[ { "id": "https://openalex.org/W2990746220", "doi": "10.1145/3345838.3355998", "title": "A Multilayer Low-Altitude Airspace Model for UAV Traffic Management", "year": 2019, "publication_date": "2019-11-25", "type": "conference-paper", "language": "en", "authors": [ "Nader Samir Labib", "Grégoire Danoy", "Jędrzej Musiał", "Matthias R. Brust", "Pascal Bouvry" ], "first_author": "Nader Samir Labib", "venue": null, "publisher": null, "abstract": "Over the recent years, Unmanned Aerial Vehicles' (UAVs) technology developed rapidly, in turn shedding light on a wide range of potential civil and commercial applications ranging from mapping and surveillance, parcel delivery to more demanding ones that require UAVs to operate in heterogeneous swarms. UAVs are thus expected to soon dominate the shared, low-altitude airspace over populated cities, introducing multiple new research challenges in safely managing the unprecedented traffic demands. The main contribution of this work is addressing the complex problem of UAV traffic management at an abstract level by proposing a structure for the uncontrolled low-altitude airspace. The paper proposes a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulations of UAV traffic for the validation of the model.", "keywords": [ "Computer science", "Air traffic control", "Range (aeronautics)", "Separation (statistics)", "Free flight", "Altitude (triangle)", "Ranging", "Work (physics)", "Set (abstract data type)", "Aeronautics", "Transport engineering", "Real-time computing" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3345838.3355998" }, { "id": "https://openalex.org/W4414153362", "doi": "10.1016/j.engappai.2025.112140", "title": "Emergency medical facility site selection in drone-based relief operations using an enhanced T-spherical fuzzy frank combined compromise solution method", "year": 2025, "publication_date": "2025-09-12", "type": "article", "language": "en", "authors": [ "Rajdip Mahajan", "Saptadeep Biswas", "Vladimir Šimić", "Dragan Stevan Pamucar", "Abhijit Baidya", "Uttam Kumar Bera" ], "first_author": "Rajdip Mahajan", "venue": "Engineering Applications of Artificial Intelligence", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Weighting", "Compromise", "Fuzzy logic", "Emergency management", "Site selection", "Sensitivity (control systems)", "Selection (genetic algorithm)", "Disaster area", "Field (mathematics)" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.engappai.2025.112140" }, { "id": "https://openalex.org/W2945740989", "doi": "10.1109/radar41533.2019.171305", "title": "Initial results of Radar-based classification of commercial drone carrying small payloads", "year": 2019, "publication_date": "2019-09-01", "type": "conference-paper", "language": "en", "authors": [ "Yubin Zhao", "Zhang Xiao", "Francesco Fioranelli" ], "first_author": "Yubin Zhao", "venue": null, "publisher": null, "abstract": "This student paper shows preliminary results on the possibility of using radar systems to identify small payloads carried by a commercial drone. This can be of great interest for traffic monitoring and situational awareness in near-future scenarios when drones are expected to be used for parcel delivery and many other applications. In this work, we consider a distributed and rather small payload, approximately 92g, carried by a commercial drone flying back and forth. Different combinations of features extracted from the micro-Doppler signatures and dwell time have been analyzed, showing promising results with median accuracy around 80% and maximum accuracy approaching 90% for the optimal case.", "keywords": [ "Drone", "Payload (computing)", "Radar", "Dwell time", "Computer science", "Real-time computing", "Radar tracker", "Doppler radar", "Situation awareness", "Remote sensing", "Simulation", "Aeronautics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "http://eprints.gla.ac.uk/184462/" }, { "id": "https://openalex.org/W4415402735", "doi": "10.3390/app152011249", "title": "Integrated Two-Stage Optimization of Strategic Unmanned Aerial Vehicle Allocation and Operational Scheduling Under Demand Uncertainty", "year": 2025, "publication_date": "2025-10-21", "type": "article", "language": "en", "authors": [ "Xiaojin Zheng", "Shengkun Qin", "Yanxia Zhang", "Jiazhen Huo" ], "first_author": "Xiaojin Zheng", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The rapid growth of e-commerce has intensified the need for efficient last-mile delivery, making unmanned aerial vehicles (UAVs) a promising solution. However, despite their potential, practical deployment remains limited by how to effectively plan depot locations and UAV fleet sizes under stochastic customer demands with probabilistic same-day modifications. Existing approaches often address the strategic and operational decisions separately, leading to inefficiencies and infeasible solutions in practice. This study develops a unified two-stage decision framework integrating strategic depot location and UAV fleet allocation with operational assignment and scheduling. Three strategic models are considered: a deterministic model, a stochastic model solved via Sample Average Approximation (SAA), and a robust optimization model. Operational decisions assign UAV trips to realized requests while respecting time-slot and UAV availability constraints. Deterministic and SAA models are solved directly as integer programs, whereas the robust model is tackled via a logic-based Benders decomposition framework, with all approaches evaluated through simulation. The results show that the robust model provides overly conservative solutions, resulting in higher costs; the deterministic model minimizes cost but risks service failures; and the SAA approach balances cost and service across demand scenarios. The findings demonstrate the value of jointly considering strategic and operational decisions in UAV delivery design and provide practical guidance for UAV logistics operators. The proposed framework helps firms select appropriate planning models that align with their risk tolerance and service reliability goals, thereby improving the feasibility and competitiveness of UAV-based delivery systems.", "keywords": [ "Probabilistic logic", "Scheduling (production processes)", "Software deployment", "Stochastic programming", "Reliability (semiconductor)", "Service (business)", "Robust optimization", "Operational planning", "Integer programming", "Time horizon" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/15/20/11249/pdf?version=1761025148" }, { "id": "https://openalex.org/W4387126854", "doi": "10.1145/3565287.3617633", "title": "No-Fly-Zone: Regulating Drone Fly-Overs Via Government and User-Controlled Authorization Zones", "year": 2023, "publication_date": "2023-09-28", "type": "conference-paper", "language": "en", "authors": [ "Abdullah Kamal", "Jeremy Vidaurri", "Carlos E. Rubio-Medrano" ], "first_author": "Abdullah Kamal", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Systems (UAVs), a.k.a., drones, are becoming increasingly popular, as several companies are incorporating them into highly-convenient, last-mile delivery systems. Given this market, concerns with non-consensual surveillance and destruction of drones and private property have been raised. To address these concerns, this paper presents No-Fly-Zone, a solution that proposes developing a series of physical spaces, a.k.a., zones, that utilize Attribute-Based Access Control (ABAC) to allow users, e.g., homeowners, to define flight authorization for drones. With zones that highlight no-fly air-spaces, companies will be able to launch their drones with a lowered chance of human interference or liability, allowing for property owners to determine which level of privacy they wish to enforce. Moreover, using a map that incorporates zoning preferences, a drone path planning algorithm can safely designate a path for any commercial drone flight. No-Fly-Zone also provides a a user-friendly application for collecting and deploying zoning data to pathing algorithms with limited setbacks, thus encouraging the future integration of No-Fly-Zone with existing and future drone flight management systems.", "keywords": [ "Drone", "Zoning", "Computer security", "Authorization", "Government (linguistics)", "Computer science", "SAFER", "Business", "Engineering", "Civil engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dl.acm.org/doi/pdf/10.1145/3565287.3617633" }, { "id": "https://openalex.org/W4361251532", "doi": "10.1109/itnec56291.2023.10082608", "title": "A Hybrid Variable Neighborhood Search Algorithm on Routing and Scheduling for Truck-Assisted Multi-Drone Parcel Delivery", "year": 2023, "publication_date": "2023-02-24", "type": "conference-paper", "language": "en", "authors": [ "Meng Cai", "Hongyan Qian" ], "first_author": "Meng Cai", "venue": null, "publisher": null, "abstract": "In this paper, we study the routing and scheduling for truck-assisted multi-drone parcel delivery. Considering the existing model TSP-mD, two different road networks are adopted, where truck and drones transport goods along Manhattan distance and Euclidean distance, respectively. At the same time allowing truck to launch multiple drones at the same location, the drones can fly out from the depot to serve customers before returning to the truck, or take off from the truck to serve customers eventually flying back to the depot. Based on the common TSP, an integer planning model of TSP-mD with the shortest completion time as the optimization objective is constructed, and a hybrid variable neighborhood search algorithm is designed. Finally, experimental data prove that both the proposed model and HVNS algorithm in this paper are effective.", "keywords": [ "Drone", "Truck", "Variable neighborhood search", "Scheduling (production processes)", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Route planning", "Job shop scheduling", "Travelling salesman problem", "Integer programming", "Variable (mathematics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/itnec56291.2023.10082608" }, { "id": "https://openalex.org/W3196387838", "doi": "10.1007/978-3-030-68655-0_27", "title": "A Drone System for Detecting, Classifying and Monitoring Solid Wastes Using Computer Vision Techniques in the Context of a Smart Cities Logistics Systems", "year": 2021, "publication_date": "2021-01-01", "type": "book-chapter", "language": "en", "authors": [ "Adrian Ramirez-Lopez", "Alberto Cortes-González", "Gilberto Ochoa‐Ruiz", "Alberto Ochoa", "Lina María Aguilar-Lobo", "Diego Moreno-Jacobo", "Christian Mata" ], "first_author": "Adrian Ramirez-Lopez", "venue": "Lecture notes in intelligent transportation and infrastructure", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Context (archaeology)", "Computer science", "Engineering", "Artificial intelligence", "Computer security", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-68655-0_27" }, { "id": "https://openalex.org/W3200627637", "doi": "10.3397/in-2021-2045", "title": "Sound, noise, annoyance? Information as a means to strengthen the public acceptance of civil drones", "year": 2021, "publication_date": "2021-08-01", "type": "conference-paper", "language": "en", "authors": [ "Hinnerk Eißfeldt", "Albert End" ], "first_author": "Hinnerk Eißfeldt", "venue": "NOISE-CON proceedings", "publisher": null, "abstract": "Civil drones are becoming ever more present in public perception. Ranging from parcel delivery to wildlife protection and from precision farming to law enforcement, many applications are said to have market-changing potential. Against this background, nations and institutions around the world are trying to keep up with the dynamic technological developments by means of rules and regulations. Since all parties involved expect a strong increase in both the number of drones and the range of their uses, there is a rising interest in the public acceptance of these vehicles. Widespread acceptance can promote the dissemination of new technologies. Conversely, citizens' concerns about the use of drones in their daily environment may pose barriers to the further proliferation of civil drones, especially in urban areas. The psychoacoustic properties of the vehicles have repeatedly been discussed as being one such limiting factor. This paper discusses results of a representative national study on the social acceptance of civil drones, taking a closer look at effects of information about drones as potential means to foster public acceptance. The findings underline the role of well planned information campaigns as well as community engagement in managing the contribution of drones in future urban soundscapes.", "keywords": [ "Drone", "Soundscape", "Enforcement", "Law enforcement", "Annoyance", "Business", "Perception", "Public relations", "Internet privacy", "Marketing", "Political science", "Sound (geography)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3397/in-2021-2045" }, { "id": "https://openalex.org/W4367396121", "doi": "10.1016/j.dib.2023.109192", "title": "Dataset for the van-drone routing problem with multiple delivery drop points", "year": 2023, "publication_date": "2023-04-29", "type": "data-paper", "language": "en", "authors": [ "Eleftherios Athanasiadis", "Vasilis P. Koutras", "Vasileios Zeimpekis" ], "first_author": "Eleftherios Athanasiadis", "venue": "Data in Brief", "publisher": "Elsevier BV", "abstract": "The distribution of parcels is one of the most complex and challenging processes in supply chain execution. Lately, the development of both electronic and quick commerce has driven carriers and courier operators to identify more effective methods for express parcel delivery. To this end, the development of efficient distribution networks that aim in increasing customer experience while maintaining low operating costs is significant importance both for researchers as well as for practitioners. This article presents a dataset for the Van Drone Routing Problem with Multiple Delivery Points and Cooperation (VDRPMDPC). The latter examines a van-drone team from an operational viewpoint, where a van moves along a road network while the drone egress and ingress from a van to a nearby delivery location and then travels back to the van. This problem has been created with the aim of assessing the design of more sustainable and cost-effective delivery routes in urban and semi urban environments via the use of Unmanned Aerial Vehicles (UAVs). For the development of this dataset real geographical positions have been used located at two different areas of Athens, Greece. The entire benchmark is composed of 14 instances comprised by 20, 40, 60 and 100 clients respectively. The dataset is publicly available for its use and modification.", "keywords": [ "Drone", "Computer science", "Benchmark (surveying)", "Routing (electronic design automation)", "Vehicle routing problem", "Delivery Performance", "Operations research", "Transport engineering", "Distribution (mathematics)", "Computer network", "Business", "Process management" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.dib.2023.109192" }, { "id": "https://openalex.org/W2908381555", "doi": null, "title": "Of People and drones", "year": 2018, "publication_date": "2018-01-01", "type": "conference-paper", "language": "en", "authors": [ "Hinnerk Eißfeldt", "Marcus Biella" ], "first_author": "Hinnerk Eißfeldt", "venue": "elib (German Aerospace Center)", "publisher": "Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)", "abstract": "Drones – understood here as unmanned aerial vehicles (UAV) in civil context - are becoming more and more evident in public perception. Reaching from parcel delivery to wildlife protection, from precision farming to law enforcement, and from industrial inspection to digital fireworks, nearly every month a new drone application or patent pending is presented in the news. Drones help building bridges, inspecting industrial plants, deliver medication, advertise handbags, survey whales, produce shark-warnings etc. Many applications are said to have market-changing potential (for instance the inspection of power grids, railroads, or pipelines), and nations and institutions across the world are trying to keep up with the dynamic development concerning rules and regulations. The European Commission envisions its ‘U-space’ up and running already in 2019, promising to make drone use in low-level airspace safe, secure and environmentally friendly. As a strong increase in the number and uses of drones is anticipated by all parties involved there is a rising interest concerning the acceptance of civil drones in the public. As shown with the planning of airport extensions for example lacking public acceptance can be a limit to aviation growth in general. Likewise, hopes and fears of citizens concerning the use of civil drones in their daily environment could be potential barriers or enablers to the further proliferation of civil drones. This paper reports a representative study of the acceptance of the usage of civil drones in Germany. The study questionnaire was compiled by DLR German Aerospace Research Center and conducted by infas institute for applied social services GmbH. Questions were asked by trained staff in a phone interview of about 20 minutes duration. About 830 respondents agreed to participate in the study and provided their answers to the full set of questions. Reporting detailed results this paper helps to better understand the perception of civil drones in the society and thus to describe the general acceptance of civil drones.", "keywords": [ "Drone", "Context (archaeology)", "Enforcement", "Engineering", "Civil aviation", "Law enforcement", "Business", "Computer security", "Aeronautics", "Political science", "Aviation", "Law" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://elib.dlr.de/123116/1/Ei%C3%9Ffeldt%20%26%20Biella%20%282018%29%20Of%20people%20and%20drones%20EAAP33.pdf" }, { "id": "https://openalex.org/W4415471246", "doi": "10.1016/j.tre.2025.104481", "title": "Distributionally robust scheduling optimization for pharmaceutical delivery using coordinated mother-end drones under post-earthquake road disruptions", "year": 2025, "publication_date": "2025-10-23", "type": "article", "language": "en", "authors": [ "Laijun Zhao", "Qin Huang", "Changzhi Wu" ], "first_author": "Laijun Zhao", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Robust optimization", "Scheduling (production processes)", "Robustness (evolution)", "Cluster analysis", "Stochastic programming", "Linear programming", "Optimization problem" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104481" }, { "id": "https://openalex.org/W4415707208", "doi": "10.1109/tmc.2025.3626747", "title": "Digital Twins for Low-Altitude UAV Networks–Cooperation and Learning", "year": 2025, "publication_date": "2025-10-30", "type": "article", "language": "en", "authors": [ "Longyu Zhou", "Supeng Leng", "Yuchen Liu", "Zehui Xiong", "Tony Q. S. Quek" ], "first_author": "Longyu Zhou", "venue": "IEEE Transactions on Mobile Computing", "publisher": "IEEE Computer Society", "abstract": "The Digital Twin (DT) system has become a new paradigm to empower Unmanned Aerial Vehicles (UAV) networks for low-altitude applications, such as parcel delivery. However, due to high computing complexity, traditional DT technology might confront challenges to imitating highly dynamic UAVs in large-scale parcel delivery scenarios. It causes a negative influence on low-latency and high-accuracy delivery. To address the issue, we propose a terminal-edge cooperative multi-scale DT framework. It can perform a cooperative DT implementation with a cross-layer computing resource orchestration based on a multi-scale imitation manner. Explicitly, we propose a graph matching network based DT algorithm to run macro-scale DTs at the edge. It can assist edge UAVs in exploring feasible delivery associations among UAV groups and parcel clusters based on information on UAV topology and parcel destinations for a high successful delivery ratio. We then propose a Competitive and Cooperative Reinforcement Learning (CCRL) based DT algorithm to implement micro-scale DTs at the terminal. It can enable UAVs to implement low-latency delivery by optimizing delivery paths with low energy consumption. We demonstrate the effectiveness of the proposed framework with verifications under multiple metrics. The results show that our solution provides a real-time UAV delivery performance, with up to 94% successful delivery ratio, under a low system latency compared to the state-of-the-art solutions.", "keywords": [ "Orchestration", "Reinforcement learning", "Edge computing", "Network topology", "Latency (audio)", "Matching (statistics)", "Blossom algorithm", "Low latency (capital markets)", "Graph" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://pure.qub.ac.uk/en/publications/191193ff-0ba2-475e-83db-239e9854f677" }, { "id": "https://openalex.org/W4405722266", "doi": "10.3390/math12244023", "title": "Optimizing Multi-Depot Mixed Fleet Vehicle–Drone Routing Under a Carbon Trading Mechanism", "year": 2024, "publication_date": "2024-12-22", "type": "article", "language": "en", "authors": [ "Yong Peng", "Yanlong Zhang", "Dennis Z. Yu", "Song Liu", "Yali Zhang", "Yangyan Shi" ], "first_author": "Yong Peng", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The global pursuit of carbon neutrality requires the reduction of carbon emissions in logistics and distribution. The integration of electric vehicles (EVs) and drones in a collaborative delivery model revolutionizes last-mile delivery by significantly reducing operating costs and enhancing delivery efficiency while supporting environmental objectives. This paper presents a cost-minimization model that addresses transportation, energy, and carbon trade costs within a cap-and-trade framework. We develop a multi-depot mixed fleet, including electric and fuel vehicles, and a drone collaborative delivery routing optimization model. This model incorporates key factors such as nonlinear EV charging times, time-dependent travel conditions, and energy consumption. We propose an adaptive large neighborhood search algorithm integrating spatiotemporal distance (ALNS-STD) to solve this complex model. This algorithm introduces five domain-specific operators and an adaptive adjustment mechanism to improve solution quality and efficiency. Our computational experiments demonstrate the effectiveness of the ALNS-STD, showing its ability to optimize routes by accounting for both spatial and temporal factors. Furthermore, we analyze the influence of charging station distribution and carbon trading mechanisms on overall delivery costs and route planning, underscoring the global significance of our findings.", "keywords": [ "Computer science", "Drone", "Vehicle routing problem", "Environmental economics", "Energy consumption", "Routing (electronic design automation)", "Operations research", "Transport engineering", "Simulation", "Engineering", "Computer network", "Economics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2227-7390/12/24/4023/pdf?version=1734858364" }, { "id": "https://openalex.org/W4366775089", "doi": "10.54097/ijeh.v8i1.7255", "title": "Urban Area End Logistics Drones Distribution Route Planning", "year": 2023, "publication_date": "2023-04-10", "type": "article", "language": "en", "authors": [ "Fang Tan" ], "first_author": "Fang Tan", "venue": "International Journal of Education and Humanities", "publisher": null, "abstract": "With the development of logistics technology, the application of drones in the last mile of logistics distribution has also become a hot topic. Due to the complexity and particularity of the flight environment in urban areas, UAVs need to comprehensively consider safety performance and flight energy consumption when conducting terminal logistics distribution. Based on this, this paper constructs a mathematical model with the goal of shortening flight paths, reducing energy consumption, and improving flight safety, and completes environmental modeling using grid method and genetic algorithm to solve the model. At the same time, the battery energy consumption of the UAV is calculated using the energy consumption segmentation mode. The experimental results of a numerical example verify the effectiveness of the model and algorithm, and also prove that considering segmented energy consumption can more accurately calculate the battery energy consumption of unmanned aerial vehicles.", "keywords": [ "Drone", "Energy consumption", "Computer science", "Grid", "Fuel efficiency", "Battery (electricity)", "Automotive engineering", "Consumption (sociology)", "Genetic algorithm", "Energy (signal processing)", "Real-time computing", "Simulation" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://drpress.org/ojs/index.php/ijeh/article/download/7255/7055" }, { "id": "https://openalex.org/W4327774314", "doi": "10.1109/candarw57323.2022.00068", "title": "Delivery Routing to Reduce Calculation Load of Drones on Divided Logistics Areas for Drone Logistics Networks", "year": 2022, "publication_date": "2022-11-01", "type": "conference-paper", "language": "en", "authors": [ "Kazuya Matsutani", "Shigetomo Kimura" ], "first_author": "Kazuya Matsutani", "venue": null, "publisher": null, "abstract": "In a drone logistics system, a message delivery system in which the drone delivers messages for other users on the way to a parcel delivery destination has been proposed. To reduce the complexity of message delivery routes, this paper proposes a message delivery method that divides logistics areas and determines the message delivery routes in each area. The method also makes it possible for a later departure drone and an early departure drone to exchange information to add, cancel, and/or exchange their messages and delivery points. The simulation experiments show that compared with the previous method, the proposed method has lower computational complexity and can be assumed to cover almost the same the average delivery distance. It is also shown that when an early departure drone and a later departure drone exchange many message delivery points, the average delivery distance is reduced.