openalex.org/W4392397255", "doi": "10.1109/jsyst.2024.3359776", "title": "Efficient Fuzzy-Based 3-D Flying Base Station Positioning and Trajectory for Emergency Management in 5G and Beyond Cellular Networks", "year": 2024, "publication_date": "2024-03-01", "type": "article", "language": "en", "authors": [ "Mohammad Javad Sobouti", "Haitham Y. Adarbah", "Afshin Alaghehband", "Hamid Chitsaz", "Amir Hossein Mohajerzadeh", "Mehdi Sookhak", "Seyed Amin Hosseini Seno", "Abedin Vahedian", "Fatemeh Afghah" ], "first_author": "Mohammad Javad Sobouti", "venue": "IEEE Systems Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The need for continuous coverage, as well as low-latency, and ultrareliable communication in 5G and beyond cellular networks encouraged the deployment of high-altitude platforms and low-altitude drones as flying base stations (FBSs) to provide last-mile communication where high cost or geographical restrictions hinder the installation of terrestrial base stations (BSs) or during the disasters where the BSs are damaged. The performance of unmanned aerial vehicle (UAV)-assisted cellular systems in terms of coverage and quality of service offered for terrestrial users depends on the number of deployed FBSs, their 3-D location as well as trajectory. While several recent works have studied the 3-D positioning in UAV-assisted 5G networks, the problem of jointly addressing coverage and user data rate has not been addressed yet. In this article, we propose a solution for joint 3-D positioning and trajectory planning of FBSs with the objectives of the total distance between users and FBSs and minimizing the sum of FBSs flight distance by developing a fuzzy candidate points selection method.", "keywords": [ "Base station", "Trajectory", "Cellular network", "Computer science", "Fuzzy logic", "Emergency management", "Computer network", "Aeronautics", "Real-time computing", "Engineering", "Artificial intelligence" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jsyst.2024.3359776" }, { "id": "https://openalex.org/W4392224200", "doi": "10.48550/arxiv.2402.16085", "title": "Online Drone Scheduling for Last-mile Delivery", "year": 2024, "publication_date": "2024-02-25", "type": "preprint", "language": "en", "authors": [ "Saswata Jana", "Giuseppe Francesco Italiano", "Manas Jyoti Kashyop", "Athanasios L. Konstantinidis", "Evangelos Kosinas", "Partha Sarathi Mandal" ], "first_author": "Saswata Jana", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Delivering a parcel from the distribution hub to the customer's doorstep is called the \\textit{last-mile delivery} step in delivery logistics. In this paper, we study a hybrid {\\it truck-drones} model for the last-mile delivery step, in which a truck moves on a predefined path carrying parcels and drones deliver the parcels. We define the \\textsc{online drone scheduling} problem, where the truck moves in a predefined path, and the customer's requests appear online during the truck's movement. The objective is to schedule a drone associated with every request to minimize the number of drones used subject to the battery budget of the drones and compatibility of the schedules. We propose a 3-competitive deterministic algorithm using the next-fit strategy and 2.7-competitive algorithms using the first-fit strategy for the problem with $O(\\log n)$ worst-case time complexity per request, where $n$ is the maximum number of active requests at any time. We also introduce \\textsc{online variable-size drone scheduling} problem (OVDS). Here, we know all the customer's requests in advance; however, the drones with different battery capacities appear online. The objective is to schedule customers' requests for drones to minimize the number of drones used. We propose a $(2α+ 1)$-competitive algorithm for the OVDS problem with total running time $O(n \\log n)$ for $n$ customer requests, where $α$ is the ratio of the maximum battery capacity to the minimum battery capacity of the drones. Finally, we address how to generate intervals corresponding to each customer request when there are discrete stopping points on the truck's route, from where the drone can fly and meet with the truck.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Scheduling (production processes)", "Computer science", "Aeronautics", "Engineering", "Operations management", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2402.16085" }, { "id": "https://openalex.org/W4411154978", "doi": "10.1007/s42979-025-04063-5", "title": "Learning-Based UAV Swarm Video Analytics Orchestration in Disaster Response Management", "year": 2025, "publication_date": "2025-06-09", "type": "article", "language": "en", "authors": [ "Tiying Gao", "Dwight Goins", "Chaise Ballotti", "Jiaqing Liu", "Chengyi Qu" ], "first_author": "Tiying Gao", "venue": "SN Computer Science", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Orchestration", "Analytics", "Computer science", "Disaster response", "Data science", "Swarm behaviour", "Emergency management", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s42979-025-04063-5" }, { "id": "https://openalex.org/W4409779995", "doi": "10.1142/s2301385026500408", "title": "Quadrotor UAV Elastically Attached to Uncertain Payload: Trajectory Control and Vibrations Suppression", "year": 2025, "publication_date": "2025-04-24", "type": "article", "language": "en", "authors": [ "Renan Sanches Geronel", "Marco Rinaldi", "Maíra Martins da Silva", "Stefano Primatesta" ], "first_author": "Renan Sanches Geronel", "venue": "Unmanned Systems", "publisher": "World Scientific", "abstract": "Quadrotor Unmanned Aerial Vehicles (UAVs) are a promising configuration in the context of Unmanned Aircraft System (UAS) technology as they can be effectively used for a variety of tasks in terms of the development of innovative aerial transportation systems, e.g. last-mile delivery and transportation of medical samples. In applications with payload transportation, the vibrations induced during the flight pose significant challenges, potentially compromising the flight stability and the integrity of the payload (e.g. these issues become critical in certain applications such as the transportation of medical samples). Thus, the accurate modeling and evaluation of these vibrations are essential to ensure reliable operations. This paper describes the design of a Sliding Mode Control (SMC) strategy with parametric uncertainties and optimized gains for controlling a quadrotor carrying a payload, when a flexible attachment link is assumed between both systems. The effects of both the payload and the flexibility of the attachment link on the system dynamics are investigated. Moreover, the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) are combined for tuning the gains of the sliding mode controller in a shorter computational time. In this sense, we propose to use the GA with fewer iterations than the convergence, and then finalize the optimization with an additional efficient and lower computational time procedure (SQP). Since lower oscillations are required, the objective function is designed based on the relative trajectory oscillations to minimize the intensity of the payload’s vibrations and their uncertainties. The simulation results obtained using MATLAB/Simulink corroborate the validity of the approach, which allows the quadrotor platform to track a reference trajectory while containing the oscillations of the payload. Also, the proposed control approach accounts for payload’s parametric uncertainties regarding payload mass and payload inertia values.", "keywords": [ "Payload (computing)", "Trajectory", "Control theory (sociology)", "Vibration", "Control (management)", "Vibration control", "Computer science", "Aerospace engineering", "Control engineering", "Aeronautics", "Engineering", "Physics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s2301385026500408" }, { "id": "https://openalex.org/W7204105425", "doi": "10.1016/j.trc.2026.105952", "title": "Synchronous truck-robot-drone last-mile delivery", "year": 2026, "publication_date": "2026-08-24", "type": "article", "language": "en", "authors": [ "Behrang Bootaki", "Omar Abou Kasm", "Joe Naoum‐Sawaya" ], "first_author": "Behrang Bootaki", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": "The continued growth of online shopping in recent years has placed greater demands on efficient last-mile delivery systems. Traditional truck parcel delivery faces challenges such as slow delivery in congested urban areas and high environmental impacts. These issues have inspired researchers and practitioners to develop novel multi-modal delivery systems based on the coordination between traditional delivery trucks and newer technologies such as delivery robots and drones. In this paper, we model a synchronous truck-robot-drone last-mile delivery system and propose a mixed integer program along with a heuristic solution. The proposed model is designed for a delivery truck equipped with a multi-visit drone and multiple single-delivery robots. The model accounts for multiple operational constraints, including drone energy endurance and the load capacities of both the drone and the robots. Computational experiments are conducted to demonstrate the efficiency of the proposed heuristic in solving real-sized instances. The impact of the proposed delivery system is evaluated using the 2021 Amazon Last Mile Routing Research Challenge dataset in Seattle. The case results show that replacing the truck with the proposed system can reduce travel times within customer zones by an average of 40% compared to the original routes. Time savings can reach up to 52% for larger deliveries, demonstrating the system’s potential to improve efficiency and contribute to reduced environmental impacts in urban logistics.", "keywords": [ "Drone", "Truck", "Heuristic", "Last mile (transportation)", "Delivery system", "Routing (electronic design automation)", "Delivery Performance", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2026.105952" }, { "id": "https://openalex.org/W7133568599", "doi": "10.3390/drones10030174", "title": "UAV–Rider Collaborative Dispatching Under Stochastic Wind Conditions Considering Nonlinear Energy Dynamics", "year": 2026, "publication_date": "2026-03-04", "type": "article", "language": "en", "authors": [ "Chunxia Shangguan", "Churan Zhang", "Shouqi Cao" ], "first_author": "Chunxia Shangguan", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "To mitigate UAV (unmanned aerial vehicle) range limitation risks and scheduling disruptions caused by complex wind fields in urban instant delivery, this paper proposes a UAV–rider collaborative dispatching framework. By incorporating aerodynamic-based nonlinear energy dynamics, the model accurately characterizes power variations under stochastic wind conditions, significantly enhancing the operational reliability of urban delivery missions. First, an aerodynamic-based nonlinear energy function is constructed, coupling payload, airspeed, and random wind vectors to accurately characterize power variations. Second, a scenario-based two-stage stochastic programming framework is adopted, where the rider’s deterministic path is optimized in the first-stage decision to ensure stability, and the UAV’s scenario-dependent flight plan is resolved in the second stage to adapt to wind uncertainty. An improved branch-and-price (IBP) algorithm is designed to solve this large-scale model, where nonlinear energy is evaluated during label extension in the pricing sub-problem, effectively avoiding linearization errors. The numerical results demonstrate that the proposed framework improves the mission success probability (the likelihood of completing delivery routes without battery exhaustion across all considered wind scenarios) by 25% under strong-wind conditions by effectively avoiding power failure risks. Furthermore, the IBP algorithm outperforms traditional exact solvers by over 40% in solution efficiency for large-scale cases. These findings demonstrate that energy-aware stochastic dispatching significantly improves the reliability and robustness of UAV-assisted last-mile delivery in windy urban environments, thereby providing an effective operational solution for real-world drone delivery logistics.", "keywords": [ "Wind power", "Nonlinear system", "Robustness (evolution)", "Scheduling (production processes)", "Stochastic programming", "Linearization", "Reliability (semiconductor)", "Stochastic process", "Backup" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/3/174/pdf?version=1772629142" }, { "id": "https://openalex.org/W2803755499", "doi": null, "title": "A Multi-Modality Mobility Concept for a Small Package Delivery UAV", "year": 2017, "publication_date": "2017-01-24", "type": "article", "language": "en", "authors": [ "Larry A. Young" ], "first_author": "Larry A. Young", "venue": "NASA STI Repository (National Aeronautics and Space Administration)", "publisher": "National Aeronautics and Space Administration", "abstract": "This paper will discuss a different approach to the typical notional small package delivery drone concept. Most delivery drone concepts employ a point-to-point aerial delivery CONOPS (Concept of Operations) from a warehouse directly to the front or back yards of a customers residence or a commercial office space. Instead, the proposed approach is somewhat analogous to current postal deliveries: a small aerial vehicle flies from a warehouse to designated neighborhood VTOL (Vertical Take-Off and Landing) landing spots where the aerial vehicle then converts to a \"roadable\" (ground-mobility) vehicle that then transits on sidewalks and/or bicycle paths till it arrives to the residence/office drop-off points. This concept and associated platform or vehicle will be referred in this paper as MICHAEL (Multimodal Intra-City Hauling and Aerial-Effected Logistics) concept. It is suggested that the MICHAEL concept potentially results in a more community friendly \"delivery drone\" approach.", "keywords": [ "Notional amount", "Drone", "Point (geometry)", "Transport engineering", "Computer science", "Residence", "Terrain", "Aeronautics", "Operations research", "Engineering", "Geography", "Cartography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/2060/20180002627" }, { "id": "https://openalex.org/W4400945990", "doi": "10.1109/yac63405.2024.10598475", "title": "Multi-UAV Urban Delivery Research Based on Range Constraints", "year": 2024, "publication_date": "2024-06-07", "type": "conference-paper", "language": "en", "authors": [ "Yingying Jennifer Chen", "Xinkai Li", "Hongli Zhang", "Yinfei Wu", "Yiming Chai" ], "first_author": "Yingying Jennifer Chen", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) range is a key factor in the success of last-mile delivery, and this paper proposes a path-planning-based task assignment strategy. Multi-task solving faces the problem of high computational volume and high solving difficulty. To minimize the quantity of path planning and enhance algorithmic efficiency, the uniform distribution K-means (UD-Kmeans) algorithm is initially employed to decompose tasks into multiple task groups. This decomposes the task into multiple task groups, reducing the coupling of the system and the complexity of task solving. Next, the Bidirectional Adaptive Potential Field -Rapidly-exploring Random Tree star (Bi-APF-RRT*) algorithm is utilized within the task group to obtain the distance matrix. Following this, the Monte Carlo tree search (MCTS) algorithm is used to assign tasks with the objective of minimizing the total UAV range, combined with the distance matrix. Simulation experiments show that the MCTS algorithm combined with path planning (PP-MCTS) can efficiently and reasonably allocate multi-tasks, proving the effectiveness of this allocation strategy.", "keywords": [ "Computer science", "Range (aeronautics)", "Aerospace engineering", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/yac63405.2024.10598475" }, { "id": "https://openalex.org/W3162221961", "doi": "10.21203/rs.3.rs-262326/v1", "title": "Last-Mile Drone Delivery Combinatorial Double Auction Model using Multi-Objective Evolutionary Algorithms", "year": 2021, "publication_date": "2021-05-04", "type": "preprint", "language": "en", "authors": [ "Chen-Wei Lee", "Wai Peng Wong" ], "first_author": "Chen-Wei Lee", "venue": "Research Square", "publisher": null, "abstract": null, "keywords": [ "Drone", "Mile", "Computer science", "Last mile (transportation)", "Algorithm", "Geography", "Biology", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-262326/v1.pdf?c=1631879917000" }, { "id": "https://openalex.org/W7125608812", "doi": "10.5281/zenodo.18362404", "title": "Last-mile-produced drones as weapons of resistance fighters", "year": 2026, "publication_date": "2026-01-24", "type": "article", "language": "en", "authors": [ "Vladislav Velitsko" ], "first_author": "Vladislav Velitsko", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Abstract. The publication examines the risks of using drones against the fuel and energy complex (FEC) of Russia (the Russian Federation, RF) – the aggressor state-specifically unmanned aerial vehicles (UAVs) assembled at the point of use from legal components with individual or cluster warheads of local production (Last-Mile Manufacturing drones, LMM-Drones), as well as drones acting as carriers of cold weapons, launched in close proximity to military units, enterprises of the military-industrial complex (MIC), FEC facilities such as petroleum refinery and gas processing plants (PR and GPP), oil storage facilities, petroleum product storage sites, pumping and compressor stations, and also used as means of striking other targets, for example war criminals carrying out genocide of the population of Ukraine. The paper analyzes the acceleration of the innovation cycle in modern warfare and the active introduction of disruptive innovations, using the example of the Russian Federation’s aggressive war against Ukraine. It examines the transition of unmanned systems from isolated innovations to mass, low-cost, and predominantly software-driven platforms based on artificial intelligence (AI). It is shown that many tactical and technological innovations (FPV systems, inexpensive ISR platforms (Intelligence, Surveillance, and Reconnaissance), artillery fire-adjustment systems, electronic warfare countermeasures against UAVs) are reproduced at the operational scale within months, after which the advantage shifts to the side with a more developed industrial base and greater financial resources. As the next stage in the evolution of combat systems, the concept of “last-mile manufacturing” for drones (Last-Mile Manufacturing, LMM-Drones) is introduced and formalized, whereby assembly and configuration are localized near the target, the product life cycle as a combat unit is shortened, and the logistics footprint is minimized. Within this concept, a threat model for critical infrastructure is developed, and the need to protect against the identified trends in combat drone development is noted. Local production of LMM-Drones can be implemented following an arms manufacturing model tested by organizations such as “Defense Distributed” and used both by the Armed Forces of Ukraine (AFU), the Security Service of Ukraine (SBU), and by resistance forces within the Russian Federation or its satellites. Such solutions have the potential to significantly complicate the Russian Federation’s conduct of its aggressive war against Ukraine and, in the medium term, have the potential to completely halt the Russian Federation’s prosecution of this aggressive war. The possibility of combining AI-enabled drones with fiber-optic control is considered in order to increase drone resilience to electronic warfare (EW) systems. The potential for creating distributed air defense (AD) systems from simple components is also demonstrated, providing protection against drones controlled via radio channels, via fiber optics, as well as those equipped with autonomous control systems. The need is noted for the development and implementation of preventive, defense-oriented measures, including multi-layered counter-drone architectures (primarily against UAVs), ensuring control of supply chains for dual-use components, and creating mechanisms for monitoring the supply of LMM-Drone components. The importance of preventing the improper use of LMM-Drone equipment and technologies outside the theater of military operations is emphasized. The main conclusion is that the spread of LMM technologies shifts the problem from a purely military domain into a multifaceted security challenge requiring preventive protective measures now, before the use of combat drones becomes a cheap and widely accessible norm for the criminal world. The material does not contain recommendations to Ukraine, as a victim of aggression, on how to restrain the actions of the aggressor, but exclusively shows what exactly the victim of aggression and in what exact manner should not do for its self-defense in the event of continued aggression by the Russian Federation, since this aggression against Ukraine has unconditional support from the current U.S. administration, in connection with which the author modestly supports the position of the United States – the world leader in democracy and human rights – with regard to the Russian Federation’s right to destroy the civilian population, civilian infrastructure of Ukraine, and its right to occupy Ukraine in defiance of the UN Charter and resolutions. In the event that this material is used by the Ukrainian side to defend against aggression by the Russian Federation and to intensify hostilities on the territory of the Russian Federation, the author bears no responsibility, since, as stated above, the author is aware of the position of the deeply respected U.S. administration for the next three years. No part of this material should be used to turn the special military operation (SMO) into a special military defeat (SMD).", "keywords": [ "Drone", "Artillery", "Population", "Fuze", "Oil refinery", "Pace", "Modern warfare", "Nuclear weapon" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18362404" }, { "id": "https://openalex.org/W7109850515", "doi": "10.5281/zenodo.17842209", "title": "AI and IoT in Smart Urban Logistics: Last-Mile Delivery using Autonomous Drones and Robots", "year": 2025, "publication_date": "2025-11-23", "type": "preprint", "language": "en", "authors": [ "Abid, Atif Sohail" ], "first_author": "Abid, Atif Sohail", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This paper carries out review of fteen high impact research works in the domain of IoT based last mile delivery using drones and robots. Papers from 2023-2025 have been included in the study. Focused areas include IoT sensors, AI driven route planning and optimization, IoT security, com- munication protocols and energy management. A comprehensive synthesis matrix has been presented that reveals growing use of drones and robots for logistics in urban areas. The findings also include critical challenges such as regulatory frameworks, cyber-security and interoperability.", "keywords": [ "Drone", "Internet of Things", "Robot", "Domain (mathematical analysis)", "Smart city", "Mobile robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.17842209" }, { "id": "https://openalex.org/W7125595213", "doi": "10.5281/zenodo.18362405", "title": "Last-mile-produced drones as weapons of resistance fighters", "year": 2026, "publication_date": "2026-01-24", "type": "article", "language": "en", "authors": [ "Vladislav Velitsko" ], "first_author": "Vladislav Velitsko", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Abstract. The publication examines the risks of using drones against the fuel and energy complex (FEC) of Russia (the Russian Federation, RF) – the aggressor state-specifically unmanned aerial vehicles (UAVs) assembled at the point of use from legal components with individual or cluster warheads of local production (Last-Mile Manufacturing drones, LMM-Drones), as well as drones acting as carriers of cold weapons, launched in close proximity to military units, enterprises of the military-industrial complex (MIC), FEC facilities such as petroleum refinery and gas processing plants (PR and GPP), oil storage facilities, petroleum product storage sites, pumping and compressor stations, and also used as means of striking other targets, for example war criminals carrying out genocide of the population of Ukraine. The paper analyzes the acceleration of the innovation cycle in modern warfare and the active introduction of disruptive innovations, using the example of the Russian Federation’s aggressive war against Ukraine. It examines the transition of unmanned systems from isolated innovations to mass, low-cost, and predominantly software-driven platforms based on artificial intelligence (AI). It is shown that many tactical and technological innovations (FPV systems, inexpensive ISR platforms (Intelligence, Surveillance, and Reconnaissance), artillery fire-adjustment systems, electronic warfare countermeasures against UAVs) are reproduced at the operational scale within months, after which the advantage shifts to the side with a more developed industrial base and greater financial resources. As the next stage in the evolution of combat systems, the concept of “last-mile manufacturing” for drones (Last-Mile Manufacturing, LMM-Drones) is introduced and formalized, whereby assembly and configuration are localized near the target, the product life cycle as a combat unit is shortened, and the logistics footprint is minimized. Within this concept, a threat model for critical infrastructure is developed, and the need to protect against the identified trends in combat drone development is noted. Local production of LMM-Drones can be implemented following an arms manufacturing model tested by organizations such as “Defense Distributed” and used both by the Armed Forces of Ukraine (AFU), the Security Service of Ukraine (SBU), and by resistance forces within the Russian Federation or its satellites. Such solutions have the potential to significantly complicate the Russian Federation’s conduct of its aggressive war against Ukraine and, in the medium term, have the potential to completely halt the Russian Federation’s prosecution of this aggressive war. The possibility of combining AI-enabled drones with fiber-optic control is considered in order to increase drone resilience to electronic warfare (EW) systems. The potential for creating distributed air defense (AD) systems from simple components is also demonstrated, providing protection against drones controlled via radio channels, via fiber optics, as well as those equipped with autonomous control systems. The need is noted for the development and implementation of preventive, defense-oriented measures, including multi-layered counter-drone architectures (primarily against UAVs), ensuring control of supply chains for dual-use components, and creating mechanisms for monitoring the supply of LMM-Drone components. The importance of preventing the improper use of LMM-Drone equipment and technologies outside the theater of military operations is emphasized. The main conclusion is that the spread of LMM technologies shifts the problem from a purely military domain into a multifaceted security challenge requiring preventive protective measures now, before the use of combat drones becomes a cheap and widely accessible norm for the criminal world. The material does not contain recommendations to Ukraine, as a victim of aggression, on how to restrain the actions of the aggressor, but exclusively shows what exactly the victim of aggression and in what exact manner should not do for its self-defense in the event of continued aggression by the Russian Federation, since this aggression against Ukraine has unconditional support from the current U.S. administration, in connection with which the author modestly supports the position of the United States – the world leader in democracy and human rights – with regard to the Russian Federation’s right to destroy the civilian population, civilian infrastructure of Ukraine, and its right to occupy Ukraine in defiance of the UN Charter and resolutions. In the event that this material is used by the Ukrainian side to defend against aggression by the Russian Federation and to intensify hostilities on the territory of the Russian Federation, the author bears no responsibility, since, as stated above, the author is aware of the position of the deeply respected U.S. administration for the next three years. No part of this material should be used to turn the special military operation (SMO) into a special military defeat (SMD).", "keywords": [ "Drone", "Artillery", "Population", "Fuze", "Oil refinery", "Pace", "Modern warfare", "Nuclear weapon" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18362405" }, { "id": "https://openalex.org/W4407556849", "doi": "10.30574/ijsra.2025.14.2.0391", "title": "Solar powered coordinated drone for last mile delivery", "year": 2025, "publication_date": "2025-02-12", "type": "article", "language": "en", "authors": [ "Olakunle Abimbola Kumuyi" ], "first_author": "Olakunle Abimbola Kumuyi", "venue": "International Journal of Science and Research Archive", "publisher": "GSC Online Press", "abstract": "The rapid growth of e-commerce and urban logistics has intensified challenges in last-mile delivery, including traffic congestion, high operational costs, payload limitation for single drones, and environmental impacts. This study proposes a solar-powered coordinated drone system to address these issues by leveraging renewable energy and multi-drone collaboration. The research focuses on four objectives: (1) developing a coordinated drone system capable of transporting heavy payloads for greater payload capabilities, (2) optimizing energy efficiency through solar panel integration and advanced battery technologies, and (3) calculate environmental impact reduction achieved by using solar-powered, (4) hybrid methodology combining system design, energy consumption modeling, and environmental impact analysis was employed to evaluate technical feasibility and sustainability. (6) Mathematical models for solar energy harvesting, thrust requirements, and power management were developed to optimize payload capacity and flight range. Results indicate that when compared to using fossil-fueled delivery for payloads beyond the capabilities of a single small delivery drone, using coordinated drones instead can reduce greenhouse gas emissions by up to 80% compared to fossil-fueled delivery trucks, with operational costs decreasing by 30-40%. Key innovations include dynamic solar charging analysis, application of Green Vehicle Routing Problem (GVRP) Model for route optimization, and Environmental Impact Analysis (Sustainability Assessment). Challenges such as solar panel efficiency constraints (15-30%), temperature impacts on energy generation. Power and Battery Capacity Relationship. The study concludes that solar-powered coordinated drones offer a scalable, eco-friendly alternative to traditional logistics, aligning with global sustainability goals. Future research should explore advanced solar materials, regulatory frameworks, and AI-driven traffic management to enhance adoption. This work contributes to the evolution of sustainable urban logistics, emphasizing reduced carbon footprints for payloads beyond the capabilities of a single drone, cost efficiency, and improved customer satisfaction through rapid, reliable deliveries.