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Cover (algebra)", "Information exchange", "Computer network", "Operations research", "Engineering", "Telecommunications" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/candarw57323.2022.00068" }, { "id": "https://openalex.org/W4311562645", "doi": "10.21203/rs.3.rs-2365573/v1", "title": "Evaluating the sustainability of lightweight drones for delivery: towards a suitable methodology for assessment", "year": 2022, "publication_date": "2022-12-15", "type": "preprint", "language": "en", "authors": [ "Sinéad Mitchell", "Juliana Steinbach", "Tomás Flanagan", "Pouyan Ghabezi", "Noel M. Harrison", "Simon O’Reilly", "Stephen Killian", "William G. Finnegan" ], "first_author": "Sinéad Mitchell", "venue": "Research Square", "publisher": null, "abstract": "Abstract Drone technology is widely available and is rapidly becoming a crucial instrument in the functions of businesses and government agencies worldwide. The demand for delivery services is accelerating particularly since the Covid-19 pandemic. Both companies and customers want these services to be efficient, timely, safe, and sustainable, but these are major challenges. Last-mile delivery by lightweight short-range drones has the potential to address these challenges. However, there is a lack of consistency and transparency in assessing and reporting the sustainability of last-mile delivery services and drones. This paper critically reviews published assessments (specifically lifecycle assessment and circularity). The study reveals a lack of comprehensive studies, and a need to examine composite and battery manufacturing developments and provides key considerations for future study development.", "keywords": [ "Drone", "Sustainability", "Transparency (behavior)", "Business", "Government (linguistics)", "Consistency (knowledge bases)", "Last mile (transportation)", "Pandemic", "Risk analysis (engineering)", "Coronavirus disease 2019 (COVID-19)", "Process management", "Mile" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-2365573/latest.pdf" }, { "id": "https://openalex.org/W4405885010", "doi": "10.58915/ijact.v4.2024.1647", "title": "Bibliometric Analysis Of Travelling Salesman Problem With Drones (TSP-D)", "year": 2024, "publication_date": "2024-12-30", "type": "article", "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": "International Journal of Advanced Communication Technology (IJACT)", "publisher": null, "abstract": "The growing need for efficient package delivery systems in Last Mile Delivery (LMD) has attracted significant attention, bringing the Travelling Salesman Problem with Drones (TSP-D) to the forefront of scholarly research. The research under consideration generated a surge in scientific output across various domains, including economics, industry, and logistics, owing to the potential benefits it presents. The primary objective of this study is to compile, organize, and analyze the literature on TSP-D in the context of LMD. A comprehensive bibliometric analysis scrutinized the progress made in this field during the period spanning 2017 to 2024. The study encompassed an extensive analysis of a corpus of 2,397 scholarly publications, yielding numerous intriguing conclusions derived from a meticulously constructed framework. This review addresses inquiries about the most prevalent research categories on resolving TSP-D, focusing on exact and metaheuristic approaches.", "keywords": [ "Travelling salesman problem", "Drone", "Context (archaeology)", "Operations research", "Computer science", "Traveling purchaser problem", "Field (mathematics)", "Metaheuristic", "Data science", "Management science", "2-opt", "Economics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ejournal.unimap.edu.my/index.php/ijact/article/download/1647/1010" }, { "id": "https://openalex.org/W4390136526", "doi": "10.48550/arxiv.2312.13551", "title": "Elevating Industries with Unmanned Aerial Vehicles: Integrating Sustainability and Operational Innovation", "year": 2023, "publication_date": "2023-12-21", "type": "preprint", "language": "en", "authors": [ "Ali Kaan Kurbanzade", "Ansaar M. Baig", "Sanjay Mehrotra" ], "first_author": "Ali Kaan Kurbanzade", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned aerial vehicles, commonly known as drones, have emerged as a disruptive technology with the potential to revolutionize operations across various industries. Drones are the fast-growing internet-of-things technology and are estimated to have a $100 billion market value in the next decade. Exploring drone operations through research has the potential to yield innovative academic insights and create significant practical effects in diverse industries, offering a competitive edge. Drawing insights from both academic and industry literature, this article describes how technological advancements in UAVs may disrupt traditional operational practices in different industries (e.g., commercial last-mile delivery, commercial pickup and delivery, telecommunication, insurance, healthcare, humanitarian, environmental, urban planning, homeland security), identifies the value of this evolving disruptive technology from sustainability and operational innovation perspectives, argues the significance of this area for operations management by conceptualizing a research agenda. The current state of the art focuses on the computing aspect of analytical models to tackle a variety of synthetic drone-related problems, with mixed integer optimization being the primary tool. There is a very significant research gap that should focus on drone operations management with industry know-how by partnering with actual stakeholders and using a variety of tools (i.e., econometrics, field experiments, game theory, optimal control, utility functions). This article aims to promote research on UAVs from operations management and industry-specific point of view.", "keywords": [ "Drone", "Variety (cybernetics)", "Value proposition", "Homeland security", "Sustainability", "Business", "Competitive advantage", "Disruptive innovation", "Marketing", "Computer science", "Political science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2312.13551" }, { "id": "https://openalex.org/W4391857154", "doi": "10.1109/aece59614.2023.10428514", "title": "Design of AED Based Drone to Deliver the Medical Kit on an Emergency Basis", "year": 2023, "publication_date": "2023-11-23", "type": "conference-paper", "language": "en", "authors": [ "P. Nagarani", "Mohd Wazih Ahmad", "R. Shanmuka Shalini", "Pavitar Parkash Singh" ], "first_author": "P. Nagarani", "venue": null, "publisher": null, "abstract": "Access to reliable modes of transportation is essential. Postal delivery to multi-million-dollar freight transports, practically everything is linked to its connections. Wheeled motor vehicles and manned aeroplanes are now the only options for transporting medical supplies at times of urgent need, which may be expensive, time-consuming, and often impossible if the emergency site is far. Drones are a game-changer in the medical industry because they can transfer vital supplies like blood and medical equipment. Unfortunately, given the state of the art, the drone's battery life is a constraint that may slow down the delivery process. This article details the research into the mechanisms designed for drones to increase their battery life and efficiently deliver first aid supplies. The drone's system safely delivers and drops medical supplies, reducing the drone's takeoffs and landings. The primary goal of this research is to develop a device that can safely transport medical equipment to its destination and then carefully unload it there. Results showed the system could safely transport and deploy first aid supplies during flight and landing.", "keywords": [ "Drone", "Computer science", "Aeronautics", "Basis (linear algebra)", "Medical emergency", "Medicine", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/aece59614.2023.10428514" }, { "id": "https://openalex.org/W4406611545", "doi": "10.1109/smc54092.2024.10831970", "title": "Depth Camera-LiDAR Fusion Based UAV Autonomous Navigation for Parcel Delivery in Complex Urban Environment", "year": 2024, "publication_date": "2024-10-06", "type": "conference-paper", "language": "en", "authors": [ "Chengdao Chi", "Bing Li", "Hao Deng", "Shengjie Zhao" ], "first_author": "Chengdao Chi", "venue": null, "publisher": null, "abstract": "Unmanned aerial vehicle (UAV) delivery in urban environments demonstrates significant growth potential due to its efficiency and eco-friendliness. The challenges of autonomous navigation and obstacle avoidance in complex environments are crucial study aspects of UAV delivery. This paper proposes a depth camera-LiDAR fusion based UAV autonomous navigation protocol, enabling autonomous navigation and obstacle avoidance for UAV parcel delivery in complex urban environments. Experiments are conducted in a highly realistic simulation environment to validate performance of the proposed method. The results indicate that the proposed method can efficiently and accurately achieve autonomous navigation and obstacle avoidance with high robustness.", "keywords": [ "Lidar", "Computer science", "Computer vision", "Remote sensing", "Sensor fusion", "Fusion", "Artificial intelligence", "Environmental science", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/smc54092.2024.10831970" }, { "id": "https://openalex.org/W4406356702", "doi": "10.1109/iccasit62299.2024.10828038", "title": "Optimization of Urban Logistics Obstacle Avoidance UAV Path Based on Improved Crowned Porcupine Pig Algorithm", "year": 2024, "publication_date": "2024-10-23", "type": "conference-paper", "language": "en", "authors": [ "Shengyu Jiang", "Baotong Wang" ], "first_author": "Shengyu Jiang", "venue": null, "publisher": null, "abstract": "Path planning for unmanned aerial vehicles (UAVs) in complex urban logistics is a core component of UAV mission planning systems. In order to solve the problem of safe and efficient logistics drone path planning, this paper proposes a crowned porcupine Pig defense mechanism based on the crowned porcupine Pig optimization algorithm, which relies on four defense mechanisms: odor, sound, line of sight, and physical attack. This algorithm has made the following contributions: targeting the problem of urban logistics drone transportation tasks, and comprehensively considering the restrictions of different cities’ no fly airspace (different flight heights), minimizing economic costs, optimizing time efficiency, and improving drone performance. It has established an adjusted terrain map, obstacle size and range, and drone flight altitude to determine the optimal flight path of the drone from the starting point to the end point through the algorithm. The simulation results show that after 500 iterations of optimization using crowned porcupine Pig, the convergence of path planning is good. This article can provide a reference for drone path planning and a solution for drone path optimization.", "keywords": [ "Porcupine", "Computer science", "Obstacle", "Obstacle avoidance", "Path (computing)", "Collision avoidance", "Optimization algorithm", "Real-time computing", "Algorithm", "Artificial intelligence", "Mathematical optimization", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iccasit62299.2024.10828038" }, { "id": "https://openalex.org/W4399039538", "doi": "10.1109/ispa59904.2024.10536779", "title": "Drone-Enabled Connectivity: Advancements and Challenges in B5G / 6G Networks", "year": 2024, "publication_date": "2024-04-21", "type": "conference-paper", "language": "en", "authors": [ "Khaled Telli", "Okba Kraa", "Yassine Himeur", "Mohamed Boumehraz", "Abdelmalik Ouamane", "Mohamed Kamel Benbraika", "Shadi Atalla", "Wathiq Mansoor" ], "first_author": "Khaled Telli", "venue": null, "publisher": null, "abstract": "The rapid advancement of networking and broad-band communications has created a demand for efficient relaying methods, particularly in the context of high-fidelity and low-latency last-mile communications. This paper explores the utilization of cellular-enabled-drones as Aerial-Base-Station (Aerial-BS) in cellular networks next-generation, specifically focusing on their potential as flying base stations in Beyond 5G/6G networks. The study summarises various aspects, including energy availability, mobility, optimal positioning, and node localization, handover efficiency. Additionally, multi-objective optimizations for bandwidth allocation, accommodating highly mobile User Equipment (UE), mitigating interference, and ensuring quality of service (QoS) are discussed. The paper highlights the use of drone-base-stations (DBS) as extending terrestrial network capacity and coverage, assisting mobile ad-hoc-networks, and enabling beam-forming applications. The challenges associated with optimal positioning, energy availability, path planning mobility, security, and QoS are examined, providing insights into future research directions. The findings contribute to the enhancement of cellular connectivity and offer valuable insights into the utilization of drones as flying aerial base stations in B5G / 6G network.", "keywords": [ "Base station", "Drone", "Computer science", "Cellular network", "Computer network", "Quality of service", "Efficient energy use", "Handover", "Mobile telephony", "Mobile radio", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ispa59904.2024.10536779" }, { "id": "https://openalex.org/W3046476802", "doi": null, "title": "What impedes Consumers’ Delivery Drone Service Adoption?", "year": 2020, "publication_date": "2020-06-10", "type": "article", "language": "en", "authors": [ "Marius Knobloch", "Mario Schaarschmidt" ], "first_author": "Marius Knobloch", "venue": null, "publisher": null, "abstract": "Despite widespread plans of big companies like Amazon and Google to develop unmanned delivery drones, scholarly research in this field is scarce, especially in the information systems field. From technical and legal perspectives, drone delivery in last-mile scenarios is in a quite mature state. However, estimates of user acceptance are varying between high skepticism and exaggerated optimism. This research follows a mixed method approach consisting both qualitative and quantitative research, to identify and test determinants of consumer delivery drone service adoption. The qualitative part rests on ten interviews among average consumers, who use delivery services on a regular basis. Insights gained from the qualitative part were used to develop an online survey and to assess the influence of associated risks on adoption intentions. The quantitative results show that especially financial and physical risks impede drone delivery service adoption. Delivery companies who are currently thinking about providing a delivery drone service may find these results useful when evaluating usage behaviors in the future market for delivery drones.", "keywords": [ "Drone", "Business", "Service delivery framework", "Marketing", "Service (business)", "Qualitative research", "Sociology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hbz.opus.hbz-nrw.de/opus45-kola/frontdoor/index/index/docId/2070" }, { "id": "https://openalex.org/W4414614639", "doi": "10.1080/15568318.2025.2564851", "title": "Investigating the emissions effect of integrating drones into mixed-mode logistics – A case study of a healthcare setting", "year": 2025, "publication_date": "2025-09-29", "type": "article", "language": "en", "authors": [ "Matt Grote", "Andy Oakey", "Aliaksei Pilko", "Jakub Krol", "Alex Blakesley", "Tom Cherrett", "James P. Scanlan", "Bani Anvari", "Antonio Martínez-Sykora" ], "first_author": "Matt Grote", "venue": "International Journal of Sustainable Transportation", "publisher": "Taylor & Francis", "abstract": "Interest is growing in the potential of using Uncrewed Aerial Vehicles (UAVs; known as drones) for logistics applications (i.e. last-mile payload delivery). Based on case studies of pathology specimens taken from patients at community clinics and transported to central laboratories for analysis, the effect on greenhouse gas emissions of using drones alongside more traditional transport modes (i.e. electric vans (e-vans) and bicycle couriers) in mixed-mode logistics systems was investigated for networks in locations with contrasting geographic characteristics. Results suggested that reductions in emissions of up to 83% were possible compared to e-van-only solutions. Notably, bicycle couriers made a considerable contribution to these reductions in some cases. In general, serving clinics that were remote and/or isolated tended to be where drones could offer a beneficial effect. Using drones was also associated with decreases in payload transit times (up to 76%) but increases in costs (up to 134%), raising a question regarding the true value of expedited delivery in a medical context.", "keywords": [ "Drone", "Payload (computing)", "Greenhouse gas", "Transit (satellite)", "Health care", "City logistics", "Poison control" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.tandfonline.com/doi/pdf/10.1080/15568318.2025.2564851?needAccess=true" }, { "id": "https://openalex.org/W4391790578", "doi": "10.1007/978-3-031-47718-8_19", "title": "AI-Driven Runtime Monitoring of Energy Consumption in Autonomous Delivery Drones", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Moritz Urban", "Adina Aniculăesei", "Andreas Rausch" ], "first_author": "Moritz Urban", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Energy consumption", "Computer science", "Energy (signal processing)", "Computer security", "Engineering", "Aeronautics", "Physics", "Electrical engineering", "Biology" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-47718-8_19" }, { "id": "https://openalex.org/W7124150147", "doi": "10.1109/codit66093.2025.11321813", "title": "Drone-Based Delivery in Logistics: Interdisciplinary Challenges", "year": 2025, "publication_date": "2025-07-15", "type": "conference-paper", "language": "en", "authors": [ "Mariem Belhor", "Danielle Nyakam Nya" ], "first_author": "Mariem Belhor", "venue": null, "publisher": null, "abstract": "As urban logistics continue to evolve, there is a growing need for innovative delivery solutions that can meet rising demand while addressing sustainability and cost-effectiveness. To tackle these challenges, ecofriendly transportation methods must be adopted to minimize environmental impact, enhance safety and optimize costs. Drone delivery has emerged as a transformative approach that offers speed, adaptability and reduced emissions compared to conventional logistics. However, its large-scale deployment presents technical and operational challenges that require an interdisciplinary approach. This paper examines these challenges through the integration of control engineering and computer science, identifying how their synergies can enhance efficiency and scalability in drone logistics by developing complementary solutions that address various logistical challenges.", "keywords": [ "Adaptability", "Software deployment", "Drone", "Sustainability", "Flexibility (engineering)", "Transformative learning" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/codit66093.2025.11321813" }, { "id": "https://openalex.org/W3200985582", "doi": "10.3926/jairm.182", "title": "Management of urban air logistics with unmanned aerial vehicles: The case of medicine supply in Aveiro, Portugal", "year": 2021, "publication_date": "2021-06-30", "type": "article", "language": "en", "authors": [ "Mário J. Silva", "Jorge Miguel Reis Silva" ], "first_author": "Mário J. Silva", "venue": "Journal of Airline and Airport Management", "publisher": "OmniaScience", "abstract": "Purpose: This research aims to investigate the relation between urban logistics and all delivery systems used. The unmanned aerial vehicle (UAV) and the unmanned aerial system (UAS) have been under investigation in the world of logistics, having been pointed as the next logistic technology. For that reason, this article proposes the use of UAV in urban logistics.Design/methodology/approach: We set for the methodology study the current state of this system and analyze what lies ahead soon. Based on this information, we intend to implement a scenario of deliveries in an urban environment. This scenario will be in the city of Aveiro and consists of the delivery of medicines into pharmacies located in an urban environment. With this study, we pretend to find the best current and future solution to operate in an urban environment, with the conventional vehicle or the UAV/UAS.Findings: The legal implications for the use of UAV/UAS are one important factor that can set back the use of these technologies due to the lack of legislation. Although this technology has some limitations in endurance and payload, this investigation reached a consensus in the use of UAV for logistics in urban areas, in small payloads (around 15 kg), and low endurance (around 25 min). The UAV/UAS brings excellent advantages that the conventional vehicle cannot overcome: direct routes; traffic congestion; and environmental legislation.Practical implications: This study brings an overview of a possible scenario for urban logistics. Although this is currently not possible, soon, this scenario could be implemented. This would bring a reduction in operation costs and reduce the congestion in the urban cores of the cities.Originality/value: Based on this research, this delivery system could, in the future, help and be the starting point for UAV/UAS logistics, specifically in the delivery of medicines.", "keywords": [ "Payload (computing)", "City logistics", "Legislation", "Transport engineering", "Computer science", "Urban environment", "Supply chain", "Business", "Environmental planning", "Computer security", "Environmental science", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.jairm.org/index.php/jairm/article/download/182/146" }, { "id": "https://openalex.org/W3155281209", "doi": "10.1117/12.2588153", "title": "Challenges and opportunities of unmanned aerial vehicles in agriculture in the next decade", "year": 2021, "publication_date": "2021-04-09", "type": "conference-paper", "language": "en", "authors": [ "Alfonso F. Torres‐Rua" ], "first_author": "Alfonso F. Torres‐Rua", "venue": null, "publisher": null, "abstract": "Unmanned aerial vehicles have been available for more than a decade and have been part of tests and efforts to be incorporated in agricultural applications, which was achieved to a minor degree. Still, advancements in science and technology in the same period have achieved maturity allowing the transition of this found knowledge into farming decision-making agents. Still, challenges are present towards the “last mile problem” or the seamless integration of UAV products into farming activities. Besides, having addressed the fundamental questions about UAV technology in the last decade, UAVs can answer questions that have been challenging to respond to in agriculture and open opportunities to advance the technification and automation of farming further. This presentation will provide a personal view of unmanned aerial vehicles’ challenges, and opportunities as regulation, technology, and algorithms evolve for the next decade.", "keywords": [ "Agriculture", "Presentation (obstetrics)", "Maturity (psychological)", "Automation", "Last mile (transportation)", "Precision agriculture", "Computer science", "Drone", "Mile", "Engineering", "Geography", "Political science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2588153" }, { "id": "https://openalex.org/W7147508291", "doi": "10.1016/j.tre.2026.104825", "title": "Air-ground multimodal transport planning for joint passenger mobility and parcel delivery: integration of drones, aircraft, and ground vehicles", "year": 2026, "publication_date": "2026-03-31", "type": "article", "language": "en", "authors": [ "Yimeng Zhang", "Chenjie Yang", "Haoning Xi", "Songhan Peng", "Junjie Yang", "Mi Gan", "Xiaobo Liu", "Ruixue Ai" ], "first_author": "Yimeng Zhang", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "Drones and electric Vertical Takeoff and Landing aircraft (eVTOLs) offer potential to improve transport efficiency and flexibility by circumventing ground transport constraints such as traffic congestion and infrastructure limitations. However, aerial vehicles alone often cannot meet transport demands, necessitating integration with ground-based systems. To address this challenge, this study investigates the Integrated Air-Ground Multimodal Transport Planning Problem (IAG-MTPP) for joint passenger mobility and parcel delivery. The IAG-MTPP integrates fixed-route and flexible-route vehicles, accommodating operational scenarios involving eVTOL aircraft, drones, and ground vehicles. The proposed IAG-MTPP is formulated as a Mixed-Integer Linear Programming (MILP) model that optimizes multimodal operations to minimize transshipment costs, delay penalties, and carbon emissions, while also incorporating capital costs and battery-energy constraints for electric air and ground vehicles. The model is tailored to the complexities of air-ground vehicle routing, considering transfer operations, inter-modal coordination, and routing flexibility. It enables switches between parcel-only, passenger-only, and integrated operations, providing adaptability to evolving transport demands. To solve large-scale instances, we customize an Adaptive Large Neighborhood Search (ALNS) algorithm with insertion, removal, and swap operators, feasibility checks, and an operator-selection scheme. Benchmarking against a commercial exact solver and multiple heuristic algorithms demonstrates the robustness and scalability of the proposed ALNS. The effectiveness and applicability of the proposed model and algorithm are validated through numerical experiments in scenarios including emergency rescue operations in Jiuzhaigou and urban transport in Chengdu, China. The results demonstrate that the integrated air-ground system optimizes multimodal routing while reducing transport costs and improving service coverage compared to alternatives. The proposed ALNS algorithm solves large-scale instances efficiently where commercial exact solvers fail, supporting real-world deployment in large networks and high-demand settings. This study offers insights and guidelines to support efficient resource allocation in the rapidly evolving low-altitude economy.", "keywords": [ "Flexibility (engineering)", "Adaptability", "Transshipment (information security)", "Multimodal transport", "Routing (electronic design automation)", "Takeoff", "Stochastic programming", "Drone", "Bilevel optimization" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104825" }, { "id": "https://openalex.org/W4406267786", "doi": "10.1109/vtc2024-fall63153.2024.10757589", "title": "Bayesian Optimization for Fast Radio Mapping and Localization with an Autonomous Aerial Drone", "year": 2024, "publication_date": "2024-10-07", "type": "conference-paper", "language": "en", "authors": [ "Paul S. Kudyba", "Qin Lu", "Haijian Sun" ], "first_author": "Paul S. Kudyba", "venue": null, "publisher": null, "abstract": "This paper explores how a flying drone can autonomously navigate while constructing a narrowband radio map for signal localization. As flying drones become more ubiquitous, their wireless signals will necessitate new wireless technologies and algorithms to provide robust radio infrastructure while preserving radio spectrum usage. A potential solution for this spectrum-sharing localization challenge is to limit the bandwidth of any transmitter beacon. However, location signaling with a narrow bandwidth necessitates improving a wireless aerial system’s ability to filter a noisy signal, estimate the transmitter’s location, and self-pilot to improve the location estimate. By showing results through simulation, emulation, and a final drone flight experiment, this work provides an algorithm using a Gaussian process for radio signal estimation and Bayesian optimization for drone automatic guidance. This research supports advanced radio and aerial robotics applications in critical areas such as search-and-rescue, last-mile delivery, and large-scale platform digital twin development.", "keywords": [ "Drone", "Computer science", "Bayesian optimization", "Artificial intelligence", "Simultaneous localization and mapping", "Bayesian probability", "Computer vision", "Mobile robot", "Robot" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/vtc2024-fall63153.2024.10757589" }, { "id": "https://openalex.org/W7128957790", "doi": "10.1007/s44163-026-00940-x", "title": "Research on dual objective optimization of location and allocation for urban logistics drone distribution centers based on cost and timeliness", "year": 2026, "publication_date": "2026-02-15", "type": "article", "language": "en", "authors": [ "Qing Liu", "Haonan Ge", "Yang Song" ], "first_author": "Qing Liu", "venue": "Discover Artificial Intelligence", "publisher": "Springer Nature", "abstract": "Abstract This study proposes an innovative dual-objective optimization model for the location and allocation problem of drone delivery centers in urban logistics, aiming to simultaneously minimize the overall cost and maximize delivery time efficiency. To address the complexity of this NP-hard problem, an Improved Genetic-Particle Swarm Optimization (IGA-PSO) hybrid algorithm was designed, which integrates a simulated annealing selection strategy and a Gaussian noise perturbation mechanism to enhance solution quality and robustness. Simulation results based on a 50-node urban scenario demonstrate that the proposed IGA-PSO significantly outperforms standard GA and PSO, converging to the optimal solution (Total Cost: 300,704 CNY; Average Time Utility: 0.894) by the 20th iteration. Sensitivity analysis further reveals the critical impact of airspace configurations: expanding feasible airspace reduces total costs by 15.4% (to 254,438 CNY), whereas introducing new no-fly zones increases costs by 9.6%. Additionally, scalability tests confirm that the model maintains linear time complexity ( $${R}^{2}\\approx0.99$$ ) as the number of demand nodes increases. This study provides theoretical support and data-driven guidance for the coordinated optimization of economy, efficiency, and sustainability in urban drone logistics systems.", "keywords": [ "Drone", "Simulated annealing", "Multi-objective optimization", "Particle swarm optimization", "Scalability", "Sensitivity (control systems)", "Genetic algorithm", "Location-allocation" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s44163-026-00940-x" }, { "id": "https://openalex.org/W3131569636", "doi": "10.2139/ssrn.3476824", "title": "Retail Deliveries by Drones: How Will Logistics Networks Change?", "year": 2019, "publication_date": "2019-01-01", "type": "preprint", "language": "en", "authors": [ "Sandun C. Perera", "Milind Dawande", "Ganesh Janakiraman", "Vijay S. Mookerjee" ], "first_author": "Sandun C. Perera", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Business", "Marketing", "Industrial organization" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.3476824" }, { "id": "https://openalex.org/W2974100732", "doi": "10.22214/ijraset.2019.5053", "title": "Delivery Drone System", "year": 2019, "publication_date": "2019-05-31", "type": "article", "language": "en", "authors": [ "Vikram Pratap Singh" ], "first_author": "Vikram Pratap Singh", "venue": "International Journal for Research in Applied Science and Engineering Technology", "publisher": "International Journal for Research in Applied Science and Engineering Technology (IJRASET)", "abstract": "Drones have recently become a promising solution for rapid parcel delivery due to advances in battery technology and navigation systems.Drones have inherited limitations in battery capacity and payload, which make their efficient operation and management a critical problem for a successful delivery system.Adopting modularity in the drone design can provide operational benefits to increase overall fleet readiness and reduce overall fleet size.This paper discusses the potential value of introducing modular design to a drone delivery system.We propose an optimization method for the operation management of a fleet of modular delivery drones.This paper presents simulation results that compare the proposed method with existing operation management methods.The results show that a simple operation management strategy can make a drone delivery system unstable with increasing demand on certain types of modules in the fleet.", "keywords": [ "Drone", "Computer science", "Aeronautics", "Engineering", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.22214/ijraset.2019.5053" }, { "id": "https://openalex.org/W4407893139", "doi": "10.4271/2025-01-7144", "title": "Regulatory and Policy Framework for Urban Drone Logistics Systems", "year": 2025, "publication_date": "2025-02-21", "type": "conference-paper", "language": "en", "authors": [ "Jie Ma", "Junjie Yang", "Weile Diao", "Yan‐Jun Du", "Weiqi Chen" ], "first_author": "Jie Ma", "venue": "SAE technical papers on CD-ROM/SAE technical paper series", "publisher": null, "abstract": "The swift and relentless progression of drone technology has ushered in novel opportunities within the realm of urban logistics, especially for the potential of drones to modify last-mile delivery and improve customer fulfillment through mobile application integration, offering the potential for delivery systems that are both efficient and environmentally sustainable. This development is not just a technological leap but a transformative shift in how goods are moved within urban spaces, potentially reducing traffic congestion and emissions from traditional vehicles. Nevertheless, the safety issues of drone flights in cities are becoming increasingly serious, and the accountability related to drone accidents is not clear, raising concerns in society regarding the use and safety of drones. Therefore, to fully utilize the potential of drones in urban logistics, the incorporation of drones into the urban airspace environment necessitates the establishment of a strong regulatory and policy framework, one that is capable of addressing a multitude of concerns, including safety, security, environmental impact, and social equity. This particular research paper undertakes a detailed examination of the existing regulatory landscape that pertains to urban drone logistics systems. It aims to pinpoint the primary challenges that are currently faced in this domain, and subsequently proposes a comprehensive policy framework designed to facilitate the effective implementation of urban drone logistics. To achieve a thorough understanding of the subject matter, this study meticulously reviews and analyzes more than 30 scholarly articles, industry reports, and policy documents, synthesizing the insights gained to provide a well-rounded perspective and policy framework suggestions on the topic.", "keywords": [ "Drone", "Business", "Computer science", "Computer security", "Risk analysis (engineering)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4271/2025-01-7144" }, { "id": "https://openalex.org/W3178822418", "doi": "10.1007/s10619-021-07347-w", "title": "Multiple ground/aerial parcel delivery problem: a Weighted Road Network Voronoi Diagram based approach", "year": 2021, "publication_date": "2021-07-12", "type": "article", "language": "en", "authors": [ "Po-Wei Harn", "Ji Zhang", "Ting Shen", "Wenlu Wang", "Xunfei Jiang", "Wei‐Shinn Ku", "Min-Te Sun", "Yao‐Yi Chiang" ], "first_author": "Po-Wei Harn", "venue": "Distributed and Parallel Databases", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Voronoi diagram", "Truck", "Computer science", "Heuristic", "Transport engineering", "Partition (number theory)", "Mathematics", "Engineering", "Artificial intelligence", "Automotive engineering", "Geometry" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10619-021-07347-w" }, { "id": "https://openalex.org/W4402958310", "doi": "10.1007/978-981-97-3690-4_45", "title": "Optimizing Parcel Delivery Routes", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Sulabh Bansal", "Priyanshi Vyas" ], "first_author": "Sulabh Bansal", "venue": "Smart innovation, systems and technologies", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Computer science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-97-3690-4_45" }, { "id": "https://openalex.org/W4411560114", "doi": "10.3390/drones9070454", "title": "Parametric Modeling and Evaluation of Departure and Arrival Air Routes for Urban Logistics UAVs", "year": 2025, "publication_date": "2025-06-23", "type": "article", "language": "en", "authors": [ "Zhong‐Ming Li", "Yifei Zhao", "Xinhui Ren" ], "first_author": "Zhong‐Ming Li", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid development of the low-altitude economy, the intensive take-offs and landings of Unmanned Aerial Vehicles (UAVs) performing logistics transport tasks in urban areas have introduced significant safety risks. To reduce the likelihood of collisions, logistics operators—such as Meituan, Antwork, and Fengyi—have established fixed departure and arrival air routes above vertiports and designed fixed flight air routes between vertiports to guide UAVs to fly along predefined paths. In the complex and constrained low-altitude urban environment, the design of safe and efficient air routes has undoubtedly become a key enabler for successful operations. This research, grounded in both current theoretical research and real-world logistics UAV operations, defines the concept of UAV logistics air routes and presents a comprehensive description of their structure. A parametric model for one-way round-trip logistics air routes is proposed, along with an air route evaluation model and optimization method. Based on this framework, the research identifies four basic configurations that are commonly adopted for one-way round-trip operations. These configurations can be further improved into two optimized configurations with more balanced performance across multiple metrics. Simulation results reveal that Configuration 1 is only suitable for small-scale transport; as the number of delivery tasks increases, delays grow linearly. When the task volume exceeds 100 operations per 30 min, Configurations 2, 3, and 4 reduce average delay by 88.9%, 89.2%, and 93.3%, respectively, compared with Configuration 1. The research also finds that flight speed along segments and the cruise segment capacity have the most significant influence on delays. Properly increasing these two parameters can lead to a 28.4% reduction in the average delay. The two optimized configurations, derived through further refinement, show better trade-offs between average delay and flight time than any of the fundamental configurations. This research not only provides practical guidance for the planning and design of UAV logistics air routes but also lays a methodological foundation for future developments in UAV scheduling and air route network design.", "keywords": [ "Parametric statistics", "Arrival time", "Parametric model", "Computer science", "Transport engineering", "Aeronautics", "Environmental science", "Geography", "Engineering", "Statistics", "Mathematics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9070454" }, { "id": "https://openalex.org/W4383860621", "doi": "10.48550/arxiv.2307.03523", "title": "Parallel drone scheduling vehicle routing problems with collective drones", "year": 2023, "publication_date": "2023-07-07", "type": "preprint", "language": "en", "authors": [ "Roberto Montemanni", "Mauro Dell’Amico", "Corsini, Andrea" ], "first_author": "Roberto Montemanni", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We study last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers. In particular, we focus on problem settings where either a single truck or a fleet with several homogeneous trucks work in parallel to drones, and drones have the capability of collaborating for delivering missions. This cooperative behaviour of the drones, which are able to connect to each other and work together for some delivery tasks, enhance their potential, since connected drone has increased lifting capabilities and can fly at higher speed, overcoming the main limitations of the setting where the drones can only work independently. In this work, we contribute a Constraint Programming model and a valid inequality for the version of the problem with one truck, namely the \\emph{Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones} and we introduce for the first time the variant with multiple trucks, called the \\emph{Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones}. For the latter variant, we propose two Constraint Programming models and a Mixed Integer Linear Programming model. An extensive experimental campaign leads to state-of-the-art results for the problem with one truck and some understanding of the presented models' behaviour on the version with multiple trucks. Some insights about future research are finally discussed.", "keywords": [ "Drone", "Truck", "Travelling salesman problem", "Vehicle routing problem", "Computer science", "Scheduling (production processes)", "Integer programming", "Routing (electronic design automation)", "Job shop scheduling", "Constraint programming", "Operations research", "Mathematical optimization" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2307.03523" }, { "id": "https://openalex.org/W4310929467", "doi": "10.1109/iecon49645.2022.9968605", "title": "Enhanced V-SLAM combining SVO and ORB-SLAM2, with reduced computational complexity, to improve autonomous indoor mini-drone navigation under varying conditions", "year": 2022, "publication_date": "2022-10-17", "type": "conference-paper", "language": "en", "authors": [ "Amin Basiri", "Valerio Mariani", "Luigi Glielmo" ], "first_author": "Amin Basiri", "venue": "IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society", "publisher": null, "abstract": "Mini-drones have a wide range of applications, including weather monitoring, parcel delivery, search and rescue, and entertainment. As their functionality, safety, and performance heavily relies on ubiquitously reliable positioning and navigation, their applications are relatively limited to outdoor environments where Global Positioning System (GPS) and/or similar are available. Indoor localization is improving, e.g, using Visual Simultaneous Localization and Mapping (V-SLAM). However, for the applications of mini-drone navigation with a higher safety requirements, further improvements are still needed. This paper proposes a novel approach to improve the localization performance for mini-drone indoor navigation. The proposed approach enhances V-SLAM techniques by combining Oriented Rotated Brief SLAM (ORB-SLAM2) and Semi-direct monocular Visual Odometry (SVO) algorithms along with an Adaptive Complementary Filter (ACF). The results show that the proposed approach improvement in position estimation in the different conditions (low light, low texture and dynamic environments) in comparison with other commonly used indoor positioning approaches.", "keywords": [ "Drone", "Computer science", "Simultaneous localization and mapping", "Orb (optics)", "Computer vision", "Artificial intelligence", "Visual odometry", "Global Positioning System", "Search and rescue", "Odometry", "Real-time computing", "Mobile robot" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iecon49645.2022.9968605" }, { "id": "https://openalex.org/W4381301560", "doi": "10.2139/ssrn.4463493", "title": "Pseudo Node Insertion Method for Synchronization in Drone-Truck Combined Operations", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Sung Hoon Chung", "Bhawesh Sah", "Jinkun Lee" ], "first_author": "Sung Hoon Chung", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Node (physics)", "Synchronization (alternating current)", "Computer science", "Aeronautics", "Time synchronization", "Real-time computing", "Engineering", "Computer network", "Aerospace engineering", "Biology" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4463493" }, { "id": "https://openalex.org/W4400921595", "doi": "10.1177/03611981241243076", "title": "Flying Sidekick Traveling Salesman Problem in Truck–Drone Team Logistics with Energy Issues", "year": 2024, "publication_date": "2024-07-23", "type": "article", "language": "en", "authors": [ "Beste Aksit", "Gökçe Özden", "İnci Sarıçiçek" ], "first_author": "Beste Aksit", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "Last-mile delivery has become increasingly important in recent years because of changes in customer needs. In this study, single-truck–multi-drone team logistics is considered. A mixed integer mathematical model is developed for the flying sidekick traveling salesman problem, a single-truck–multi-drone problem by considering technical and energy issues. Two different objective functions are used in the model. The first objective function is to minimize the sum of arrival times. Minimizing the sum of arrival times for customers also minimizes the distance traveled by the vehicles and, accordingly, the cost. The second objective function is to minimize total tardiness. A dataset is generated to test the proposed model. Trade-offs between the results for the two objective functions are analyzed. The technical requirements of the drone, energy limits, and fleet size are considered in the proposed mathematical model. The effects of the flight ranges of the drones are investigated with respect to the energy constraint. It has been observed that changes in the number of customers and drones affect the results of the objective functions. The total arrival time and tardiness decreased with increasing number of drones. Thus, the number of deliveries by truck decreases for customers. In addition, the advantages of this hybrid approach have been proven by comparing situations where only land vehicles are used with truck–drone team logistics.", "keywords": [ "Drone", "Travelling salesman problem", "Truck", "Tardiness", "Computer science", "Operations research", "Function (biology)", "Mathematical optimization", "Transport engineering", "Engineering", "Automotive engineering", "Mathematics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981241243076" }, { "id": "https://openalex.org/W3213255077", "doi": "10.1117/12.2613944", "title": "Optimization of drone and truck emergency delivery using artificial bee colony-Clarke-Wright algorithm", "year": 2021, "publication_date": "2021-11-10", "type": "conference-paper", "language": "en", "authors": [ "Shuguang Han", "Hang Yang", "Jueliang Hu", "Tianhui Zhang" ], "first_author": "Shuguang Han", "venue": "International Conference on Smart Transportation and City Engineering 2021", "publisher": null, "abstract": "It is very important to provide material support for people in disaster area in order to save lives and reduce losses. With the development of science and technology and the popularization of 5G network, Unmanned Aerial Vehicle (UAV, or drone) will be widely used in urban logistics delivery and emergency rescue. Using trucks as a launch platform for drones, and delivering logistics in a coordinated distribution mode of trucks and drones, which is expected to further improve distribution efficiency. A mixed integer programming model with the shortest total delivery time is established. An artificial bee colony (ABC) algorithm embedded with improved mileage-saving (the Clarke-Wright or C-W) algorithm is developed to solve this model. The results show that the established model and the proposed algorithm are effective and can provide guidance for the application of drones in emergency rescue.", "keywords": [ "Drone", "Truck", "Computer science", "Artificial bee colony algorithm", "Wright", "Operations research", "Algorithm", "Transport engineering", "Engineering", "Artificial intelligence", "Automotive engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2613944" }, { "id": "https://openalex.org/W7133742907", "doi": "10.1016/j.cie.2026.111941", "title": "A traveling salesman problem with drone stations and speed-optimized drones", "year": 2026, "publication_date": "2026-03-05", "type": "article", "language": "en", "authors": [ "Ya Qiu", "Chun Hung Cheng", "Peisong Liu", "Lina Yu" ], "first_author": "Ya Qiu", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2026.111941" }, { "id": "https://openalex.org/W4309226867", "doi": "10.1109/icnp55882.2022.9940417", "title": "Poster: Data-Driven Studies of UAV-sharing in Parcel Delivery and Surveillance", "year": 2022, "publication_date": "2022-10-30", "type": "conference-paper", "language": "en", "authors": [ "Junhui Gao", "Yan Pan", "Zhigang Li", "Qingye Han" ], "first_author": "Junhui Gao", "venue": null, "publisher": null, "abstract": "Parcel delivery and Point of Interest (PoI) surveillance are two fundaches were conducted for an isolated application, separately. UAV-sharing in the two heterogeneous applications bears significant but unexplored benefit potentials, similar to current sharingmental applications of Unmanned Aerial Vehicles (UAVs) in city. Traditional resear economy such as taxi sharing. However, the inconsistency of the two heterogeneous applications in both temporal and spatial domains would impact the sharing performance. This