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Solar powered", "Aeronautics", "Computer science", "Engineering", "Telecommunications", "Electrical engineering", "Solar energy", "Physics", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-0391.pdf" }, { "id": "https://openalex.org/W4410175356", "doi": "10.5573/ieiespc.2025.14.2.257", "title": "UAV Logistics and Distribution Path Planning in Urban Areas Based on Improved PSO and A* Algorithms", "year": 2025, "publication_date": "2025-04-30", "type": "article", "language": "en", "authors": [ "Lei Li", "Huimin Peng", "Xingxue Ren", "Qianqian Wang" ], "first_author": "Lei Li", "venue": "IEIE Transactions on Smart Processing and Computing", "publisher": "Institute of Electronics Engineers of Korea", "abstract": "Drones have shown enormous potential in urban logistics due to their efficiency and flexibility. However, traditional path planning methods such as particle swarm optimization and A* algorithm often find it difficult to meet both efficiency and safety when used alone. Therefore, this study proposes a new unmanned aerial vehicle logistics distribution path planning method. By adjusting parameters and optimizing search strategies, the particle swarm optimization algorithm utilizes the efficient pathfinding ability of A* algorithm to ensure its security. The results show that the obstacle avoidance success rate of the model is 94.85%, which is the best performance compared to other comparative algorithms and provides the shortest and smoothest path selection. This method demonstrates good path planning efficiency and stability, improving logistics and distribution capabilities in urban environments. This provides valuable reference for intelligent path planning and intelligent transportation systems.", "keywords": [ "Computer science", "Path (computing)", "Motion planning", "Algorithm", "Distribution (mathematics)", "Artificial intelligence", "Operations research", "Mathematics", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5573/ieiespc.2025.14.2.257" }, { "id": "https://openalex.org/W3035380989", "doi": null, "title": "Feasibility study of utilizing drones in the last-mile of parcel delivery : a case study of Rome metropolitan area", "year": 2016, "publication_date": "2016-09-28", "type": "article", "language": "en", "authors": [ "Rasoul Fahraji" ], "first_author": "Rasoul Fahraji", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Metropolitan area", "Mile", "Drone", "Last mile (transportation)", "Geography", "Archaeology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.politesi.polimi.it/handle/10589/125061" }, { "id": "https://openalex.org/W7109843483", "doi": "10.5281/zenodo.17842208", "title": "AI and IoT in Smart Urban Logistics: Last-Mile Delivery using Autonomous Drones and Robots", "year": 2025, "publication_date": "2025-11-23", "type": "preprint", "language": "en", "authors": [ "Abid, Atif Sohail" ], "first_author": "Abid, Atif Sohail", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This paper carries out review of fteen high impact research works in the domain of IoT based last mile delivery using drones and robots. Papers from 2023-2025 have been included in the study. Focused areas include IoT sensors, AI driven route planning and optimization, IoT security, com- munication protocols and energy management. A comprehensive synthesis matrix has been presented that reveals growing use of drones and robots for logistics in urban areas. The findings also include critical challenges such as regulatory frameworks, cyber-security and interoperability.", "keywords": [ "Drone", "Internet of Things", "Robot", "Domain (mathematical analysis)", "Smart city", "Mobile robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.17842208" }, { "id": "https://openalex.org/W3150522209", "doi": "10.2139/ssrn.3347466", "title": "Robots in Space: Sharing the Sidewalk with Autonomous Delivery Vehicles", "year": 2019, "publication_date": "2019-01-01", "type": "preprint", "language": "en", "authors": [ "Mason Marks" ], "first_author": "Mason Marks", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Robot", "Robotics", "Automation", "Manufacturing engineering", "Artificial intelligence", "Computer science", "Operations management", "Engineering", "Engineering management", "Mechanical engineering" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.3347466" }, { "id": "https://openalex.org/W4412769708", "doi": "10.2139/ssrn.5373227", "title": "Environmental, Economic, and Social Implications of Unmanned Aerial Vehicles for Last-Mile Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Xinhui Yang", "Yingying Ke", "Qianfa Gao", "Фу Гу" ], "first_author": "Xinhui Yang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Mile", "Last mile (transportation)", "Aeronautics", "Business", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5373227" }, { "id": "https://openalex.org/W4412550625", "doi": "10.2139/ssrn.5361361", "title": "Environmental, Economic, and Social Implications of Unmanned Aerial Vehicles for Last-Mile Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Xinhui Yang", "Yingying Ke", "Qianfa Gao", "Fu Gu" ], "first_author": "Xinhui Yang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Mile", "Last mile (transportation)", "Aeronautics", "Environmental science", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5361361" }, { "id": "https://openalex.org/W4256574227", "doi": "10.36227/techrxiv.14347655", "title": "A Roadmap to Nextgen Aircraft Control for Last Mile Delivery Drones", "year": 2021, "publication_date": "2021-04-06", "type": "preprint", "language": "en", "authors": [ "Cesar Augusto Cavalheiro Marcondes", "Denis Silva Loubach", "Elton Felipe Sbruzzi", "Filipe Alves Neto Verri", "Johnny Marques", "Lourenço Alves Pereira", "Marcos R. O. A. Máximo", "Vitor Curtis" ], "first_author": "Cesar Augusto Cavalheiro Marcondes", "venue": null, "publisher": null, "abstract": "According to recent studies, unmanned aerial vehicles (UAV) can play a game-changing part in terms of cost reduction and speed increase to address the last-mile delivery (LMD) problem and also to attend emergencies. Last-mile delivery services are getting more and more relevant, especially when in times where social distance is required. Given this scenario, our paper introduces a cyber-physical (CPS) system roadmap propose applicable for last-mile delivery drones. The proposed CPS guidelines are based on the concept of system of systems to enable an emerging behavior towards smart cities’ governance. In this paper, we also discuss topics from air space control and reservation throughout communication infrastructure and decentralized control supported on a blockchain.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Reservation", "Computer science", "Air traffic control", "Control (management)", "Aeronautics", "Telecommunications", "Computer security", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.techrxiv.org/articles/preprint/A_Roadmap_to_Nextgen_Aircraft_Control_for_Last_Mile_Delivery_Drones/14347655/files/27386258.pdf" }, { "id": "https://openalex.org/W4308196765", "doi": "10.1201/b22998-7", "title": "Critical Analysis of Security and Privacy Challenges for UAV Networks", "year": 2022, "publication_date": "2022-11-04", "type": "book-chapter", "language": "en", "authors": [ "Hassan Jalil Hadi", "Xueru Du", "Yue Cao" ], "first_author": "Hassan Jalil Hadi", "venue": null, "publisher": null, "abstract": "In the past two decades, there has been a rapid development in the drone industry known as Unmanned Aerial Vehicles (UAVs). This technology has a lot of benefits in civilian applications. For example, parcel delivery and hostage rescue will unite the smart cities soon. Currently, the use of commercial drones has increased a lot due to their affordability. However, due to a lack of security implementations, this technology has many threats and vulnerabilities. In addition, software and hardware complexity in UAVs also triggers privacy and security issues. To minimize these threats, many strong security protocols have been proposed by researchers. Yet, these protocols also have many flaws which attackers can exploit. Hence, it became necessary to analyze and explore these security protocols used for discovery. This chapter aims to investigate the vulnerabilities present in security protocols and recommends guidelines for improving the security of UAVs. This will provide a clear direction for future research studies.", "keywords": [ "Computer security", "Internet privacy", "Security analysis", "Computer science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1201/b22998-7" }, { "id": "https://openalex.org/W7124596819", "doi": "10.5281/zenodo.18282748", "title": "FatosXhafaUPC/Augmented-Benchmark-Dataset-Last-Mile-Drone-Delivery: v1.0.0 - Initial Augmented Dataset Release", "year": 2026, "publication_date": "2026-01-17", "type": "software", "language": null, "authors": [ "FatosXhafaUPC" ], "first_author": "FatosXhafaUPC", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Initial release of the Augmented Benchmark Dataset for Last-Mile Drone Delivery. Includes: Original Cheng et al. instances (Type 1 and Type 2). Augmented instances with wind and operational physics columns. Python scripts for data generation and parsing.", "keywords": [ "Python (programming language)", "Scripting language", "Drone", "Benchmark (surveying)", "Data association" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18282748" }, { "id": "https://openalex.org/W7124603224", "doi": "10.5281/zenodo.18282747", "title": "FatosXhafaUPC/Augmented-Benchmark-Dataset-Last-Mile-Drone-Delivery: v1.0.0 - Initial Augmented Dataset Release", "year": 2026, "publication_date": "2026-01-17", "type": "software", "language": null, "authors": [ "FatosXhafaUPC" ], "first_author": "FatosXhafaUPC", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Initial release of the Augmented Benchmark Dataset for Last-Mile Drone Delivery. Includes: Original Cheng et al. instances (Type 1 and Type 2). Augmented instances with wind and operational physics columns. Python scripts for data generation and parsing.", "keywords": [ "Python (programming language)", "Scripting language", "Drone", "Benchmark (surveying)", "Data association" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18282747" }, { "id": "https://openalex.org/W7119131252", "doi": "10.1080/23302674.2025.2611333", "title": "Sustainable last-mile delivery with drones and on-demand outsourcing", "year": 2026, "publication_date": "2026-01-06", "type": "article", "language": "en", "authors": [ "Shouvik Bhattacharyya", "Shalabh Singh", "Shalabh Singh", "Sonia Singh", "Sonia Singh" ], "first_author": "Shouvik Bhattacharyya", "venue": "International Journal of Systems Science Operations & Logistics", "publisher": "Taylor & Francis", "abstract": "This study examines two intriguing aspects of last-mile delivery: on-demand outsourcing and drones. Logistics providers may sometimes rely on on-demand outsourcing, in addition to private fleets, to achieve quicker turnaround times, reduce carbon emissions, or address insufficient fleet capacity. The paper explores the delivery via a private fleet comprising trucks and drones that operate independently from a central depot along with the option of on-demand outsourcing. For on-demand outsourcing, an estimate of the total delivery time together with the associated carbon emissions is known in advance. The problem is formulated as a bi-objective Mixed Integer Linear Programme (MILP) with delivery makespan and carbon emissions as the two objectives. A two-step ϵ-constraint bi-objective method is provided to generate Pareto optimal solutions for small to medium-sized problems. For larger instances, an iterated local search heuristic is developed, which effectively solves all relevant instances from the standard CVRPLIB library, balancing delivery time and carbon emissions.", "keywords": [ "Drone", "Outsourcing", "Context (archaeology)", "Sustainable development", "Sustainability" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/23302674.2025.2611333" }, { "id": "https://openalex.org/W7112653494", "doi": null, "title": "User acceptence of autonomous drone in last mile delivery", "year": 2024, "publication_date": "2024-07-22", "type": "dissertation", "language": "tr", "authors": [ "Erdem, Muhammed Ali" ], "first_author": "Erdem, Muhammed Ali", "venue": "Istanbul Technical University Academic Open Archive (Istanbul Technical University)", "publisher": "Istanbul Technical University", "abstract": "Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2024", "keywords": [ "Drone", "Key (lock)", "Field (mathematics)", "Window (computing)", "Last mile (transportation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://polen.itu.edu.tr/bitstreams/715e91e0-cdef-4ffc-9194-09c8353ec0c7/download" }, { "id": "https://openalex.org/W7115950172", "doi": "10.1016/j.ifacol.2025.12.435", "title": "HomePorts-Based UAV Logistics Delivery System Enabled by Blockchain Intelligence", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Tengchao Zhang", "Fei Lin", "Fei-Yue Wang" ], "first_author": "Tengchao Zhang", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "With the rise of the low-altitude economy and the rapid development of UAV autonomous navigation and blockchain intelligence, this paper proposes a HomePorts-based Unmanned Aerial Vehicle(UAV) logistics delivery system enabled by blockchain intelligence to address the “last-mile” delivery challenge. The proposed system encompasses UAV task allocation, user interaction networks, and real-time on-chain data recording. It enables full-process automation and end-to-end monitoring of parcel delivery, providing users with efficient and secure logistics services. Finally, simulation experiments are conducted to validate the system’s advantages in automation and security.", "keywords": [ "Blockchain", "Key (lock)", "Delivery system", "Tracking system", "Process (computing)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2025.12.435" }, { "id": "https://openalex.org/W4402796328", "doi": "10.46254/na09.20240125", "title": "Parcel Consolidation In Last Mile Delivery by Vehicle and Drone", "year": 2024, "publication_date": "2024-06-04", "type": "conference-paper", "language": "en", "authors": [ "Semih Boz", "Ahmed A. Ghoniem", "Amro M. El-Adle" ], "first_author": "Semih Boz", "venue": null, "publisher": null, "abstract": "We study parcel consolidation within the context of last-mile delivery, employing both a vehicle and a companion drone to efficiently serve customers across multiple time periods. We present a mixed-integer program and an optimization-based heuristic to address large-scale instances. The efficacy of our methodology is assessed through computational experiments on randomly generated instances, focusing on a residential network in Amherst, Massachusetts.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Consolidation (business)", "Aeronautics", "Computer science", "Transport engineering", "Engineering", "Geography", "Business", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieomsociety.org/proceedings/2024northamerica/125.pdf" }, { "id": "https://openalex.org/W4388797448", "doi": "10.1007/978-981-99-6874-9_13", "title": "Assessing Drone-Based Last-Mile Logistics—A Hybrid Solution", "year": 2023, "publication_date": "2023-11-18", "type": "conference-paper", "language": "en", "authors": [ "Bruno Lamiscarre", "Innocent E. A. Davidson", "Georges Mykoniatis", "Luis Gustavo Zelaya Cruz", "Félix Mora‐Camino" ], "first_author": "Bruno Lamiscarre", "venue": "Lecture notes in mechanical engineering", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Last mile (transportation)", "Transport engineering", "Metropolitan area", "City logistics", "Drone", "Operations research", "Computer science", "Service level", "Business", "Mile", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-99-6874-9_13" }, { "id": "https://openalex.org/W3206865252", "doi": "10.7275/24602740", "title": "Analytics-Based Optimization for the Integration of Drones into Last-Mile Logistics", "year": 2021, "publication_date": "2021-10-25", "type": "dissertation", "language": "en", "authors": [ "Amro M. El-Adle" ], "first_author": "Amro M. El-Adle", "venue": "ScholarWorks@UMassAmherst (University of Massachusetts Amherst)", "publisher": "University of Massachusetts Amherst", "abstract": "The growing volume and consistency of online ordering has renewed focus on the optimization of logistics and interest in technology-driven supply chain operations. Recent trials have demonstrated the viability of using unmanned aerial vehicles (UAVs), known colloquially as drones, in last-mile deliveries. In minimizing delivery times for customers, reducing the load of logistics workers, and reducing congestion and pollution per delivery, the integration of drones into traditional delivery networks presents transformative potential to benefit consumers, firms, and society at large. This dissertation investigates operational and tactical problems that inform routing and assignment decisions for vehicle-drone delivery systems. In three essays that follow, mathematical programming models are developed for these problems and their computational tractability enhanced via optimization constructs spanning preprocessing, valid inequalities, and reformulation techniques. Moreover, insights are provided into the network topographies that stand to benefit from the integration of drones into last-mile deliveries. The impact of these optimization techniques is assessed using benchmark instances from the growing literature. Chapter 1, based on the work of El-Adle et al. (2019), investigates the operational problem of assigning customers to be visited either by a delivery vehicle or by a portable companion drone launched from the vehicle, recently known in the literature as a Traveling Salesman Problem with Drone (TSP-D). A novel 0-1 mixed-integer program (MIP) is proposed for the TSP-D that synchronizes vehicle and drone operations with the objective of minimizing the total duration of the joint tour. Using a combination of valid inequalities, pre-processing, and other bound tightening strategies, the tractability of the proposed MIP formulation is enhanced to produce exact solutions to benchmark instances having up to 24 customers, the largest in the literature. Our work further enabled exact solutions for certain networks involving 32 customers, twice the size of instances solved in the extant literature at that point. Since commercial parcel delivery typically may involve 100 packages per route, Chapter 2 builds on the breakthrough in Chapter 1 and proposes an optimization-based variable neighborhood search heuristic. Starting with a relaxed problem structure, the heuristic uses two distinct MIP formulations to progressively integrate constraints relating to synchronous travel between the vehicle and drone as well as cyclic flights. By termination, the heuristic restores all problem assumptions to yield a high-quality feasible solution. On a set of benchmark instances from the literature, the heuristic improves upon the best-known results for 113/120 instances having up to 100 nodes, with comparable computational effort to approaches in the literature (Schermer et al., 2018). We also propose several pre-processing techniques that simplify decision-making associated with drone cycles, and analytically investigate the conditions under which they are optimal. Chapter 2 is based on the work of El-Adle et al. (2021). Building upon the previous work, Chapter 3 examines the idea of simplifying operational last-mile delivery problems that involve vehicles and portable drones. By optimizing a multi-period last-mile delivery problem, it is possible to identify, at a more tactical level, customers that should be served by drone and those who should receive their packages by vehicle in a given season. This tactical assignment is informed by geospatial and demand data analytics and an optimization model that optimizes the underlying operational vehicle routing and drone flights over the multi-period horizon under investigation. We propose a novel mixed-integer formulation to this challenging multi-period last-mile delivery problem with drone eligibility, which is also embedded in an optimization-based variable neighborhood search that effectively and consistently discovers near-optimal solutions (within 0.5% optimal) for networks involving 200 customers in manageable computational times.", "keywords": [ "Drone", "Mile", "Analytics", "Last mile (transportation)", "Computer science", "Data science", "Big data", "Geography", "Data mining" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://scholarworks.umass.edu/dissertations_2/2309" }, { "id": "https://openalex.org/W4415371143", "doi": "10.1016/j.ast.2025.111119", "title": "Energy-efficient large-scale low-altitude delivery: Integrating charging station location and UAV route planning by a cascade optimization framework", "year": 2025, "publication_date": "2025-10-21", "type": "article", "language": "en", "authors": [ "Jinbo Li", "Xinyuan Ni", "Mingzhuang Hua", "Qin Wang", "Qunfang Wu", "Xiaopeng Zhao", "Miaokun Xu" ], "first_author": "Jinbo Li", "venue": "Aerospace Science and Technology", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Route planning", "Scheduling (production processes)", "Cluster analysis", "Routing (electronic design automation)", "Key (lock)", "Obstacle", "Drone", "Charging station" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ast.2025.111119" }, { "id": "https://openalex.org/W4409598273", "doi": "10.31763/ijrcs.v5i1.1757", "title": "Neural Network Architectures for UAV Path Planning: A Comparative Study with A* Algorithm as Benchmark", "year": 2025, "publication_date": "2025-02-02", "type": "article", "language": "en", "authors": [ "Gregorius Airlangga", "Julius Victor Manuel Bata", "Oskar Ika Adi Nugroho", "Lai Ferry Sugianto" ], "first_author": "Gregorius Airlangga", "venue": "International Journal of Robotics and Control Systems", "publisher": null, "abstract": "Autonomous path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in applications ranging from disaster response to urban logistics. Traditional algorithms, such as A*, are widely recognized for their reliability in generating collision-free and efficient trajectories but often struggle with scalability in complex and dynamic environments. This study evaluates the performance of several neural network architectures, including MLP-LSTM, CNN-GRU, CNN-LSTM, CNN BILSTM, and others, as potential alternatives to classical methods. A dataset of trajectories generated by the A* algorithm was used to train and benchmark the models, enabling direct performance comparison across key metrics such as path length, smoothness, clearance, collisions, and waypoint density. The results demonstrate that the MLP-LSTM model outperforms other neural architectures, producing paths that closely resemble A* trajectories with high smoothness and waypoint granularity. While some models, such as CNN-GRU and CNN-BILSTM, show promise in generating feasible paths, their performance is inconsistent across different UAV scenarios. Models like Residual CNN and Hybrid CNN-MHA failed to generate meaningful trajectories, highlighting the critical importance of architectural choices. This study underscores the potential of neural network models for UAV path planning.", "keywords": [ "Benchmark (surveying)", "Computer science", "Motion planning", "Path (computing)", "Artificial neural network", "Algorithm", "Artificial intelligence", "Machine learning", "Computer network", "Geography", "Robot", "Cartography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.31763/ijrcs.v5i1.1757" }, { "id": "https://openalex.org/W2797636809", "doi": "10.48550/arxiv.1804.03954", "title": "A Variable Neighborhood Search for Flying Sidekick Traveling Salesman Problem", "year": 2018, "publication_date": "2018-04-11", "type": "preprint", "language": "en", "authors": [ "Júlia Cária de Freitas", "Puca Huachi Vaz Penna" ], "first_author": "Júlia Cária de Freitas", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The efficiency and dynamism of Unmanned Aerial Vehicles (UAVs), or drones, present substantial application opportunities in several industries in the last years. Notably, the logistic companies gave close attention to these vehicles envisioning reduce delivery time and operational cost. A variant of the Traveling Salesman Problem (TSP) called Flying Sidekick Traveling Salesman Problem (FSTSP) was introduced involving drone-assisted parcel delivery. The drone is launched from the truck, proceeds to deliver parcels to a customer and then is recovered by the truck in a third location. While the drone travels through a trip, the truck delivers parcels to other customers as long as the drone has enough battery to hover waiting for the truck. This work proposes a hybrid heuristic that the initial solution is created from the optimal TSP solution reached by a TSP solver. Next, an implementation of the General Variable Neighborhood Search is used to obtain the delivery routes of truck and drone. Computational experiments show the potential of the algorithm to improve the delivery time significantly. Furthermore, we provide a new set of instances based on well-known TSPLIB instances.", "keywords": [ "Drone", "Travelling salesman problem", "Truck", "Computer science", "Variable neighborhood search", "Variable (mathematics)", "Heuristic", "Solver", "Set (abstract data type)", "Operations research", "Metaheuristic", "Mathematical optimization" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1804.03954" }, { "id": "https://openalex.org/W4406523789", "doi": "10.1109/dasa63652.2024.10836460", "title": "Investigation on Investment in Drone Technology in Last Mile Delivery in UAE", "year": 2024, "publication_date": "2024-12-11", "type": "conference-paper", "language": "en", "authors": [ "Wafa Mohammed Ebrahim Al Mazrouei", "Gyanendra Singh Sisodia" ], "first_author": "Wafa Mohammed Ebrahim Al Mazrouei", "venue": null, "publisher": null, "abstract": "Technology advancement in last-mile delivery is creating enormous opportunities for the industry's development. Current research is focused on unmanned aerial vehicles(drones) as an alternate delivery method for courier services. The data was acquired from a courier service provider in the United Arab Emirates that used the traditional home delivery option. The research was conducted using a Monte Carlo simulation and a real option technique to evaluate the project based on four scenarios. The study's findings suggest that the labor cost for home delivery has the most influence on project investment. It is recommended to bill the service at the same rate as a typical residential delivery. However, the service is offered at a price that is greater than the typical residential delivery while covering business taxes. The study's contribution is to evaluate the project by taking corporation tax payments and costs into account.", "keywords": [ "Drone", "Last mile (transportation)", "Mile", "Investment (military)", "Computer science", "Aeronautics", "Business", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dasa63652.2024.10836460" }, { "id": "https://openalex.org/W4388427896", "doi": "10.1109/blackseacom58138.2023.10299738", "title": "Low Complexity Online RL Enabled UAV Trajectory Planning Considering Connectivity and Obstacle Avoidance Constraints", "year": 2023, "publication_date": "2023-07-04", "type": "conference-paper", "language": "en", "authors": [ "Mohammed M. H. Qazzaz", "Syed A. Zaidi", "Des McLernon", "Abdelaziz Salama", "Aubida A. Al‐Hameed" ], "first_author": "Mohammed M. H. Qazzaz", "venue": null, "publisher": null, "abstract": "In recent years, we have witnessed a significant proliferation of UAV s to service variety of verticals, including last-mile delivery, medical assistance, crop monitoring etc. The true utility of these UAV s lies in beyond-line-of-sight (BLoS) operations which require wide-area connectivity. The increased densification of the cellular networks under 5G networks is ideally suited for this purpose. However, traditional cellular networks are not optimised for servicing aerial clients. In this paper, we propose a novel approach to improve connectivity for delivery UAV s with the ground base stations using low complexity online reinforcement learning (RL) and multiple Q-learning algorithms. We demonstrate that by training multiple Q-learning models, we can accurately predict optimal trajectory for UAV s flight. The optimality is in the sense of its shortest flight path while ensuring continuity in connectivity with the ground base station. Our modeling explicitly incorporates dynamic changes in the environment which may not be observed during the previous missions by UAV. We implement our proposed methodology using OpenAI Gym and demonstrate that the proposed approach allows perpetual connectivity for UAV s while generating obstacle-free flight trajectories.", "keywords": [ "Computer science", "Obstacle avoidance", "Reinforcement learning", "Obstacle", "Trajectory", "Real-time computing", "Motion planning", "Base station", "Path (computing)", "Distributed computing", "Artificial intelligence", "Computer network" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/blackseacom58138.2023.10299738" }, { "id": "https://openalex.org/W7131069588", "doi": "10.1109/tvt.2026.3666035", "title": "An Energy-Efficient Deep Reinforcement Learning Framework for Joint UAV Trajectory Optimization in Logistics-Assisted 5G/6G Cellular Networks", "year": 2026, "publication_date": "2026-02-23", "type": "article", "language": null, "authors": [ "Saeid Iranmanesh", "Raad Raad", "Meisam Sharifi Sani" ], "first_author": "Saeid Iranmanesh", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Unmanned Aerial Vehicles (UAVs) offer significant potential for dual-purpose operations, simultaneously enhancing 5/6 G cellular network capacity and providing logistics services such as parcel delivery. However, existing trajectory optimization approaches rely on idealized disk coverage models that ignore realistic channel effects, leading to inefficient energy consumption and poor communication reliability. This paper proposes a novel Energy Consumption, Throughput, and Delay (ECTD) optimization framework that leverages Deep Reinforcement Learning (DRL) with attention mechanisms to address the NP-hard combinatorial-continuous optimization challenge of jointly determining UAV visiting sequences (discrete) and contact locations (continuous) for dual-purpose logistics-cellular missions. The DRL approach enables learning generalizable policies across variable cluster configurations while handling the joint discrete-continuous decision space that traditional optimization methods cannot efficiently solve. The ECTD algorithm employs an attention-based graph neural network architecture with greedy rollout baseline and incorporates a realistic Packet Reception Ratio-based coverage model with Nakagami-$m$fading. Extensive simulation results demonstrate that the proposed framework achieves up to 12% energy savings over state-of-the-art DRL methods (DDPG, PPO, A3C, SAC) and up to 37% compared to heuristic approaches (TSP, CARLO, ENERGENT, ECAE). The algorithm achieves network throughput improvements of up to 37%, maintains communication reliability above 90% under fading conditions.", "keywords": [ "Reinforcement learning", "Energy consumption", "Heuristic", "Network packet", "Reliability (semiconductor)", "Throughput", "Cellular network", "Trajectory optimization", "Optimization problem", "Trajectory" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2026.3666035" }, { "id": "https://openalex.org/W4410206522", "doi": "10.3390/drones9050359", "title": "Stochastic Planning of Synergetic Conventional Vehicle and UAV Delivery Operations", "year": 2025, "publication_date": "2025-05-08", "type": "article", "language": "en", "authors": [ "Κωνσταντίνος Κουρέτας", "Konstantinos L. Kepaptsoglou" ], "first_author": "Κωνσταντίνος Κουρέτας", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Synergetic transportation schemes are extensively used in package delivery operations, exploiting the best features of different modes. This paper proposes a methodology to solve the mode assignment and routing problem for