work illustrates the first quantified studies of the UAV-sharing performance in the two heterogeneous applications. Specifically, some critical constraints of the UAV-sharing process are first discussed. Thereafter, a data-driven evaluation is conducted to understand the sharing process of the UAVs with a delivery dataset and a traffic accident dataset obtained from Shanghai city. Some inspiring results verify the particularly excellent UAV-sharing performance in the two heterogeneous applications.", "keywords": [ "Computer science", "Data sharing", "Process (computing)", "Bike sharing", "Transport engineering", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icnp55882.2022.9940417" }, { "id": "https://openalex.org/W7162752074", "doi": "10.1016/j.trc.2026.105780", "title": "Knowledge-aware path planning for UAV parcel delivery and road monitoring", "year": 2026, "publication_date": "2026-05-29", "type": "article", "language": "en", "authors": [ "Meng Xu", "Nikolas Geroliminis" ], "first_author": "Meng Xu", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Scalability", "Trajectory", "Baseline (sea)", "Routing (electronic design automation)", "Controller (irrigation)", "Focus (optics)", "Motion planning" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2026.105780" }, { "id": "https://openalex.org/W4413121740", "doi": "10.3390/logistics9030111", "title": "Planning of Logistic Networks with Automated Transport Drones: A Systematic Review of Application Areas, Planning Approaches, and System Performance", "year": 2025, "publication_date": "2025-08-08", "type": "review", "language": "en", "authors": [ "Lukas Ostermann", "Asrat Mekonnen Gobachew", "Andreas Schwung", "Stefan Lier" ], "first_author": "Lukas Ostermann", "venue": "Logistics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Background: The increasing integration of automated transport drones into logistics networks presents transformative potential for addressing contemporary logistics challenges, particularly in last-mile delivery, healthcare, disaster response, urban mobility, and postal services. However, their effective integration into varied logistics contexts remains hindered by infrastructure, regulatory, and operational limitations. This study aims to explore how drone-based logistics systems can be systematically planned and evaluated across diverse operational environments. Methods: A structured literature review was conducted, employing thematic synthesis to analyze current research on drone logistics. The analysis focused on identifying the key planning dimensions and interrelated components that influence the deployment of drone-based transport systems. Results: The review identified seven central planning dimensions: areas of application, system components, transport configuration, geographic areas, optimization and analysis methods, logistical planning, and performance assessment. These dimensions inform a conceptual framework designed to guide the planning and assessment of drone logistics networks. Conclusions: While existing studies contribute valuable insights into route optimization and drone deployment strategies, they often overlook integrative approaches that account for societal and environmental factors. The study emphasizes the need for interdisciplinary collaboration and context-specific planning frameworks to enhance the sustainable and effective implementation of drone-based logistics systems.", "keywords": [ "Drone", "Computer science" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2305-6290/9/3/111/pdf?version=1754655344" }, { "id": "https://openalex.org/W4399970236", "doi": "10.4018/979-8-3693-2093-8.ch009", "title": "Drones in E-Commerce", "year": 2024, "publication_date": "2024-05-31", "type": "book-chapter", "language": "en", "authors": [ "Shrishail Basaprabhu Sollapur", "Nishanth A Savanur", "Prathap M Sharma", "M Tejasvi Guruprasad", "Saikrishna Giri" ], "first_author": "Shrishail Basaprabhu Sollapur", "venue": "Advances in computational intelligence and robotics book series", "publisher": "IGI Global", "abstract": "The rapid advancement of technology is altering e-commerce with the incorporation of drones. This study digs into the complex influence of drones on logistical efficiency and user experience, addressing last-mile delivery issues. Drones improve supply chain sustainability by reducing delivery times and costs. They also improve the client experience by providing real-time order tracking and personalised delivery choices. Ongoing research looks on synergies with AR/VR to provide immersive retail experiences. Despite the promise, obstacles like law and privacy issues abound. The study's goal is to provide insights regarding drone integration in e-commerce, focusing on transformative prospects and difficulties, and anticipating a paradigm change in delivery techniques and heightened customer expectations.", "keywords": [ "Drone", "E-commerce", "Computer security", "Internet privacy", "Computer science", "Business", "Advertising", "World Wide Web", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3693-2093-8.ch009" }, { "id": "https://openalex.org/W4403964867", "doi": "10.3934/jimo.2024141", "title": "Solving the vehicle-drone pickup and delivery problem in road congestion: A heuristic and its deep reinforcement learning-based improvement", "year": 2024, "publication_date": "2024-11-01", "type": "article", "language": "en", "authors": [ "Xiwang Yang", "Zhichao He", "Ya Liu", "Shulin Liu" ], "first_author": "Xiwang Yang", "venue": "Journal of Industrial and Management Optimization", "publisher": "American Institute of Mathematical Sciences", "abstract": "In the context of last-mile logistics, the vehicle-drone delivery system increasingly snone a light on its commercial potential in logistics. It offered advantage of dispatching drones which were not influenced by road congestion, and even the parcels were transported from various origins to different destinations. Efficiently determining the appropriate synchronized vehicle-drone routes and times for pickup-and-delivery pairs in a traffic congestion scenario presented a significant challenge. We first presented a mixed-integer linear programming model and designed a tailored heuristic. Then, an improvement based on deep reinforcement learning (DRL) was proposed through learning a policy of selecting requests to remove and choose operators for the optimization of combined routes. Computational results showed the efficiency of the improved heuristic in computational time and solution quality. We have also applied the DRL-based improvement in realistic road congestion scenarios in Beijing and estimated potential annual cost savings of 10.49 billion CNY through the implementation of a vehicle-drone delivery system.", "keywords": [ "Drone", "Reinforcement learning", "Pickup", "Heuristic", "Computer science", "Artificial intelligence", "Simulation", "Operations research", "Transport engineering", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3934/jimo.2024141" }, { "id": "https://openalex.org/W4312182177", "doi": "10.1016/j.adhoc.2022.103078", "title": "FFP: A Force Field Protocol for the tactical management of UAV conflicts", "year": 2022, "publication_date": "2022-12-26", "type": "article", "language": "en", "authors": [ "Jamie Wubben", "Carlos T. Calafate", "Juan‐Carlos Cano", "Pietro Manzoni" ], "first_author": "Jamie Wubben", "venue": "Ad Hoc Networks", "publisher": "Elsevier BV", "abstract": "In recent years, we have seen a tremendous growth in the adoption of Unmanned Aerial Vehicles (UAVs). Nowadays, UAVs are used in many different industries such as agriculture, inspection (bridges, pipelines, etc.), parcel delivery, etc. In the near future, this will lead to a substantial increase of aircraft in our airspace, especially in urban areas. Many existing collision avoidance approaches rely on heavy and/or expensive sensors, which limits its use for real UAVs due to increased costs, weight and complexity. Hence, to address this problem, in this paper we present a solution for the tactical management (i.e. in-flight) of UAV conflicts outdoors that introduces minimal requirements: a wireless interface and a GPS module. Specifically, we provide a collision avoidance algorithm based on artificial potential fields to provide flight safety. Our solution, called Force Field Protocol (FFP), allows the UAVs to autonomously detect each other using wireless communications, and to maintain a safe distance between them without the intervention of any central service. Experiments performed in our multi-UAV simulator ArduSim show that, with our approach, collisions between two UAVs are completely avoided in a wide set of scenarios, while introducing low disturbances to the original flight plans. Specifically, in the scenarios that we tested, the additional flight time introduced will be only 7 s longer in the worst case; in addition, it is able to improve upon previous approaches by reducing flight time by up to 54 s. We have shown experimentally that our approach can be scaled easily up to 100 UAVs, and that the probability of a collision is very low (< 0.06) despite flying in a small area (2.5 km × 2.5 km).", "keywords": [ "Collision avoidance", "Global Positioning System", "Protocol (science)", "Computer science", "Wireless", "Field (mathematics)", "Software deployment", "Collision", "Real-time computing", "Simulation", "Engineering", "Aeronautics" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.adhoc.2022.103078" }, { "id": "https://openalex.org/W3209179218", "doi": "10.1109/airpharo52252.2021.9571035", "title": "Model Predictive Control of a Hybrid VA V for Parcel Delivery Applications", "year": 2021, "publication_date": "2021-10-01", "type": "conference-paper", "language": "en", "authors": [ "Turan Konyalıoğlu", "Sinan Alnıpak", "Erdinç Altuğ" ], "first_author": "Turan Konyalıoğlu", "venue": null, "publisher": null, "abstract": "Within the last decade, e-commerce has increased at an extraordinary level. The widespread use of Unmanned Aerial Vehicles (VAVs) in the logistics sector can reduce traffic density, improve delivery time, and can provide contactless delivery. This work proposes a new hybrid VAV, that combines the benefits of vertical take-off and land vehicle and a fixed-wing aircraft. It can carry multiple packages, which can decent automatically towards the recipients. This approach eliminates time-consuming landing maneuvers of the VA V for delivery. This work focuses on the design of the vehicle, and the challenges of the transitions from VTOL to aircraft modes and vice versa with a Model Predictive Controller.", "keywords": [ "Model predictive control", "Work (physics)", "Computer science", "Control (management)", "Controller (irrigation)", "Drone", "Fixed wing", "Versa", "Automotive engineering", "Simulation", "Aeronautics", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/airpharo52252.2021.9571035" }, { "id": "https://openalex.org/W2942409633", "doi": null, "title": "An ACO-Inspired, Probabilistic, Greedy Approach to the Drone Traveling Salesman Problem", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "Jessica Houseknecht" ], "first_author": "Jessica Houseknecht", "venue": "Digital Commons (Liberty University)", "publisher": "Liberty University", "abstract": "In recent years, major companies have done research on using drones for parcel delivery. Research has shown that this can result in significant savings, which has led to the formulation of various truck and drone routing and scheduling optimization problems. This paper explains and analyzes a new approach to the Drone Traveling Salesman Problem (DTSP) based on ant colony optimization (ACO). The ACO-based approach has an acceptance policy that maximizes the usage of the drone. The results reveal that the pheromone causes the algorithm to converge quickly to the best solution. The algorithm performs comparably to the MIP model, CP model, and EA of Rich & Ham (2018), especially in instances with a larger number of stops.", "keywords": [ "Travelling salesman problem", "Drone", "Greedy algorithm", "Probabilistic logic", "Computer science", "2-opt", "Traveling purchaser problem", "Bottleneck traveling salesman problem", "Mathematical optimization", "Artificial intelligence", "Mathematics", "Algorithm" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://digitalcommons.liberty.edu/cgi/viewcontent.cgi?article=1926&context=honors" }, { "id": "https://openalex.org/W2967724857", "doi": "10.1109/mdm.2019.00-56", "title": "A VLOS Compliance Solution to Ground/Aerial Parcel Delivery Problem", "year": 2019, "publication_date": "2019-06-01", "type": "conference-paper", "language": "en", "authors": [ "Ji Zhang", "Ting Shen", "Wenlu Wang", "Xunfei Jiang", "Wei‐Shinn Ku", "Min-Te Sun", "Yao‐Yi Chiang" ], "first_author": "Ji Zhang", "venue": null, "publisher": null, "abstract": "This paper presents an exact solution and a heuristic solution to a UAV-assisted parcel delivery problem, in which UAVs can only be operated in Visual-Line-Of-Sight (VLOS) areas. In our proposed problem, we assume that trucks travel on road networks, and UAVs move in Euclidean spaces and can launch at any locations on roads. We first demonstrate the overview of our exact solution that iterates all permutations of destinations for an optimal delivery route. Given a specific delivery order, an intuitive approach needs to check all possible locations on roads in the VLOS areas and find a globally optimal location for every destination if UAVs are used for delivery. To avoid high computational cost of searching the optimal location at runtime, we propose an advanced index-based alternative, which computes optimal delivery routes in a pre-processing stage. Due to the nature of NP-hard problems, we also propose a heuristic approach that utilizes delivery groups for the proposed problem of practical size. All proposed solutions are evaluated through extensive experiments.", "keywords": [ "Heuristic", "Computer science", "Iterated local search", "Truck", "Mathematical optimization", "Iterated function", "Euclidean geometry", "Operations research", "Local search (optimization)", "Algorithm", "Mathematics", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mdm.2019.00-56" }, { "id": "https://openalex.org/W4388506269", "doi": "10.46254/an11.20211173", "title": "Identification of Critical Success Factor for Sustainable Supply Chain Management Drone Logistics in Indonesia with ISM (Interpretive Structural Modeling) Approach", "year": 2021, "publication_date": "2021-03-07", "type": "conference-paper", "language": "en", "authors": [ "Nida An Khofiyah", "Wahyudi Sutopo", "Muhammad Hisjam" ], "first_author": "Nida An Khofiyah", "venue": null, "publisher": null, "abstract": "Infrastructure Indonesia in building national competitiveness and to support the implementation of the Master Plan for the Acceleration and Expansion of Indonesian Economic Development 2011 -2025.The main thing is logistics in urban areas, because urban logistics has a function as a link in national networks as well as global networks.The main cause in urban areas is road congestion, resulting in delays, in addition to environmental impacts such as carbon dioxide emissions.To overcome this, this study analyzes a new mode of transportation with new technology, namely drones.A drone is an unmanned aerial vehicle or Unmanned Aerial Vehicle (UAV).This drone technology can be a solution for the logistics system to be efficient.A sustainable supply chain is one that is better at identifying current and future environmental and social impacts and finding ways to reduce them.Environmental and social impacts are important in this industrial 4.0 era, especially for new technologies that will enter the Indonesian market.This research focuses on the objective of identifying the critical success factors (CSF) to implement the sustainability of the application of logistic drones as a mode of delivery in urban areas in Indonesia, to determine the contextual relationship between CSFs.The method used in this study is to use a structural hierarchy model (ISM).The results obtained were that there were 8 levels out of a total of 18 CSFs identified.Level 8, the most important is the global marketing factor, where this factor aims to provide a competitive advantage in the global marketing space by implementing a green supply chain.These results can be a recommendation to the government and business people to focus on the most influential factors for the success of a logistics drone business in Indonesia.", "keywords": [ "Drone", "Supply chain", "Identification (biology)", "Supply chain management", "Critical success factor", "Business", "Process management", "Factor (programming language)", "Computer science", "Systems engineering", "Knowledge management", "Risk analysis (engineering)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://index.ieomsociety.org/index.cfm/article/downloadArticle/ID/1295" }, { "id": "https://openalex.org/W4281701107", "doi": "10.1016/j.eswa.2022.117647", "title": "An adaptive large neighborhood search heuristic for the flying sidekick traveling salesman problem with multiple drops", "year": 2022, "publication_date": "2022-05-30", "type": "article", "language": "en", "authors": [ "Setyo Tri Windras Mara", "Achmad Pratama Rifai", "Bertha Maya Sopha" ], "first_author": "Setyo Tri Windras Mara", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Travelling salesman problem", "Computer science", "Drone", "Vehicle routing problem", "Heuristic", "Context (archaeology)", "Mathematical optimization", "Local search (optimization)", "Operations research", "Truck", "Routing (electronic design automation)", "Artificial intelligence" ], "cited_by_count": 75, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2022.117647" }, { "id": "https://openalex.org/W4401160108", "doi": "10.5875/p25fht10", "title": "UAV parcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization", "year": 2024, "publication_date": "2024-07-31", "type": "article", "language": "en", "authors": [ "Chung‐Hsien Kuo" ], "first_author": "Chung‐Hsien Kuo", "venue": "International Journal of Automation and Smart Technology", "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.", "keywords": [ "Reinforcement learning", "Payload (computing)", "Collision avoidance", "Computer science", "Drone", "Genetic algorithm", "Path (computing)", "Collision", "Software", "Simulation", "Real-time computing", "Artificial intelligence" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.jausmt.org/index.php/jausmt/article/download/4/20" }, { "id": "https://openalex.org/W4401628500", "doi": "10.1109/twc.2024.3440923", "title": "3D Network Design for Multi-UAV RAN With THz-Empowered Backhaul", "year": 2024, "publication_date": "2024-08-16", "type": "article", "language": "en", "authors": [ "Kyeongsoo Kim", "Jihwan P. Choi" ], "first_author": "Kyeongsoo Kim", "venue": "IEEE Transactions on Wireless Communications", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "For seamless and ubiquitous connectivity in 6G, three-dimensional (3D) network design using flourishing unmanned aerial vehicles (UAVs) will be an inevitable task. Notably, integrating terahertz (THz) networks with UAVs remains an open question, despite its immense potential to achieve exceptional flexibility and high capacity. Spurred by this, we delve into the promising 3D network: the multi-UAV radio access network (RAN) with THz-empowered backhaul. For reliable last-mile connectivity in the end-to-end (E2E) system, non-orthogonal multiple access (NOMA) is applied with sub-6GHz spectrum. To maximize the E2E throughput, we formulate an optimization problem with diverse design variables of access and backhaul, including 3D UAV deployment, UAV transmit power, and NOMA decoding orders, with hybrid precoding and transmit power of the backhaul terminal. For this intricate problem, we propose an optimization process where the design variables are optimized sequentially. Using numerical results, we verify the feasibility of our optimization process and demonstrate supremacy of the proposed network scheme and optimization algorithm compared to other baselines. Furthermore, we corroborate design guidelines for the proposed network over access and backhaul links with various factors, such as antenna structure, the number of antennas, transmit power level, and interference.", "keywords": [ "Backhaul (telecommunications)", "Computer science", "Computer network", "Wireless", "C-RAN", "Telecommunications", "Radio access network", "Base station" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/twc.2024.3440923" }, { "id": "https://openalex.org/W4389428647", "doi": "10.1109/tvt.2023.3340177", "title": "RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption", "year": 2023, "publication_date": "2023-12-07", "type": "article", "language": "en", "authors": [ "Nesrine Cherif", "Wael Jaafar", "Halim Yanıkömeroğlu", "Abbas Yongaçoğlu" ], "first_author": "Nesrine Cherif", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Unmanned aerial vehicle (UAV) is a promising technology for last-mile cargo delivery. However, the limited on-board battery capacity, cellular unreliability, and frequent handoffs in the airspace are the main obstacles to unleash its full potential. Given that existing cellular networks were primarily designed to service ground users, re-utilizing the same architecture for highly mobile aerial users, e.g., cargo-UAVs, is deemed challenging. Indeed, to ensure a safe delivery using cargo-UAVs, it is crucial to utilize the available energy efficiently, while guaranteeing reliable connectivity for command-and-control and avoiding frequent handoff. To achieve this goal, we propose a novel approach for joint cargo-UAV trajectory planning and cell association. Specifically, we formulate the cargo-UAV mission as a multi-objective problem aiming to 1) minimize energy consumption, 2) reduce handoff events, and 3) guarantee cellular reliability along the trajectory. We leverage reinforcement learning (RL) to jointly optimize the cargo-UAV's trajectory and cell association. Simulation results demonstrate a performance improvement of our proposed method, in terms of handoffs, disconnectivity, and energy consumption, compared to benchmarks.", "keywords": [ "Handover", "Energy consumption", "Computer science", "Trajectory", "Base station", "Leverage (statistics)", "Cellular network", "Reliability (semiconductor)", "Computer network", "Real-time computing", "Engineering" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2023.3340177" }, { "id": "https://openalex.org/W4405180792", "doi": "10.1016/j.rineng.2024.103627", "title": "Travel route and formation optimization for flocks of drones in package delivery by using an ACO based V-Shape algorithm", "year": 2024, "publication_date": "2024-12-01", "type": "article", "language": "en", "authors": [ "Erik De Kuyffer", "Wout Joseph", "Luc Martens", "Toon De Pessemier" ], "first_author": "Erik De Kuyffer", "venue": "Results in Engineering", "publisher": "Elsevier BV", "abstract": "Despite a lot of research throughout the last half century about the fact that geese and other bird species have the tendency to fly in a V-Shape configuration, investigation about applying this phenomenon to swarms of drones used in parcel delivery is limited. The extensive increase in online shopping and the resulting intensified package transport have urged the importance to optimize travel routes and swarms configurations in order to reduce the energy needed to carry out these tasks by the use of drones. To minimize the amount of energy needed by a swarm to travel an optimized distance, an Ant Colony Optimization (ACO) based V-Shape algorithm is applied on different configurations of swarms, each with a different drag coefficient, a distinctive surface opposite to drag, and a separate energy recovery parameter. For groups of up to 10 drones it is best to move in a V-Shape, being a 37.3% better configuration for 4 drones and 15.7% more efficient for 9 drones than the next best, being the vertical line formation. For larger groups of 10 drones or more, the vertical line becomes the most energy efficient being 22.3% more (energy) efficient than the V-Shape for 16 drones and 38.6% more (energy) efficient for 25 drones.", "keywords": [ "Flock", "Drone", "Computer science", "Ant colony optimization algorithms", "Algorithm", "Mathematical optimization", "Mathematics", "Biology", "Medicine", "Veterinary medicine", "Genetics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.rineng.2024.103627" }, { "id": "https://openalex.org/W3115078114", "doi": "10.1088/1757-899x/995/1/012015", "title": "A prototype of Fixed Wing UAV for delivery of Medical Supplies", "year": 2020, "publication_date": "2020-11-01", "type": "conference-paper", "language": "en", "authors": [ "R Pavithran", "V Lalith", "C Naveen", "S P Sabari", "MST Ajay Kumar", "V Hariprasad" ], "first_author": "R Pavithran", "venue": "IOP Conference Series Materials Science and Engineering", "publisher": "IOP Publishing", "abstract": "Abstract A prototype Unmanned Aerial Vehicle (UAV) is designed for the purpose of last mile delivery to supply medicines and vaccines. This work aims to ascertain that UAVs can be employed to decrease transportation times, increased power efficiency, and improved safety for transportation of necessary products. The prototype model is design to deliver 1 kg maximum payload up to 5 km with an endurance time of 30 minutes. The model is fabricated with a wing span of 1.5 m, Aspect ratio 7, and contains NACA 6 series airfoil shape as its cross section. During the transportation of payload, it has to fly beyond the line of sight which requires an autonomous Pixhawk flight controller installed in the model. Autonomous module aids in the safe flight avoiding the obstacles during the operation. To comply with government regulations for flying the model cruises at an altitude of 350 feet. After reaching the desired GPS destination, the payload is delivered to the target customer via parachute. However the model requires a runway length of 200 m for its takeoff and landing operation.", "keywords": [ "Payload (computing)", "Takeoff", "Runway", "Fixed wing", "Aeronautics", "Marine engineering", "Drone", "Takeoff and landing", "Automotive engineering", "Computer science", "Engineering", "Aerospace engineering" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://iopscience.iop.org/article/10.1088/1757-899X/995/1/012015/pdf" }, { "id": "https://openalex.org/W7144166351", "doi": "10.1016/j.trb.2026.103449", "title": "Real-time order dispatch for on-demand food-delivery platforms with a mixed fleet of drones and couriers", "year": 2026, "publication_date": "2026-03-31", "type": "article", "language": "en", "authors": [ "Tingting Dong", "Yitong Shang", "Zhijie Lai", "Sen Li" ], "first_author": "Tingting Dong", "venue": "Transportation Research Part B Methodological", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Order (exchange)", "Aviation", "Mathematical model" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trb.2026.103449" }, { "id": "https://openalex.org/W4387485778", "doi": "10.2139/ssrn.4598462", "title": "Improved Utaut2 Model for Uav Urban Logistics Delivery Acceptance", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Xinhui Ren", "Fang Yu", "Hong Jiang" ], "first_author": "Xinhui Ren", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Business", "Process management", "Computer science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4598462" }, { "id": "https://openalex.org/W4391128660", "doi": "10.1109/ictc58733.2023.10392747", "title": "Multi-UAV Employed Secure Parcel Delivery System using Blockchain", "year": 2023, "publication_date": "2023-10-11", "type": "conference-paper", "language": "en", "authors": [ "Ramdhan Nugraha", "Md Masuduzzaman", "Munkyu Choi", "Soo Young Shin" ], "first_author": "Ramdhan Nugraha", "venue": null, "publisher": null, "abstract": "The optimization of parcel delivery services to consumers has become a priority due to the rapid development of the logistics industry, particularly in relation to the primary application of Industry 4.0. It is difficult to maintain trust between a travel company and its consumers. Although many of the current solutions identified in the research address trust issues, most of them are based on centralized architectures, such as cloud computing, where data is vulnerable to a variety of security threats. In this article, a multi-UAV-based secure parcel delivery system using blockchain has been proposed to increase trust among entities. Moreover, genetic algorithms (GA) are exploited to generate routing paths for multiple heterogeneous UAVs. Initially, optimizing fitness function, selection, crossover, and mutation operators are generated by utilizing the GA. The novelty of this research is to propose a secure delivery system using GA for heterogeneous multiple UAVs. Finally, the GA method is constructed to optimize the time constraint issue, and different consensus mechanisms are scrutinized to verify the performance (e.g., latency and average transaction) while storing the parcel delivery information in the blockchain network.", "keywords": [ "Computer science", "Crossover", "Blockchain", "Distributed computing", "Cloud computing", "Database transaction", "Routing (electronic design automation)", "Computer security", "Relation (database)", "Computer network", "Data mining", "Database" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ictc58733.2023.10392747" }, { "id": "https://openalex.org/W4406611318", "doi": "10.1109/wsc63780.2024.10838959", "title": "Designing the Charging Stations Network for Freight Delivery by Drones Using Simulation-Optimization", "year": 2024, "publication_date": "2024-12-15", "type": "conference-paper", "language": "en", "authors": [ "Irene Izco", "Adrián Serrano-Hernández", "Javier Faulín" ], "first_author": "Irene Izco", "venue": null, "publisher": null, "abstract": "Drones are a promising choice to the last-mile delivery in place of conventional vehicles that contribute to road congestion and air pollution. Despite the autonomy, flexibility, and agility of drones, their limited battery capacity and payload compromise their flight range. In this paper, this challenge is faced through the placement of charging stations where drone batteries are recharged to expand their flying span. This work relies on a simulation-optimization solution approach to determine the optimal number of the drone hubs to serve a given delivery order demand. The optimization aims at minimizing charging station installation costs, drone energy consumption, and operational costs. The simulation model is run to simulate the parcel delivery flights as well as the drone battery consumption and charging cycles. Moreover, allocating the customer demands to stations and dimensioning the drones fleet to deliver packages efficiently pinpoint the station locations and size.", "keywords": [ "Drone", "Computer science", "Transport engineering", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wsc63780.2024.10838959" }, { "id": "https://openalex.org/W4399714938", "doi": "10.1007/978-3-031-62912-9_13", "title": "Combining Heuristics and Constraint Programming for the Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Roberto Montemanni", "Mauro Dell’Amico", "Andrea Corsini" ], "first_author": "Roberto Montemanni", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Heuristics", "Scheduling (production processes)", "Constraint programming", "Job shop scheduling", "Routing (electronic design automation)", "Mathematical optimization", "Computer network", "Mathematics", "Operating system" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://iris.unimore.it/bitstream/11380/1342368/2/chapter.pdf" }, { "id": "https://openalex.org/W4225426204", "doi": "10.48550/arxiv.2201.07992", "title": "Electric Vehicle Traveling Salesman Problem with Drone with Fixed-time-full-charge Policy", "year": 2022, "publication_date": "2022-01-20", "type": "preprint", "language": "en", "authors": [ "Tengkuo Zhu", "Stephen D. Boyles", "Avinash Unnikrishnan" ], "first_author": "Tengkuo Zhu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The idea of deploying electric vehicles and unmanned aerial vehicles (UAVs), also known as drones, to perform \"last-mile\" delivery in logistics operations has attracted increasing attention in the past few years. In this paper, we propose the electric vehicle traveling salesman problem with drone (EVTSPD), in which the electric vehicle (EV) and the drone perform delivery tasks coordinately while the electric vehicle may need to visit charging stations occasionally to recharge. We further assume that the EV can refresh its energy to full battery capacity with fixed time at charging stations. Thus, the proposed problem is termed EVTSPD-FF. In this paper, an arc-based mixed-integer programming model defined in a multigraph is presented for EVTSPD-FF. An exact branch-and-price (BP) algorithm and a variable neighborhood search heuristic are developed to solve instances with up to 25 customers in one minute. Numerical experiments show that the heuristic is much more efficient than solving the arc-based model using the ILOG CPLEX solver and BP algorithm. A real-world case study on the Austin network and the sensitivity analysis of different parameters are also conducted and presented. The results indicate that drone speed has a more significant effect on delivery time than the EV's driving range.", "keywords": [ "Drone", "Travelling salesman problem", "Solver", "2-opt", "Computer science", "Electric vehicle", "Heuristic", "Mathematical optimization", "Bottleneck traveling salesman problem", "Traveling purchaser problem", "Battery (electricity)", "Driving range" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2201.07992" }, { "id": "https://openalex.org/W7154580749", "doi": "10.3390/drones10040292", "title": "Recent Advances for Generative AI-Enabled Unmanned Aerial Vehicle Systems and Applicable Technologies", "year": 2026, "publication_date": "2026-04-16", "type": "article", "language": "en", "authors": [ "Hyunbum Kim" ], "first_author": "Hyunbum Kim", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned Aerial Vehicles (UAVs) have been key platforms to perform sensing, analytics and automation across intelligent transportation, construction, smart agriculture, logistics and defense. Generative AI (GenAI) accelerates intelligence of UAVs by creating synthetic data, simulating environments and improving learning with restricted data conditions. When integrated with digital twin and AI frameworks, GenAI enables advanced design, modeling, adaptation and making a decision. In this paper, we survey recent advances for generative AI-enabled UAVs systems and applicable scenarios. Then, we categorize four applicable research branches using generative AI-enabled UAVs for intelligent transportation systems, digital twin and smart infrastructure, smart agriculture, last-mile logistics and delivery.", "keywords": [ "Generative grammar", "Automation", "Categorization", "Analytics", "Key (lock)", "Adaptation (eye)", "Drone", "Intelligent decision support system" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/4/292/pdf?version=1776337395" }, { "id": "https://openalex.org/W7117543502", "doi": "10.1016/j.tre.2025.104594", "title": "Integrated routing of drones and public transportation vehicles for simultaneous parcel pickup and delivery", "year": 2025, "publication_date": "2025-12-30", "type": "article", "language": "en", "authors": [ "Wei Xu", "Zhixiao Wang", "Zhenjie Zheng", "Zhengli Wang", "Hai Yang" ], "first_author": "Wei Xu", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Solver", "Scheduling (production processes)", "Public transport", "Vehicle routing problem", "Linear programming", "Integer programming", "Routing (electronic design automation)", "TRIPS architecture" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104594" }, { "id": "https://openalex.org/W2947448184", "doi": "10.1109/tase.2019.2914113", "title": "Efficient Routing for Precedence-Constrained Package Delivery for Heterogeneous Vehicles", "year": 2019, "publication_date": "2019-05-29", "type": "article", "language": "en", "authors": [ "Xiaoshan Bai", "Ming Cao", "Weisheng Yan", "Shuzhi Sam Ge" ], "first_author": "Xiaoshan Bai", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms.", "keywords": [ "Truck", "Computer science", "Vehicle routing problem", "Sorting", "Task (project management)", "Routing (electronic design automation)", "Set (abstract data type)", "Heuristic", "Mathematical optimization", "Assignment problem", "Drone", "Constraint (computer-aided design)" ], "cited_by_count": 108, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/11370/cd2aa47c-e03d-4ae9-b4a2-28f3331af3a3" }, { "id": "https://openalex.org/W4295026952", "doi": "10.1016/j.tre.2022.102872", "title": "A route network planning method for urban air delivery", "year": 2022, "publication_date": "2022-09-09", "type": "article", "language": "en", "authors": [ "Xinyu He", "Fang He", "Lishuai Li", "Lei Zhang", "Gang Xiao" ], "first_author": "Xinyu He", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.", "keywords": [ "Drone", "Service (business)", "Computer science", "Network planning and design", "Air traffic management", "Traffic congestion", "Transport engineering", "Function (biology)", "Air traffic control", "Operations research", "Computer network", "Engineering" ], "cited_by_count": 71, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S1366554522002526/pdf" }, { "id": "https://openalex.org/W4286851329", "doi": "10.48550/arxiv.2111.11463", "title": "Drone flight data reveal energy and greenhouse gas emissions savings for\\n small package delivery", "year": 2021, "publication_date": "2021-11-22", "type": "preprint", "language": "en", "authors": [ "Thiago A. Rodrigues", "Jay Patrikar", "Natalia L. Oliveira", "H. Scott Matthews", "Sebastian Scherer", "Constantine Samaras" ], "first_author": "Thiago A. Rodrigues", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will\\naffect the energy productivity of package delivery and require new methods to\\nunderstand the associated energy consumption and greenhouse gas (GHG)\\nemissions. Here we combine empirical testing of 187 quadcopter flights with\\nfirst principles analysis to develop a usable energy model for drone package\\ndelivery. We develop a machine-learning algorithm to assess energy use across\\nthree different flight regimes: takeoff, cruise, and landing. Our model shows\\nthat, in the US, a small electric quadcopter drone with a payload of 1 kg would\\nconsume approximately 0.05 MJ/km and result in 41 g of CO$_{2}$e per package.\\nThe energy per package delivered by drones (0.19 MJ/package) can be up to 96\\\\%\\nlower than conventional transportation modes. Our open model and generalizable\\ncoefficients can assist stakeholders in understanding and improving the energy\\nuse of drone package delivery.\\n", "keywords": [ "Quadcopter", "Drone", "Takeoff", "Greenhouse gas", "Payload (computing)", "Energy consumption", "Automotive engineering", "Environmental science", "Engineering", "Computer science", "Simulation", "Aeronautics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2111.11463" }, { "id": "https://openalex.org/W4226333468", "doi": "10.48550/arxiv.2204.01335", "title": "Logistics in the Sky: A Two-phase Optimization Approach for the Drone Package Pickup and Delivery System", "year": 2022, "publication_date": "2022-04-04", "type": "preprint", "language": "en", "authors": [ "Fangyu Hong", "Guohua Wu", "Qizhang Luo", "Huan Liu", "Fang, Xiaoping", "Witold Pedrycz" ], "first_author": "Fangyu Hong", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The application of drones in the last-mile distribution is a research hotspot in recent years. Different from the previous urban distribution mode that depends on trucks, this paper proposes a novel package pick-up and delivery mode and system in which multiple drones collaborate with automatic devices. The proposed mode uses free areas on the top of residential buildings to set automatic devices as delivery and pick-up points of packages, and employs drones to transport packages between buildings and depots. Integrated scheduling problem of package drop-pickup considering m-drone, m-depot, m-customer is crucial for the system. We propose a simulated-annealing-based two-phase optimization approach (SATO) to solve this problem. In the first phase, tasks are allocated to depots for serving, such that the initial problem is decomposed into multiple single depot scheduling problems with m-drone. In the second phase, considering the drone capability constraints and task demand constraints, we generate the route planning scheme for drones in each depot. Concurrently, an improved variable neighborhood descent algorithm (IVND) is designed in the first phase to reallocate tasks, and a local search algorithm (LS) are proposed to search the high-quality solution in the second phase. Finally, extensive experiments and comparative studies are conducted to test the effectiveness of the proposed approach. Experiments indicate that the proposed SATO-IVND can reduce the cost by more than 14% in a reasonable time compared with several other peer algorithms.", "keywords": [ "Drone", "Computer science", "Scheduling (production processes)", "Pickup", "Simulated annealing", "Last mile (transportation)", "Real-time computing", "Mathematical optimization", "Simulation", "Algorithm", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2204.01335" }, { "id": "https://openalex.org/W2747616223", "doi": "10.2197/ipsjjip.25.655", "title": "Routing of Carrier-vehicle Systems with Dedicated Last-stretch Delivery Vehicle and Fixed Carrier Route", "year": 2017, "publication_date": "2017-01-01", "type": "article", "language": "en", "authors": [ "Mohd Shahrizan Othman", "Aleksandar Shurbevski", "Yoshiyuki Karuno", "Hiroshi Nagamochi" ], "first_author": "Mohd Shahrizan Othman", "venue": "Journal of Information Processing", "publisher": null, "abstract": "We examine a routing problem that arises when an unmanned aerial vehicle (UAV), or drone, is used in the last-stretch of parcel delivery to end customers. In the scenario that we study, a delivery truck is dispatched carrying a shipment of parcels to be delivered to customers. While the truck is following a predetermined route, a drone is charged with making the last-stretch delivery of a parcel from the truck to a customer's doorstep. Given a set of customers to be served and a set of rendezvous points where the drone can meet with the truck to pick up a parcel, we ask what the quickest way is of delivering all parcels to the end customers. We model this problem as a problem of finding a special type of a path in a graph of a special structure, and show that the graph problem is NP-hard even when all edge weights are restricted to be 1 or 2. Furthermore, we identify a special instance type that can be solved optimally in polynomial time. Finally, we propose a polynomial-time approximation algorithm for the graph problem in metric graphs, and show that its approximation ratio is bounded above by 2 in restricted metric graphs.", "keywords": [ "Truck", "Drone", "Computer science", "Bounded function", "Metric (unit)", "Routing (electronic design automation)", "Rendezvous", "Set (abstract data type)", "Graph", "Mathematical optimization", "Computer network", "Theoretical computer science" ], "cited_by_count": 48, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.jstage.jst.go.jp/article/ipsjjip/25/0/25_655/_pdf" }, { "id": "https://openalex.org/W4391184877", "doi": "10.18048/2023.63.02", "title": "Utilization of Aerial Drone Technology in Logistics", "year": 2023, "publication_date": "2023-07-01", "type": "article", "language": "en", "authors": [ "Saša Aksentijević", "Karlo Martišković", "Edvard Tijan", "Marija Jović" ], "first_author": "Saša Aksentijević", "venue": "Journal of Maritime & Transportation Science", "publisher": null, "abstract": "In order to create a supply chain that is both economically and ecologically sustainable and complies with contemporary standards, the logistics chain must be modernized and should rely on new technology. In this article, the authors will attempt to elaborate the development direction of delivery systems through examples of technology-oriented companies such as Amazon, focusing on the use of unmanned aerial vehicles (drones) for delivering smaller packages within urban and suburban environments. The main goal of using drones in urban logistics is to reduce the burden on city roads and the use of road vehicles that generate the highest gaseous emissions footprint. The use of drones has proven to be extremely flexible and useful in port logistics as well, eliminating the need to send supply vessels into rough seas or putting people in danger during exhaust emission control. However, drones are a relatively new technology, and they have their own limitations, which are still being discovered. The main constraints of the devices themselves are power sources and the power of the aircraft, which currently and likely in the future won’t be able to transport heavier commercial loads. Increased use leads to congestion in airspace, posing risks to other modes of transportation such as road or air. Research shows that the current use of drones mainly revolves around test flights or limited applications within a regulated environment.", "keywords": [ "Drone", "Aeronautics", "Computer science", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hrcak.srce.hr/file/442050" }, { "id": "https://openalex.org/W2549499959", "doi": "10.1109/fuzz-ieee.2016.7737777", "title": "A new neural-dynamic control method of position and angular stabilization for autonomous quadrotor UAVs", "year": 2016, "publication_date": "2016-07-01", "type": "conference-paper", "language": "en", "authors": [ "Zhijun Zhang", "YU Jian-li", "Yuanqing Li", "Xiaoyan Zhang" ], "first_author": "Zhijun Zhang", "venue": null, "publisher": null, "abstract": "Quadrotor unmanned aerial vehicles (UAVs) have been widely used or have great potential applications in military, entertainment, postal delivery, agriculture for working aloft, photographing, etc. The position and angular stabilization of quadrotor UAVs is very significant and it is a challenging work because of the nonlinear dynamic behavior. In this paper, a neural dynamic method based control system is designed and investigated by combination of Zhang dynamics and gradient dynamic (ZD-GD) methods. Quadrotor UAVs equipped with the ZD-GD controllers can realize position and angular stabilization autonomously. Computer simulation results substantiate the efficiency and accuracy of the proposed neural dynamic method based ZD-GD controllers. Besides, the performance of the controllers can be remarkably advanced.", "keywords": [ "Position (finance)", "Control theory (sociology)", "Computer science", "Angular displacement", "Artificial neural network", "Angular velocity", "Nonlinear system", "Attitude control", "Vehicle dynamics", "Control (management)", "Control engineering", "Artificial intelligence" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/fuzz-ieee.2016.7737777" }, { "id": "https://openalex.org/W7125255873", "doi": "10.3390/su18021091", "title": "Towards Sustainable Urban Logistics: Route Optimization for Collaborative UAV–UGV Delivery Systems Under Road Network and Energy Constraints", "year": 2026, "publication_date": "2026-01-21", "type": "article", "language": "en", "authors": [ "Cunming Zou", "Qiaoran Yang", "Junyu Li", "Wei Yue", "Na Yu" ], "first_author": "Cunming Zou", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km2, the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions.", "keywords": [ "Energy consumption", "Initialization", "Genetic algorithm", "Crossover", "Adaptability", "Optimization problem", "Drone", "Sustainability", "Intelligent transportation system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/18/2/1091/pdf?version=1768998852" }, { "id": "https://openalex.org/W3110428426", "doi": "10.21203/rs.3.rs-113835/v1", "title": "Swarm-based Counter UAV Defense System", "year": 2020, "publication_date": "2020-12-01", "type": "preprint", "language": "en", "authors": [ "Matthias R. Brust", "Grégoire Danoy", "Daniel H. Stolfi", "Pascal Bouvry" ], "first_author": "Matthias R. Brust", "venue": "Research Square", "publisher": null, "abstract": "Abstract Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities thanks to their mobility, agility, and onboard sensors' customizability. UAVs are used in many applications expanding beyond the military to more commercial ones, ranging from monitoring, surveillance, mapping to parcel delivery. As governments plan using UAVs to build fresh economic potential for innovation, urban planners are moving forward to incorporate so-called UAV flight zones and UAV highways in their smart city design. However, the high-speed and dynamic flow of UAVs needs to be monitored to detect and, subsequently, to deal with intruders, rough drones, and UAVs with a malicious intent. Autonomous defense systems consisting of collaboratively working UAVs as a swarm will gain increasing importance, since a manually conducted defense is bound to fail due to the high dynamics involved in UAV maneuvering. This paper proposes a UAV defense system for intercepting and escorting intruders. The proposed UAV defense system consists of a defense UAV swarm, which is capable to self-organize their defense formation in the event of intruder detection, and chase the malicious UAV. Our fully localized approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient against communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach.", "keywords": [ "Swarm behaviour", "Drone", "Computer science", "Modular design", "Real-time computing", "Computer security", "Artificial intelligence" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-113835/latest.pdf" }, { "id": "https://openalex.org/W7133334238", "doi": "10.1016/j.team.2026.02.001", "title": "Air-ground collaborative routing problem for parcel deliveries under soft time windows", "year": 