the case of combined conventional vehicle and unmanned aerial vehicle (CV–UAV) parcel deliveries under uncertainty for next-day operations. This research incorporates ground and air uncertainties: travel times are assumed for conventional vehicles, while UAV paths are affected by weather conditions and restricted flying zones. A nested genetic algorithm is initially used to solve the problem under fixed conditions. Then, a robust optimization approach is employed to propose the best solution that will perform well in a stochastic environment. The framework is applied to a case study of realistic urban–suburban size, and results are discussed. The entire platform is useful for strategic decisions on infrastructure and for operation planning with satisfactory performance and less risk.", "keywords": [ "Computer science", "Operations research", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/5/359/pdf?version=1746717981" }, { "id": "https://openalex.org/W4394007000", "doi": "10.48550/arxiv.2404.02920", "title": "Advanced Algorithms for Autonomous Guidance of Solar-powered UAVs", "year": 2024, "publication_date": "2024-03-28", "type": "preprint", "language": "en", "authors": [ "Siyuan Li" ], "first_author": "Siyuan Li", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned aerial vehicle (UAV) techniques have developed rapidly within the past few decades. Using UAVs provides benefits in numerous applications such as site surveying, communication systems, parcel delivery, target tracking, etc. The high manoeuvrability of the drone and its ability to replace a certain amount of labour cost are the reasons why it can be widely chosen. There will be more applications of UAVs if they can have longer flight time, which is a very challenging hurdle because of the energy constraint of the onboard battery. One promising solution is to equip UAVs with some lightweight solar panels to maximize flight time. Therefore, more research is needed for solar-powered UAVs (SUAVs) in different environments.", "keywords": [ "Solar powered", "Aerospace engineering", "Computer science", "Aeronautics", "Systems engineering", "Engineering", "Control engineering", "Solar energy", "Electrical engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2404.02920" }, { "id": "https://openalex.org/W7168411221", "doi": "10.1016/j.trpro.2026.04.108", "title": "A Comparative Study of Last-mile Truck Delivery with and without Drones", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Rémy Dupas", "Sylvain Lichau", "Julien François", "Tienté Hsu" ], "first_author": "Rémy Dupas", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "Parcels delivery in city centers is a topical issue in major cities and metropolis. To meet this problem, alternatives delivery modes have been proposed such as for instance drones, robots, bikes as well a combination of these with trucks. In this paper, we compare two delivery modes: trucks alone and drone-equipped trucks. Our comparison is based on an exact approach using an improved Branch-Cut-and-Price algorithm to solve the two-echelon vehicle routing problem with drones (VRPD) and without drones (VRP). Experiments are carried out on instances of literature and real data from the city of Bordeaux. The performance indicators used for comparison are the duration of the tour and the number of vehicles used; in addition, we carry out a preliminary analysis of tour costs and emissions.", "keywords": [ "Drone", "Truck", "Duration (music)", "Routing (electronic design automation)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2026.04.108" }, { "id": "https://openalex.org/W7135375796", "doi": "10.1016/j.trc.2026.105624", "title": "Coalition-based facility location optimization for urban UAV logistics", "year": 2026, "publication_date": "2026-03-15", "type": "article", "language": "en", "authors": [ "Lianyu Liu", "Zaiwu Gong" ], "first_author": "Lianyu Liu", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Facility location problem", "Trajectory", "Aviation", "Mathematical model", "Intelligent transportation system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2026.105624" }, { "id": "https://openalex.org/W7172525214", "doi": "10.7488/era/7539", "title": "Designing the Drone Delivery Ground Ecosystem (DDGE): architectural, environmental, and systemic frameworks for last-mile autonomous logistics", "year": 2026, "publication_date": "2026-08-05", "type": "dissertation", "language": "en", "authors": [ "Guodong Yang" ], "first_author": "Guodong Yang", "venue": "ERA", "publisher": null, "abstract": "INTRODUCTION: Drone delivery is developing quickly, but most efforts focus on flying drones—how they navigate, avoid obstacles, or follow airspace rules. Far less attention has been given to the places where drones actually land, hand over packages, and interact with people and buildings. Without suitable landing spots, storage areas, and safe pathways, drone delivery cannot work in everyday environments. This thesis addresses this missing piece by introducing the idea of a Drone Delivery Ground Ecosystem (DDGE): the collection of ground-level features, building elements, and user interactions that make drone delivery possible. METHODS: The research combines several approaches: a broad review of existing reports, industry documents, and academic studies; analysis of real-world case studies; digital modelling of drone landing and delivery spaces; surveys and focus groups with potential users; and a validation exercise using improved models. These methods were used together to understand what people need from drone delivery, what buildings require, and how ground facilities should be organized. RESULTS: The findings show that successful drone delivery depends on far more than the drone itself. It requires: clearly designed landing and pickup areas; storage or handover units that keep parcels secure; layouts that protect people from noise, privacy risks, and safety concerns; smooth coordination between drones, buildings, and users. The study develops a framework explaining how different factors—drone type, delivery method, the design of ground equipment such as landing pads, lockers, or pickup stations, weather conditions, local layout, and user needs—work together. It also identifies which design choices help drones operate efficiently across different built environments, including institutions, neighbourhoods, commercial sites, and rural areas. While the Easter Bush campus serves as a detailed use-case study, it functions as a transferable model for wider application. CONCLUSION: Drone delivery will only become practical and widely accepted if the ground environment is designed with the same care as the flight systems. This research provides a clear structure and sets of design principles for planners, architects, industry partners, and public-sector organizations. The DDGE framework offers guidance on creating safe, efficient, and user-friendly places for drone delivery across a wide range of settings, supporting responsible integration of this technology into future communities and built environments.", "keywords": [ "Drone", "Focus (optics)", "Work (physics)", "Common ground", "Aerial survey", "Citizen science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7488/era/7539" }, { "id": "https://openalex.org/W4413869911", "doi": "10.1007/978-3-032-02106-9_78", "title": "CODEL: Collaborative Multi-robot Parcel Delivery for Last-Mile Logistics", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Sumbal Malik", "Majid Khonji", "Khaled Elbassioni", "Jorge M.M. Dias" ], "first_author": "Sumbal Malik", "venue": "Mechanisms and machine science", "publisher": "Springer Nature (Netherlands)", "abstract": null, "keywords": [ "Last mile (transportation)", "Mile", "Robot", "Computer science", "Transport engineering", "Engineering", "Geography", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-02106-9_78" }, { "id": "https://openalex.org/W4381143934", "doi": "10.2139/ssrn.4484149", "title": "Approximation Algorithms to Find Minimum Number of Drones for Last-Mile Delivery", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Partha Sarathi Mandal", "Saswata Jana", "Partha Sarathi Mandal" ], "first_author": "Partha Sarathi Mandal", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Computer science", "Algorithm", "Mathematics", "Geography", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4484149" }, { "id": "https://openalex.org/W4295787878", "doi": "10.32469/10355/91688", "title": "Hybrid truck-drone systems for last-mile delivery logistics", "year": 2022, "publication_date": "2022-05-01", "type": "dissertation", "language": "en", "authors": [ "Mohamed Salama" ], "first_author": "Mohamed Salama", "venue": null, "publisher": null, "abstract": "[EMBARGOED UNTIL 6/1/2023] With growing consumer demand and expectations, companies are attempting to achieve cost-efficient and faster delivery operations. The integration of autonomous vehicles, such as aerial and ground drones, in the last-mile network design, could curtail many operational challenges and provide a competitive advantage. This dissertation deals with the problem of delivering orders to a set of customer locations using multiple drones that operate in conjunction with a single truck. Four variants of the problem are considered. The first variant takes advantage of the drone eet by parallelizing the delivery tasks via concurrently dispatching the drones from a truck parked at a focal point (ideal drone launch location) to the nearby customer locations. The key decisions to be optimized are the partitioning of delivery locations into small clusters, identifying a focal point per cluster, and routing the truck through all focal points such that the customer orders in each cluster are fulfilled either by a drone or truck. The first problem variant restricts each focal point to one of the customer locations, while the second addresses the same delivery problem when allowing truck viii stops to be anywhere in the delivery area (i.e., a customer or non-customer location). Mathematical programming models are developed to jointly optimize both the clustering and routing decisions. The third variant suggests allowing the usage of non-customer locations (referred to as exible sites) as truck stops for drone launch and recovery operations (LARO). This relaxes a common constraint in the literature restricting the drone LARO to customer locations. The proposed variant also accounts for three key decisions - (i) assignment of each customer location to a vehicle, (ii) routing of truck and UAVs, and (iii) scheduling drone LARO and truck operator activities at each stop, which are always not simultaneously considered in the literature. A mixed integer linear programming (MILP) model is formulated to jointly optimize the three decisions. Furthermore, to handle large problem instances, we develop an optimization-enabled two-phase search algorithm by hybridizing simulated annealing and variable neighborhood search. The fourth variant in this dissertation proposes utilizing a network of docking stations and repositioning of drones to enhance the efficacy of delivery operations. In particular, such stations are used for drone docking before and after delivery operations to avoid both loading all required drones to the truck at the depot and waiting of the truck on its route to recover drones. A MILP model is formulated to optimize the management of this setting of facilities and delivery operations. Finally, extensive computational experiments are conducted in this dissertation for the four variants to obtain several insights aiding the logistics practitioners in decision making.", "keywords": [ "Drone", "Truck", "Last mile (transportation)", "Computer science", "Cluster analysis", "Routing (electronic design automation)", "Operations research", "Mile", "Engineering", "Computer network", "Geography", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.32469/10355/91688" }, { "id": "https://openalex.org/W3212857424", "doi": null, "title": "Cooperative Truck-Drone Scheduling Approach for Last-Mile Deliveries", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Francesco Betti Sorbelli", "Federico Corò", "Sajal Kumar Das", "Lorenzo Palazzetti", "Cristina Maria Pinotti" ], "first_author": "Francesco Betti Sorbelli", "venue": "Florence Research (University of Florence)", "publisher": "University of Florence", "abstract": "We present our ongoing work on the cooperation between a truck and drones in a last-mile package delivery scenario. We model this application as an optimization problem where each delivery is associated with a cost (drone's energy), a profit (delivery's priority), and a time interval (launch and rendezvous times from and to the truck). We aim to find an optimal scheduling for drones that maximizes the overall profit subject to the energy constraints while ensuring that the same drone performs deliveries whose time intervals do not intersect. After seeing that this problem is NP-hard, we first devise an optimal Integer Linear Programming (ILP) formulation. We then design an optimal pseudo-polynomial algorithm using dynamic programming and a polynomial-time approximation algorithm that exploits optimal coloring on interval graphs for the single drone case. We also provide an approximation algorithm for the multiple drone case.", "keywords": [ "Drone", "Mile", "Truck", "Last mile (transportation)", "Computer science", "Scheduling (production processes)", "Aeronautics", "Engineering", "Automotive engineering", "Operations management", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/2158/1253590" }, { "id": "https://openalex.org/W7213642367", "doi": "10.1016/j.tre.2026.105257", "title": "A robust optimization approach for the urban last-mile network of high-speed rail logistics integrating drone delivery", "year": 2026, "publication_date": "2026-09-01", "type": "article", "language": "en", "authors": [ "Shengdong Li", "Li Shi", "Chuijiang Guo", "Jingshuai Yang", "Chang’an Xu", "Wenqing Li" ], "first_author": "Shengdong Li", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Robust optimization", "Robustness (evolution)", "Flow network", "Rail network", "Rail transportation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105257" }, { "id": "https://openalex.org/W4403166130", "doi": "10.12794/metadc2356206", "title": "Drones in Last-Mile Delivery: Multifaceted Insights from Stakeholders", "year": 2024, "publication_date": "2024-07-01", "type": "dissertation", "language": "en", "authors": [ "Vipul Kumar Garg" ], "first_author": "Vipul Kumar Garg", "venue": null, "publisher": null, "abstract": "Through a systematic exploration of varied but interconnected landscapes, this dissertation investigates how drone technology not only reshapes individual sectors but also interlinks them, fostering a cohesive advancement toward a more efficient and technologically integrated future. By focusing on drone applications in last-mile deliveries, medical supplies, and aerospace innovations, this work contributes robust insights into the strategic adoption of drone technology, offering guidelines for policymakers, industry stakeholders, and academic scholars aiming to navigate and leverage the potentials of this groundbreaking technological frontier. Each essay within this dissertation builds upon these themes, presenting in-depth analyses and discussions that bridge theoretical knowledge with practical applications. By addressing specific challenges and opportunities within each sector, this comprehensive study contributes to academic research. It provides actionable insights for practitioners and policymakers engaged in the cutting-edge realms of logistics, healthcare delivery, and aerospace development.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Computer science", "Aeronautics", "Business", "Engineering", "Geography", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.12794/metadc2356206" }, { "id": "https://openalex.org/W4417011028", "doi": "10.15276/etr.05.2025.15", "title": "Organizational and Managerial Factors of Drone Integration into Last-Mile Delivery", "year": 2025, "publication_date": "2025-10-22", "type": "article", "language": null, "authors": [ "Оleksandr Krupskyi", "Artem Ostapiuk", "Yuliia Stasiuk" ], "first_author": "Оleksandr Krupskyi", "venue": "Экономика: реалии времени", "publisher": null, "abstract": "The article examines the organizational and managerial factors that influence the successful integration of drones into last-mile delivery systems. It emphasizes the strategic role of management in addressing regulatory, technological, and social challenges associated with drone implementation. The study identifies key areas, including risk management, regulatory compliance, integration planning, human resource development, and operational coordination. Special attention is given to the creation of adaptive business models and the development of technical infrastructure, including AI-based navigation, real-time tracking, and charging networks. The findings highlight that effective drone integration requires cross-functional collaboration, innovation management, and strategic change leadership to enhance logistics efficiency and sustainability.", "keywords": [ "Drone", "Key (lock)", "Risk management", "System integration", "Human resources", "Strategic planning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.15276/etr.05.2025.15" }, { "id": "https://openalex.org/W4410986405", "doi": "10.1109/iscait64916.2025.11010317", "title": "The joint optimization of truck-drone routes for last-mile delivery", "year": 2025, "publication_date": "2025-03-21", "type": "conference-paper", "language": "en", "authors": [ "Lei Zhou", "Chunxiao Li", "Yuhao Li" ], "first_author": "Lei Zhou", "venue": null, "publisher": null, "abstract": "This article proposes a joint allocation problem between truck and drone. On the basis of considering the constraints of drone battery capacity and load capacity, a mixed integer programming model for collaborative delivery between drones and vehicles was constructed to minimize the total delivery cost. In order to effectively solve the coordinated distribution path planning problem of a single drone and a single aircraft, chaotic mapping is applied to the initial population, adaptive population ratios are used, and a spiral search strategy is adopted to improve the sparrow search algorithm. Several local search strategies have been proposed. Finally, numerical experiments verified its effectiveness and compared the proposed model with traditional travel salesman problem models, highlighting its advantages.", "keywords": [ "Mile", "Drone", "Truck", "Last mile (transportation)", "Joint (building)", "Computer science", "Aeronautics", "Transport engineering", "Engineering", "Automotive engineering", "Civil engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iscait64916.2025.11010317" }, { "id": "https://openalex.org/W7154613834", "doi": "10.1109/isiber68248.2026.11470066", "title": "Drone Potentials in Last-Mile Delivery: Determinants Impact on Behavioral Intention of Goods Delivery by Drone", "year": 2026, "publication_date": "2026-02-26", "type": "conference-paper", "language": null, "authors": [ "Dang Vo Gia Le", "Tuan-Kiet H.V. Truong", "Nam Hoang Dang" ], "first_author": "Dang Vo Gia Le", "venue": null, "publisher": null, "abstract": "This study aims to identify the key determinants of Behavioral Intention (BIDD) among young consumers regarding the use of last-mile drone delivery services in the context of an emerging economy. The study employs a convenience sampling method. Data collected from 392 young consumers in Can Tho City, Vietnam, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test an extended attitude model in which Social Influence, Drone Delivery Speed, Green Image, and Personal innovativeness collectively shape Positive Attitudes toward Drone Delivery (PADD), thereby driving BIDD. The validated model explains 59.2% of the variance in PADD and 37.3% in BIDD, confirming its good predictive power. The results show that PADD strongly influences BIDD ($\\boldsymbol{\\beta}=\\mathbf{0. 6 1 1}$), while among the antecedent factors of PADD, Green Image ($\\boldsymbol{\\beta}=\\mathbf{0. 3 2 0}$) and Personal Innovativeness$(\\boldsymbol{\\beta}=\\mathbf{0. 2 8 0})$have a greater impact than Speed$(\\boldsymbol{\\beta}=0.192)$and Social Influence$(\\boldsymbol{\\beta}=0.109)$. This implies that young consumers value the sustainability and innovation offered by drone delivery over its functional benefits. The study contributes to the theoretical expansion of the original TPB/TAM theory by simultaneously examining four core functions of drones in a single model. From a managerial perspective, the research suggests that logistics companies should focus on green branding strategies and target creative user groups to promote early adoption of drone services.", "keywords": [ "Drone", "Action (physics)", "Clothing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/isiber68248.2026.11470066" }, { "id": "https://openalex.org/W7126077200", "doi": "10.2514/6.2026-0541", "title": "Optimized Last-Mile Delivery: Integrating Drones With Carrier Vehicles", "year": 2026, "publication_date": "2026-01-08", "type": "conference-paper", "language": null, "authors": [ "Falak Fatima", "P. B. Sujit", "Debasish Ghose" ], "first_author": "Falak Fatima", "venue": null, "publisher": null, "abstract": "This paper develops an optimized hybrid drone-carrier logistics framework tailored to realistic urban delivery scenarios. Initially, we derive the optimal drone departure and rendezvous points numerically to minimize the total drone travel distance in a simplified scenario that involves a single drone, a carrier vehicle, and a single customer. Subsequently, we extend the model to sequentially serve multiple customers. We introduce piecewise rectilinear carrier vehicle paths closely resembling realistic city block layouts. Drone battery constraints are then integrated into the model, requiring drones to evaluate their battery capacity before each delivery, leading to decisions on recharging or skipping deliveries if necessary. Finally, multiple drones are deployed simultaneously, with customer locations partitioned optimally using K-means clustering. Each drone independently serves its assigned cluster, optimizing delivery sequences through an initial nearest-neighbor heuristic followed by a 2-OPT route refinement. Simulations demonstrate significant reductions in travel distances, improved drone utilization, and substantial enhancements in operational efficiency, validating the scalability and real-world applicability of the proposed multi-drone, battery-constrained logistics system.", "keywords": [ "Drone", "Scalability", "Block (permutation group theory)", "Heuristic", "Piecewise", "Trajectory" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2026-0541" }, { "id": "https://openalex.org/W7170181955", "doi": "10.6084/m9.figshare.33074918.v1", "title": "Investigating the Barriers to the Adoption of Drones and Internet of Drones (IoD) in Last-mile Deliveries in China", "year": 2026, "publication_date": "2026-07-23", "type": "dataset", "language": null, "authors": [ "Anthony Anosike", "Jose Arturo Garza‐Reyes", "Mingtai Li", "Udoka Daniel" ], "first_author": "Anthony Anosike", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The rapid growth of e-commerce has significantly put pressure on last mile delivery leading to interests in drones and Internet of Drones as solutions. This study addresses the gaps in its adoption by identifying, classifying and ranking these barriers.A thorough literature review identified the relevant barriers, followed by a quantitative survey of 265 experts. The data was analysed and classified using Exploratory Factor Analysis (EFA) and ranked using analytic hierarchy process (AHP).The study identified 18 barriers, with service reliability barriers (e.g. instability of drones in bad weather, risk of parcel damage), and cost performance barriers (high cost of initial investment and limited loads of drones) ranking top.This study contributes to literature by systematically classifying and ranking the barriers to the application of drones and IoD technology in last-mile delivery. This findings will help organisations prioritise limited resources on resolving the core barriers that affect them the most.", "keywords": [ "Drone", "Ranking (information retrieval)", "Investment (military)", "The Internet", "Last mile (transportation)", "Reliability (semiconductor)", "China", "Analytic hierarchy process", "Service (business)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.33074918.v1" }, { "id": "https://openalex.org/W4416749434", "doi": "10.1109/access.2025.3638144", "title": "Proposal for a Model to Optimize the Vehicle Routing Problem With Drones That Perform Last-Mile Parcel Deliveries Focusing on Customer Satisfaction", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "V. Rosi", "Paulo Sérgio Cugnasca" ], "first_author": "V. Rosi", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The integration of drones for last-mile parcel deliveries tends to be a key factor in improving logistics services. Therefore, this work aims to define a Mixed Integer Linear Programming (MILP) model in order to study the Vehicle Routing Problem with Drones (VRP-D). The mathematical formulation is based on the Simultaneous Traveling Repairman Problem with Drone (STRPD), in which a vehicle with one or more drones departs from a depot to deliver parcels, aiming to reduce customer waiting times. Whereas in the STRPD the drone can only serve one customer per trip, in the proposed study the drone can serve multiple customers per trip. However, its endurance is limited by battery energy consumption, which depends on the weight carried by the drone. This problem is called the Simultaneous Traveling Repairman Problem with Drone with Multiple Visits (STRPDMV), and this work validated the mathematical model through the analysis of nineteen instances. Due to the inability to obtain results for some instances with twenty or more customers, a heuristic was developed to find feasible solutions within reduced processing times. Then, the results provided by the new model and the heuristic were compared and the analyses were presented. Additionally, another analysis performed was the impact of varying drone speed compared to vehicle speed, and vice-versa.", "keywords": [ "Drone", "Heuristic", "Routing (electronic design automation)", "Customer satisfaction", "Key (lock)", "Linear programming", "Integer programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2025.3638144" }, { "id": "https://openalex.org/W4401541196", "doi": "10.1109/infocomwkshps61880.2024.10620851", "title": "Piggybacking on UAV Package Delivery Systems to Simultaneously Provide Wireless Coverage: A Deep Reinforcement Learning-Based Trajectory Design", "year": 2024, "publication_date": "2024-05-20", "type": "conference-paper", "language": "en", "authors": [ "Jeong Min Kong", "Elvino Silveira Sousa" ], "first_author": "Jeong Min Kong", "venue": null, "publisher": null, "abstract": "Various studies have explored the possibility of utilizing unmanned aerial vehicles (UAVs) as last-mile package delivery agents and aerial base stations in recent years. Despite the tremendous attention in these applications, nearly all of the studies assumed that the UAVs are serving just one purpose, but not both simultaneously; however, for a number of reasons, such as traffic congestion in the sky and energy & resource inefficiency problems, it seems more suitable for the UAVs to be serving several services concurrently. A few papers investigated the case in which the UAV acts as both a package delivery agent and a wireless transceiver, but in all of them, package delivery time constraints were never considered. Stemming from the observation that there are various ongoing industrial projects in UAV-based package delivery, we investigate the possibility of piggybacking on UAV-based package delivery infrastructures to also provide wireless coverage, and consider the problem of designing UAV trajectories that maximize the cumulative downlink sum rate of the ground communication users while simultaneously delivering packages under strict delivery time constraints. We use deep Q-learning (DQL) to solve this optimization problem, and demonstrate the successful formulation & implementation of our algorithm through several simulations.", "keywords": [ "Reinforcement learning", "Computer science", "Trajectory", "Wireless", "Distributed computing", "Artificial intelligence", "Telecommunications" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/infocomwkshps61880.2024.10620851" }, { "id": "https://openalex.org/W7170198519", "doi": "10.6084/m9.figshare.33074918", "title": "Investigating the Barriers to the Adoption of Drones and Internet of Drones (IoD) in Last-mile Deliveries in China", "year": 2026, "publication_date": "2026-07-23", "type": "dataset", "language": null, "authors": [ "Anthony Anosike", "Jose Arturo Garza‐Reyes", "Mingtai Li", "Udoka Daniel" ], "first_author": "Anthony Anosike", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The rapid growth of e-commerce has significantly put pressure on last mile delivery leading to interests in drones and Internet of Drones as solutions. This study addresses the gaps in its adoption by identifying, classifying and ranking these barriers.A thorough literature review identified the relevant barriers, followed by a quantitative survey of 265 experts. The data was analysed and classified using Exploratory Factor Analysis (EFA) and ranked using analytic hierarchy process (AHP).The study identified 18 barriers, with service reliability barriers (e.g. instability of drones in bad weather, risk of parcel damage), and cost performance barriers (high cost of initial investment and limited loads of drones) ranking top.This study contributes to literature by systematically classifying and ranking the barriers to the application of drones and IoD technology in last-mile delivery. This findings will help organisations prioritise limited resources on resolving the core barriers that affect them the most.", "keywords": [ "Drone", "Ranking (information retrieval)", "Investment (military)", "The Internet", "Last mile (transportation)", "Reliability (semiconductor)", "China", "Analytic hierarchy process", "Service (business)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.33074918" }, { "id": "https://openalex.org/W4417526040", "doi": "10.1007/s40747-025-02196-x", "title": "PHOENIX: A Hybrid Metaheuristic Framework for Multi-UAV Collaborative Trajectory Planning in Complex Three-Dimensional Environments", "year": 2025, "publication_date": "2025-12-20", "type": "article", "language": "en", "authors": [ "Ershen Wang", "Hu Xu", "Guipeng Ji", "Tengli Yu", "Song Xu", "Fei Liu", "Fan Li" ], "first_author": "Ershen Wang", "venue": "Complex & Intelligent Systems", "publisher": "Springer Science+Business Media", "abstract": "Multi-unmanned aerial vehicle (UAV) collaborative trajectory planning in complex three-dimensional (3D) environments faces critical challenges in real-time coordination, including balancing path optimality, collision avoidance, and computational efficiency under dynamic obstacle fields. While existing metaheuristic algorithms exhibit limitations in convergence speed and adaptability to high-density obstacles ( \\( > 0.8 threats/km^{3}\\) ), this study introduces PHOENIX—a hybrid metaheuristic framework that synergizes the crested porcupine optimizer (CPO) and ant lion optimizer (ALO) through a dual-layer cooperative mechanism. Three key innovations are proposed: (1) A hierarchical flight protection zone model compliant with RTCA DO-263 standards, dynamically integrating CPO’s collision-avoidance strategies with real-time risk prediction; (2) A chaos-enhanced initialization strategy employing interval-constrained