2026, "publication_date": "2026-03-03", "type": "article", "language": "en", "authors": [ "Rongfei Du", "W. D. Sun", "Fangni Zhang", "Jinping Guan" ], "first_author": "Rongfei Du", "venue": "Transport Economics and Management", "publisher": "Elsevier BV", "abstract": "Rapid growth in e-commerce is putting unprecedented pressure on urban logistics to reduce costs, emissions, and delivery times. Leveraging complementary strengths of ground vehicles and drones, this study investigates an Electric Vehicle and Drone Routing Problem with soft Time Windows (EVRPD-TW) for parcel deliveries. Specifically, the system consists of multiple Electric Unmanned Ground Vehicles (E-UGVs), each capable of deploying a drone at one node to serve a customer and retrieving it at another. Therefore, customers may be served by either E-UGVs or drones, depending on energy-endurance constraints. The objective is to minimize the total cost, including the travel cost, vehicle activation fees, and penalties for early or late deliveries beyond time windows. To solve the proposed problem, we design a two-level Hybrid Genetic Algorithm with Dynamic Iteration (HGA-DI). The HGA-DI adopts a permutation chromosome that encodes the sequence of customer visits for each E-UGV, and explores the space of ground routes by classical genetic operators. Moreover, the HGA-DI is integrated with a dynamic iteration method that optimizes drone launches for every candidate route, guaranteeing solution feasibility. We evaluate the proposed problem in a real-world scenario in Shenzhen, China. The HGA-DI can achieve the optimal solutions on small instances over thirty times faster than Gurobi. Compared to vehicle-only delivery, the E-UGVs and drones collaborative system can better meet the time windows and reduce the total cost by 1.2%. Sensitivity analyses reveal that the total cost is most sensitive to the relative per-kilometer travel cost of E-UGVs and drones, followed by time-window penalty rates and drone endurance. These findings demonstrate the economic and environmental benefits of applying air-ground collaborations and offer practical insights for implementing the proposed system in urban logistics.", "keywords": [ "Drone", "Routing (electronic design automation)", "Genetic algorithm", "Vehicle routing problem", "Node (physics)", "Dynamic programming", "Chromosome" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.team.2026.02.001" }, { "id": "https://openalex.org/W4413506043", "doi": "10.3390/sym17091382", "title": "A New Vehicle–Multi-Drone Collaborative Delivery Path Optimization Approach", "year": 2025, "publication_date": "2025-08-24", "type": "article", "language": "en", "authors": [ "Jinhui Li", "Meng Wang" ], "first_author": "Jinhui Li", "venue": "Symmetry", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "To address the logistical challenges of traffic congestion and environmental concerns associated with carbon emissions in last-mile delivery, this paper explores the potential of vehicle–drone cooperative delivery. The existing studies are predominantly confined to single-drone scenarios, failing to simultaneously consider the constraints of drone payload capacity and endurance. This limitation leads to task allocation imbalance in large-scale customer deliveries and low distribution efficiency. Firstly, a mathematical model for vehicle–multi-drone collaborative delivery with payload and endurance constraint (VMDCD-PEC) is proposed. Secondly, an improved genetic algorithm (IGA) is developed, as follows: 1. designing a hybrid selection strategy to achieve symmetrical equilibrium between exploration and exploitation by adjusting the weights of dynamic fitness–distance balance, greedy selection, and random selection; and 2. introducing the local search operator composed of gene sequence reversal, single-gene slide-down, and random half-swap to improve the neighborhood quality solution mining efficiency. Finally, the experimental results show that compared with a traditional genetic algorithm (GA) and adaptive large neighborhood search (ALNS), the IGA requires less time to find solutions in various test cases and reduces the average cost of the optimal solution by up to 30%. In addition, an analysis of drone payload sensitivity showed that drone payload capacity is negatively correlated with delivery time, and that larger customer sizes corresponded to higher sensitivity.", "keywords": [ "Drone", "Computer science", "Path (computing)", "Artificial intelligence", "Computer network", "Biology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2073-8994/17/9/1382/pdf?version=1756100190" }, { "id": "https://openalex.org/W4406972787", "doi": "10.1007/978-3-031-78241-1_24", "title": "Optimal Charging Station Deployment for Drone-Assisted Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Irene Izco", "Adrián Serrano-Hernández", "Javier Faulín" ], "first_author": "Irene Izco", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Software deployment", "Drone", "Computer science", "Aeronautics", "Real-time computing", "Simulation", "Operating system", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://academica-e.unavarra.es/bitstreams/93f6ec9f-07ff-4af8-b212-ebe81f133472/download" }, { "id": "https://openalex.org/W4386961255", "doi": "10.54254/2755-2721/9/20230068", "title": "Analysis of UAV path planning effectiveness and evaluation index scores combined with urban logistics scenarios", "year": 2023, "publication_date": "2023-09-22", "type": "article", "language": "en", "authors": [ "Jiawei Zhang" ], "first_author": "Jiawei Zhang", "venue": "Applied and Computational Engineering", "publisher": null, "abstract": "Using logistics drones for distribution is an optional solution to the problems of saturated logistics industry, insufficient transportation capacity, and high work pressure on employees in real cities. In a realistic urban environment, it is of practical significance to establish a logistics drone path planning model to improve the efficiency of logistics drones by fully considering the multiple requirements of both logistics service parties, such as time and location, and aiming at minimizing transportation costs. Aiming at this problem, this paper analyzes and studies the path planning problem of unmanned aerial vehicles based on the factors that need to be considered in actual life logistics distribution. Firstly, the actual scenario requirements of logistics UAV applications are analyzed and a path planning algorithm for this scenario is enumerated. Subsequently, mathematical models are established from both path planning and customer satisfaction to assess the performance of logistics drones. Then, a comparative analysis of four existing classical path planning algorithms is conducted, and simulated annealing algorithm is used to optimize logistics delivery scenarios for path planning. Finally, combining the mathematical model and simulated annealing algorithm constructed in the previous article, logistics UAV simulation experiments are conducted in urban distribution scenarios and an ideal path planning model is obtained. The work of this article provides a certain reference basis for logistics companies in the application of logistics UAVs and the selection of path planning methods.", "keywords": [ "Simulated annealing", "Motion planning", "Computer science", "Drone", "Operations research", "Path (computing)", "Gray (unit)", "Transport engineering", "Engineering", "Artificial intelligence", "Algorithm" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.ewadirect.com/proceedings/ace/article/view/4207/pdf" }, { "id": "https://openalex.org/W4400013179", "doi": "10.35534/dav.2024.10008", "title": "An Integer Programming Approach to Multi-Trip Routing of Delivery Drones at Load-Dependent Flight Speed", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Mao Nishira", "Hiroki Nishikawa", "Xiangbo Kong", "Hiroyuki Tomiyama" ], "first_author": "Mao Nishira", "venue": "Drones and autonomous vehicles/Drones and Autonomous Vehicles", "publisher": null, "abstract": "In recent years, there has been a growing interest in utilizing drones for parcel delivery among companies, aiming to address logistical challenges. However, effective optimization of delivery routes is essential. A theoretical framework termed the Flight Speed-aware Vehicle Routing Problem (FSVRP) has emerged to address the variability in drone flight speed based on payload weight. Several approximate methods have been proposed to solve the FSVRP. Our research endeavors to optimize parcel delivery efficiency and reduce delivery times by introducing a novel delivery problem. This problem accounts for multiple deliveries while considering the variability in flight speed due to diverse payloads. Through experimentation, we evaluate the efficacy of our proposed method compared to existing approaches. Specifically, we assess total flight distance and flight time. Our findings indicate that even in cases where the payload exceeds maximum capacity, all parcels can be delivered through multiple trips. Furthermore, employing a multi-trip FSVRP approach results in an average reduction of 10% in total flight time, even when payload capacities are not exceeded.", "keywords": [ "Drone", "Integer programming", "Routing (electronic design automation)", "Computer science", "Mathematical optimization", "Computer network", "Mathematics", "Algorithm", "Biology" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciepublish.com/index/article/view_full_text/id/220" }, { "id": "https://openalex.org/W4318778215", "doi": "10.3390/app13031842", "title": "A Logistics UAV Parcel-Receiving Station and Public Air-Route Planning Method Based on Bi-Layer Optimization", "year": 2023, "publication_date": "2023-01-31", "type": "article", "language": "en", "authors": [ "Honghai Zhang", "Fei Wang", "Dikun Feng", "Sen Du", "Gang Zhong", "Cheng Dan Deng", "Ji Zhou" ], "first_author": "Honghai Zhang", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The popularity of unmanned aerial vehicle (UAV) technology has made UAV logistics transportation possible. However, based on the current development status of logistics UAVs, there are difficulties in using UAVs directly for door-to-door logistics transportation. Therefore, it is necessary to establish UAV parcel-receiving stations that can gather logistics needs in a small area. The construction of stations allows the UAVs to transport back and forth between the distribution warehouse and the established stations, enabling customers to send and receive packages at the more convenient stations. Based on the current situation, it is a more appropriate air–ground cooperative transport mode to solve the “last-mile” cargo transportation problem. In this paper, a bi-layer UAV parcel-receiving station and public air-route planning method is proposed to explore the interaction between station location and public route planning, and is solved with a genetic algorithm and max–min ant system (GA-MMAS). The model proposed in this paper can determine the location of the stations and plan the public air routes between the warehouse and stations simultaneously. Simulation results show that the planning results of the bi-layer optimization model proposed in this paper meet the requirements of station location and public air-route planning. Compared with the layered planning results, the cost of the upper-layer model is reduced by 5.12% on average, and the cost of the lower-layer model is reduced by 4.48%.", "keywords": [ "Transport engineering", "Public transport", "Plan (archaeology)", "Route planning", "Computer science", "Genetic algorithm", "Operations research", "Popularity", "Engineering", "Geography" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/13/3/1842/pdf?version=1675168261" }, { "id": "https://openalex.org/W4407842663", "doi": "10.3390/su17051838", "title": "Improving Small Parcel Delivery Efficiency and Sustainability: A Study of Lithuanian Private Delivery Company", "year": 2025, "publication_date": "2025-02-21", "type": "article", "language": "en", "authors": [ "Kristina Čižiūnienė", "Greta Draugelytė", "Edgar Sokolovskij", "Jonas Matijošius" ], "first_author": "Kristina Čižiūnienė", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The paper provides an in-depth investigation of techniques for improving small parcel delivery services in a private logistics company, addressing significant difficulties in customer logistics service, particularly in the growing e-commerce industry. The study addresses a gap in the existing literature by assessing 170 documented customer complaints, with an emphasis on recurring issues such as improper delivery, delays, and damaged parcels. The methodological approach uses statistical tools to determine the magnitude of delivery challenges, integrating a review of the scientific literature with real data analysis. There are 28% complaints about faulty delivery and 26% about delays, according to the statistics. It is clear that systemic improvements are urgently needed. One strategy to improve service reliability and efficiency is to use automation technologies, such as drones, smart route optimization systems, and constant human training programs. While ensuring operational sustainability, these strategies aim to address the underlying causes of consumer dissatisfaction.", "keywords": [ "Lithuanian", "Sustainability", "Business", "Delivery system", "Environmental economics", "Industrial organization", "Engineering", "Economics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/17/5/1838/pdf?version=1740142439" }, { "id": "https://openalex.org/W2904994834", "doi": "10.1016/j.osn.2018.12.003", "title": "Adaptive resource allocation in FSO/RF multiuser system with proportional fairness for UAV application", "year": 2018, "publication_date": "2018-12-14", "type": "article", "language": "en", "authors": [ "Pengfei Zhu", "Jiawei Zhang", "Zhengguang Gao", "Lin Feng Bai", "Yuefeng Ji" ], "first_author": "Pengfei Zhu", "venue": "Optical Switching and Networking", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Throughput", "Resource allocation", "Transmission (telecommunications)", "Channel (broadcasting)", "Telecommunications link", "Transmitter", "Spectral efficiency", "Bandwidth (computing)", "Computer network", "Real-time computing", "Wireless" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.osn.2018.12.003" }, { "id": "https://openalex.org/W4411359188", "doi": "10.1109/iccies63851.2025.11032336", "title": "Autonomous Logistics: The Role of Artificial Intelligence, Drones, And Robotics In Supply Chain Management", "year": 2025, "publication_date": "2025-04-24", "type": "conference-paper", "language": "en", "authors": [ "Durbek Sayfullaev", "Zayd Ajsan Balsem", "Asmaa Abdullah Majeed", "Azibaev Akhmadkhon Gulomjonugli", "Ghorpade Bipin Shivaji" ], "first_author": "Durbek Sayfullaev", "venue": null, "publisher": null, "abstract": "Autonomous logistics is revolutionizing supply chain management through the integration of Artificial Intelligence (AI), drones, and robotics, enhancing efficiency, speed, and accuracy. The synergy of these technologies optimizes warehouse operations, transportation, and last-mile delivery, reducing human intervention and operational costs. Traditional logistics systems face several challenges, including delays due to traffic congestion, high labor costs, inefficiencies in warehouse management, and the environmental impact of fuel-based transportation. These limitations hinder the ability to meet the growing demand for faster and more reliable deliveries. To address these challenges, this study proposes an AI-powered Drones and Autonomous Vehicles (AI-DAV) Framework for last-mile delivery. AI-driven systems optimize route planning, ensure real-time traffic management, and enhance operational efficiency using machine learning algorithms, IoT-based tracking, and automated navigation technologies. The proposed method employs AI-powered drones for small package deliveries in urban areas and autonomous ground vehicles for bulk transportation, reducing human dependency and enhancing precision. Robotics in warehouses further streamlines inventory handling and order fulfillment, improving overall supply chain performance. Findings suggest that the AI-DAV framework significantly reduces delivery time, operational costs, and carbon emissions while increasing accuracy and scalability in logistics operations. By leveraging AI, drones, and robotics, supply chain management becomes more efficient, sustainable, and resilient, meeting the dynamic demands of modern commerce.", "keywords": [ "Drone", "Supply chain", "Robotics", "Supply chain management", "Artificial intelligence", "Computer science", "Robot", "Engineering management", "Engineering", "Systems engineering", "Business", "Marketing" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iccies63851.2025.11032336" }, { "id": "https://openalex.org/W4414364323", "doi": "10.1155/joro/8839629", "title": "LSTM‐KAN‐Based Energy Consumption Prediction and Optimization Method for Quadcopter Drone Under Unknown Load", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Hongsheng Ren", "Guangyan Wang", "Qing Chang", "Sen Gao", "Xi Chen" ], "first_author": "Hongsheng Ren", "venue": "Journal of Robotics", "publisher": "Hindawi Publishing Corporation", "abstract": "With the rapid expansion of drones in last‐mile delivery, high energy consumption has become a critical challenge, affecting both operational efficiency and sustainability. Among the various factors influencing unmanned aerial vehicle (UAV) energy consumption, payload weight plays a significant role in determining power usage. However, most existing studies assume known payload conditions, while in real‐world applications, payload is often unknown, limiting the accuracy of energy consumption prediction and optimization. To address this issue, this study proposes a long short‐term memory (LSTM)‐Kolmogorov–Arnold network (KAN) energy consumption modeling approach, integrating deep learning with prior knowledge of quadrotor dynamics to enhance UAV energy prediction under unknown load conditions. The proposed framework consists of a load prediction model based on an improved LSTM network and an energy consumption model employing the KAN for power estimation, effectively capturing the impact of payload variations on energy consumption. Additionally, a particle swarm optimization (PSO) algorithm is applied to dynamically adjust flight parameters, further improving energy efficiency. Experimental validation on a customized quadrotor platform demonstrates that the proposed model achieves R 2 values of 0.9852 and 0.9235 for load and power prediction, respectively, indicating high predictive accuracy. When combined with PSO, the approach enhances energy efficiency by 23.14%–28.21% under various payload conditions. This study provides an effective solution for UAV energy consumption prediction and optimization, contributing to improved operational efficiency and the sustainability of drone‐based logistics.", "keywords": [ "Payload (computing)", "Quadcopter", "Energy consumption", "Drone", "Energy (signal processing)", "Efficient energy use" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/joro/8839629" }, { "id": "https://openalex.org/W3023023980", "doi": "10.1016/j.comnet.2020.107451", "title": "A survey on cellular-connected UAVs: Design challenges, enabling 5G/B5G innovations, and experimental advancements", "year": 2020, "publication_date": "2020-08-06", "type": "article", "language": "en", "authors": [ "Debashisha Mishra", "Enrico Natalizio" ], "first_author": "Debashisha Mishra", "venue": "Computer Networks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Cellular network", "Software deployment", "Computer science", "Open research", "Field (mathematics)", "Standardization", "Systems engineering", "Telecommunications", "Engineering" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.comnet.2020.107451" }, { "id": "https://openalex.org/W4415243345", "doi": "10.3390/automation6040054", "title": "Artificial Intelligence Approaches for UAV Deconfliction: A Comparative Review and Framework Proposal", "year": 2025, "publication_date": "2025-10-11", "type": "article", "language": "en", "authors": [ "Fábio Suim Chagas", "Neno Ruseno", "Aurilla Aurelie Arntzen Bechina" ], "first_author": "Fábio Suim Chagas", "venue": "Automation", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The increasing capabilities of Unmanned Aerial Vehicles (UAVs) or drones are opening up diverse business opportunities. Innovations in drones, U-space, and UTM systems are driving the rapid development of new air mobility applications, often outpacing current regulatory frameworks. These applications now span multiple sectors, from infrastructure monitoring to urban parcel delivery, resulting in a projected increase in drone traffic within shared airspace. This growth introduces significant safety concerns, particularly in managing the separation between drones and manned aircraft. Although various research efforts have addressed this deconfliction challenge, a critical need remains for improved automated solutions at both strategic and tactical levels. In response, our SESAR-funded initiative, AI4HyDrop, investigates the application of machine learning to develop an intelligent system for UAV deconfliction. As part of this effort, we conducted a comprehensive literature review to assess the application of Artificial Intelligence (AI) in this domain. The AI algorithms used in drone deconfliction can be categorized into three types: deep learning, reinforcement learning, and bio-inspired learning. The findings lay a foundation for identifying the key requirements of an AI-based deconfliction system for UAVs.", "keywords": [ "Drone", "Key (lock)", "Reinforcement learning", "Foundation (evidence)", "Air traffic control", "Intelligent transportation system" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2673-4052/6/4/54/pdf?version=1760177092" }, { "id": "https://openalex.org/W4392346071", "doi": "10.7307/ptt.v36i1.261", "title": "Optimisation of Multi-Type Logistics UAV Scheduling under High Demand", "year": 2024, "publication_date": "2024-03-01", "type": "article", "language": "en", "authors": [ "Hong Jiang", "Tong Qing Wu", "Xinhui Ren", "Lizhen Gou" ], "first_author": "Hong Jiang", "venue": "PROMET - Traffic&Transportation", "publisher": "University of Zagreb, Faculty of Transport and Traffic Sciences", "abstract": "At present, interest in the application of unmanned aerial vehicles (UAV) for delivery is growing. A new “multi-type of UAV collaborative delivery” mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there is high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones. This study explores logistics delivery strategies to solve problems caused by high demand. In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load and distribution centre conditions. An improved ant colony algorithm was designed and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3% while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems.", "keywords": [ "Computer science", "Scheduling (production processes)", "Operations research", "Operations management", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://traffic2.fpz.hr/index.php/PROMTT/article/download/261/119" }, { "id": "https://openalex.org/W7125899544", "doi": "10.1080/13675567.2026.2621325", "title": "Energy-efficient drone technologies and carbon-aware systems in sustainable supply chains: innovations, challenges, and policy pathways", "year": 2026, "publication_date": "2026-01-28", "type": "article", "language": "en", "authors": [ "Jalel Euchi", "Abdeljawed Sadok", "Majdi Argoubi" ], "first_author": "Jalel Euchi", "venue": "International Journal of Logistics Research and Applications", "publisher": "Taylor & Francis", "abstract": "Traditional logistics networks face increasing environmental pressure from carbon-intensive operations, prompting the need for solutions that balance efficiency and sustainability. This review synthesises academic literature, industry practices and case studies to assess how energy-efficient drone hubs, renewable energy integration and carbon-aware delivery systems enable low-emission, resilient last-mile logistics. Evidence from real-world applications, including UPS's solar-charged UAVs, DHL's warehouse drone pilots and Zipline's healthcare deliveries, indicates that renewable-powered docking stations and real-time route optimisation can reduce energy consumption by 54–84% compared with conventional methods. While these systems offer reduced emissions and improved service reliability, challenges remain, notably regulatory fragmentation, battery limitations and economic barriers to clean energy adoption. The paper proposes policy measures, such as streamlined certification and targeted subsidies, and highlights the role of decentralised energy systems and AI-driven fleet coordination. The findings provide actionable guidance for advancing scalable, sustainable logistics aligned with global goals such as the EU Green Deal.", "keywords": [ "Drone", "Supply chain", "Sustainability", "Key (lock)", "Sustainable development", "Emerging technologies" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/13675567.2026.2621325" }, { "id": "https://openalex.org/W7153024469", "doi": "10.1016/j.swevo.2026.102382", "title": "Solving electric vehicle routing problem with heterogeneous drones and no-fly zones using enhanced adaptive large neighborhood search algorithm", "year": 2026, "publication_date": "2026-04-01", "type": "article", "language": "en", "authors": [ "Wanting Chen", "Shuai Zhang", "Kang Jiang" ], "first_author": "Wanting Chen", "venue": "Swarm and Evolutionary Computation", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Drone", "Electric vehicle", "Routing (electronic design automation)", "Search algorithm", "Local search (optimization)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.swevo.2026.102382" }, { "id": "https://openalex.org/W4409881423", "doi": "10.1117/12.3060784", "title": "Urban logistics UAV vertiport location and layout planning method", "year": 2025, "publication_date": "2025-04-28", "type": "conference-paper", "language": "en", "authors": [ "Hairu Wang", "Qiqian Zhang", "Honghai Zhang", "Dikun Feng" ], "first_author": "Hairu Wang", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Computer science", "Transport engineering", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3060784" }, { "id": "https://openalex.org/W4379507617", "doi": "10.1007/978-3-031-31066-9_78", "title": "The Possibility of Using Drones in the Process of Delivery in the Area of the City of Sarajevo", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Amel Kosovac", "Adisa Medić", "Muharem Šulić", "Haris Mehičić" ], "first_author": "Amel Kosovac", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Last mile (transportation)", "Process (computing)", "Function (biology)", "Supply chain", "Work (physics)", "Business", "Engineering", "Transport engineering", "Architectural engineering", "Computer science", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-31066-9_78" }, { "id": "https://openalex.org/W4408856948", "doi": "10.1007/s11227-025-07124-x", "title": "State of the art and taxonomy survey on federated learning and blockchain integration in UAV applications", "year": 2025, "publication_date": "2025-03-24", "type": "article", "language": "en", "authors": [ "Hela Alaya", "Asma Ben Letaïfa", "Abderrezak Rachedi" ], "first_author": "Hela Alaya", "venue": "The Journal of Supercomputing", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Blockchain", "Computer science", "Taxonomy (biology)", "Federated learning", "Data science", "State (computer science)", "State of art", "Data integration", "Computer security", "Database", "World Wide Web", "Artificial intelligence" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s11227-025-07124-x" }, { "id": "https://openalex.org/W4387067280", "doi": "10.1007/978-981-99-5056-0_3", "title": "Analysis of Geospatial Data Collected by Drones as Part of Aerial Computing", "year": 2023, "publication_date": "2023-01-01", "type": "book-chapter", "language": "en", "authors": [ "Galiveeti Poornima", "Deepak S. Sakkari", "T N Manjunath", "M. A. Sukruth Gowda", "R. Pallavi" ], "first_author": "Galiveeti Poornima", "venue": "Transactions on computer systems and networks", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Photogrammetry", "Geospatial analysis", "Plan (archaeology)", "Identification (biology)", "Global Positioning System", "Remote sensing", "Geomatics", "Geography", "Aerial photos", "Aerial photography", "Aerial survey" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-99-5056-0_3" }, { "id": "https://openalex.org/W7128471767", "doi": "10.1080/00207543.2026.2626537", "title": "Collaborative truck multi-drone pollution routing problem with pickup and delivery under variable truck speeds: a cost minimisation approach", "year": 2026, "publication_date": "2026-02-10", "type": "article", "language": "en", "authors": [ "Arash Alizadeh", "Sharan Srinivas", "James S. Noble", "Alena Otto" ], "first_author": "Arash Alizadeh", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "Collaborative truck-drone systems offer significant potential for reducing cost and emissions in last-mile logistics. However, prior studies often assume constant truck speeds, oversimplify fuel and drone energy use, and overlook differences between labor- and energy-dominant cost structures. This study introduces the Truck Multi-Drone Pollution Routing Problem with Pickup and Delivery under Variable Truck Speeds (TMD-PRP-PDVS), a generalisation of the PRP that integrates multi-drone coordination, segment-specific truck speed control, and realistic cost modelling. It is the first to unify variable truck speeds and coordinated truck-drone operations within a PRP framework for comparative analysis across distinct cost environments. A mixed-integer programming model is developed to minimise total cost, comprising truck fuel consumption, drone energy usage, and driver labor cost. For larger instances, an Adaptive Large Neighborhood Search heuristic is proposed with an embedded local search procedure that refines truck speeds at the route-segment level in polynomial time. Computational results show that drone deployment reduces total cost by about 40%, while speed optimisation yields an additional 7-11% on average and up to 18% in some cases. Case studies on real-world networks demonstrate applicability across contrasting cost regimes. The findings highlight the joint influence of truck speed regulation, drone deployment, and cost structures.", "keywords": [ "Truck", "Minimisation (clinical trials)", "Vehicle routing problem", "Pickup", "Variable (mathematics)", "Routing (electronic design automation)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2026.2626537" }, { "id": "https://openalex.org/W4405787520", "doi": "10.1109/iros58592.2024.10801949", "title": "SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle Routing Problem with Drones Using Reinforcement Learning", "year": 2024, "publication_date": "2024-10-14", "type": "conference-paper", "language": "en", "authors": [ "Navid Mohammad Imran", "Myounggyu Won" ], "first_author": "Navid Mohammad Imran", "venue": null, "publisher": null, "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, the definition of which can vary depending on various heuristic approaches, e.g., the total operation 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, regardless of how the solution quality is defined. 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", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Mathematical optimization", "Artificial intelligence", "Computer network", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros58592.2024.10801949" }, { "id": "https://openalex.org/W3120097964", "doi": null, "title": "Drone noise, a new public health challenge?", "year": 2020, "publication_date": "2020-10-19", "type": "article", "language": "en", "authors": [ "Antonio J. Torija" ], "first_author": "Antonio J. Torija", "venue": null, "publisher": null, "abstract": "There is a rapidly growing interest in manned and unmanned aerial vehicles of different size from small multi-rotor drones to Urban Air Mobility (UAM) vehicles. With the projected expansion of the sector, for a variety of applications from parcel delivery to transportation of people, it is very likely that urban and rural soundscapes will be altered by drone noise. This can lead to a significant source of community noise annoyance. This is the reason why it is widely accepted that noise is one of the largest limiting factors for public acceptance and adoption of drone technology. Unquestionable, if not tackled appropriately, the noise generated by drone operations might lead to significant tension with exposed communities. This paper aims to introduce the important uncertainty as to how communities will react to a new source of noise. Compared to conventional aircraft, drones generate an unconventional noise signature to which people are completely unfamiliar. Although with important broadband contributions, drone noise is highly tonal and has irritating frequency and amplitude modulations due to varying rotors rotational speeds. They will also flight closer to people and in a bigger number. An important factor to consider is that new communities, not currently affected by aircraft noise, will most likely be affected. Moreover, an overview on challenges and research gaps on noise effects of drones is provided:\r\n(1) It is uncertain whether the current evidence of health effects of aircraft noise will be of application. If not, new evidence will need to be gathered as to health effects of drone noise.\r\n(2) There are neither metrics able to account for the particular characteristics of drone noise nor information about acceptable levels.\r\n(3) There is no understanding on how the deployment of drones will affect the perception of current urban and rural soundscapes.\r\nPage | 2\r\n(4) Community annoyance will be different depending on context, e.g. drone delivering medicines to remote areas vs. drone delivering parcels to your neighbours\r\n(5) For planning purposes, exposure-response functions for drone noise will need to be derived. This introduces the challenge of how to predict long-term effects.", "keywords": [ "Drone", "Noise (video)", "Annoyance", "Noise control", "Aeronautics", "Noise pollution", "Traffic noise", "Computer science", "Noise reduction", "Engineering", "Artificial intelligence" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://usir.salford.ac.uk/id/eprint/59203/" }, { "id": "https://openalex.org/W3034405176", "doi": null, "title": "Hybrid Vehicle-drone Routing Problem For Pick-up And Delivery Services Mathematical Formulation And Solution Methodology", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Aline Karak" ], "first_author": "Aline Karak", "venue": "SMU Scholar (Southern Methodist University)", "publisher": "Southern Methodist University", "abstract": "The fast growth of online retail and associated increasing demand for same-day delivery have pushed online retail and delivery companies to develop new paradigms to provide faster, cheaper, and greener delivery services. Considering drones’ recent technological advancements over the past decade, they are increasingly ready to replace conventional truck-based delivery services, especially for the last mile of the trip. Drones have significantly improved in terms of their travel ranges, load-carrying capacity, positioning accuracy, durability, and battery charging rates. Substituting delivery vehicles with drones could result in $50M of annual cost savings for major U.S. service providers. The first objective of this research is to develop a mathematical formulation and efficient solution methodology for the hybrid vehicle-drone routing problem (HVDRP) for pick-up and delivery services. The problem is formulated as a mixed-integer program, which minimizes the vehicle and drone routing cost to serve all customers. The formulation captures the vehicle-drone routing interactions during the drone dispatching and collection processes and accounts for drone operation constraints related to flight range and load carrying capacity limitations. A novel solution methodology is developed which extends the classic Clarke and Wright algorithm to solve the HVDRP. The performance of the developed heuristic is benchmarked against two other heuristics, namely, the vehicle-driven routing heuristic and the drone-driven routing heuristic. Anticipating the potential risk of using drones for delivery services, aviation authorities in the U.S. and abroad have mandated necessary regulatory rules to ensure safe operations. The U.S. Federal Aviation Administration (FAA) is examining the feasibility of drone flights in restricted airspace for product delivery, requiring drones to fly at or below 400-feet and to stay within the pilot’s line of sight (LS). Therefore, a second objective of this research is considered to develop a modeling framework for the integrated vehicle-drone routing problem for pick-up and delivery services considering the regulatory rule requiring all drone flights to stay within the pilot’s line of sight (LS). A mixed integer program (MIP) and an efficient solution methodology were developed for the problem. The solution determines the optimal vehicle and drone routes to serve all customers without violating the LS rule such that the total routing cost of the integrated system is minimized. Two different heuristics are developed to solve the problem, which extends the Clarke and Wright Algorithm to cover the multimodality aspects of the problem and to satisfy the LS rule. The first heuristic implements a comprehensive multimodal cost saving search to construct the most efficient integrated vehicle-drone routes. The second heuristic is a light version of the first heuristic as it adopts a vehicle-driven cost saving search. Several experiments are conducted to examine the performance of the developed methodologies using hypothetical grid networks of different sizes. The capability of the developed model in answering a wide variety of questions related to the planning of the vehicle-drone delivery system is illustrated. In addition, a case study is presented in which the developed methodology is applied to provide pick-up and delivery services in the downtown area of the City of Dallas. The results show that mandating the LS rule could double the overall system operation cost especially in dense urban areas with LS obstructions.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Engineering", "Operations research", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://scholar.smu.edu/cgi/viewcontent.cgi?article=1008&context=engineering_civil_etds" }, { "id": "https://openalex.org/W4417169013", "doi": "10.1109/jiot.2025.3641887", "title": "A Two-Stage Framework Based on RL for Truck–Drone Collaborative Delivery Problem", "year": 2025, "publication_date": "2025-12-09", "type": "article", "language": null, "authors": [ "Yuanbo Li", "Chengwei Zhang", "Wanting Liu", "Chao Li", "Dou An", "Qi Wang" ], "first_author": "Yuanbo Li", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "With the explosive growth of e-commerce, efficient last-mile delivery has emerged as a critical challenge. Truck-drone collaborative delivery has garnered significant attention as a promising solution to this problem. In this work, we formulate the truck-drone collaborative delivery problem as a collaborative optimization problem, aiming to minimize the total completion time for delivering packages to customers by leveraging the complementary strengths of the drone’s speed and the truck’s endurance. We propose a two-stage framework to address this challenge. In the first stage, the Lin-Kernighan Helsgaun (LKH) algorithm is employed to generate a high-quality initial Traveling Salesman Problem (TSP) solution, serving as a robust starting point. In the second stage, a Sequence Allocate Policy (SAPPO), based on Proximal Policy Optimization, refines the TSP solution by optimizing the truck-drone collaborative path using a specially designed action space. Extensive experiments conducted on both random dataset and TSPLIB benchmarks demonstrate that our method significantly outperforms existing algorithms regarding delivery time, while exhibiting improved scalability and less training time.", "keywords": [ "Scalability", "Travelling salesman problem", "Path (computing)", "Collaborative software", "Sequence (biology)", "Optimization problem" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2025.3641887" }, { "id": "https://openalex.org/W4225749429", "doi": "10.1007/978-981-16-9492-9_149", "title": "Hybridization of Variable Neighborhood Search and Simulated Annealing for the Collaborative Truck-Drone Routing Problem", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Qizhang Luo", "Guohua Wu", "Ni Mao", "Jianmai Shi" ], "first_author": "Qizhang Luo", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Simulated annealing", "Drone", "Metaheuristic", "Vehicle routing problem", "Variable neighborhood search", "Computer science", "Benchmark (surveying)", "Truck", "Variable (mathematics)", "Mathematical optimization", "Routing (electronic design automation)", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-16-9492-9_149" }, { "id": "https://openalex.org/W4413159539", "doi": "10.2991/978-94-6463-811-0_105", "title": "Optimizing Urban Logistics through Low-Altitude Aerial Routes: A Study on UAV Integration in Supply Chains", "year": 2025, "publication_date": "2025-01-01", "type": "book-chapter", "language": "en", "authors": [ "Dongyang Zhang" ], "first_author": "Dongyang Zhang", "venue": "Advances in economics, business and management research/Advances in Economics, Business and Management Research", "publisher": "Atlantis Press", "abstract": null, "keywords": [ "Low altitude", "Supply chain", "Business", "Aeronautics", "Transport engineering", "Altitude (triangle)", "Engineering", "Marketing" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2991/978-94-6463-811-0_105" }, { "id": "https://openalex.org/W4360764516", "doi": "10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00092", "title": "Efficient Face Recognition via Multi-UAV-Edge Collaboration in UAV Delivery Service", "year": 2022, "publication_date": "2022-12-01", "type": "conference-paper", "language": "en", "authors": [ "Yi Xu", "Ruyi Guo", "Xiao Liu", "Haoyu Luo", "Chengzu Dong", "Aiting Yao", "Xuejun Li" ], "first_author": "Yi Xu", "venue": null, "publisher": null, "abstract": "In recent years, smart unmanned aerial vehicle (UAV) 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 target consignee is a critical task. Face recognition as an important AI application for user authentication, existing cloud computing based face recognition methods often face several critical issues such as massive data transmission to the cloud, slow response time, and the concern over user data privacy. To address these issues, in this paper, we propose a multi-UAV-edge collaborative framework for accurate, efficient and secure face recognition in the edge computing environment. Specifically, the feature values are extracted from the target consignee's face images and then stored on the permissioned blockchain node. Second, a parallel task processing framework for face recognition on edge servers is proposed. Finally, the results of face recognition are broadcasted to all UAVs so that they are working collaboratively to identify their target consignee. Simulation experiments based on a real-world smart UAV delivery system demonstrate the effectiveness of the proposed multi-UAV-edge collaborative framework.", "keywords": [ "Computer science", "Facial recognition system", "Cloud computing", "Edge computing", "Enhanced Data Rates for GSM Evolution", "Server", "Task (project management)", "Authentication (law)", "Feature extraction", "Embedded system", "Computer security", "Artificial intelligence" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/conference_contribution/Efficient_Face_Recognition_via_Multi-UAV-Edge_Collaboration_in_UAV_Delivery_Service/22680766" }, { "id": "https://openalex.org/W4411550988", "doi": "10.1109/jiot.2025.3582058", "title": "Distributed Knowledge-Enhanced Multi-Agent Reinforcement Learning for Internet of Drones", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Ye Wang", "Jingjing Wang", "Ruijie Zhu", "Ziyang Wang", "Pujie Xin", "Peng Pan" ], "first_author": "Ye Wang", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "With the growing demand for low-altitude transportation, the application of the Internet of drones (IoD) in urban logistics has become increasingly significant. However, the complex obstacles present in urban environments, such as tall buildings and no-fly zones, pose numerous challenges for the IoD, including low data-driven efficiency and difficulties in representing implicit knowledge. To address these challenges, this paper proposes an IoD swarm collaborative scheduling method based on distributed knowledge-enhanced multi-agent reinforcement learning (DKEMARL). The method leverages prior environmental knowledge to design a knowledge embedding and expansion module, which provides comprehensive and detailed environmental observation data during training and introduces a reward mechanism that balances task timeliness with flight safety. Within a centralized training and decentralized execution framework, the multi-agent deep deterministic policy gradient (MADDPG) approach is employed to facilitate efficient cooperation among the IoD. Specifically, we develop a scenario model for low-altitude transportation tasks involving an IoD swarm, considering factors such as task timeliness, flight distance cost, and safety constraints, and propose an optimization objective to balance timely task completion with flight safety. Simulation results demonstrate that the proposed DKEMARL algorithm can significantly enhance task completion efficiency compared to baseline methods that do not incorporate knowledge enhancement.", "keywords": [ "Computer science", "Reinforcement learning", "The Internet", "Drone", "Multi-agent system", "Artificial intelligence", "Distributed computing", "World Wide Web" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2025.3582058" }, { "id": "https://openalex.org/W4400650222", "doi": "10.1109/iv55156.2024.10588709", "title": "A Lagrangian Relaxation Algorithm for the Drone Routing Problem with Backhauls and Wind", "year": 2024, "publication_date": "2024-06-02", "type": "conference-paper", "language": "en", "authors": [ "Ting Jiang", "Riju Lavanya", "Yihuai Liang", "HanByul Ryu", "Daisik Nam" ], "first_author": "Ting Jiang", "venue": null, "publisher": null, "abstract": "Most urban logistics providers are able to deliver goods efficiently using conventional vehicles, except in areas that have low quality road infrastructure and steep road slopes, which pose a challenge for delivery. This lowers the quality of delivery services, creating pockets of underserved populations in urban areas, which is undesirable from an economic and social perspective. The flexibility of drones has made them an attractive option for last-mile delivery in this context. Optimizing the utilization of remaining energy and payload capacity during drone return trips is essential for maximizing overall utility. However, prior studies have overlooked the consideration of backhaul in the context of drone-only systems, as well as the assessment of energy consumption under wind conditions. This paper proposes a new model for the Drone Delivery Routing Problem with Backhaul (DDRPB), which incorporates both wind conditions and backhauling requirements, and is applicable for areas that have high delivery demand and return requests, but suffer from low delivery service quality. We introduce a linearized drone-wind-energy consumption model and design a Lagrangian relaxation heuristic algorithm to solve the resulting NP-hard optimization problem efficiently. A case study of an urban area with delivery challenges in Seoul, South Korea is presented. The results show that the proposed model effectively decreases overall costs.", "keywords": [ "Drone", "Lagrangian relaxation", "Computer science", "Relaxation (psychology)", "Lagrangian", "Routing (electronic design automation)", "Vehicle routing problem", "Mathematical optimization", "Algorithm", "Mathematics", "Computer network", "Applied mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/iv55156.2024.10588709" }, { "id": "https://openalex.org/W4416692040", "doi": "10.1016/j.trc.2025.105454", "title": "A branch-and-cut algorithm for routing a heterogeneous drone-ground vehicle fleet to deliver time-sensitive products", "year": 2025, "publication_date": "2025-11-26", "type": "article", "language": "en", "authors": [ "Sayed Hamid Hosseini Dolatabadi", "Tanveer Hossain Bhuiyan", "Waquar Kaleem", "Anirudh Subramanyam", "Taner Cokyasar" ], "first_author": "Sayed Hamid Hosseini Dolatabadi", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Payload (computing)", "Energy consumption", "Routing (electronic design automation)", "Heuristic", "Range (aeronautics)", "Linear programming", "Vehicle routing problem" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2025.105454" }, { "id": "https://openalex.org/W4285295307", "doi": "10.1007/978-981-19-1292-4_5", "title": "Location Prediction and Trajectory Optimization in Multi-UAV Application Missions", "year": 2022, "publication_date": "2022-01-01", "type": "book-chapter", "language": "en", "authors": [ "Rounak Singh", "Chengyi Qu", "Alicia Esquivel Morel", "Prasad P. Calyam" ], "first_author": "Rounak Singh", "venue": "Unmanned system technologies", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Reinforcement learning", "Trajectory", "Computer science", "Trajectory optimization", "Kalman filter", "Real-time computing", "Network packet", "Artificial intelligence", "Computer security" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-19-1292-4_5" }, { "id": "https://openalex.org/W7161697463", "doi": "10.1109/tem.2026.3695002", "title": "Digital Twin