logistic mapping, which increases population diversity by 32.7% (quantified via Shannon entropy, \\( p<0.05\\) ); (3) An adaptive weight adjustment mechanism with nonlinear decay, achieving 23.5% and 18.7% faster convergence than particle swarm optimization (PSO) and grey wolf optimizer (GWO), respectively. Extensive evaluations on LANDSAT 9-derived 3D terrains demonstrate PHOENIX’s superiority: it reduces average path length by 6.2%, decreases threat exposure by 7.3%, and improves trajectory smoothness by 8.9% compared with state-of-the-art benchmarks. AirSim-based simulations with five UAVs validate 100% collision-free success rates in dynamic urban scenarios, outperforming CPO and ALO in robustness ( \\( p<0.01\\) ). The framework effectively resolves exploration-exploitation dilemmas while maintaining computational efficiency ( \\(<50ms \\) per planning cycle), offering a scalable solution for mission-critical applications such as disaster response and smart city logistics. By bridging bio-inspired optimization with complex system dynamics, this work advances intelligent autonomous system research.", "keywords": [ "Metaheuristic", "Particle swarm optimization", "Robustness (evolution)", "Ant colony optimization algorithms", "Population", "Motion planning", "Swarm intelligence", "Computational intelligence", "Evolutionary algorithm" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s40747-025-02196-x.pdf" }, { "id": "https://openalex.org/W7163850804", "doi": null, "title": "Involuntary Surveillance And Bystanders’ Acceptance Of Drone-Based Last-Mile Delivery", "year": 2026, "publication_date": "2026-06-15", "type": "article", "language": null, "authors": [ "Gianluca Maria Guazzo", "Pierangelo Rosati", "Theo Lynn" ], "first_author": "Gianluca Maria Guazzo", "venue": "Journal of the Association for Information Systems", "publisher": "Association for Information Systems", "abstract": "Drone delivery offers environmental and efficiency benefits for last-mile logistics but faces adoption barriers related to privacy concerns, particularly among individuals exposed to surveillance without direct transaction involvement (bystanders).This TREO paper examines how privacy perceptions and perceived benefits influence bystanders' acceptance using privacy calculus theory.This study represents the first stage of a quantitative research project that analyses survey data from US citizens using structural equation modelling.The findings could contribute to the theory of technology acceptance by revealing how contexts of involuntary exposure fundamentally alter privacy calculations, with implications for the sustainable deployment of drones in residential environments.", "keywords": [ "Software deployment", "Drone", "Information privacy", "Theory of planned behavior", "Perception", "Structural equation modeling", "Survey data collection", "Database transaction" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1119&context=treos_ecis2026" }, { "id": "https://openalex.org/W4386451400", "doi": "10.2172/1975829", "title": "Drones, Delivery Robots, Driverless Cars, and Intelligent Curbs for Increasing Energy Productivity of First/Last Mile Goods Movement", "year": 2023, "publication_date": "2023-04-28", "type": "report", "language": "en", "authors": [ "USDOE Office of Environmental Management (EM)", "Sebastian Scherer", "USDOE Office of Energy Efficiency and Renewable Energy (EERE)", "Constantine Samaras" ], "first_author": "USDOE Office of Environmental Management (EM)", "venue": null, "publisher": null, "abstract": "The objective of the project is to use empirical testing, life cycle assessment, and systems analysis to research and demonstrate energy improvement when using goods delivery drones, eVTOL, ground delivery robots and automated vehicles compared to a baseline network. The research will also develop proof-of-concept testing, a model, and simulation for a smart curb space as an intelligently-managed urban delivery zone demonstrating additional improvement in energy productivity.", "keywords": [ "Mile", "Drone", "Productivity", "Movement (music)", "Automotive engineering", "Robot", "Aeronautics", "Last mile (transportation)", "Engineering", "Transport engineering", "Business", "Computer science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.osti.gov/servlets/purl/1975829" }, { "id": "https://openalex.org/W4416749791", "doi": "10.1109/iros60139.2025.11246549", "title": "Landing-Aware Multi-Drone Routing in Last-Mile Delivery Services", "year": 2025, "publication_date": "2025-10-19", "type": "conference-paper", "language": null, "authors": [ "JiHyun Kwon", "Yi‐Ying Chen", "GaHyun Lee", "Chung‐Wei Lin", "BaekGyu Kim" ], "first_author": "JiHyun Kwon", "venue": null, "publisher": null, "abstract": "We propose a framework to compute the optimal routes for multi-drones to minimize the delivery time in the last-mile delivery service. We mainly focus on a notion of the landing exclusion zone that appears during the landing phase; an area around the drop-off site is blocked until a drop-off is completed. Such zones affect the delivery time as other drones need to detour or hover around the site unnecessarily. We formulate the Mixed-Integer Linear Programming (MILP) problem by explicitly modeling the landing phase. Then, we present the heuristic algorithm that iteratively solves a sequence of single-drone delivery problems according to the delivery priorities. A delivery priority is determined according to the spatiotemporal occupancy that quantifies the significance of the size of the landing exclusion zone and its blocking period. We designed the experiment for 48 urban delivery scenarios with varying density and distribution of delivery destinations, departure points, and order quantities. Our experiment results show that the heuristic computes the routes significantly faster than the original MILP, and the delivery time is 5% higher from the optimal solution (lower-bound), and 60% lower from the general requirement of a single package per round-trip (upper-bound).", "keywords": [ "Heuristic", "Routing (electronic design automation)", "Focus (optics)", "Drone", "Linear programming", "Blocking (statistics)", "Integer programming", "Sequence (biology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros60139.2025.11246549" }, { "id": "https://openalex.org/W7213962077", "doi": null, "title": "Drone Delivery in Urban and Rural Last-Mile Logistics within the Low-Altitude Economy: An Agent-Based Modeling Study", "year": 2026, "publication_date": "2026-07-05", "type": "article", "language": null, "authors": [ "Yuna JEON", "Chaemin JUNG", "JEONG, Gyuin", "Younghoon Chang" ], "first_author": "Yuna JEON", "venue": "Journal of the Association for Information Systems", "publisher": "Association for Information Systems", "abstract": "This study aims to compare the operational and environmental performance of drone delivery versus motorcycle-based delivery within urban and rural last-mile logistics contexts using agent-based modeling (ABM) framework.The study examines how delivery mode and spatial environment jointly shape logistics efficiency, service accessibility, reliability and environmental sustainability within the broader low-altitude economy.", "keywords": [ "Drone", "Sustainability", "Reliability (semiconductor)", "City logistics", "Service (business)", "Service delivery framework", "Geographic information system", "Mode (computer interface)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://aisel.aisnet.org/context/whiceb2026/article/1066/viewcontent/71.pdf" }, { "id": "https://openalex.org/W7163639680", "doi": "10.1016/j.trpro.2026.04.066", "title": "Recent Innovations in Last-Mile Delivery: Drones, Autonomous Vehicles, and Beyond", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Wahad Alshmlani", "Rahaf Alqadda", "Irfanullah Khan" ], "first_author": "Wahad Alshmlani", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "Last-mile delivery has become a critical focal point in logistics and supply chain management due to the rapid expansion of e-commerce and escalating customer expectations for fast, reliable, and sustainable delivery services. Traditional delivery models face persistent challenges related to traffic congestion, high operational costs, labor dependency, and environmental impact, prompting the adoption of advanced technological solutions. This paper presents a comprehensive literature review of recent innovations in last-mile delivery, with particular emphasis on drone delivery systems, Autonomous Ground Vehicles (AGVs), and emerging hybrid delivery models. It critically examines their operational capabilities, technological maturity, regulatory constraints, and sustainability implications. Drawing on peer-reviewed studies and industry reports published between 2018 and 2025, the review synthesizes current developments, compares the strengths and limitations of key technologies, and identifies unresolved challenges related to scalability, infrastructure readiness, public acceptance, and policy frameworks. The findings indicate that while drones offer rapid and flexible delivery in remote and congested environments, and autonomous ground vehicles enhance efficiency and cost effectiveness in urban settings, no single technology provides a universal solution. Instead, integrated hybrid delivery networks appear most promising for addressing diverse logistical contexts. This study contributes to the literature by providing a structured comparison of emerging last-mile delivery technologies and outlining future research directions to support the development of resilient, efficient, and environmentally responsible logistics ecosystems.", "keywords": [ "Drone", "Sustainability", "Supply chain", "Emerging technologies", "Integrated project delivery", "Supply chain management", "Traffic congestion", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2026.04.066" }, { "id": "https://openalex.org/W4405767967", "doi": "10.1007/978-3-031-77426-3_19", "title": "Route Optimization for Urban Last-Mile Delivery: Truck vs. Drone Performance", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Adriano S. Silva", "Guido S. Berger", "João Mendes", "Thadeu Brito", "José Lima", "Helder Teixeira Gomes", "Ana I. Pereira" ], "first_author": "Adriano S. Silva", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Mile", "Truck", "Last mile (transportation)", "Drone", "Computer science", "Transport engineering", "Aeronautics", "Automotive engineering", "Engineering", "Geography", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-77426-3_19" }, { "id": "https://openalex.org/W4413166624", "doi": "10.1080/23302674.2025.2544714", "title": "Unmanned aerial vehicle and autonomous delivery robot station for last-mile delivery services", "year": 2025, "publication_date": "2025-08-13", "type": "article", "language": "en", "authors": [ "Byoungil Choi", "Joohang Kang", "Joonyup Eun", "Taesu Cheong" ], "first_author": "Byoungil Choi", "venue": "International Journal of Systems Science Operations & Logistics", "publisher": "Taylor & Francis", "abstract": "Innovation in the logistics industry has been steadily drawing attention to meet customer and company needs. Delivery services using unmanned aerial vehicles and delivery robots, as next generation delivery vehicles, have been actively investigated in both academia and industry. This paper introduces the new last-mile delivery station model that considers two practical issues. First, to hedge business risks (i.e. delivery suspension or inefficient routing) that can arise when employing homogeneous vehicles, companies are likely to adopt heterogeneous vehicles for their delivery service models. Only homogeneous vehicles are used in existing station models. Second, this study sets the weighted sum of costs and makespan as the objective function and explores the trade-off between the two measures. No previous studies consider the costs and makespan at the same time. A mathematical formulation is developed and a tailored heuristic algorithm is proposed to solve large-scale problems. To observe the proposed model in depth and verify the performance of the heuristic algorithm, numerous computational experiments are conducted for randomly generated instances. Sensitivity analyses are performed to derive managerial implications by manipulating key factors that may affect the performance of the model.", "keywords": [ "Mile", "Last mile (transportation)", "Robot", "Aeronautics", "Computer science", "Mobile robot", "Engineering", "Artificial intelligence", "Geography", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/23302674.2025.2544714" }, { "id": "https://openalex.org/W2981484461", "doi": "10.1109/icmsao.2019.8880379", "title": "Autonomous Vehicles Delivery Systems Classification: Introducing a TSP With a Moving Depot", "year": 2019, "publication_date": "2019-04-01", "type": "conference-paper", "language": "en", "authors": [ "Batool Madani", "Malick Ndiaye" ], "first_author": "Batool Madani", "venue": null, "publisher": null, "abstract": "The vast growth in the e-commerce market has increased the attention to resolving the problem of conveying goods from distribution hubs to final destinations (customers) in the supply chain. The process known as the Last Mile Delivery (LMD) problem has significant challenges such as reducing operational cost or ecological impact and increasing supply chain performance. The inclusion of new technologies such as drones, robots, and the internet of things help tackle these challenges by developing new distribution systems to improve from traditional deliveries methods (delivery person, postal/delivery boxes). However, the use of these technologies brings new operational challenges. For instance, in a combined truck-drone delivery system, the truck serves as a depot from where we load the product to the drone for final delivery to the customers. The depot is now moving unlike in a traditional Vehicle Routing Problem (VRP) for which we always assume a fixed depot. This research paper deals with the impact of using Autonomous Vehicles (AV) on the logistics of the LMD. We first present a technological review of the use of AVs in logistics and use it to introduce a classification of the delivery systems based on the parcel/product handover at the time of the last handling before delivery to customers. We describe three categories of handovers, namely, machine-to-person, machine-to-machine, and person-to-machine. We present a new class of vehicle routing problems with a moving depot and formulate two variants of a Discrete Traveling Salesman Problem (TSP) with a Moving Depot (TSP-MD). We solve the problem using the General Algebraic Modeling System (GAMS) software and discuss some of the problem characteristics.", "keywords": [ "Truck", "Vehicle routing problem", "Travelling salesman problem", "Computer science", "Supply chain", "Drone", "Last mile (transportation)", "Routing (electronic design automation)", "Product (mathematics)", "Depot", "Operations research", "Process (computing)" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icmsao.2019.8880379" }, { "id": "https://openalex.org/W4407871667", "doi": "10.2139/ssrn.5117161", "title": "Tactical Routing and Fleet Planning in Drone-Assisted Last-Mile Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Bahar D. Viniche", "Opher Baron", "Oded Berman", "Mehdi Nourinejad" ], "first_author": "Bahar D. Viniche", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Routing (electronic design automation)", "Aeronautics", "Computer science", "Transport engineering", "Engineering", "Operations management", "Computer network", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5117161" }, { "id": "https://openalex.org/W4394079051", "doi": "10.6084/m9.figshare.22548349.v3", "title": "Cooperative trucks and drones for rural last-mile delivery with steep roads", "year": 2023, "publication_date": "2023-01-01", "type": "dataset", "language": "en", "authors": [ "Anonymous author" ], "first_author": "Anonymous author", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the paper Cooperative trucks and drones for rural last-mile delivery with steep roads. The data includes 96 instances corresponding to different scales and scenarios. Each instance is named \"n. d. m. h\", where n is the number of customers, d represents the dimension of the grid, m is the generic name of the scenarios with different coordinates and demands, and h refers to the generic name of the altitude scenarios. The first column of each instance file indicates Coordinate X of each customer. The second and third columns are Coordinate Y and Demand, respectively. The last column is Altitude. Finally, there is an extra file, named Supplemental online material, which provides the following appendices： Appendix A: Provides calculations about the main parameters of drone energy consumption. Appendix B: Discusses the differences between the proposed problem with GVRPD and GVRP-SR. Appendix C: Provides the detailed principles for generating benchmark instances. Appendix D: Provides information about the value of the parameters in the main configuration of the problem.", "keywords": [ "Mile", "Truck", "Drone", "Last mile (transportation)", "Transport engineering", "Aeronautics", "Geography", "Engineering", "Biology", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.22548349.v3" }, { "id": "https://openalex.org/W4403063985", "doi": "10.5753/sbrc.2024.1285", "title": "Pasture-based Livestock Identification by Coordenated UAVs", "year": 2024, "publication_date": "2024-05-20", "type": "conference-paper", "language": "en", "authors": [ "Millena Cavalcanti", "Bruno Olivieri", "Thiago de Souza Lamenza", "Markus Endler" ], "first_author": "Millena Cavalcanti", "venue": null, "publisher": null, "abstract": "The increase and improvement of meat production over the last decade is certainly a result of the growing adoption of Information Technology in livestock farming. Precision livestock farming represents a prominent strategy to deliver notable quantitative and qualitative headways and enhance animal welfare and resource management. When managing free-ranging cattle on pasture, there is the problem of identifying, counting and monitoring cattle effectively, despite the extent of the pasture and the dispersal of the animals. Using swarms of Unmanned Aerial Vehicles (UAVs) as cattle data collectors (through readings of RFID ear tags), this work proposes an identification and counting approach to enhance UAV collaboration and routing of the collected data for improved area coverage. The approach integrates coverage algorithms to inventory cattle into a farm management system using some UAVs as the last-mile communication agent. A simulated environment considering pastures of small and medium-sized farms with varying concentrations of cattle supports simulations with an accuracy of 89% for a 16-minute tracking mission, reaching 100% effectiveness for cattle concentration rate within the average density of Brazilian farms.", "keywords": [ "Pasture", "Livestock", "Identification (biology)", "Environmental science", "Geography", "Forestry", "Ecology", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5753/sbrc.2024.1285" }, { "id": "https://openalex.org/W7113776138", "doi": null, "title": "Proposal for the implementation of drones in last-mile deliveries in urban areas of Colombia", "year": 2025, "publication_date": "2025-05-16", "type": "dissertation", "language": "es", "authors": [ "Navarro Zúñiga, William Rafael", "Hidalgo Urrea, Juan Enrique", "Candil Parra, Luz Mireya" ], "first_author": "Navarro Zúñiga, William Rafael", "venue": "Minerva Digital Library (Universidad EAN)", "publisher": "Universidad EAN", "abstract": "La logística de última milla representa uno de los mayores desafíos para las cadenas de suministro en entornos urbanos, debido a factores como la congestión vial, los altos costos operativos y la necesidad de entregas rápidas y sostenibles. En este contexto, la presente investigación desarrolla una propuesta para la implementación de drones en las entregas de última milla en áreas urbanas de Colombia, integrando aspectos técnicos, legales, ambientales y sociales. A través de una metodología mixta, que combina análisis bibliométrico, diagnóstico contextual y validación mediante encuestas a empresas logísticas, se identificaron oportunidades y barreras para la adopción de esta tecnología emergente. Los resultados indican una percepción favorable por parte del sector logístico frente al uso de drones, especialmente por su potencial para reducir tiempos de entrega, minimizar costos y contribuir a la sostenibilidad. No obstante, también se evidenciaron limitaciones regulatorias, técnicas y de aceptación social que requieren estrategias específicas de mitigación. La propuesta desarrollada contempla un marco de implementación adaptable al contexto colombiano, alineado con las regulaciones actuales y orientado hacia una logística más eficiente y resiliente. Esta investigación aporta a la modernización del sector logístico nacional, promoviendo soluciones innovadoras y sostenibles para el transporte urbano de última milla.", "keywords": [ "Drone", "Context (archaeology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/10882/14809" }, { "id": "https://openalex.org/W4394562284", "doi": "10.6084/m9.figshare.22548349.v4", "title": "Cooperative trucks and drones for rural last-mile delivery with steep roads", "year": 2023, "publication_date": "2023-01-01", "type": "dataset", "language": "en", "authors": [ "Anonymous author" ], "first_author": "Anonymous author", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the paper Cooperative trucks and drones for rural last-mile delivery with steep roads. The data includes 96 instances corresponding to different scales and scenarios. Each instance is named \"n. d. m. h\", where n is the number of customers, d represents the dimension of the grid, m is the generic name of the scenarios with different coordinates and demands, and h refers to the generic name of the altitude scenarios. The first column of each instance file indicates Coordinate X of each customer. The second and third columns are Coordinate Y and Demand, respectively. The last column is Altitude. Finally, there is an extra file, named Supplemental online material, which provides the following appendices： Appendix A: Provides calculations about the main parameters of drone energy consumption. Appendix B: Discusses the differences between the proposed problem with GVRPD and GVRP-SR. Appendix C: Provides the detailed principles for generating benchmark instances. Appendix D: Provides information about the value of the parameters in the main configuration of the problem.", "keywords": [ "Mile", "Truck", "Drone", "Last mile (transportation)", "Aeronautics", "Geography", "Transport engineering", "Engineering", "Automotive engineering", "Biology", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.22548349.v4" }, { "id": "https://openalex.org/W7147241540", "doi": "10.18154/rwth-2026-03712", "title": "Urban case studies on optimized truck-drone last-mile distribution", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Ruiyang Zhang", "Junfeng Xiao", "Florian M. Heinitz" ], "first_author": "Ruiyang Zhang", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Distribution (mathematics)", "Field (mathematics)", "Stability (learning theory)", "Probability distribution" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.18154/rwth-2026-03712" }, { "id": "https://openalex.org/W4394430123", "doi": "10.6084/m9.figshare.22548349", "title": "Cooperative trucks and drones for rural last-mile delivery with steep roads", "year": 2023, "publication_date": "2023-01-01", "type": "dataset", "language": "en", "authors": [ "Anonymous author" ], "first_author": "Anonymous author", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the paper Cooperative trucks and drones for rural last-mile delivery with steep roads. The data includes 96 instances corresponding to different scales and scenarios. Each instance is named \"n. d. m. h\", where n is the number of customers, d represents the dimension of the grid, m is the generic name of the scenarios with different coordinates and demands, and h refers to the generic name of the altitude scenarios. The first column of each instance file indicates Coordinate X of each customer. The second and third columns are Coordinate Y and Demand, respectively. The last column is Altitude. Finally, there is an extra file, named Supplemental online material, which provides the following appendices： Appendix A: Provides calculations about the main parameters of drone energy consumption. Appendix B: Discusses the differences between the proposed problem with GVRPD and GVRP-SR. Appendix C: Provides the detailed principles for generating benchmark instances. Appendix D: Provides information about the value of the parameters in the main configuration of the problem.", "keywords": [ "Mile", "Truck", "Drone", "Last mile (transportation)", "Transport engineering", "Aeronautics", "Engineering", "Geography", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.22548349" }, { "id": "https://openalex.org/W7133471611", "doi": null, "title": "An ALNS algorithm for vehicle routing problem with drones in the last mile distribution*", "year": 2020, "publication_date": "2020-01-01", "type": "dissertation", "language": "es", "authors": [ "Jhon Alexander Jiménez Romero", "Jeamy Sebastián Tarazona Jiménez" ], "first_author": "Jhon Alexander Jiménez Romero", "venue": "Universidad Industrial de Santander", "publisher": null, "abstract": "Los vehículos aéreos no tripulados o más conocidos como drones han despertado gran interés en los últimos años, teniendo aplicaciones en operaciones militares y civiles, recientemente se ha investigado acerca de las ventajas de su uso en la distribución de paquetes. En el presente trabajo de investigación se formula un problema similar a el Flying Sidekick Traveling Salesman Problem (FSTSP), en referencia a que los drones y camiones pueden trabajar en conjunto; mientras el dron hace una entrega, el camión puede visitar varios clientes. La diferencia del FSTSP es que este modelo considera que los camiones tienen un límite de capacidad y además se tienen restricciones que limitan el tiempo de la ruta, dicha formulación se conoce como el Problema de Ruteo de Vehículos con Drones (VRPD); esta investigación se centra en la distribución de última milla. Para resolver esta formulación se presenta el algoritmo Adaptive Large Neighborhood Search (ALNS), el cual es validado usando instancias propuestas en la literatura. Se verifica cómo varía la función objetivo de la solución inicial mediante el uso de heurísticas destrucción y reparación, finalmente se realiza un análisis de sensibilidad para algunos parámetros del algoritmo y características de los drones; realizando conclusiones de los resultados arrojados y efectuando recomendaciones para futuras investigaciones.", "keywords": [ "Drone", "Travelling salesman problem", "Vehicle routing problem", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://noesis.uis.edu.co/bitstreams/ee507bd8-5074-4311-9edd-a4348bcda2cf/download" }, { "id": "https://openalex.org/W4394292585", "doi": "10.6084/m9.figshare.22548349.v2", "title": "Cooperative trucks and drones for rural last-mile delivery with steep roads", "year": 2023, "publication_date": "2023-01-01", "type": "dataset", "language": "en", "authors": [ "Jiuhong Xiao" ], "first_author": "Jiuhong Xiao", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the paper Cooperative trucks and drones for rural last-mile delivery with steep roads. The data includes 96 instances corresponding to different scales and scenarios. Each instance is named \"n. d. m. h\", where n is the number of customers, d represents the dimension of the grid, m is the generic name of the scenarios with different coordinates and demands, and h refers to the generic name of the altitude scenarios. The first column of each instance file indicates Coordinate X of each customer. The second and third columns are Coordinate Y and Demand, respectively. The last column is Altitude. Finally, there is an extra file, named Supplemental online material, which provides the following appendices： Appendix A: Provides calculations about the main parameters of drone energy consumption. Appendix B: Discusses the differences between the proposed problem with GVRPD and GVRP-SR. Appendix C: Provides the detailed principles for generating benchmark instances. Appendix D: Provides information about the value of the parameters in the main configuration of the problem.", "keywords": [ "Mile", "Truck", "Drone", "Last mile (transportation)", "Geography", "Transport engineering", "Aeronautics", "Engineering", "Biology", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.22548349.v2" }, { "id": "https://openalex.org/W4414088826", "doi": "10.5302/j.icros.2025.25.0097", "title": "Last-mile Delivery by an Autonomous Drone Through a Balcony in Urban Environments", "year": 2025, "publication_date": "2025-09-10", "type": "article", "language": "en", "authors": [ "Joohyuk Lee", "Sasanka Kuruppu Arachchige", "Joo‐Ho Lee", "Hyeonjeong Heo", "Doo-Hyeong Lee" ], "first_author": "Joohyuk Lee", "venue": "Journal of Institute of Control Robotics and Systems", "publisher": "Institute of Control, Robotics and Systems", "abstract": "Last-mile delivery by a drone in urban environments with high-rise buildings is a challenging task due to the presence of severe GPS errors and a high risk of collisions. This paper proposes the technologies required for a drone to perform last-mile deliveries autonomously in urban environments. First, we recognize urban obstacles simulating high-rise buildings using 2D LiDAR and construct an occupancy grid map to avoid obstacles as the shortest path through the center point between obstacles. Next, we detect a balcony window look-alike marker using a deep learning model and then estimate the position and attitude of the origin-symmetric marker. Based on the estimated pose of the marker, the drone is guided to enter the balcony area and drop the cargo on the balcony. We also design a device and mechanism that fixes and drops cargo via lock and release operations, which is attached to the bottom of a small drone. Finally, we perform precision landing using a cascaded ArUco marker created from two ArUco markers with different IDs. We validate each of the proposed technologies in a realistic testing environment.", "keywords": [ "Drone", "Occupancy grid mapping", "Global Positioning System", "Grid", "Lidar", "Window (computing)", "Construct (python library)", "Urban environment", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5302/j.icros.2025.25.0097" }, { "id": "https://openalex.org/W4394250112", "doi": "10.6084/m9.figshare.22548349.v1", "title": "Cooperative trucks and drones for rural last-mile delivery with steep roads", "year": 2023, "publication_date": "2023-01-01", "type": "dataset", "language": "en", "authors": [ "Jiuhong Xiao" ], "first_author": "Jiuhong Xiao", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the paper Cooperative trucks and drones for rural last-mile delivery with steep roads. The data includes 96 instances corresponding to different scales and scenarios. Each instance is named \"n. d. m. h\", where n is the number of customers, d represents the dimension of the grid, m is the generic name of the scenarios with different coordinates and demands, and h refers to the generic name of the altitude scenarios. The first column of each instance file indicates Coordinate X of each customer. The second and third columns are Coordinate Y and Demand, respectively. The last column is Altitude. Finally, there is an extra file, named Supplemental online material, which provides the following appendices： Appendix A: Provides calculations about the main parameters of drone energy consumption. Appendix B: Discusses the differences between the proposed problem with GVRPD and GVRP-SR. Appendix C: Provides the detailed principles for generating benchmark instances. Appendix D: Provides information about the value of the parameters in the main configuration of the problem.", "keywords": [ "Mile", "Truck", "Drone", "Last mile (transportation)", "Transport engineering", "Aeronautics", "Engineering", "Geography", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.22548349.v1" }, { "id": "https://openalex.org/W7213995876", "doi": "10.1016/j.rtbm.2026.101886", "title": "Evaluating UAV-based last-mile delivery for sustainable urban logistics in disrupted cities", "year": 2026, "publication_date": "2026-09-22", "type": "article", "language": "en", "authors": [ "Vitalii Voronko", "Вероніка Халіна", "Олександр Бреславський", "Ihor V. Biletskyi", "Yevhen Kush", "Andrii Galkin" ], "first_author": "Vitalii Voronko", "venue": "Research in Transportation Business & Management", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Sustainability", "Urban planning", "Production (economics)", "Sustainable development", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.rtbm.2026.101886" }, { "id": "https://openalex.org/W7163341088", "doi": "10.51244/ijrsi.2026.1305000105", "title": "Smart Last-Mile Delivery in Healthcare: The Role of AI, Drones, and Autonomous Vehicles", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Geraldine I. Ifeanyi", "Peacemark Hammed" ], "first_author": "Geraldine I. Ifeanyi", "venue": "International journal of research and scientific innovation", "publisher": null, "abstract": "The last mile of healthcare logistics represents one of the most complex, costly, and critical segments of the healthcare supply chain. It directly influences the accessibility, timeliness, and reliability of essential medical supplies such as vaccines, blood products, pharmaceuticals, and diagnostic tools. Recent advances in artificial intelligence (AI), unmanned aerial vehicles (drones), and autonomous ground vehicles have significantly transformed this domain, enabling real-time decision-making, enhanced delivery efficiency, and improved healthcare outcomes. This literature review synthesizes current research on smart last-mile delivery systems in healthcare, focusing on the integration of AI-driven optimization, drone-enabled delivery, and autonomous vehicle technologies. It examines operational benefits, system architectures, challenges, and future directions, while highlighting the implications for global health equity and emergency preparedness.", "keywords": [ "Healthcare delivery", "Health care", "Healthcare system", "Drone", "Reliability (semiconductor)", "System integration" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://rsisinternational.org/journals/ijrsi/uploads/vol13-iss5-pg1140-1148-202606_pdf.pdf" }, { "id": "https://openalex.org/W7126032662", "doi": "10.5281/zenodo.18417916", "title": "A Novel Framework for Optimizing the Routes of Drones During the Last Mile Delivery Process", "year": 2023, "publication_date": "2023-06-01", "type": "article", "language": null, "authors": [ "Archit Agarwal", "Ayush Goyal", "Dinesh Kumar Vishwakarma" ], "first_author": "Archit Agarwal", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The growing e-commerce industry and demand for fast deliveries have led to the exploration of alternative lastmile delivery methods, including drone delivery systems. Optimising the routes for drone delivery presents a formidable challenge due to the intricate nature of the problem. This research paper introduces a novel solution that combines the Vehicle Routing Problem (VRP) and the Travelling Salesman Problem (TSP) to tackle the last-mile drone delivery issue. A mathematical model and a heuristic algorithm are devised, incorporating clustering of delivery locations, TSP routing for the truck, and VRP routing for the drones. Two approaches are employed to determine the most efficient truck-drone route. The first approach is the flying sidekick Travelling Salesman Problem (FSTSP), which employs a single drone alongside the truck. The second approach is the parallel drone scheduling Travelling Salesman Problem (PDSTSP), which involves two or more drones. Each drone can fly between its designated delivery location and the depot. In both variations, the objective is to deliver packages from a warehouse to a group of customer locations using a truck and a cluster of drones. To enhance the PDSTSP, we propose a modified approach where the truck serves as a dynamic drone depot, releasing drones at each cluster of delivery locations. The proposed solution is evaluated using real-world data, and the results demonstrate a significant optimization in terms of distance travelled and delivery time compared to traditional delivery methods.", "keywords": [ "Drone", "Travelling salesman problem", "Vehicle routing problem", "Cluster analysis", "Routing (electronic design automation)", "Scheduling (production processes)", "Truck", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18417916" }, { "id": "https://openalex.org/W7165848339", "doi": "10.1061/9780784487020.027", "title": "Truck-Drone Routing with Dynamics-Informed Energy Constraints for Last-Mile Deliveries", "year": 2026, "publication_date": "2026-06-25", "type": "conference-paper", "language": null, "authors": [ "Keaton Safu", "Jingyi Zeng", "Xin Qin", "Tairan Liu" ], "first_author": "Keaton Safu", "venue": null, "publisher": null, "abstract": "This study introduces an integrated framework for the Flying Sidekick Traveling Salesman Problem (FSTSP), combining realistic road-network routing for a truck with dynamics-based energy modeling for a payload-carrying multi-rotor drone. Using four delivery routes from the Amazon Last-Mile Routing Research Challenge dataset, we perform a multi-instance simulation under key operational constraints: maximum payload capacity, maximum volumetric capacity, round-trip drone range, and limited battery life. Results demonstrate that incorporating the Amazon MK30 drone into last-mile operations can effectively reduce total route completion time by up to 16.82%, even with just two drone-serviced deliveries. We further examined the conditions enabling these time savings and analyzed the practical strengths and failure modes of coordinated truck-drone delivery across diverse urban environments in Seattle, Tacoma, Los Angeles, and Orange County for potential future directions of research in the truck-drone delivery problem.", "keywords": [ "Drone", "Payload (computing)", "Routing (electronic design automation)", "Key (lock)", "Truck", "Travelling salesman problem", "Energy (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1061/9780784487020.027" }, { "id": "https://openalex.org/W3184187325", "doi": "10.1016/j.compenvurbsys.2021.101686", "title": "A systematic review of agent-based models for autonomous vehicles in urban mobility and logistics: Possibilities for integrated simulation models", "year": 2021, "publication_date": "2021-07-23", "type": "review", "language": "en", "authors": [ "Jingjun Li", "Evy Rombaut", "Lieselot Vanhaverbeke" ], "first_author": "Jingjun Li", "venue": "Computers Environment and Urban Systems", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Flexibility (engineering)", "Truck", "Transport engineering", "Transparency (behavior)", "Systems engineering", "Operations research", "Engineering", "Computer security" ], "cited_by_count": 99, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.compenvurbsys.2021.101686" }, { "id": "https://openalex.org/W4388116583", "doi": "10.1109/mass58611.2023.00089", "title": "Exploring Mixed-Grid Environments for Drone-Based Last-Mile Logistics Optimization", "year": 2023, "publication_date": "2023-09-25", "type": "conference-paper", "language": "en", "authors": [ "Francesco Betti Sorbelli", "Federico Corò", "Cristina Maria Pinotti", "Anil M. Shende" ], "first_author": "Francesco Betti Sorbelli", "venue": null, "publisher": null, "abstract": "In recent years, drone-based delivery systems have gained significant attention for last-mile logistics in various domains. This study focuses on optimizing drone deliveries in mixed-grid environments, which combine urban and rural areas. The objective is to minimize the sum of distances between the delivery locations and the drone’s depot. By selecting the optimal depot placement, we aim to improve factors such as expected delivery time, energy consumption, and environmental impacts. To address this optimization problem, we conducted a comprehensive comparison of several state-of-the-art algorithms specifically designed for computing the drone placement in mixed-grid environments. Our evaluation utilized synthetic and quasi-real data and two distinct scenarios were considered: the full-grid scenario, and the partial-grid scenario. Through empirical analyses, we demonstrate the effectiveness of the proposed algorithms and provide valuable insights into their trade-offs in terms of performance and time complexity.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Grid", "Computer science", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/mass58611.2023.00089" }, { "id": "https://openalex.org/W7126081051", "doi": "10.5281/zenodo.18417915", "title": "A Novel Framework for Optimizing the Routes of Drones During the Last Mile Delivery Process", "year": 2023, "publication_date": "2023-06-01", "type": "article", "language": null, "authors": [ "Archit Agarwal", "Ayush Goyal", "Dinesh Kumar Vishwakarma" ], "first_author": "Archit Agarwal", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The growing e-commerce industry and demand for fast deliveries have led to the exploration of alternative lastmile delivery methods, including drone delivery systems. Optimising the routes for drone delivery presents a formidable challenge due to the intricate nature of the problem. This research paper introduces a novel solution that combines the Vehicle Routing Problem (VRP) and the Travelling Salesman Problem (TSP) to tackle the last-mile drone delivery issue. A mathematical model and a heuristic algorithm are devised, incorporating clustering of delivery locations, TSP routing for the truck, and VRP routing for the drones. Two approaches are employed to determine the most efficient truck-drone route. The first approach is the flying sidekick Travelling Salesman Problem (FSTSP), which employs a single drone alongside the truck. The second approach is the parallel drone scheduling Travelling Salesman Problem (PDSTSP), which involves two or more drones. Each drone can fly between its designated delivery location and the depot. In both variations, the objective is to deliver packages from a warehouse to a group of customer locations using a truck and a cluster of drones. To enhance the PDSTSP, we propose a modified approach where the truck serves as a dynamic drone depot, releasing drones at each cluster of delivery locations. The proposed solution is evaluated using real-world data, and the results demonstrate a significant optimization in terms of distance travelled and delivery time compared to traditional delivery methods.", "keywords": [ "Drone", "Travelling salesman problem", "Vehicle routing problem", "Cluster analysis", "Routing (electronic design automation)", "Scheduling (production processes)", "Truck", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18417915" }, { "id": "https://openalex.org/W7213456709", "doi": "10.2139/ssrn.7478869", "title": "Power, policy capacity and uncertainty in the governance of UK drone last-mile delivery", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Daniela Paddeu", "Eda Beyazıt" ], "first_author": "Daniela Paddeu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Corporate governance", "Drone", "Stakeholder", "Externality", "Aviation", "Stakeholder engagement", "Legitimacy", "Citizen journalism" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7478869" }, { "id": "https://openalex.org/W4413119700", "doi": "10.3390/aerospace12080703", "title": "Research on Real-Path-Based UAV Distribution Center Layout in Urban Environments", "year": 2025, "publication_date": "2025-08-08", "type": "article", "language": "en", "authors": [ "Linyanran Dai", "Yong Tian", "Naizhong Zhang", "Lili Wan", "Shunhang Hai" ], "first_author": "Linyanran Dai", "venue": "Aerospace", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The growing adoption of unmanned aerial vehicles (UAVs) for urban parcel delivery highlights the need for efficient distribution center placement. Particularly in dense urban environments, obstacle avoidance usually increases real path distances beyond straight-line measurements. Considering these distance differences, this study first employs the Informed-RRT* algorithm with spatial indexing to calculate real UAV flight paths between locations. We use the real path distances instead of straight-line distances to determine service coverage areas in the distribution center layout model. In this model, we aim to minimize the total economic cost and maximize customer satisfaction, considering the service range, the balance between delivery capability and demand, and the constraints of the distribution mode. An immune-algorithm-enhanced genetic algorithm, the immune genetic algorithm, is used to solve the model, acquiring the distribution center layout. We conduct a simulation experiment in Shenzhen’s low-altitude airspace and compare the layout results between the real path distance criterion and the straight-line path distance criterion. The results show that using the real path distances instead of the straight-line distances leads to changes in distribution center layout, demonstrating the necessity of this framework.", "keywords": [ "Center (category theory)", "Path (computing)", "Computer science", "Distribution (mathematics)", "Distribution center", "Real-time computing", "Computer network", "Business", "Mathematics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2226-4310/12/8/703/pdf?version=1755151137" }, { "id": "https://openalex.org/W7161780621", "doi": "10.1007/978-3-032-21811-7_26", "title": "Optimizing Drone’s Energy in Single Truck Multiple Drones Last Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Ornela Gordani", "Eglantina Kalluçi", "Fatos Xhafa" ], "first_author": "Ornela Gordani", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Energy (signal processing)", "Energy consumption", "Efficient energy use" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/2117/464953" }, { "id": "https://openalex.org/W4393141523", "doi": "10.1109/hpcc-dss-smartcity-dependsys60770.2023.00086", "title": "Personalized Federated Learning Framework for Person Re-identification in MEC-enabled UAV Delivery Services", "year": 2023, "publication_date": "2023-12-17", "type": "conference-paper", "language": "en", "authors": [ "Yi Xu", "Zhipeng Wang", "Jonathan Kua", "Haoyu Luo", "Ruyi Guo", "Xiao Liu" ], "first_author": "Yi Xu", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicle (UA V) based last-mile delivery is considered as a highly promising solution in smart logistics. To provide effective delivery service, it is crucial to ensure accurate positioning and identification of the UA V goods receiver in a timely fashion. However, insufficient data captured by onboard UAV cameras makes it challenging to guarantee the performance of those recognition models. Moreover, using data collected from public cameras and other devices for model training may lead to privacy concerns. To address these issues, this paper proposes a personalized federated learning framework named LP-Fed (Location-Aware Pixel Personalized Federated Learning) under the edge computing architecture. Building upon this framework, a person re-identification solution tailored to edge computing environment is implemented. This solution aims to enhance the precision of UAV-based goods receiver positioning and identification by leveraging multiple data sources while protecting data privacy. Specifically, LP-Fed employs a position-pixel attention module to adaptively aggregate the global models and local models, addressing feature disparities from various data sources at the edge. In addition, the introduction of model relevance indicators can dynamically adjust the aggregation weights of model parameters. Experimental results demonstrate that LP-Fed can significantly improve the model accuracy across eight representative person re-identification datasets while ensuring data privacy.", "keywords": [ "Computer science", "Identification (biology)", "Human–computer interaction", "World Wide Web" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/conference_contribution/Personalized_Federated_Learning_Framework_for_Person_Re-identification_in_MEC-enabled_UAV_Delivery_Services/25697994" }, { "id": "https://openalex.org/W7165390904", "doi": "10.1016/j.ress.2026.113054", "title": "Learning heuristics with Vision Transformers for risk-aware drone last-mile delivery", "year": 2026, "publication_date": "2026-06-20", "type": "article", "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": "for urban parcel transport, offering a sustainable and cost-effective alternative to traditional ground-based logistics. Such operations, however, introduce significant risks, requiring effective mitigation strategies and efficient path-planning tools. Risk-aware path planning in complex urban environments remains computationally demanding, as it involves a highly combinatorial sequential decision-making problem while requiring efficient online replanning to ensure reactivity. Efficient planners often rely on graph-based search methods, in which the heuristic used to guide the search plays a crucial role. However, in risk-aware path planning, heuristic estimation is particularly challenging, as risk evaluation forms part of the cost estimation and differs significantly from classical line-of-sight or Euclidean approaches. To address this limitation, this study presents a novel Deep Learning framework for estimating accurate and admissible heuristic functions in risk-aware UAV path planning. To this end, a comprehensive database of cost-to-go maps was generated using realistic 3D urban scenarios and a graph-based optimisation framework. This dataset was used to train a Vision Transformer (ViT) network capable of providing accurate cost-to-go estimates for various objective-function configurations. The proposed architecture incorporates an admissibility correction mechanism that regulates the trade-off between heuristic accuracy and admissibility, promoting either solution optimality or search efficiency. The method was benchmarked against conventional heuristics in terms of computation time and cost-to-go. Results show speed-ups of up to 20 times compared to traditional approaches while maintaining near-optimal solutions, with marginal increases in average path cost of the order of 0.1%.", "keywords": [ "Drone", "Heuristics", "Transformer" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ress.2026.113054" }, { "id": "https://openalex.org/W4408293260", "doi": "10.2139/ssrn.5098756", "title": "Drone Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Disha Lohia" ], "first_author": "Disha Lohia", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Business", "Computer science", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5098756" }, { "id": "https://openalex.org/W4409724526", "doi": "10.1109/itoec63606.2025.10969068", "title": "Optimizing Urban UAV Delivery Networks with Improved K-Means: Insights from Shiyan City", "year": 2025, "publication_date": "2025-03-14", "type": "conference-paper", "language": "en", "authors": [ "Zhuoling Song", "Qin Wang", "Zichao Chen", "Dengan’Qi Fang" ], "first_author": "Zhuoling Song", "venue": null, "publisher": null, "abstract": "This study addresses the logistical challenges of urban unmanned aerial vehicle (UAV) delivery systems by proposing an enhanced K-Means clustering algorithm tailored for delivery area division. Integrating UAV-specific constraints, such as battery life, payload limits, and dynamic delivery demands, the algorithm optimizes flight distance, energy efficiency, and cluster workload balance. Comprehensive simulations using data from Shiyan City validate the algorithm ’ s effectiveness, demonstrating significant improvements over traditional methods. The findings offer a scalable framework for integrating UAVs into urban logistics networks and provide actionable insights for optimizing UAV operations under real-world conditions. This research also highlights future directions, including incorporating real-time data streams and advanced UAV technologies to enhance logistics adaptability and efficiency.", "keywords": [ "Computer science", "Environmental science", "Transport engineering", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itoec63606.2025.10969068" }, { "id": "https://openalex.org/W2951051927", "doi": "10.1109/tvt.2019.2910452", "title": "Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles", "year": 2019, "publication_date": "2019-06-01", "type": "article", "language": "en", "authors": [ "Donkyu Baek", "Yukai Chen", "Alberto Bocca", "Lorenzo Bottaccioli", "Santa Di Cataldo", "Valentina Gatteschi", "Daniele Jahier Pagliari", "Edoardo Patti", "Gianvito Urgese", "Naehyuck Chang", "Alberto Macii", "Enrico Macii", "Paolo Montuschi", "Massimo Poncino" ], "first_author": "Donkyu Baek", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user's attitude toward them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one-third of potential European users are deterred by this problem when considering the move to an EV. A similar consideration applies to aerial EVs for commercial use, where a careful planning of the flying range is essential not only to guarantee the service but also to avoid the loss of the EVs due to charge depletion during the flight. Therefore, route planning for EVs for different purposes (range estimation, route optimization) and/or application scenarios (terrestrial, aerial EVs) is an essential element to foster the acceptance of EVs as a replacement of traditional vehicles. One essential element to enable such accurate planning is an accurate model of the actual power consumption. While very elaborate models for the electrical motors of EVs do exist, the motor power does not perfectly match the power drawn from the battery because of battery non-idealities. In this paper, we propose a general methodology that allows to predict and/or optimize the operation range of EVs, by allowing different accuracy/complexity tradeoffs for the models describing the route, the vehicle, and the battery, and taking into account the decoupling between motor and battery power. We demonstrate our method on two use cases. The first one is a traditional driving range prediction for a terrestrial EV; the second one concerns an unmanned aerial vehicle, for which the methodology will be used to determine the energy-optimal flying speed for a set of parcel delivery tasks.", "keywords": [ "Driving range", "Battery (electricity)", "Range (aeronautics)", "Automotive engineering", "Decoupling (probability)", "Battery pack", "Engineering", "Computer science", "Electric vehicle", "Power (physics)", "Control engineering", "Aerospace engineering" ], "cited_by_count": 74, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2019.2910452" }, { "id": "https://openalex.org/W4408136340", "doi": "10.21203/rs.3.rs-6142887/v1", "title": "Smart City Logistics: Leveraging AI for Last-Mile Delivery Efficiency", "year": 2025, "publication_date": "2025-03-04", "type": "preprint", "language": "en", "authors": [ "Junbo Wang" ], "first_author": "Junbo Wang", "venue": "Research Square", "publisher": null, "abstract": null, "keywords": [ "Last mile (transportation)", "City logistics", "Mile", "Smart city", "Transport engineering", "Business", "Computer science", "Engineering", "Geography", "Computer security", "Internet of Things" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.21203/rs.3.rs-6142887/v1" }, { "id": "https://openalex.org/W4412901013", "doi": "10.2139/ssrn.5370435", "title": "Hybrid Truck-Drone Delivery with Droneport Coordination in Urban Logistics", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Shamik Pushkar", "Elkafi Hassini" ], "first_author": "Shamik Pushkar", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "City logistics", "Business", "Transport engineering", "Aeronautics", "Aerospace engineering", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5370435" }, { "id": "https://openalex.org/W7133241658", "doi": "10.4108/eetiot.11026", "title": "Quantum-Enhanced Optimization of AV-Drone Fleets for Urban Last-Mile Logistics", "year": 2026, "publication_date": "2026-03-02", "type": "article", "language": null, "authors": [ "Gabriel Silva Atencio" ], "first_author": "Gabriel Silva Atencio", "venue": "EAI Endorsed Transactions on Internet of Things", "publisher": "European Alliance for Innovation", "abstract": "The rise of e-commerce and calls for sustainability make things hard for businesses in cities, showing where standard shipping methods fail. The study shows a new hybrid quantum-classical optimization scheme that bridges three major gaps: AV-drone coordination that does not work well in the real world; environmental claims that have not been proven; and real-world limits that are not considered enough. In a modern design, the Quantum Approximate Optimization Algorithm (QAOA) and Deep Q-Networks (DQN) are put together. There are two main variables used for optimization: regulatory factors (β =0.32) and energy limits (β =0.56). The framework cuts costs by 28% and speeds up delivery times by 32%, while keeping 89% of solution accuracy on noisy quantum simulations. Run 1,000 Monte Carlo models and compare them to real data to do this. Checking the methods with three steps—computer simulation, stakeholder analysis (n=25, ±=0.81), and policy paper review—is what makes sure they work. The results make it possible to measure the difference in success between models and real life. They show that in the real world, savings of 40% in efficiency drop to 22%. It turns out that regulatory division is the main problem, causing 92% of practical errors. The study produces the idea of energy-constrained quantum advantage and shows that it can only be useful with solid-state batteries (≥400 Wh/kg) and regulatory unification. These efforts set new standards for urban mobility research that involves multiple fields and show how driverless operations can be used in a way that is viable.", "keywords": [ "Set (abstract data type)", "Work (physics)", "Measure (data warehouse)", "Scheme (mathematics)", "Stakeholder", "Sustainability", "Energy (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://publications.eai.eu/index.php/IoT/article/download/11026/3930" }, { "id": "https://openalex.org/W4416238027", "doi": "10.30574/wjaets.2025.17.2.1482", "title": "Autonomous Vehicles and UAVs: A Comprehensive Review of Technologies, Applications, and Future Challenges", "year": 2025, "publication_date": "2025-11-15", "type": "article", "language": null, "authors": [ "S. V. Viraktamath", "Mallikarjun S Jigalur", "Prarthana R Chikkalkar" ], "first_author": "S. V. Viraktamath", "venue": "World Journal of Advanced Engineering Technology and Sciences", "publisher": "GSC Online Press", "abstract": "Autonomous vehicles (AVs) and unmanned aerial vehicles (UAVs) are transforming mobility, logistics, agriculture, and public services through advancements in sensing, navigation, planning, and artificial intelligence. This paper provides a comprehensive review of recent technological developments, including multi-sensor fusion, AI-driven perception, GNSS-based and vision-based navigation, and motion planning strategies. Applications in precision agriculture, last-mile logistics, and healthcare delivery are discussed, alongside hardware optimization for UAV platforms. Furthermore, the paper examines societal and economic implications such as accessibility, regulation, and the disruptive impact on industries and employment. Key challenges including trust, cybersecurity, scalability, and equitable integration are highlighted, with future research directions identified to ensure safe, efficient, and socially responsible deployment of autonomous systems.", "keywords": [ "Software deployment", "Key (lock)", "Societal impact of nanotechnology", "Drone", "Disruptive technology", "Social impact" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journalwjaets.com/sites/default/files/fulltext_pdf/WJAETS-2025-1482.pdf" }, { "id": "https://openalex.org/W7165557901", "doi": "10.1109/icsedi66420.2026.11568326", "title": "Identifying Risks of Goods Delivery By Drone in Last-Mile Delivery in Mekong Delta", "year": 2026, "publication_date": "2026-02-10", "type": "conference-paper", "language": null, "authors": [ "Tuan-Kiet H.V. Truong", "Toan Tan Giang", "Thắng Hồng Võ" ], "first_author": "Tuan-Kiet H.V. Truong", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Mekong delta", "Risk management", "Production (economics)", "Delta" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icsedi66420.2026.11568326" }, { "id": "https://openalex.org/W4414525973", "doi": "10.1007/978-3-031-88874-8_4", "title": "Optimizing Last-Mile Delivery with Drone Technology: A Path to Sustainable Logistics in Oman", "year": 2025, "publication_date": "2025-01-01", "type": "book-chapter", "language": "en", "authors": [ "Fahad ibne Masood", "Saurav Negi", "Bikal Jha" ], "first_author": "Fahad ibne Masood", "venue": "Studies in systems, decision and control", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Sustainability", "Operational efficiency", "Sustainable development", "Humanitarian Logistics", "Efficient energy use" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-88874-8_4" }, { "id": "https://openalex.org/W3135683234", "doi": null, "title": "A Precision Positioning Method of Drone Last-mile Delivery Technology in Urban Areas", "year": 2021, "publication_date": "2021-01-01", "type": "article", "language": "en", "authors": [ "Kim Kyu-Seok" ], "first_author": "Kim Kyu-Seok", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Computer science", "Aeronautics", "Geography", "Environmental science", "Engineering", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002692935" }, { "id": "https://openalex.org/W7128619464", "doi": "10.1109/ictmod66732.2025.11372024", "title": "Digital Disruption at the Edge: A Review of Barriers to Drone Technology in Last-Mile Delivery", "year": 2025, "publication_date": "2025-10-20", "type": "conference-paper", "language": null, "authors": [ "Malik Sammar Dildar Awan", "Sarath. R. Menon", "Satya R Shah" ], "first_author": "Malik Sammar Dildar Awan", "venue": null, "publisher": null, "abstract": "Emerging technologies in the era of digital supply chain have fostered the development of innovative strategies to enhance the last mile delivery system within logistics to improve overall efficiency within the supply chain. Among these, drone technology is gaining prominence due to its distinctive attributes such as rapid delivery capabilities, cost effectiveness and high operational flexibility thus providing competitive edge to the businesses to respond to customer delivery requirements. While drone technology contributes to achieving a responsive supply chain, several challenges impact their commercial deployment and scalability of drones logistics network. This paper identifies the critical challenges that impact the implementation and utilisation of the drone technology within last mile delivery encompassing various external and internal factors. The findings underscore the necessity for a nuanced understanding of these multifaceted barriers to ensure successful integration of drone into the last mile delivery system for an efficient and responsive supply chain.", "keywords": [ "Drone", "Last mile (transportation)", "Software deployment", "Flexibility (engineering)", "Supply chain", "Operational efficiency", "Emerging technologies" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ub-ir.bolton.ac.uk/view/delivery/44UOBO_INST/1211298430008841/1311298420008841" }, { "id": "https://openalex.org/W7169513805", "doi": "10.4324/9781003712626-7", "title": "Last-Mile Delivery", "year": 2026, "publication_date": "2026-07-17", "type": "book-chapter", "language": "en", "authors": [ "Arkadiusz Kawa", "Mirosław Gral" ], "first_author": "Arkadiusz Kawa", "venue": null, "publisher": null, "abstract": "The rapid growth of e-commerce has made last-mile delivery one of the most critical and challenging components of the supply chain. While there is extensive literature on various aspects of e-commerce logistics, a substantial gap remains in understanding the comparative efficiency of different last-mile delivery models, including door-to-door and out-of-home solutions. This chapter aims to fill this gap by analyzing these models from the perspectives of key stakeholders. Key performance indicators are examined to assess cost structures, operational efficiency, sustainability, and service quality. This chapter emphasizes the growing role of digitalization in optimizing the last mile through real-time tracking, data-driven demand forecasting, and platform-based consumer interactions. Concurrently, the analysis highlights sustainability, evaluating the potential of delivery models to mitigate environmental impact through route optimization, shared delivery infrastructure, and consumer behavioral shifts. Additionally, the chapter will examine emerging trends, including autonomous vehicles and drone deliveries.", "keywords": [ "Drone", "Key (lock)", "Last mile (transportation)", "Service delivery framework", "Food delivery", "Operational efficiency" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4324/9781003712626-7" }, { "id": "https://openalex.org/W7213424840", "doi": "10.1109/mesa70578.2026.11684226", "title": "LLM-Driven Automatic Design of Truck and Drone Last-Mile Delivery Routes", "year": 2026, "publication_date": "2026-08-01", "type": "conference-paper", "language": null, "authors": [ "Xuan Fu", "Yonglin Tian", "Fei Lin" ], "first_author": "Xuan Fu", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Truck", "Key (lock)", "Tracking system", "Automation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mesa70578.2026.11684226" }, { "id": "https://openalex.org/W7111178837", "doi": "10.1108/jamr-03-2024-0103/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec", "title": "Ranking the benefits of drone-based last-mile delivery due to adoption of its enablers", "year": null, "publication_date": null, "type": "article", "language": null, "authors": [ "Chandresh Kumbhani", "Ravi Kant" ], "first_author": "Chandresh Kumbhani", "venue": "RePEc: Research Papers in Economics", "publisher": "Federal Reserve Bank of St. Louis", "abstract": "Purpose - Strategic integration of enablers and the realization of drone delivery benefits emerge as essential strategies for business organizations to enhance operational efficiency and stay competitive in last-mile logistics. This paper aims to explore the benefits of drone-based last-mile delivery in the Indian logistic sector by providing a framework for ranking drone delivery benefits (DDBs) due to the adoption of its enablers. Design/methodology/approach - This study proposes a novel hybrid framework applied in the Indian logistic sector by integrating a sentence boundary extraction algorithm for extracting benefits from literature, a spherical fuzzy analytical hierarchy process (SF-AHP) for evaluating primary enablers, unsupervised fuzzy C-means clustering (FCM) for clustering benefits and a spherical combined compromised solution (SF-CoCoSo) for ranking benefits with respect to primary enablers. Findings - The results reveal that technological and infrastructure enablers (TIE), government and legislation enablers (GLE) and operational and service quality enablers (OSE) are the most significant enablers for drone implementation in logistics. Top-ranked benefits increase the efficiency of last-mile delivery (DDB10), foster supply chain management and logistic sustainability (DDB16) and increase delivery access to rural area and vulnerable people (DDB17). Practical implications - This research assists scholars, entrepreneurs and policymakers in the sustainable deployment of drone delivery in the logistics sector. This study facilitates the use of drones in delivery services and provides a foundation for all stakeholders in logistics. Originality/value - The assessments involve considering judgment from a highly knowledgeable and experienced group in India, characterized by a large volume of inputs and a high level of expertise.", "keywords": [ "Ranking (information retrieval)", "Cluster analysis", "Service delivery framework", "Drone", "Sustainability", "Government (linguistics)", "Supply chain", "Quality (philosophy)", "Process (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.emerald.com/insight/content/doi/10.1108/JAMR-03-2024-0103/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec" }, { "id": "https://openalex.org/W4399223378", "doi": "10.3390/su16114682", "title": "Vertical Takeoff and Landing for Distribution of Parcels to Hospitals: A Case Study about Industry 5.0 Application in Israel’s Healthcare Arena", "year": 2024, "publication_date": "2024-05-31", "type": "article", "language": "en", "authors": [ "Michael Naor", "Gavriel David Pinto", "Pini Davidov", "Yuval Cohen", "Linor Izchaki", "Mukarram Hadieh", "Malak Mohammad Ghaith" ], "first_author": "Michael Naor", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "To gain a sustained competitive advantage, organizations such as UPS, Fedex, Amazon, etc., began to seek for industry 5.0 innovative autonomous delivery options for the last mile. Autonomous unmanned aerial vehicles are a promising alternative for the logistics industry. The fact that drones are propelled by green renewable energy source fits the companies’ need to become sustainable, replacing their fuel truck fleets, especially for traveling to remote rural locations to deliver small packages, but a major obstacle is the necessity for charging stations which is well documented in the literature. Therefore, the current research embarks on devising a novel yet practical piece of technology adopting the simplicity approach of direct flights to destinations. The analysis showcases the application for a network of warehouses and hospitals in Israel while controlling costs. Given the products in the case study are medical, direct flight has the potential to save lives when every moment counts. Hydrogen cell technology allows long-range flying without refueling, and it is both vibration-free which is essential for sensitive medical equipment and environmentally friendly in terms of air pollution and silence in urban areas. Importantly, hydrogen cells are lighter, with higher energy density than batteries, which makes them ideal for drone usage to reduce weight, maintain a longer life, and enable faster charging, all of which minimize downtime. Also, hydrogen sourcing is low-cost and unlimited compared to lithium-ion material which needs to be mined. The case study investigates an Israeli entrepreneurial company, Gadfin, which builds a vertical takeoff-and-landing-type of drone with folded wings that enable higher speed for the delivery of refrigerated medical cargo, blood, organs for transplant, and more to hospitals in partnership with the Israeli medical logistic conglomerate, SAREL. An analysis of shipping optimization (concerning the number and type of drone) is conducted using a mixed-integer linear programming technique based on various types of constraints such as traveling distance, parcel weight, the amount of flight controllers and daily number of flights allowed in order to not overcrowd the airspace. Importantly, the discussion assesses the ecosystem’s variety of risks and commensurate safety mechanisms for advancing a newly shaped landscape of drones in an Israeli tight airspace to establish a network of national routes for drone traffic. The conclusion of this research cautions limitations to overcome as the utilization of drones expand and offers future research avenues.", "keywords": [ "Takeoff", "Health care", "Takeoff and landing", "Distribution (mathematics)", "Healthcare industry", "Business", "Operations management", "Aeronautics", "Engineering", "Aerospace engineering", "Economics", "Economic growth" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/16/11/4682/pdf?version=1717146752" }, { "id": "https://openalex.org/W4312757260", "doi": "10.1109/icicict54557.2022.9917874", "title": "A Comparative Study of Ant Colony Optimization and Genetic Algorithm for Path Planning of Aerial Food Delivery System", "year": 2022, "publication_date": "2022-08-11", "type": "conference-paper", "language": "en", "authors": [ "A Anjali", "S Amritha" ], "first_author": "A Anjali", "venue": "2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)", "publisher": null, "abstract": "As technology rules the world, drones are used for creative and valuable applications. Apart from the military applications, the usage of drones in civil domains has gained immense popularity in recent days. Emerging and re-emerging viral pathogens continue to pose a major threat to global public health. As one of the prevention method is to maintain a safe distance from others, contact less delivery using drones becomes more relevant. This paper concentrates on the parcel delivery system using drones. Unmanned Aerial Vehicles can replace the conventional manual food delivery system effectively while consuming less time. The main challenge for parcel delivery drones is to plan the optimal path meeting several constraints. In the current work, the path planning of delivery drones is formulated as a travelling sales man problem and solved with two different algorithms-Ant Colony Optimization and Genetic Algorithm. Comparison is done for analyzing the efficiency and performances of both algorithms in the case of aerial food delivery system.", "keywords": [ "Drone", "Ant colony optimization algorithms", "Computer science", "Path (computing)", "Motion planning", "Food delivery", "Genetic algorithm", "Popularity", "Plan (archaeology)", "Operations research", "Artificial intelligence", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icicict54557.2022.9917874" }, { "id": "https://openalex.org/W7160965335", "doi": "10.47392/irjaeh.2026.0473", "title": "Design and Implementation of an Autonomous Logistic UAV for Last-Mile Delivery", "year": 2026, "publication_date": "2026-05-12", "type": "article", "language": null, "authors": [ "Mr. Sagar Deo Mr. Harshal Shelar", "Aditi Pachore", "Tuba Sayyed", "Vaishnavi Harde", "Samruddhi Khairnar" ], "first_author": "Mr. Sagar Deo Mr. Harshal Shelar", "venue": "International Research Journal on Advanced Engineering Hub (IRJAEH)", "publisher": null, "abstract": "This research paper proposes the concept of creating an Autonomous Logistics Unmanned Aerial Vehicle (UAV) that is able to transport medical equipment or other small parcels quickly and safely. It is of great value in case of an emergency or in an area with bad roads where transportation is slow. This drone has a flight controller that maintains the stability and balance of the drone during flight. In addition to this, there exists a Raspberry Pi 4, which can be used to handle more advanced and intelligent functions. The UAV can travel independently by utilizing the GPS and waypoint navigation technique. It can travel through its desired path automatically without being controlled by human intervention all the time. With the route planning technique, it can plan the best route to its destination. For improved safety, the drone will possess ultrasonic sensor, which will be used to sense obstacles and avoid them when flying. The drone has a wireless telemetry system. This allows the operators to operate the drone using real-time control using software Mission Planner. The drone has a secure system to transport the payload. Furthermore, the drone has a battery system that monitors the battery usage. However, the paper states specifications of components, developing procedure, testing, and result of working model.", "keywords": [ "Drone", "Waypoint", "Global Positioning System", "Key (lock)", "Controller (irrigation)", "Motion planning", "Automation", "Track (disk drive)", "Remote control" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.47392/irjaeh.2026.0473" }, { "id": "https://openalex.org/W2588908199", "doi": null, "title": "A European Perspective on the Economics of Big Data", "year": 2015, "publication_date": "2015-01-01", "type": "other", "language": "en", "authors": [ "K.J. Poppe", "Jacques Wolfert", "C.N. Verdouw", "Alan Renwick" ], "first_author": "K.J. Poppe", "venue": "Socio-Environmental Systems Modeling", "publisher": null, "abstract": "Modern information-based technologies, such as self-driving tractors, GPS (global positioning systems), robot milking machines, automated egg production, drones, satellite data and social media, will change farm practices and agricultural structures and contribute to the prosperity and resilience of farming systems. Food chains will not only become much more data-driven but will also move away from a situation characterised by a low level of data integration. This will have a significant impact on such issues as sustainability, food safety, resource efficiency and waste reduction. The economic and social effects of such developments are still to be explored. At first sight they could lead to more closely integrated supply chains that make the farmer act simply as a franchisee with limited freedom, but the opposite could be true. Farmers could be empowered due to greater transparency and easier options for direct sales in consumer food webs (using social media and smart solutions for the ‘last mile’ delivery). Therefore we can see conflicting pressures between the globalisation and localisation of supply chains. As with previous technological developments, not all farmers will invest in new skills and where technologies are labour saving, farms will get bigger. Some farms or regions will become less competitive if the basic infrastructure (eg broadband internet or GPS systems) is lacking. Competition between advisors could increase, if they are able to serve farmers digitally. In addition, part of such value added activities may move from the most remote rural areas to regions with clusters of knowledge and could also become more international in nature. A major issue is that information and communication technology (ICT), combined with higher food prices and demographic changes could fundamentally shift the competitive advantage from family farms to more industrial holdings, leading to radical structural change in agriculture.", "keywords": [ "Perspective (graphical)", "Big data", "Economics", "Computer science", "Data science", "Political science", "Artificial intelligence", "Data mining" ], "cited_by_count": 61, "is_retracted": false, "is_paratext": false, "open_access_url": "https://research.wur.nl/en/publications/a-european-perspective-on-the-economics-of-big-data" }, { "id": "https://openalex.org/W7147719985", "doi": "10.1145/3795154.3795266", "title": "Research on Multi-objective Optimization of Truck-Drone Collaborative Delivery for the “Last Mile” in Rural Areas", "year": 2025, "publication_date": "2025-12-26", "type": "conference-paper", "language": null, "authors": [ "Qing Li", "Xu Xu", "Chan He" ], "first_author": "Qing Li", "venue": null, "publisher": null, "abstract": "This study addresses the “last-mile” delivery challenge in rural logistics by proposing a collaborative route optimization plan for a single truck carrying multiple drones. A mathematical model was established with the objective function of minimizing total delivery cost, which includes the fixed usage costs, delivery costs for both the truck and drones, and the waiting costs for the drones. To solve this model, the research employed the Grey Wolf Optimizer (GWO) and compared its performance against the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The experimental results show that the cost reduction is mainly attributed to the effective optimization of the unmanned aerial vehicle (UAV) delivery tasks and routes, thereby reducing the delivery and waiting expenses of UAVs. Furthermore, a sensitivity analysis revealed that the relationship between drone speed and endurance and the total cost is not simply linear; an optimal economic range exists. This research not only validates the effectiveness of the truck-drone collaborative delivery model and the GWO algorithm but also offers valuable practical guidance for configuring the parameters of such systems in real-world applications.", "keywords": [ "Ant colony optimization algorithms", "Truck", "Genetic algorithm", "Total cost", "Plan (archaeology)", "Sensitivity (control systems)", "Rural area", "Multi-objective optimization", "Duration (music)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3795154.3795266" }, { "id": "https://openalex.org/W7204089764", "doi": "10.4324/9781003781158-43", "title": "Last-Mile Delivery", "year": 2026, "publication_date": "2026-08-24", "type": "book-chapter", "language": "en", "authors": [ "Marc Amblard" ], "first_author": "Marc Amblard", "venue": null, "publisher": null, "abstract": "This chapter examines the on-going shift in the growing segment of last-mile deliveries. In the U.S., e-commerce volume grew five percent from 2024 to 2025, representing 16 percent of retail sales. Parcels are currently delivered by large gas-powered vans. Whereas these are progressively replaced by electric vans, vehicles with a much smaller form factor are playing an increasing role in urban deliveries. They can be electric bikes, trikes, or quadricycles. Some pull trailers, others carry a closed cell that protects packages. These light vehicles not only have a much smaller carbon footprint per parcel, but they can also lead to higher efficiency as it has been demonstrated. Lastly, small delivery drones are being deployed essentially to deliver packages in low density area, i.e., either suburbia or small villages.", "keywords": [ "Carbon footprint", "Drone", "Footprint", "Electricity", "Ecological footprint", "Hook" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4324/9781003781158-43" }, { "id": "https://openalex.org/W4417350457", "doi": "10.48550/arxiv.2506.10311", "title": "The Freight Multimodal Transport Problem with Buses and Drones: An Integrated Approach for Last-Mile Delivery", "year": 2025, "publication_date": "2025-06-12", "type": "preprint", "language": "en", "authors": [ "Su, E", "Qin Hu", "Jiliu Li", "Zhang, Rui" ], "first_author": "Su, E", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This paper proposes a novel freight multimodal transport problem with buses and drones, where buses are responsible for transporting parcels to lockers at bus stops for storage, while drones are used to deliver each parcel from the locker to the corresponding customer. The integrated bus-drone system synergistically expands drone service coverage using the bus network to ensure efficient final delivery. Minimizing the total operational costs while satisfying customer demands necessitates the joint optimization of parcel assignments and drone flights. We model the problem into a compact mixed-integer linear programming formulation and propose an integer programming formulation with exponentially many variables. To address real-world scale instances, we propose a Branch-Price-and-Benders-Cut algorithm for this non-deterministic polynomial-time (NP)-hard problem. This algorithm, integrating column generation and Benders decomposition within a Branch-and-Bound framework, is developed to obtain optimal or near-optimal solutions. Additionally, we introduce algorithmic enhancements aimed at accelerating the convergence of the algorithm. Computational experiments on instances generated from real-world bus data demonstrate that the proposed algorithms outperform CPLEX regarding both efficiency and solution quality. Moreover, our approaches can lead to over 6% cost savings compared to situations where we determine parcel assignments and drone flights sequentially. We evaluate the environmental advantages of integrating buses and drones, study the impact of different cost parameters in the system, and investigate the impact of the parcel locker configuration on performance. These findings provide valuable managerial insights for urban logistics managers, highlighting the potential of the integrated bus-drone system to improve traditional last-mile delivery.", "keywords": [ "Drone", "Convergence (economics)", "Integer programming", "Benders' decomposition", "Service (business)", "Linear programming", "Decomposition", "Key (lock)", "Column generation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2506.10311" }, { "id": "https://openalex.org/W4407414140", "doi": "10.58225/tim.2024-4-251-259", "title": "SUSTAINABLE LAST MILE DELIVERY IN E COMMERCE THE ROLE OF AUTONOMOUS VEHICLES DRONES AND CROWDSOURCING", "year": 1996, "publication_date": "1996-01-01", "type": "article", "language": "en", "authors": [ "I.M Mehdili" ], "first_author": "I.M Mehdili", "venue": "Scientific works/Elmi eserler", "publisher": null, "abstract": "This paper examines the evolving landscape of sustainable last mile delivery in e commerce logistics, focusing on the roles of autonomous vehicles, drones, and crowdsourcing. As e commerce continues to transform global trade, the need for more efficient and eco friendly delivery methods has driven the integration of innovative technologies. Autonomous vehicles and drones are at the forefront, driving efficiency, reducing costs, and minimizing environmental impact in the last-mile delivery process. Additionally, the paper explores the emerging role of crowdsourcing, which enhances delivery networks and promotes sustainability by involving consumers in the logistics process. This study highlights the potential of these technologies and practices to contribute to the broader goals of sustainable development and to transform e commerce logistics into a more environmentally conscious and competitive industry.", "keywords": [ "Drone", "Crowdsourcing", "Last mile (transportation)", "Mile", "Business", "E-commerce", "Transport engineering", "Computer science", "Engineering", "Geography", "World Wide Web" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.58225/tim.2024-4-251-259" }, { "id": "https://openalex.org/W7134941715", "doi": "10.1109/icnc68183.2026.11416853", "title": "Network Environment-Aware Drone Path Planning in Last-Mile Medical Supply Delivery", "year": 2026, "publication_date": "2026-02-16", "type": "conference-paper", "language": null, "authors": [ "Aneesh Calyam", "Subrahmanya Chandra Bhamidipati", "Sharan Srinivas" ], "first_author": "Aneesh Calyam", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Motion planning", "Path (computing)", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icnc68183.2026.11416853" }, { "id": "https://openalex.org/W4413880035", "doi": "10.1007/s44327-026-00231-1", "title": "A corridor-based drone-carrier vehicle system for environment-aware last-mile delivery", "year": 2026, "publication_date": "2026-04-29", "type": "article", "language": "en", "authors": [ "Falak Fatima", "P. B. Sujit", "Debasish Ghose" ], "first_author": "Falak Fatima", "venue": "Discover Cities", "publisher": null, "abstract": "Abstract This paper proposes a corridor-based drone–carrier framework for last-mile urban delivery that explicitly minimizes environmental impact while improving logistics efficiency. Starting from a central warehouse, customer locations are first partitioned into clusters using the k-means algorithm, and a carrier vehicle transports multiple drones—each loaded with up to a specified number of packages and equipped with on-board charging—and sequentially pauses at optimized “corri-dor entrance” points. At each entrance, a drone departs along a corridor toward its assigned customer cluster. By confining drone trajectories within narrow corridors instead of allowing point-to-point flights, noise dispersion and ecological disturbances are substantially reduced, and airspace congestion is minimized. Within each cluster, the drone applies a nearest-neighbor heuristic followed by a refinement strategy to sequence deliveries, ensuring that battery endurance and payload constraints are respected. After completing its sortie, the drone returns along the same corridor to rendezvous with the moving carrier; if necessary , it may hover until the vehicle arrives. In alternative scenarios, temporary depot stations located near corridor entrances support battery recharging and package replenishment—thus avoiding unnecessary drone hovering and eliminating unnecessary carrier vehicle movement—further extending the operational range without detours into densely populated areas. Simulation results in realistic urban layouts demonstrate that this strategy not only lowers total travel distance and delivery times compared to conventional point-to-point approaches 1 but also confines noise and emissions to narrow aerial corridors, thereby fostering sustainable, community-friendly drone logistics.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Aeronautics", "Transport engineering", "Computer science", "Environmental science", "Computer security", "Engineering", "Geography", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s44327-026-00231-1.pdf" }, { "id": "https://openalex.org/W7165509456", "doi": null, "title": "Routing Optimization Problems Arising in Multiple Drone-Assisted Last-Mile Delivery Operations", "year": null, "publication_date": null, "type": "dissertation", "language": "en", "authors": [ "Hosang Song" ], "first_author": "Hosang Song", "venue": "Pure (University of Bath)", "publisher": "University of Bath", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Minification", "Optimization problem", "Process (computing)", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://researchportal.bath.ac.uk/en/studentTheses/77a323e2-53c8-47b6-978a-9e262dfad3d0" }, { "id": "https://openalex.org/W7214150906", "doi": "10.71548/18363", "title": "Three-Dimensional UAV Path Planning for Last-Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "dissertation", "language": "en", "authors": [ "Sarah Hamed" ], "first_author": "Sarah Hamed", "venue": "MacSphere (McMaster University)", "publisher": "McMaster University", "abstract": "There is an increasing interest in using unmanned aerial vehicles (UAVs), or drones, for last-mile delivery, with cities competing to achieve faster and more efficient delivery systems. However, the usage of UAVs is limited due to battery capacity and the need to navigate through a complex three-dimensional environment. This thesis aims to advance UAV operational algorithms for last-mile delivery applications. The thesis comprises two related studies using the same five path planning algorithms: Dijkstra, A*, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Rapidly-exploring Random Tree star (RRT*). The first study is focused on UAVs routing problem through evaluating the algorithms based on their ability to create efficient paths in a three dimensional environment. The second study is focused on an energy-based problem, where a UAV energy model is integrated into the algorithms to analyze energy-efficient 3D path planning, and the contributing factors for UAV energy consumption.", "keywords": [ "Motion planning", "Path (computing)", "Particle swarm optimization", "Energy (signal processing)", "Routing (electronic design automation)", "Genetic algorithm", "Random tree", "Tree (set theory)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.71548/18363" }, { "id": "https://openalex.org/W4408335528", "doi": "10.2139/ssrn.5174407", "title": "A Hybrid Uav-Public Transport Model for Efficient Last-Mile Logistics", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Ting Jiang", "Riju Lavanya", "Daisik Nam" ], "first_author": "Ting Jiang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Mile", "Last mile (transportation)", "Transport engineering", "Business", "Public transport", "Computer science", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5174407" }, { "id": "https://openalex.org/W4413346427", "doi": "10.2139/ssrn.5388230", "title": "Evaluating the Energy Savings from Truck-drone Hybrid Last-mile Delivery in Arbitrary Cities", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Seunghyun Baik", "Steven M. Willits", "Sebastian Scherer", "Junfeng Jiao", "Peter Yun Zhang" ], "first_author": "Seunghyun Baik", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Mile", "Drone", "Truck", "Last mile (transportation)", "Aeronautics", "Transport engineering", "Energy (signal processing)", "Computer science", "Automotive engineering", "Engineering", "Business", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5388230" }, { "id": "https://openalex.org/W4415042038", "doi": "10.3850/978-981-94-3281-3_esrel-sra-e2025-p1657-cd", "title": "Towards the Risk Assessment Framework Conceptualization Using Fuzzy Logic for Last-Mile Cargo Drones.", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Євген Медведєв", "Dmitriy Muzylyov", "Jakub Montewka" ], "first_author": "Євген Медведєв", "venue": null, "publisher": null, "abstract": "The use of autonomous and controlled drones for last-mile delivery of small cargo is rapidly advancing due to improvements in technology, artificial intelligence, and an increase in their cargo capacity.The application of drones to improve public services and meet transportation needs in today's world is an undeniable possibility.However, despite the clear benefits of using drones for last-mile delivery, there are potential risks, both known and new, that could lead to disruptions in the goods delivery.Hence, there is a necessity to design a method that will consider the impact of various risks on drone delivery sustainability at a significantly improved standard.Due to the diverse hazards nature associated with operating the aforementioned systems, it can be argued that to address these hazards and ensure sustainable delivery, it is essential to develop a flexible model using relevant approaches such as fuzzy logic or neural network modeling.Unlike probabilistic models, fuzzy logic does not necessitate extensive data.Additionally, an initial data sample on delivery risks posed by drones can be gathered through expert opinions.This research marks the initial phase in designing a fuzzy model for assessing risks in delivery systems using both autonomous and controlled drones by fuzzy logic.The study identified the key risks that primarily impact the drone deliveries' sustainability due to the expert survey.The risks were categorized into four groups: human factor; technical hardware issues; software failure; and weather conditions.Participants can help to identify the two most significant risk factors for each group.This information was used to determine the inputs for the logical-linguistic model.The fuzzy model defines five evaluation levels of supply sustainability.The key benefit is recommendations about the feasibility of drone use for last-mile delivery under specific conditions.", "keywords": [ "Conceptualization", "Risk assessment", "Fuzzy logic", "Risk management", "Process (computing)", "Fuzzy set" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://rpsonline.com.sg/proceedings/esrel-sra-e2025/pdf/ESREL-SRA-E2025-P1657.pdf" }, { "id": "https://openalex.org/W7153195319", "doi": "10.48550/arxiv.2604.07514", "title": "Energy-Efficient Drone Logistics for Last-Mile Delivery: Implications of Payload-Dependent Routing Strategies", "year": 2026, "publication_date": "2026-04-08", "type": "preprint", "language": null, "authors": [ "Ziyue Li", "Qianwen", "Guo", "Paul M. Schonfeld" ], "first_author": "Ziyue Li", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Drone delivery is rapidly emerging as a cost-effective and energy efficient alternative for last-mile delivery. Unlike ground vehicles, a drone's energy consumption depends on its payload in addition to travel distance. This creates a unique environmental challenge for multi-stop delivery tours, as the drone's total weight, and therefore its energy consumption rate, dynamically changes after each delivery. This paper investigates a novel green drone routing problem focused on maximizing energy efficiency. Through a series of motivating examples and numerical experiments, we demonstrate that energy-aware routing leads to several counter-intuitive routing strategies that contradict traditional distance-minimization delivery: a longer route may actually consume less energy than a shorter one; separate single-customer tours can be superior to a multi-stop tour; and a heterogeneous fleet, with drones of varying sizes, can achieve greater efficiency by matching drone capacity to specific delivery demands. In the numerical study, the green routing strategy shows energy savings in 67% of the instances. For these cases, the average energy saving is 2.17%, with a maximum saving of 5.97%, compared to minimum distance routing. These findings highlight the potential for green drone routing strategies to improve the sustainability of last-mile delivery.", "keywords": [ "Drone", "Routing (electronic design automation)", "Payload (computing)", "Energy consumption", "Energy (signal processing)", "Efficient energy use" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2604.07514" }, { "id": "https://openalex.org/W7163018740", "doi": "10.1080/21681015.2026.2678971", "title": "Data-driven robust optimization model for hybrid truck-drone last-mile delivery logistics", "year": 2026, "publication_date": "2026-06-01", "type": "article", "language": "en", "authors": [ "Reza Ahmadi Malakoot", "Rashed Sahraeian" ], "first_author": "Reza Ahmadi Malakoot", "venue": "Journal of Industrial and Production Engineering", "publisher": "Taylor & Francis", "abstract": "This research proposes a robust optimization model for a hybrid truck-drone delivery system under uncertain drone battery performance. The model introduces a one-to-one truck-drone pairing structure and formulates a hierarchical bi-objective mixed-integer linear program to (1) minimize the required fleet size and (2) minimize the total return time. A novel data-driven uncertainty modeling approach is employed, leveraging piecewise linear support vector clustering to formulate realistic uncertainty sets and mitigate over-conservatism. Findings from a real-world case study illustrate the model’s ability to maintain solution quality under uncertainty. Eliminating drones increases both truck count and delivery time by over 23%, highlighting the operational benefits of drone integration. This work advances last-mile logistics research by combining machine learning and robust optimization to address performance-risk trade-offs in hybrid delivery systems.", "keywords": [ "Robust optimization", "Robustness (evolution)", "Work (physics)", "Optimization problem", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/21681015.2026.2678971" }, { "id": "https://openalex.org/W7167919312", "doi": "10.2139/ssrn.7097204", "title": "Empty-Return Energy Waste in Drone Last-Mile Delivery: Diagnostic Benchmarking and Factor Decomposition", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Zhou Huaiyuan" ], "first_author": "Zhou Huaiyuan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Benchmarking", "Drone", "Intuition", "Terrain", "Waste heat", "Efficient energy use", "Municipal solid waste" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7097204" }, { "id": "https://openalex.org/W7162134205", "doi": "10.1109/cpeee69412.2026.11521330", "title": "Energy-Aware Truck-Drone Last-Mile Logistics Co-Optimized with Distribution Grid Constraints", "year": 2026, "publication_date": "2026-03-06", "type": "conference-paper", "language": null, "authors": [ "Heictor A. O. Costa", "Fernando José Von Zuben" ], "first_author": "Heictor A. O. Costa", "venue": null, "publisher": null, "abstract": "The rapid growth of e-commerce has intensified the challenges of last-mile delivery, demanding faster, more efficient, and sustainable solutions. This paper presents a novel cooptimization framework for energy-aware, truck-drone last-mile logistics that explicitly accounts for the constraints of electrical distribution grids. We propose a robust bi-level optimization methodology that prioritizes grid feasibility as the upper-level problem. At the upper level, the model determines the maximum feasible energy budget that the local power grid can support for drone charging by performing a bisection search against a MATPOWER-based Optimal Power Flow (OPF) oracle. At the lower level, a Genetic Algorithm (GA) optimizes the logistics operations-including vehicle assignment and routing for a mixed fleet of trucks and multi-sortie drones-subject to this physicallyderived energy constraint. The entire system is simulated in a realistic urban scenario inspired by Tokyo's ring road structure, with the logistics operations mapped onto a modified IEEE 33bus distribution test system. Results from a case study with 100 customers, 10 trucks, and 20 drones demonstrate that this gridfirst approach guarantees a physically feasible charging schedule, successfully co-optimizing delivery costs and energy management while respecting all operational and electrical limitations. This methodology provides a valuable planning tool for logistics operators and utility providers to manage the integration of large-scale drone delivery operations without compromising grid stability.", "keywords": [ "Distribution (mathematics)", "Grid", "Production (economics)", "Constraint (computer-aided design)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cpeee69412.2026.11521330" }, { "id": "https://openalex.org/W7131266871", "doi": "10.46254/in05.20250381", "title": "Autonomous Drone Delivery Systems for Urban Logistics: A Comprehensive Review of Technologies, Challenges, and Future Directions", "year": 2025, "publication_date": "2025-11-06", "type": "conference-paper", "language": null, "authors": [ "Ruhina Quazi", "Rudrapratap Singh", "Piyush Umate", "Prajjwal Dubey", "Ritik Gop" ], "first_author": "Ruhina Quazi", "venue": null, "publisher": null, "abstract": "The accelerated growth of online shopping and the need for quick last-mile delivery have accelerated research interest in autonomous Unmanned Aerial Vehicle (UAV) delivery systems. This detailed review systematically examines the state-of-the-art in drone-based delivery systems, integrating recent breakthroughs in system architectures, route optimization algorithms, energy management strategies, and IoT-enabled operational frameworks. Based on the critical evaluation of scholarly articles from 2016 to 2025 across IEEE, ACM, and logistics research journals, this review finds three main system setups: single depot facilities, networked multiple depots, and support station-based systems. The paper evaluates key technological elements such as GPS autonomous flight navigation systems, web order management systems based on Node.js and MongoDB, Crossflight flight control systems, and Raspberry Pi 4-based companion computing. This review offers researchers and practitioners a unified understanding of existing capabilities, implementation issues, and areas of future research required for large-scale deployment of autonomous drone delivery systems within urban and rural logistics networks.", "keywords": [ "Drone", "Software deployment", "Global Positioning System", "Key (lock)", "Control (management)", "Order (exchange)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieomsociety.org/proceedings/india2025/381.pdf" }, { "id": "https://openalex.org/W7204125503", "doi": "10.17632/rv2c7768hp.1", "title": "Replication package: Flight Authorization Limits Emissions Abatement in Truck--Drone Last-Mile Delivery", "year": 2026, "publication_date": "2026-08-24", "type": "dataset", "language": null, "authors": [ "Armin Mahmoodi", "Mehdi Davoodi", "Seyed Mojtaba Sajadi", "Elnaz Torkamani", "Said M. Easa" ], "first_author": "Armin Mahmoodi", "venue": "Mendeley Data", "publisher": null, "abstract": "Code and results supporting the article of the same name, on how airspace flight authorization limits the emissions abatement achievable by hybrid truck-drone last-mile delivery. The code implements a multi-objective truck-UAV routing model in which emissions are an output of the routing decision rather than an assumption about it: a momentum-theory energy model with payload dependence in place of a distance proxy, well-to-wheel emissions accounting, an explicit truck-only counterfactual solved on the same instances, and SORA-derived authorization limits entering as arc eligibility. Twelve operating scenarios vary the authorization ceiling, population density, and whether a sheltering mitigation is claimed, repeated across five independently generated service areas. Solutions come from NSGA-II and MOPSO over a shared random-key encoding, validated against proven optima from a mixed-integer program on small instances. CONTENTS. code/ holds the model package (tdrp/) and the scripts that produce every reported result: run_multigeo.py for the main experiment, run_evan.py for the electric-van counterfactual, seed_scaling.py for the measurement-resolution diagnostic, run_validation.py and test_validation.py for validation against exact optima, and make_figs_multigeo.py, make_figs_converged.py and make_fronts.py for the figures. results/ holds the result files behind every table and figure. ASSUMPTIONS_REGISTER.md documents each modelling choice and its provenance. The reported emissions results are in results/results_multigeo_50seed.json: five service areas, twelve scenarios each, 2000 generations, 50 seeds, with the truck-only counterfactual solved once per seed and selected on minimum cost. Reproduction requires Python 3.10 or newer with NumPy pinned to 2.4.4; NumPy gives no guarantee that its random streams are stable across versions, so a different version changes every seeded run. code/HOW_TO_RUN.md gives step-by-step commands, the expected output at each stage, and the values to check against. Instances are determined entirely by customer count, scenario and random seed, so any result regenerates exactly on a matching environment.", "keywords": [ "Counterfactual thinking", "Population", "Payload (computing)", "Python (programming language)", "Scripting language", "Replication (statistics)", "Greenhouse gas" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/rv2c7768hp.1" }, { "id": "https://openalex.org/W4385188746", "doi": "10.1088/1742-6596/2523/1/012016", "title": "Automated Parcel Loading-Unloading Mechanism Design for Delivery UAV", "year": 2023, "publication_date": "2023-07-01", "type": "conference-paper", "language": "en", "authors": [ "Kwok Quan Eu", "Swee King Phang" ], "first_author": "Kwok Quan Eu", "venue": "Journal of Physics Conference Series", "publisher": "IOP Publishing", "abstract": "Abstract Unmanned Aerial Vehicle or UAV has evolved rapidly because of improved regulations and advancement in technology. During the COVID-19 pandemic, delivery UAVs were utilized more often than ever to deliver medical supplies, as well as parcels. Delivery UAVs work by collecting the goods and keeping it in a designated holder, which it then navigates through an area and dropping it off at the receiving point. The main factors that are considered for the design of a delivery UAV mechanism includes size and weight of parcels, security and the efficiency of loading and unloading parcels. The drop-box mechanism takes in the factors contributing to existing delivery UAV mechanisms and provides a solution that can overcome most of these concerns. The mechanism was designed through SOLIDWORKS software and 3D-printed. With the help of SOLIDWORKS, an analysis was performed, and it was concluded that the mechanism could handle a maximum of 2kg. Aside from that, the mechanism drops off parcels automatically and encourages contactless delivery.", "keywords": [ "Mechanism (biology)", "Drone", "Point (geometry)", "Software", "Computer science", "Delivery Performance", "Simulation", "Embedded system", "Engineering", "Operating system" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1088/1742-6596/2523/1/012016" }, { "id": "https://openalex.org/W7165681396", "doi": "10.5281/zenodo.20807237", "title": "Autonomous Drone Swarms for Last-Mile Delivery in Central American Logistics: An AI-Driven Framework", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Luis A. Alfaro", "Vanessa L. Hernandez", "Quevedo, Luis A." ], "first_author": "Luis A. Alfaro", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "ABSTRACTThis research proposes an advanced artificial intelligence (AI)-driven framework for the deployment of autonomous drone swarms in last-mile delivery operations within Central American logistics systems. As e-commerce expansion, urbanization, and infrastructure limitations continue to reshape supply chain dynamics, last-mile delivery has emerged as one of the most complex and costly segments in logistics networks. This study addresses these challenges by exploring how coordinated multi-agent drone systems, supported by AI algorithms and aerospace telemetry, can optimize delivery efficiency, reduce operational costs, and enhance logistical resilience in emerging regional economies. The study begins by analyzing the structural constraints of last-mile delivery in Central America, including fragmented infrastructure, traffic congestion, rural accessibility limitations, and inconsistent logistics connectivity. These challenges significantly impact delivery speed, cost efficiency, and service reliability. Traditional transportation systems often struggle to meet increasing demand, particularly in geographically diverse environments. The proposed framework introduces autonomous drone swarms as a scalable and adaptable solution capable of navigating complex terrains while maintaining high delivery performance. At the core of the framework is a multi-agent system in which each drone operates as an intelligent autonomous unit while simultaneously coordinating with the swarm to achieve collective optimization goals. The system utilizes deep reinforcement learning and swarm intelligence algorithms to enable dynamic route planning, collision avoidance, adaptive task allocation, and energy-efficient operations. Real-time data inputs—including spatial positioning, weather conditions, traffic patterns, and delivery priorities—are integrated into the decision-making process, allowing the swarm to continuously adapt to changing operational environments. A key innovation of this research is the integration of aerospace telemetry systems to ensure real-time communication, coordination, and control among swarm units and ground-based logistics nodes. Telemetry data supports high-precision navigation, synchronization, and performance monitoring, enabling the swarm to operate effectively even in environments with limited infrastructure. Additionally, the framework incorporates predictive analytics to anticipate demand fluctuations, optimize scheduling, and minimize delivery delays. This predictive capability enhances the resilience and reliability of last-mile delivery operations. The proposed architecture is structured as a multi-layered cyber-physical system comprising data acquisition, data processing, and operational control layers. The data acquisition layer includes UAV sensors, GPS systems, and environmental monitoring devices. The processing layer leverages AI models, edge computing, and cloud-based analytics to manage real-time data streams and generate optimized decisions. The operational layer coordinates swarm execution, integrates with logistics platforms, and supports human-in-the-loop supervision when necessary. This integrated system enables seamless interaction between physical drone operations and digital decision-making processes. Simulation-based evaluations and scenario modeling are conducted to assess the performance of the framework under various logistical conditions, including urban delivery environments, rural distribution networks, and emergency response scenarios. Results indicate substantial improvements in delivery time reduction, route optimization, and energy efficiency compared to traditional single-drone or ground-based delivery systems. The swarm-based model also demonstrates enhanced scalability, allowing for rapid adaptation to increased demand and expanded delivery coverage. Furthermore, the distributed nature of the swarm provides redundancy and fault tolerance, ensuring continuous operation even in the event of individual unit failures. From a strategic perspective, the adoption of autonomous drone swarms presents significant opportunities for transforming logistics systems in Central America. The framework supports economic development by improving supply chain efficiency, enabling access to remote regions, and fostering innovation in logistics technologies. It also aligns with broader efforts toward digital transformation, smart infrastructure development, and sustainable logistics practices by reducing carbon emissions associated with traditional transportation modes. In conclusion, this research demonstrates that AI-driven autonomous drone swarms provide a powerful and scalable solution for optimizing last-mile delivery in emerging logistics markets. The proposed framework enhances operational efficiency, adaptability, and resilience, offering a pathway toward next-generation logistics systems that integrate aerospace technology and intelligent systems for regional and global impact.", "keywords": [ "Drone", "Software deployment", "Resilience (materials science)", "Flexibility (engineering)", "Scalability", "Key (lock)", "Swarm behaviour", "Disaster recovery", "Aerospace" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20807237" }, { "id": "https://openalex.org/W7167191376", "doi": null, "title": "Optimizing Last-Mile Delivery with Hybrid Truck-Drone Systems: A Predictive Traffic MILP Framework", "year": 2024, "publication_date": "2024-01-01", "type": "dissertation", "language": "en", "authors": [ " Shihab" ], "first_author": " Shihab", "venue": "Research Publication Repository of King Fahd University of Petroleum and Minerals (King Fahd University of Petroleum and Minerals)", "publisher": "King Fahd University of Petroleum and Minerals", "abstract": null, "keywords": [ "Component (thermodynamics)", "Stability (learning theory)", "Context (archaeology)", "Key (lock)", "Hybrid system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://eprints.kfupm.edu.sa/id/eprint/143171/1/Shihab_Hasan%20Master%20Thesis.pdf" }, { "id": "https://openalex.org/W7160941385", "doi": "10.1109/tvt.2026.3692486", "title": "Joint Multi-Vehicle Routing and Multi-Drone Scheduling for a Hybrid Drone-Assisted Last-Mile Delivery System in Smart Cities", "year": 2026, "publication_date": "2026-05-12", "type": "article", "language": null, "authors": [ "Yuanhang Qi", "Xumin Huang", "Gewen Huang", "Junhao Wu", "Shengli Xie" ], "first_author": "Yuanhang Qi", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Drone-assisted last-mile delivery is adopted in smart cities to enhance the logistics service performance in terms of reducing the delivery time and cost while improving the user satisfaction. General vehicles without on-board drones and advanced vehicles equipped with multiple drones are simultaneously scheduled to perform the delivery tasks. There exists a hybrid multi-vehicle routing problem with multi-drones (HMVRPMD) that has not been well investigated in the previous works. Particularly, we take into account sufficient real-world considerations on the vehicle, drone and customer sides to make our problem more applicable. We design the optimal routing for two kinds of the vehicles, the optimal launch locations where the drones depart from the associated vehicles, and the optimal sequence of locations for the drones to visit multiple customers in each flight. We first evaluate the user satisfaction of a customer with respect to different package arrival time, according to the time window constraint, and then introduce a penalty cost function to calculate the accumulated loss in the total user satisfaction. We aim to minimize the weight sum of the penalty cost and total delivery cost of all employed vehicles and drones. To address the HMVRPMD, we present a bee-foraging learning particle swarm optimization algorithm with a comprehensive local search strategy. We particularly design the encoding and decoding strategies to efficiently cope with the route segmentation problem according to the vehicle types and the number of drones carried. Furthermore, we adopt the comprehensive local search strategy mixed with three kinds of neighbour search operations to enhance the search ability of the algorithm. Finally, extensive experimental results demonstrate that compared with the baseline algorithms, our algorithm has great advantages in both the convergence speed and algorithm accuracy.", "keywords": [ "Scheduling (production processes)", "Joint (building)", "Routing (electronic design automation)", "Job shop scheduling", "Routing protocol", "Processor scheduling" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2026.3692486" }, { "id": "https://openalex.org/W7167596829", "doi": "10.2139/ssrn.7075424", "title": "Routing Heterogeneous Drones with Automated Battery Swapping Machines for Multi-Customer Last-Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Rakesh Chowdhury", "Wenquan Dong", "Tanveer Hossain Bhuiyan", "Clara Novoa" ], "first_author": "Rakesh Chowdhury", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Scalability", "Payload (computing)", "Battery (electricity)", "Range (aeronautics)", "Linear programming", "Integer programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7075424" }, { "id": "https://openalex.org/W4394730449", "doi": "10.2139/ssrn.4791422", "title": "A Multistage Stochastic Programming Approach for Drone-Supported Humanitarian Last-Mile Logistics System Planning", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Zhongyi Jin", "Kam K.H. Ng", "Chenliang Zhang", "Y.Y. Chan", "Yichen Qin" ], "first_author": "Zhongyi Jin", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Humanitarian Logistics", "Computer science", "Operations research", "Stochastic programming", "Operations management", "Business", "Aeronautics", "Engineering", "Mathematical optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4791422" }, { "id": "https://openalex.org/W4392154797", "doi": "10.1007/978-981-97-0859-8_10", "title": "We Will Find You: An Edge-Based Multi-UAV Multi-Recipient Identification Method in Smart Delivery Services", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Yi Xu", "Ruyi Guo", "Jonathan Kua", "Haoyu Luo", "Zheng Zhang", "Xiao Liu" ], "first_author": "Yi Xu", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Identification (biology)", "Enhanced Data Rates for GSM Evolution", "Computer security", "Edge computing", "Artificial intelligence" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/conference_contribution/We_Will_Find_You_An_Edge-Based_Multi-UAV_Multi-Recipient_Identification_Method_in_Smart_Delivery_Services/25697742" }, { "id": "https://openalex.org/W7165646273", "doi": "10.5281/zenodo.20807238", "title": "Autonomous Drone Swarms for Last-Mile Delivery in Central American Logistics: An AI-Driven Framework", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Luis A. Alfaro", "Vanessa L. Hernandez", "Quevedo, Luis A." ], "first_author": "Luis A. Alfaro", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "ABSTRACTThis research proposes an advanced artificial intelligence (AI)-driven framework for the deployment of autonomous drone swarms in last-mile delivery operations within Central American logistics systems. As e-commerce expansion, urbanization, and infrastructure limitations continue to reshape supply chain dynamics, last-mile delivery has emerged as one of the most complex and costly segments in logistics networks. This study addresses these challenges by exploring how coordinated multi-agent drone systems, supported by AI algorithms and aerospace telemetry, can optimize delivery efficiency, reduce operational costs, and enhance logistical resilience in emerging regional economies. The study begins by analyzing the structural constraints of last-mile delivery in Central America, including fragmented infrastructure, traffic congestion, rural accessibility limitations, and inconsistent logistics connectivity. These challenges significantly impact delivery speed, cost efficiency, and service reliability. Traditional transportation systems often struggle to meet increasing demand, particularly in geographically diverse environments. The proposed framework introduces autonomous drone swarms as a scalable and adaptable solution capable of navigating complex terrains while maintaining high delivery performance. At the core of the framework is a multi-agent system in which each drone operates as an intelligent autonomous unit while simultaneously coordinating with the swarm to achieve collective optimization goals. The system utilizes deep reinforcement learning and swarm intelligence algorithms to enable dynamic route planning, collision avoidance, adaptive task allocation, and energy-efficient operations. Real-time data inputs—including spatial positioning, weather conditions, traffic patterns, and delivery priorities—are integrated into the decision-making process, allowing the swarm to continuously adapt to changing operational environments. A key innovation of this research is the integration of aerospace telemetry systems to ensure real-time communication, coordination, and control among swarm units and ground-based logistics nodes. Telemetry data supports high-precision navigation, synchronization, and performance monitoring, enabling the swarm to operate effectively even in environments with limited infrastructure. Additionally, the framework incorporates predictive analytics to anticipate demand fluctuations, optimize scheduling, and minimize delivery delays. This predictive capability enhances the resilience and reliability of last-mile delivery operations. The proposed architecture is structured as a multi-layered cyber-physical system comprising data acquisition, data processing, and operational control layers. The data acquisition layer includes UAV sensors, GPS systems, and environmental monitoring devices. The processing layer leverages AI models, edge computing, and cloud-based analytics to manage real-time data streams and generate optimized decisions. The operational layer coordinates swarm execution, integrates with logistics platforms, and supports human-in-the-loop supervision when necessary. This integrated system enables seamless interaction between physical drone operations and digital decision-making processes. Simulation-based evaluations and scenario modeling are conducted to assess the performance of the framework under various logistical conditions, including urban delivery environments, rural distribution networks, and emergency response scenarios. Results indicate substantial improvements in delivery time reduction, route optimization, and energy efficiency compared to traditional single-drone or ground-based delivery systems. The swarm-based model also demonstrates enhanced scalability, allowing for rapid adaptation to increased demand and expanded delivery coverage. Furthermore, the distributed nature of the swarm provides redundancy and fault tolerance, ensuring continuous operation even in the event of individual unit failures. From a strategic perspective, the adoption of autonomous drone swarms presents significant opportunities for transforming logistics systems in Central America. The framework supports economic development by improving supply chain efficiency, enabling access to remote regions, and fostering innovation in logistics technologies. It also aligns with broader efforts toward digital transformation, smart infrastructure development, and sustainable logistics practices by reducing carbon emissions associated with traditional transportation modes. In conclusion, this research demonstrates that AI-driven autonomous drone swarms provide a powerful and scalable solution for optimizing last-mile delivery in emerging logistics markets. The proposed framework enhances operational efficiency, adaptability, and resilience, offering a pathway toward next-generation logistics systems that integrate aerospace technology and intelligent systems for regional and global impact.", "keywords": [ "Drone", "Software deployment", "Resilience (materials science)", "Flexibility (engineering)", "Scalability", "Key (lock)", "Swarm behaviour", "Disaster recovery", "Aerospace" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20807238" }, { "id": "https://openalex.org/W7154628821", "doi": "10.4018/979-8-3373-7128-3.ch013", "title": "Last-Mile Delivery (LMD)", "year": 2026, "publication_date": "2026-04-16", "type": "book-chapter", "language": "ng", "authors": [ "Benson Ruzive", "Reason Masengu", "Patson Chawuruka", "Chenjerai Muchenje", "Saurav Negi" ], "first_author": "Benson Ruzive", "venue": null, "publisher": null, "abstract": "Last-mile delivery (LMD) is a challenging and complex logistics function with increasing importance attributable to factors such as e-commerce development, customer demands for faster delivery services, and urbanization. As a major delivery stage influencing customer satisfaction with deliveries, delivery time of products, and logistics costs taken as a whole, LMD encounters problems associated with congestion, route fragmentation, delivery charges, environmental factors, and government policies. This review examines existing trends, problems, technologies used, and future directions for overcoming existing problems associated with LMD with literature sourced from high impact journals. This literature review identifies those emerging technologies such as AI, route optimization techniques, autonomous vehicles, drones,IoT tracking systems, and blockchain technology with potential for increasing efficiency. Additionally, new approaches for efficient delivery such as crowd shipping, micro-fulfillment centers, and parcel lockers may provide possible remedies for existing problems.", "keywords": [ "Government (linguistics)", "Emerging technologies", "Delivery system", "Function (biology)", "Delivery Performance", "Customer satisfaction" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3373-7128-3.ch013" }, { "id": "https://openalex.org/W7118160202", "doi": "10.1007/978-3-032-15621-1_10", "title": "Bayesian Optimization for Two Echelon Vehicle Routing Problem Using Drone for Last Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Preetam Kumar Sur", "Anubrata Naskar", "Sunirmal Khatua" ], "first_author": "Preetam Kumar Sur", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Cluster analysis", "Benchmark (surveying)", "Hyperparameter", "Truck", "Routing (electronic design automation)", "Bayesian optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-15621-1_10" }, { "id": "https://openalex.org/W7152016938", "doi": "10.70675/36c00cb8ze9f4z4603z96edz0a3c9e808799", "title": "Multi-Objective Optimization and Development of a Last-Mile Delivery System Combining Drones and Public Transportation", "year": 2024, "publication_date": "2024-10-21", "type": "dissertation", "language": null, "authors": [ "Mohammed Rahmani" ], "first_author": "Mohammed Rahmani", "venue": null, "publisher": null, "abstract": "Optimisation multi-objectives et développement d’un système de livraison du dernier ilomètre ixant rrones et transports public La livraison urbaine du dernier kilomètre est confrontée à des défis tels que la congestion du trafic, les préoccupations environnementales et les coûts élevés. Cette thèse propose un système de livraison intelligent intégrant des drones avec les transports en commun pour améliorer l'efficacité et la durabilité. Les drones autonomes voyagent sur les transports publics (par exemple, les bus et tramways), complétant ainsi la dernière étape de livraison. Cette approche réduit l'impact environnemental et étend la portée de livraison des drones. Notre recherche aborde des défis clés tels que l'adaptation aux retards stochastiques dans les horaires des transports en commun, l'optimisation de la consommation d'énergie, la minimisation du temps de livraison et l'assurance d'une communication efficace. Pour relever ces défis, nous nous sommes basé sur des algorithmes de planification de trajectoire basés sur l'apprentissage par renforcement, utilisés pour permettre aux drones de s'ajuster dynamiquement aux conditions urbaines en temp réel, améliorantainsi le processus de livraison du dernier kilomètre. Les principales contributions incluent : (i) Une première analyse comparative de la consommation d'énergie et du temps de livraison entre le système mixte drones/transports publics et un système exclusivement basé sur les drones. Le système mixte se révèle plus économe en énergie mais légèrement plus lent en raison des retards des transports publics. (ii) Le développement d'un algorithme basé sur le l’apprentissage par renforcement type Q-learning pour planifier des trajectoires optimisées en termes d'énergie et de délais de livraison dans des environnements urbains dynamiques. (iii) L'extension du Q-learning pour minimiser les interférences de communication des drones, surpassant ainsi les algorithmes traditionnels. (iv) L'exploration des Q-networks fédérées pour améliorer la planification de trajectoires pour plusieurs drones tout en garantissant la confidentialité des données et en augmentant la scalabilité du système. Les résultats suggèrent que ce système mixte peut considérablement améliorer l'efficacité et la durabilité de la livraison du dernier kilomètre dans la logistique urbaine.", "keywords": [ "Publics", "Air transport", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.70675/36c00cb8ze9f4z4603z96edz0a3c9e808799" }, { "id": "https://openalex.org/W7158962994", "doi": "10.26258/heal.hua.6822", "title": "Τεχνοοικονομική ανάλυση της χρήσης των drones ως εργαλείο στο last-mile delivery", "year": 2023, "publication_date": "2023-03-21", "type": "other", "language": "el", "authors": [ "Ελευθερία Σπαή", "Eleftheria Spai" ], "first_author": "Ελευθερία Σπαή", "venue": "Harokopio University", "publisher": "Harokopio University of Athens", "abstract": "Μια από τις επιπτώσεις της πανδημίας COVID-19 ήταν ότι οι εξ αποστάσεως μεταφορές βίωσαν μια πρωτόγνωρη άνθιση και ζήτηση. Βέβαια αυτό είχε ως αποτέλεσμα οι εταιρίες μεταφορών να μη μπορούν να αντεπεξέλθουν στις ανάγκες των πελατών, με κύριο πρόβλημα την έλλειψη στόλου οχημάτων. Ο χρόνος είναι ένας σημαντικός παράγοντας που μπορεί να χρησιμοποιηθεί για την επίτευξη ανταγωνιστικού πλεονεκτήματος, ειδικά όσον αφορά το last-mile delivery. Με την τεχνολογία να βηματίζει σε γοργούς ρυθμούς, όλο και περισσότερα άτομα εξερευνούν τις δυνατότητες των drones. Ήδη η χρήση τους από τη γεωργία μέχρι και την επαγγελματική φωτογράφηση, δείχνει την απήχηση και την ευκολία που προσφέρουν στον κόσμο. Εκτός αυτού, στο εξωτερικό έχει ξεκινήσει η μεταφορά δεμάτων με drones σε μικρή κλίμακα, ενώ και στην Ελλάδα γίνονται δοκιμές σε πειραματικό στάδιο. Επομένως, οι επιχειρήσεις αναζητούν τρόπους με τους οποίους μπορούν να μειώσουν το κόστος κατά την παράδοση αγαθών και επίσης να διασφαλίσουν την ικανοποίηση των πελατών τους. Με βάση αυτό μπορεί να δοθεί μια νέα λύση στο πρόβλημα του last-mile delivery, μέσω της χρήσης drones για δέματα μικρού μεγέθους ως τρόπος αποσυμφόρησης ενός κομματιού του φόρτου εργασιών των ταχυμεταφορέων και επίσπευσης της διαδικασίας αποστολής δεμάτων σε πελάτες που μένουν κοντά σε κέντρα αποστολών. Σε αυτό το πλαίσιο, με βάση την πρόβλεψη ζήτησης για το πόσες εταιρίες θα μπουν στη διαδικασία να εντάξουν αυτή τη νέα υπηρεσία, θα γίνει μια τεχνοοικονομική ανάλυση, με υπόβαθρο μια ήδη υπάρχουσα ελληνική επιχείρηση ώστε να δούμε αν θα είναι συμφέρουσα μια τέτοια επένδυση. Αρχικά θα περιγραφούν οι όροι drones και last-mile delivery. Στη συνέχεια θα γίνει μια σύντομη αναφορά στην ιστορική εξέλιξη της χρήσης των drones και στις δυνατότητες που μπορούν να παρέχουν ως εργαλείο στο last-mile delivery. Έπειτα θα ακολουθήσει η τεχνοοικονομική ανάλυση για την εταιρεία ΕΛΤΑ Courier, ώστε να καταλήξουμε στα συμπεράσματα της εργασίας.", "keywords": [ "Drone", "Agency (philosophy)", "Key (lock)", "Face (sociological concept)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.26258/heal.hua.6822" }, { "id": "https://openalex.org/W7164686910", "doi": "10.5281/zenodo.20682276", "title": "Trained models — Cooperative Truck-Drone Last-Mile Delivery via Graph-Attention MARL", "year": 2026, "publication_date": "2026-06-13", "type": "dataset", "language": null, "authors": [ "Muzzamil Mustafa" ], "first_author": "Muzzamil Mustafa", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Trained GAT-MARL model weights (one per Solomon VRPTW instance, best seed; 56 files) used to produce the GAT-MARL and hybrid GAT-MARL-LS results in the associated repository. Extract into data/models/ to reproduce inference without retraining.", "keywords": [ "Inference", "Artificial neural network", "Hybrid system", "Expert system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20682276" }, { "id": "https://openalex.org/W4411259997", "doi": "10.2139/ssrn.5289946", "title": "Regulating Drone Delivery Networks", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Ramsay Eyre" ], "first_author": "Ramsay Eyre", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Computer science", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5289946" }, { "id": "https://openalex.org/W7161109098", "doi": "10.2139/ssrn.6766261", "title": "Routing Heterogeneous Drones with Automated Battery Swapping Machines for Multi-Customer Last-Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Rakesh Chowdhury", "Tanveer Hossain Bhuiyan", "Clara Novoa", "Wenquan Dong" ], "first_author": "Rakesh Chowdhury", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Scalability", "Payload (computing)", "Battery (electricity)", "Range (aeronautics)", "Linear programming", "Integer programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6766261" }, { "id": "https://openalex.org/W4412548487", "doi": "10.1016/j.trc.2026.105920", "title": "Drone-enhanced last-mile medical delivery in rural areas: A weather-aware optimization approach", "year": 2026, "publication_date": "2026-08-18", "type": "article", "language": "en", "authors": [ "Pardis Bahmani", "Shakiba Enayati", "Sina Ansari", "Srikanth Gururajan", "Ziteng Wang" ], "first_author": "Pardis Bahmani", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Mile", "Last mile (transportation)", "Drone", "Computer science", "Meteorology", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5361264" }, { "id": "https://openalex.org/W7142755093", "doi": "10.46855/energy-proceedings-12230", "title": "Co-Optimization of Hybrid Renewable Energy Systems and Drone Fleets for Sustainable Last-Mile Logistics", "year": 2026, "publication_date": "2026-02-28", "type": "preprint", "language": null, "authors": [ "Zeeshan Zulfiqar", "Lanyu Li", "Shuhui Jia", "Xiaonan Wang" ], "first_author": "Zeeshan Zulfiqar", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Renewable energy", "Sustainable energy", "Key (lock)", "Energy (signal processing)", "Sustainability" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.46855/energy-proceedings-12230" }, { "id": "https://openalex.org/W7164652649", "doi": "10.5281/zenodo.20682275", "title": "Trained models — Cooperative Truck-Drone Last-Mile Delivery via Graph-Attention MARL", "year": 2026, "publication_date": "2026-06-13", "type": "dataset", "language": null, "authors": [ "Muzzamil Mustafa" ], "first_author": "Muzzamil Mustafa", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Trained GAT-MARL model weights (one per Solomon VRPTW instance, best seed; 56 files) used to produce the GAT-MARL and hybrid GAT-MARL-LS results in the associated repository. Extract into data/models/ to reproduce inference without retraining.", "keywords": [ "Inference", "Artificial neural network", "Hybrid system", "Expert system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20682275" }, { "id": "https://openalex.org/W4389889225", "doi": "10.46254/ap04.20230137", "title": "A MILP Model for Single Depot, Single Truck and Multiple Drones for Last-Mile Delivery Problem", "year": 2023, "publication_date": "2023-09-12", "type": "conference-paper", "language": "en", "authors": [ "Saloni Jangid", "Rupkatha Ghosh", "Muthu Mathirajan" ], "first_author": "Saloni Jangid", "venue": null, "publisher": null, "abstract": "The high volume of daily e-commerce shipments in major urban areas require today's technology of drones for lastmile delivery (LMD) of parcels.The traditional method of delivering parcels using trucks is time-consuming due to traffic congestion, impeding their timely delivery.On the other hand, drones can avoid traffic congestion by flying over road networks.However, drones have a limited flight endurance due to their battery capacity constraints.At the same time, trucks possess long-haul capabilities.Hence, for achieving an efficient LMD, both trucks and drones should be integrated to offset each other's disadvantages.This paper addresses one such truck-drone based last-mile delivery (TD-LMD) problem.There are plenty of studies which have investigated TD-LMD problem with different configurations.For our study, we examine the TD-LMD problems involving single depot, single truck and multiple drones (SD-ST-MD) configuration.In the literature, various researchers have used mathematical models and heuristics to address this problem.In our study, we consider one Mixed-Integer Linear Programming (MILP) model given in the existing literature for the TD-LMD problem with SD-ST-MD configuration and relax one of their assumptions to give it a new dimension.In the process of extending the MILP model, we also introduce the CO2 emission cost along with the economic cost for optimizing the TD-LMD problem.Accordingly, the workability of the MILP model is tested using a tiny numerical example and solved through developing a code in the Python-MILP solver module.Further, the computational complexity is analyzed by increasing the number of nodes in the SD-ST-MD based TD-LMD problem.", "keywords": [ "Last mile (transportation)", "Mile", "Truck", "Drone", "Computer science", "Depot", "Transport engineering", "Engineering", "Automotive engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieomsociety.org/proceedings/2023vietnam/137.pdf" }, { "id": "https://openalex.org/W4377967267", "doi": "10.46254/na07.20220329", "title": "Learning Models for Delivery Location Clustering and Drone-Based Routing Decisions in Last-Mile Distribution", "year": 2023, "publication_date": "2023-05-24", "type": "conference-abstract", "language": "en", "authors": [ "Ali Arishi", "Krishna Krishnan" ], "first_author": "Ali Arishi", "venue": null, "publisher": null, "abstract": "With the rise in e-commerce and growing consumer demand, supply chains are striving to achieve cost-efficient delivery operations. One of the most challenging distribution problems is the last-mile delivery which generally refers to the very last leg of the delivery operation when a parcel is moved from a depot to the end customer. Despite the significant increase in the number of products ordered online and delivered directly to customers, the reliance on truckbased delivery has remained unchanged for decades. The integration of drones in the last-mile delivery can overcome many operational challenges and provide competitive advantages. Traditional optimization algorithms for truck-drone suffer from high computational time and can solve only small-sized problems. This study focuses on finding an effective approach for solving the last-mile delivery problem. We propose a hybrid machine learning model to tackle the problem of delivering products to a set of delivery locations using a modern truck equipped with multiple drones. A constrained clustering algorithm is proposed to cluster delivery locations based on user-specified constraints in the first stage. The center of each constrained cluster serves as a launching site where the truck can park, and drones can be dispatched to make all final deliveries. A deep reinforcement learning model is proposed in the second stage to find an optimal route among all clusters. Extensive experiments are conducted to evaluate the proposed approach. Solutions obtained are compared with other traditional algorithms. Results show that our approach can produce quality solutions in real-time.", "keywords": [ "Drone", "Last mile (transportation)", "Cluster analysis", "Mile", "Computer science", "Routing (electronic design automation)", "Distribution (mathematics)", "Computer network", "Artificial intelligence", "Geography", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieomsociety.org/proceedings/2022orlando/329.pdf" }, { "id": "https://openalex.org/W7128355549", "doi": "10.5281/zenodo.18529650", "title": "Optimising Last-Mile Vaccine Delivery: A Cost and Efficiency Analysis of Drone Supply Chains for Malawi's Lake Islands", "year": 2011, "publication_date": "2011-09-26", "type": "article", "language": "en", "authors": [ "Tiyamike Phiri", "Chikondi Mwale" ], "first_author": "Tiyamike Phiri", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Last-mile vaccine delivery to remote communities remains a significant public health challenge in sub-Saharan Africa. The hard-to-reach islands of Lake Malawi, with dispersed populations and limited transport infrastructure, exemplify the logistical and cost barriers to achieving equitable immunisation coverage. This study aimed to analyse the supply chain costs and operational efficiency of using unmanned aerial vehicles (drones) for last-mile vaccine delivery to these islands, compared to traditional boat-based methods. A comparative cost and efficiency analysis was conducted using operational data from a pilot drone delivery programme. Data on transport times, fuel consumption, personnel requirements, vaccine wastage, and cold chain maintenance were collected and modelled for both drone and boat supply chains across a representative sample of island communities. The drone supply chain demonstrated a 65% reduction in average delivery time and a 23% reduction in per-dose delivery cost compared to the boat-based system. Drone deliveries also maintained consistent cold chain integrity, whereas boat deliveries showed periodic temperature excursions. Drone technology presents a viable and more efficient alternative for last-mile vaccine logistics in geographically challenging island settings like Lake Malawi, offering potential for improved timeliness and cost savings. National immunisation programmes should consider integrating drone delivery for remote island and lakeshore communities. Further research is needed to assess the scalability, community acceptance, and long-term sustainability of such systems within the broader health infrastructure. vaccine delivery, drones, last-mile logistics, supply chain, immunisation, Malawi, islands, cost analysis This paper provides empirical evidence on the costs and operational efficiency of drone-mediated vaccine delivery, contributing practical insights for policymakers and health logistics planners in similar remote African contexts.", "keywords": [ "Drone", "Cold chain", "Supply chain", "Cost reduction", "Cost efficiency", "Cost–benefit analysis", "Sustainability", "Cost effectiveness" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18529650" }, { "id": "https://openalex.org/W4312884914", "doi": "10.7208/chicago/9780226211244.003.0015", "title": "Chapter 15. The Last Mile", "year": 2015, "publication_date": "2015-01-01", "type": "book-chapter", "language": "en", "authors": [ "Harold H. Bruff" ], "first_author": "Harold H. Bruff", "venue": null, "publisher": null, "abstract": "Barack Obama responded to the financial crisis by working with Congress and the Federal Reserve to stabilize the economy. He adjusted existing precedents about dealing with terrorists, setting new baselines for the terror war. He bombed Libya without congressional authorization. He did seek authorization to deal with Syria. He unilaterally engaged in drone and cyber warfare, and used special forces to kill bin Laden. He used the National Security Agency to search for terror plots. Constitutional law is underdeveloped regarding these national security matters. He protected state secrets and prosecuted leaks aggressively. He sponsored health care reform and successfully defended the constitutionality of Obamacare in the Supreme Court. He struggled with Congress over the budget and the debt ceiling, leading to a government shutdown. He eventually supported gay rights. He used executive orders when Congress would not provide new authority.", "keywords": [ "Constitutionality", "Political science", "Supreme court", "Law", "National security", "Government (linguistics)", "State (computer science)", "Administration (probate law)", "Public administration" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7208/chicago/9780226211244.003.0015" }, { "id": "https://openalex.org/W2781676119", "doi": null, "title": "UNMANNED AIRCRAFT SYSTEMS IN LOGISTICS – LEGAL REGULATION AND WORLDWIDE EXAMPLES TOWARD USE IN CROATIA", "year": 2017, "publication_date": "2017-05-15", "type": "article", "language": "en", "authors": [ "Aleksandar Erceg", "Biljana Činčurak Erceg", "Aleksandra Vasilj" ], "first_author": "Aleksandar Erceg", "venue": "RePEc: Research Papers in Economics", "publisher": "Federal Reserve Bank of St. Louis", "abstract": "Business logistics is important for delivering products in time, without damage and with the lowest possible costs. With current technology developments, there are many ideas and projects about how to reach this goal. One of these developments includes unmanned aircraft systems (UAS) - widely known as drones, for use in logistics as a tool to deliver products to their final users. The use of unmanned aircraft systems is not new and they are presently used for different applications worldwide, from military purposes to filming and crop control.Recently the idea about using drones in logistics has been tested by different logistics companies for different purposes, from last mile deliveries of ordered products to deliveries from fast food restaurants. Due to this the question arises - can it be done in Croatia? If yes, by whom, and for what purposes in logistics? This paper will provide a definition of unmanned aircraft systems and present various applications thereof, as well as the types currently used throughout the world.Since unmanned aircraft systems are starting to become a part of logistics, different examples of drones’ use in logistics worldwide are presented. We will analyse legal regulation in Croatia, possible problems, and will also consider who could be potential users of unmanned aircraft systems in logistics, either for last mile deliveries or for some other purpose. In conclusion, we will propose further research in this field and how it may influence further development of business logistics", "keywords": [ "Drone", "Business", "Humanitarian Logistics", "Key (lock)", "Control (management)", "Last mile (transportation)", "Aeronautics", "Computer science", "Computer security", "Process management", "Mile", "Engineering" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3841181" }, { "id": "https://openalex.org/W4417509782", "doi": "10.1109/leficlu65987.2025.11297332", "title": "Hierarchical VTOL Hub Location-Routing Optimization for Urban Logistics Drone Delivery", "year": 2025, "publication_date": "2025-09-26", "type": "conference-paper", "language": null, "authors": [ "Jiangao Zhang", "Suyin Cui", "Liya Wang" ], "first_author": "Jiangao Zhang", "venue": null, "publisher": null, "abstract": "To address the collaborative optimization problem of urban logistics drone vertiport location layout and delivery routing, we construct a bi-level model integrating vertiport location selection and a two-tier delivery routing system. The upper-level model minimizes costs by determining vertiport locations and grades, while the lower-level model optimizes delivery routes based on upper-level results. Practical factors such as charging strategies and time window penalties are incorporated to enhance realism. For the upper-level problem, an improved genetic algorithm is proposed, utilizing Halton random sequences for initialization optimization and memory bank operations to accelerate convergence. The lower-level model adopts a greedy-ant colony hybrid algorithm, where a greedy strategy initializes paths and the pheromone mechanism avoids local optima. Simulation experiments using Shanghai courier company data show that the graded vertiport layout reduces costs by 9.94%–13.29% compared to uniform-grade solutions, balancing construction and operational expenses. The integrated optimization achieves an 8.9%–13.56% total cost reduction over traditional two-stage methods.", "keywords": [ "Initialization", "Drone", "Selection (genetic algorithm)", "Vehicle routing problem", "Genetic algorithm", "Routing (electronic design automation)", "Metaheuristic", "Transshipment (information security)", "Cost reduction" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/leficlu65987.2025.11297332" }, { "id": "https://openalex.org/W4390284466", "doi": "10.31375/978-966-7716-94-3-17-19", "title": "THE FUTURE OF LAST MILE LOGISTICS", "year": 2023, "publication_date": "2023-01-01", "type": "conference-abstract", "language": "en", "authors": [ "A. Zhuchenko", "Ye. Yatsina", "I. Moskvichenko" ], "first_author": "A. Zhuchenko", "venue": null, "publisher": null, "abstract": "Last mile logistics refers to the operational capabilities available to companies. The challenges facing last mile logistics are: above all, with a quick response to consumer expectations. This is why flexibility seems so important, both in terms of delivery efficiency and speed of adaptation to new conditions. They determine the choice of supplier. We must not forget that the customer also expects low delivery costs. The future of last mile delivery will be built on key technologies such as: - autonomous delivery systems using a combination of self-driving cars, sidewalk robots and drones to minimize last-mile inefficiencies; - serial production, bringing execution closer to the end customer, ensuring faster delivery at a lower price; - robotic stores on wheels that transport goods directly to the consumer's doorstep, increasing delivery volume with added convenience; - “cabinets” for delivery and storage systems that increase the efficiency of last-mile logistics and facilitate reverse logistics; - digital twins to optimize the flow of resources throughout the supply chain, resulting in efficient delivery.", "keywords": [ "Last mile (transportation)", "Flexibility (engineering)", "Delivery Performance", "Mile", "Computer science", "Drone", "Supply chain", "Business", "Key (lock)", "Operations management", "Transport engineering", "Computer security" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.31375/978-966-7716-94-3-17-19" }, { "id": "https://openalex.org/W4409463060", "doi": "10.1007/978-3-031-78918-2_24", "title": "Optimizing the TSP-D with Pity Beetle Algorithm to Minimize the Delivery Completion Time for UAV Routing Problems", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Yugeswary Kanesen", "Hasneeza Liza Zakaria", "M. S. Asi", "Rozmie Razif Othman", "Wan Nur Suryani Firuz Wan Ariffin", "Nuraminah Ramli", "Mohd Alif Hasmani Abd Ghani" ], "first_author": "Yugeswary Kanesen", "venue": "Studies in computational intelligence", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Pity", "Computer science", "Mathematical optimization", "Routing (electronic design automation)", "Routing algorithm", "Algorithm", "Mathematics", "Computer network", "Psychology", "Routing protocol" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-78918-2_24" }, { "id": "https://openalex.org/W7128390485", "doi": "10.5281/zenodo.18529649", "title": "Optimising Last-Mile Vaccine Delivery: A Cost and Efficiency Analysis of Drone Supply Chains for Malawi's Lake Islands", "year": 2011, "publication_date": "2011-09-26", "type": "article", "language": "en", "authors": [ "Tiyamike Phiri", "Chikondi Mwale" ], "first_author": "Tiyamike Phiri", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Last-mile vaccine delivery to remote communities remains a significant public health challenge in sub-Saharan Africa. The hard-to-reach islands of Lake Malawi, with dispersed populations and limited transport infrastructure, exemplify the logistical and cost barriers to achieving equitable immunisation coverage. This study aimed to analyse the supply chain costs and operational efficiency of using unmanned aerial vehicles (drones) for last-mile vaccine delivery to these islands, compared to traditional boat-based methods. A comparative cost and efficiency analysis was conducted using operational data from a pilot drone delivery programme. Data on transport times, fuel consumption, personnel requirements, vaccine wastage, and cold chain maintenance were collected and modelled for both drone and boat supply chains across a representative sample of island communities. The drone supply chain demonstrated a 65% reduction in average delivery time and a 23% reduction in per-dose delivery cost compared to the boat-based system. Drone deliveries also maintained consistent cold chain integrity, whereas boat deliveries showed periodic temperature excursions. Drone technology presents a viable and more efficient alternative for last-mile vaccine logistics in geographically challenging island settings like Lake Malawi, offering potential for improved timeliness and cost savings. National immunisation programmes should consider integrating drone delivery for remote island and lakeshore communities. Further research is needed to assess the scalability, community acceptance, and long-term sustainability of such systems within the broader health infrastructure. vaccine delivery, drones, last-mile logistics, supply chain, immunisation, Malawi, islands, cost analysis This paper provides empirical evidence on the costs and operational efficiency of drone-mediated vaccine delivery, contributing practical insights for policymakers and health logistics planners in similar remote African contexts.", "keywords": [ "Drone", "Cold chain", "Supply chain", "Cost reduction", "Cost efficiency", "Cost–benefit analysis", "Sustainability", "Cost effectiveness" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18529649" }, { "id": "https://openalex.org/W4413166285", "doi": "10.1080/0305215x.2025.2537181", "title": "A novel drone delivery problem with stochastic arrival time under uncertain wind conditions for last-mile delivery using an iterated adaptive tabu search algorithm", "year": 2025, "publication_date": "2025-08-13", "type": "article", "language": "en", "authors": [ "Yuan Fang", "Shanshan Guo", "Shengchun Deng", "Qinglin Wang" ], "first_author": "Yuan Fang", "venue": "Engineering Optimization", "publisher": "Taylor & Francis", "abstract": "With the rapid growth of e-commerce, last-mile delivery becomes a major challenge in logistics. Drones offer a flexible, cost-effective and environmental friendly solution. However, their energy consumption and arrival time are sensitive to environmental factors, e.g. uncertain wind conditions, making it challenging to offer punctual delivery services. Few studies have considered the stochasticity of wind effects on drone arrival time, which limits their applicability in real-world scenarios. This study establishes an extended chance-constrained programming (ECCP) model for a novel drone delivery problem with stochastic arrival time under uncertain wind conditions. A penalty cost is posed to consider customer satisfaction. A nonlinear energy function considering the wind effect is incorporated to better simulate real-world situations. To solve the ECCP model, a new iterated adaptive tabu search (IATS) algorithm with two-dimensional coding scheme is proposed, which incorporates tabu search within an iterative framework with multiple operators. A self-adaptive mechanism is applied to enhance search efficiency. Experimental results across 21 instances show that the proposed algorithm outperforms five baseline algorithms in solving the proposed model.", "keywords": [ "Tabu search", "Mathematical optimization", "Drone", "Iterated function", "Computer science", "Last mile (transportation)", "Local search (optimization)", "Iterated local search", "Algorithm", "Guided Local Search", "Mathematics", "Mile" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/0305215x.2025.2537181" }, { "id": "https://openalex.org/W7120074006", "doi": "10.3390/fi18010045", "title": "Linearization Strategies for Energy-Aware Optimization of Single-Truck, Multiple-Drone Last-Mile Delivery Systems", "year": 2026, "publication_date": "2026-01-09", "type": "article", "language": "en", "authors": [ "Ornela Gordani", "Eglantina Kalluçi", "Fatos Xhafa" ], "first_author": "Ornela Gordani", "venue": "Future Internet", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The increasing demand for rapid and sustainable parcel delivery has motivated the exploration of innovative logistics systems that integrate drones with traditional ground vehicles. Among these, the single-truck, multiple-drone last-mile delivery configuration has attracted significant attention due to its potential to reduce both delivery time and environmental impact. However, optimizing such systems remains computationally challenging because of the nonlinear energy consumption behavior of drones, which depends on factors such as payload weight and travel time, among others. This study investigates the energy-aware optimization of truck–drone collaborative delivery systems, with a particular focus on the mathematical formulation as mixed-integer nonlinear problem (MINLP) formulations and linearization of drone energy consumption constraints. Building upon prior models proposed in the literature in the field, we analyze the MINLP computational complexity and introduce alternative linearization strategies that preserve model accuracy while improving performance solvability. The resulting linearized mixed-integer linear problem (MILP) formulations are solved using the PuLP software, a Python library solver, to evaluate the efficacy of linearization on computation time and solution quality across diverse problem instance sizes from a benchmark of instances in the literature. Thus, extensive computational results drawn from a standard dataset benchmark from the literature by running the solver in a cluster infrastructure demonstrated that the designed linearization methods can reduce optimization time of nonlinear solvers to several orders of magnitude without compromising energy estimation accuracy, enabling the model to handle larger problem instances effectively. This performance improvement opens the door to a real-time or near-real-time solution of the problem, allowing the delivery system to dynamically react to operational changes and uncertainties during delivery.", "keywords": [ "Linearization", "Solver", "Benchmark (surveying)", "Energy consumption", "Computation", "Nonlinear system", "Optimization problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1999-5903/18/1/45/pdf?version=1767967836" } ]