Architecture for Drone Logistics Operations", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Murugaiyan Pachayappan", "Tanmoy Kundu", "Stephan M. Wagner", "Balan Sundarakani" ], "first_author": "Murugaiyan Pachayappan", "venue": "IEEE Transactions on Engineering Management", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This study proposes a digital twin (DT) model for drone logistics systems. While DT models typically rely on real-time data from physical assets, identifying and structuring such parameters for drone logistics remains underdeveloped in current literature. Existing studies primarily focus on routing and fleet optimization, offering limited guidance on the systematic design of DT architectures for drone operations. To address this gap, this work conducts an evidence-based, structured review of 116 drone logistics models to extract and consolidate key operational parameters, resulting in the development of a Drone Logistics Parameter Framework (DLPF). Building on this foundation, the study introduces the Drone Logistics Digital Twin (DLDT) architecture and model. The DLDT is a five-dimensional system consisting of a physical entity, a virtual entity, a service interface, a data layer, and connection layers. The DLDT enables essential capabilities, including real-time monitoring, predictive maintenance, route optimization, last-mile delivery management, and scheduling. To validate the DLDT architecture and model, a route-optimization case demonstration within the DT environment illustrates their internal coherence and practical utility. Hence, this study contributes to a structured approach for designing DT models in drone logistics, addressing a notable gap in the literature and offering actionable insights for advancing intelligent, data-driven logistics operations.", "keywords": [ "Drone", "Architecture", "Systems architecture" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tem.2026.3695002" }, { "id": "https://openalex.org/W4288047840", "doi": "10.1109/icuas54217.2022.9836157", "title": "Preliminary Ground Risk Tiering for Small Unmanned Aerial Vehicles (sUAV) in Metropolitan Environments", "year": 2022, "publication_date": "2022-06-21", "type": "conference-paper", "language": "en", "authors": [ "Anush Kumar Sivakumar", "Mohd Hasrizam Che Man", "Kin Huat Low" ], "first_author": "Anush Kumar Sivakumar", "venue": "2022 International Conference on Unmanned Aircraft Systems (ICUAS)", "publisher": null, "abstract": "Unmanned aircraft systems (UAS) are anticipated to increase in the future and be employed for a variety of applications such as parcel delivery, structural inspections, aerial photography, and surveillance. However, the growth of the UAS industry is impeded by delayed operational approvals and risk assessments. This is likely due to the limited knowledge on the third-party risk (TPR) of UAS operation in the environment. For metropolitan environments like Singapore, the operation of UAS pose high risk to the population on ground due to high population density. Hence, it is of vital importance to assess the ground risk of UAS operations and to subsequently demarcate low, medium, and high areas within the city. In our preliminary study, a ground risk framework, adapted from Joint Authorities for Rulemaking of Unmanned Systems (JARUS) Specific Operations Risk Assessment (SORA), and a semi-quantitative ground risk matrix was established to quantitatively generate UAS failure rate thresholds as well as perform ground risk-tiering of neighborhoods. Ground risk mapping was illustrated for UAS of four different maximum take-off weight (MTOW). It was observed that larger UAS had increased ground risk especially in areas with highly dense population. Study of ground risk in urbanized environments will provide insights on the level of safety required for UAS operations. This could aid aviation authorities in formulating comprehensive risk assessments and reduce the time required to approve UAS operations in metropolitan environments. However, it is noteworthy that further studies are required to validate the results of the risk matrix.", "keywords": [ "Metropolitan area", "Population", "Risk assessment", "Risk analysis (engineering)", "Work (physics)", "Transport engineering", "Computer science", "Engineering", "Geography", "Business", "Computer security", "Environmental health" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dr.ntu.edu.sg/bitstream/10356/160615/2/220_ICUAS22_FinalSubmission.pdf" }, { "id": "https://openalex.org/W2943485978", "doi": "10.1504/ijpqm.2019.099628", "title": "Replication of a Six Sigma black belt case study: GEP box's paper helicopter experiment in a drone logistics scenario", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "Sean P. Goffnett", "Andrew N. Paquet", "Oliver M. Strong", "Kevin P. McCarron" ], "first_author": "Sean P. Goffnett", "venue": "International Journal of Productivity and Quality Management", "publisher": "Inderscience Publishers", "abstract": "Unmanned aerial vehicles (drones) have gained attention in business in recent years. Drones have demonstrated potential use in logistics operations, such as surveillance, inventory counting, order picking, and 'last-mile' delivery. Given the proliferation in potential uses of drones, it is no surprise companies are testing drone capabilities. This article presents a theoretical business case describing the Six Sigma DMAIC model applied in a logistics context involving drone capacity and reliability. Define, measure, analyse, improve and control are presented. This article builds on earlier studies on structuring Six Sigma projects. This service-related business case can be used for training and education purposes.", "keywords": [ "Drone", "Six Sigma", "DMAIC", "Context (archaeology)", "Reliability (semiconductor)", "Surprise", "Aeronautics", "Replication (statistics)", "Computer science", "Order (exchange)", "Lean Six Sigma", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijpqm.2019.099628" }, { "id": "https://openalex.org/W3216603398", "doi": "10.1109/tvt.2021.3131746", "title": "Throughput Analysis for TCP Over the FSO-Based Satellite-Assisted Internet of Vehicles", "year": 2021, "publication_date": "2021-12-01", "type": "article", "language": "en", "authors": [ "Hoang D. Le", "Phuc V. Trinh", "Thanh V. Pham", "Dimitar Radkov Kolev", "Alberto Carrasco‐Casado", "Toshihiro Kubooka", "Morio Toyoshima", "Anh T. Pham" ], "first_author": "Hoang D. Le", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Free-space optics (FSO)-based, low-earth orbit (LEO) satellite-assisted Internet of vehicles (IoVs) has recently attracted research efforts worldwide. For the majority of Internet applications, transmission control protocol (TCP) is one of the most widely deployed protocols. Nevertheless, a complete understanding of the TCP performance over FSO-based satellite-assisted IoVs networks is still lacking in the literature. This is a highly challenging issue, as a consequence of the non-trivial interdependencies between transmission losses caused by the dynamics/uncertainty of atmospheric last-mile channels and the buffer overflows on the Internet, as well as their joint impact on the TCP congestion control mechanisms. This paper offers a comprehensive analytical framework that allows assessing the TCP throughput performance under the joint impact of congestion losses occurred in the Internet and transmission errors at the last-mile link from the satellite-to-vehicle. Internet access for emerging unmanned aerial vehicles (UAVs) is considered while the analysis can be also applied to other kinds of vehicles, which, in fact, require simpler channel models. The link-layer incremental redundancy hybrid automatic repeat request (IR-HARQ) protocol is employed at satellite-to-UAV channels to further improve the TCP throughput performance. Numerical results provide non-trivial findings and insights for the TCP performance in satellite/FSO-based IoVs, in which optimal UAV’s parameters are chosen to maximize the TCP throughput.", "keywords": [ "Throughput", "Computer network", "The Internet", "Satellite", "Computer science", "Communications satellite", "Telecommunications", "Engineering", "Wireless", "Operating system", "Aerospace engineering" ], "cited_by_count": 58, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2021.3131746" }, { "id": "https://openalex.org/W4413074673", "doi": "10.1109/jiot.2025.3589644", "title": "Multistrategy Improved Particle Swarm Optimization Algorithm for Path Planning of UAV in 3-D Low Altitude Urban Environment", "year": 2025, "publication_date": "2025-07-16", "type": "article", "language": "en", "authors": [ "Fazhan Tao", "Zezheng Chen", "Zhikai Wang", "Longlong Zhu", "Jun Wang" ], "first_author": "Fazhan Tao", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The Internet of Things (IoT) system and path planning algorithm provide a technological foundation for autonomous navigation of uncrewed aerial vehicles (UAVs). Geospatial data from the IoT system is transmitted to UAVs through lightweight protocols, and UAVs make optimal path decisions based on these data through optimization algorithms. The combination of the IoT, UAV, and path planning technology constitutes a UAV delivery system, which offers an efficient and economical solution for last-mile logistics in smart cities. Among these, rapid and accurate optimal path planning is crucial for the autonomous delivery of UAVs. Therefore, this article proposes a multistrategy improved particle swarm optimization (PSO) algorithm called MSIPSO. First, the algorithm incorporates a local deadlock jump strategy to increase the success rate of path planning in dense obstacle environments. Second, to mitigate the influence of parameter selection on the algorithm’s performance, adaptive nonlinear inertia weights and learning factors are introduced to improve the algorithm’s stability. Finally, multiple population differentiation evolution strategies are designed, with different position update equations tailored for populations of varying qualities, which enhances the search efficiency of the algorithm. The simulation results show that MSIPSO outperforms PSO, gray wolf optimizer (GWO), whale optimizer (WOA), elite archive-driven PSO (EAPSO) algorithm, and hybrid GWO and differential evolution (HGWODE) algorithm in terms of convergence speed, accuracy, and stability.", "keywords": [ "Particle swarm optimization", "Motion planning", "Computer science", "Low altitude", "Algorithm", "Altitude (triangle)", "Path (computing)", "Mathematical optimization", "Real-time computing", "Artificial intelligence", "Mathematics", "Computer network" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2025.3589644" }, { "id": "https://openalex.org/W2303891912", "doi": "10.1016/j.eswa.2016.03.039", "title": "A decision support system for coordinated disaster relief distribution", "year": 2016, "publication_date": "2016-03-24", "type": "article", "language": "en", "authors": [ "Christian Fikar", "Manfred Gronalt", "Patrick Hirsch" ], "first_author": "Christian Fikar", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Tabu search", "Computer science", "Operations research", "Metaheuristic", "Decision support system", "Emergency management", "Truck", "Scheduling (production processes)", "Integer programming", "Heuristic", "Routing (electronic design automation)", "Transport engineering" ], "cited_by_count": 111, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2016.03.039" }, { "id": "https://openalex.org/W4408679371", "doi": "10.3724/sp.j.1249.2025.02205", "title": "Multi-objective optimization of truck-drone joint delivery with restricted areas", "year": 2025, "publication_date": "2025-03-01", "type": "article", "language": "en", "authors": [ "Yuchen Li", "Haibo Mu Haibo Mu", "Meng YUAN", "Huo Chai" ], "first_author": "Yuchen Li", "venue": "JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING", "publisher": "Science Press", "abstract": "Aiming at the problems of \"last mile\" cost, low customer value, and poor logistics due to regional restrictions, a truck-drone joint delivery model is proposed, which takes customer value and customer satisfaction as measurement factors under regional restrictions, in which drones can visit multiple customers in one flight. Combined with the constraints such as customer time window, unmanned aerial vehicle (UAV) load and endurance limit, vehicle load, etc., a multi-objective truck-drone joint distribution route optimization mathematical model is established to minimize cost, maximize customer value and customer satisfaction. According to the characteristics of the model, an improved non-dominated sorting genetic algorithm III (NSGA-III) is designed to solve the model, and the effectiveness of the algorithm and model is verified by using large, medium and small calculation models selected from the classical Sioux-Falls traffic network and Eastern-Massachusetts road network. The results show that considering customer value and customer satisfaction increases the cost by about 6.95%, but customer value and customer satisfaction increase significantly by 37.52% and 13.1%, respectively. This is very beneficial for enterprises to maintain quality customers and improve customer loyalty, and helps enterprises achieve sustainable development.", "keywords": [ "Drone", "Truck", "Joint (building)", "Computer science", "Aeronautics", "Engineering", "Automotive engineering", "Biology", "Structural engineering", "Genetics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciengine.com/doi/pdf/E68E19217C63402484C074A927499621" }, { "id": "https://openalex.org/W4404523968", "doi": "10.3390/drones8110688", "title": "Advancing Social Equity in Urban UAV Logistics: Insights from the Academic Literature and Social Media", "year": 2024, "publication_date": "2024-11-19", "type": "article", "language": "en", "authors": [ "Dong Zhang", "Perry Pei‐Ju Yang", "Jin Yeu Tsou" ], "first_author": "Dong Zhang", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In recent years, the rapid growth of e-commerce and on-demand delivery services has placed a significant strain on urban logistics systems. Technological advances such as unmanned aerial vehicle (UAV)-based logistics systems have thus emerged as promising solutions in urban environments and are increasingly being piloted worldwide. However, the implementation of UAV logistics risks exacerbating social inequities, particularly in marginalized communities that may disproportionately bear the noise and safety risks. To mitigate these risks, it is crucial to integrate social equity considerations into urban UAV logistics. This study explores social equity factors through a systematic literature review and social media analysis of Xiaohongshu (the Little Red Book), a popular Chinese social media platform known for its extensive user base and active discussions on social issues. This literature review involves a full-text examination, while latent Dirichlet allocation (LDA) topic modeling is used to analyze social media comment datasets. Each method identifies social equity factors and separately assesses their relative importance, resulting in the final identification of 24 key factors that provide a holistic view of public sentiment and academic discourse. The findings reveal a divide between academic concerns around systemic risks and a public focus on immediate needs. By synthesizing these insights, this study provides a social equity landscape for urban UAV logistics and actionable references for policymakers and stakeholders.", "keywords": [ "Equity (law)", "Social media", "Business", "Social equality", "Sociology", "Regional science", "Public relations", "Computer science", "Political science", "World Wide Web" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones8110688" }, { "id": "https://openalex.org/W7128679853", "doi": "10.1016/bs.atpp.2026.01.001", "title": "Sustainable city logistics", "year": 2026, "publication_date": "2026-01-01", "type": "book-chapter", "language": "en", "authors": [ "Ron van Duin", "Bram Kin", "Hans J. Quak", "W. Ploos van Amstel", "NR Anand" ], "first_author": "Ron van Duin", "venue": "Advances in transport policy and planning", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "City logistics", "Traffic management", "Drone", "Sustainable transport", "Sustainability", "Variety (cybernetics)", "Public transport", "Humanitarian Logistics", "Sustainable city" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/bs.atpp.2026.01.001" }, { "id": "https://openalex.org/W4381786735", "doi": "10.1109/wf-iot54382.2022.10152064", "title": "Enhancing UAV Flight Safety through Sensor-based Runtime Risk Assessment", "year": 2022, "publication_date": "2022-10-26", "type": "conference-paper", "language": "en", "authors": [ "Yong Zhi Lim", "Xin Xin", "Teck Ping Khoo" ], "first_author": "Yong Zhi Lim", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs, commonly re-ferred to as drones) are increasingly used in applications such as facade inspection, aerial photography, last mile delivery, and public safety. Such applications demand that the drone operates in close proximity to buildings and people, therefore posing a safety hazard. Drone flight safety currently rests on a static, one-off risk assessment prior to the flight, and safe flight execution with strict compliance to prevailing drone regulations. The static risk assessment can effectively identify risks and mitigating control measures prior to the flight, but needs to be performed regularly to ensure safety. Safe flight execution involves ensuring airspace clearance, correct drone safety settings, piloting the drone with high situational awareness, and frequently, concurrently controlling the drone payload (such as a camera) effectively. Performing the safety risk assessment is a one-off process and does not scale well, while it can be challenging to ensure all aspects of safe drone operation are covered during flight. In order to address these two pain points, we propose sensor-based runtime risk assessment. All relevant raw drone sensor readings, such as inertial measurement unit (IMU) and microelectromechanical (MEMS), are streamed into a rules engine during runtime. These readings are then used to calculate the drone's state over a historical sliding window, which is then used to compute a risk level that is comparable with prevailing drone standards. Our proposal digitises and automates risk assessment during runtime, can be customised to the risk assessment policy in different jurisdictions, has the potential to be inter-operable with different drone models, and is highly scalable. Most importantly, drone flight safety is significantly enhanced as the drone pilot will have the runtime risk level of the drone flight as a reference during flight We illustrate our proposal using actual data obtained from a DJI Matrice 300 RTK drone.", "keywords": [ "Drone", "Situation awareness", "Computer science", "Inertial measurement unit", "Aeronautics", "Risk assessment", "Computer security", "Engineering", "Artificial intelligence", "Aerospace engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wf-iot54382.2022.10152064" }, { "id": "https://openalex.org/W4360981881", "doi": "10.1007/978-3-031-21101-0_8", "title": "Intelligent Detection System for Spoofing and Jamming Attacks in UAVs", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Khadeeja Sabah Jasim", "Khattab M. Ali Alheeti", "Abdul Kareem A. Najem Alaloosy" ], "first_author": "Khadeeja Sabah Jasim", "venue": "Engineering cyber-physical systems and critical infrastructures", "publisher": null, "abstract": null, "keywords": [ "Spoofing attack", "Computer science", "Offensive", "Computer security", "Jamming", "Confusion matrix", "Artificial intelligence", "Machine learning", "Engineering", "Operations research" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-21101-0_8" }, { "id": "https://openalex.org/W4415611735", "doi": "10.3390/app152111469", "title": "Drone–Rider Joint Delivery Routing with Arc Obstacle Avoidance", "year": 2025, "publication_date": "2025-10-27", "type": "article", "language": "en", "authors": [ "Fuqiang Lu", "Jialong Liu", "Hualing Bi" ], "first_author": "Fuqiang Lu", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Drone delivery has gained significant traction in e-commerce, particularly for parcel and food delivery. However, existing systems face challenges such as limited delivery range, low efficiency, high costs, and suboptimal customer satisfaction. This paper proposes a novel drone–rider joint delivery model incorporating an Arc Obstacle Avoidance (AOA) strategy to address these issues in complex urban environments. We formulate a multi-objective optimization model aimed at minimizing delivery costs and maximizing customer satisfaction, solved by a Logistic-Logarithmic Dung Beetle Optimization algorithm (LLDBO). Using a modified Solomon dataset and real-world urban simulations in Shenzhen, our experiments demonstrate that the proposed model achieves a 15.3% reduction in delivery costs and a 27.1% increase in delivery efficiency compared to traditional rider-only delivery. Furthermore, customer satisfaction, measured by the on-time delivery rate, shows a 12.4% improvement (from 83.1% to 95.5%) over the rider-only baseline. The AOA strategy also extends the effective delivery range by up to 22.5% compared to conventional linear obstacle avoidance approaches, as measured by the maximum service radius achievable while maintaining 95% on-time delivery performance. These findings validate the practicality and scalability of the proposed approach for real-world last-mile logistics.", "keywords": [ "Scalability", "Obstacle", "Obstacle avoidance", "Range (aeronautics)", "Robust optimization", "Optimization problem", "Arc routing", "Customer satisfaction" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/15/21/11469/pdf?version=1761636182" }, { "id": "https://openalex.org/W7141727268", "doi": "10.4018/979-8-3373-7847-3.ch016", "title": "Quick Commerce Transformation Through Artificial Intelligence, Green Logistics, and Drones", "year": 2026, "publication_date": "2026-03-27", "type": "book-chapter", "language": null, "authors": [ "Duggirala Aravind", "A. Rahmath Nisha", "N. V. Suresh", "Asokan Vasudevan", "Patil Jaya Lakshmi Reddy", "Vanathi GopiKrishna" ], "first_author": "Duggirala Aravind", "venue": "Advances in computational intelligence and robotics book series", "publisher": "IGI Global", "abstract": "Quick commerce is fast revolutionizing retail by offering super rapid service, but its future depends on whether it incorporates the new super-fast technology and sustainability. The chapter examines the application of artificial intelligence in improving demand forecasting and stock management and green logistics methods to support environment negativity by electrifying and using eco-packaging. New models in drone delivery continue to undermine last-mile operations and reduce the cost of businesses, enhancing the efficiency of services. Together, these innovations redefine supply chains, as well as consumer expectations and urban retail ecosystems, in the post-pandemic era. The chapter identifies the opportunities and challenges of reaching artificial, sustainable logistics, and autonomous delivery but also provides a roadmap to the next stage of rapid transformation of quick commerce.", "keywords": [ "Drone", "Transformation (genetics)", "Order (exchange)", "Operational efficiency", "Fast fashion", "Demand forecasting" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3373-7847-3.ch016" }, { "id": "https://openalex.org/W7153135557", "doi": "10.3390/en19081854", "title": "Energy Management for a Fuel Cell Hybrid-Powered Unmanned Aerial Vehicle Based on Optimal Path Planning", "year": 2026, "publication_date": "2026-04-09", "type": "article", "language": "en", "authors": [ "Yong Ji", "Xingpeng Ling", "Xiaojuan Wu", "Jiangping Hu" ], "first_author": "Yong Ji", "venue": "Energies", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned Aerial Vehicles (UAVs) present a promising solution for urban logistics, where an effective energy management strategy guided by optimal path planning is crucial for reducing operational costs and extending system lifespan. This study begins by analyzing the wind field distribution in a specific urban area of Chengdu using Computational Fluid Dynamics, and establishes a data-driven power prediction model to evaluate UAV energy consumption. A hybrid wind-field-aware A* with Ant Colony Optimization algorithm is subsequently proposed to compute the optimal flight path that balances energy consumption and distance, generating corresponding power demand profiles for the ensuing energy management strategy. Finally, a Deep Q-Learning (DQN)-based energy management strategy is implemented to regulate power distribution between the fuel cell and the battery, aiming to minimize hydrogen consumption and stabilize the power output of the primary source. Experimental results demonstrate that the proposed path planning method can effectively reduce energy consumption across different scenarios while causing only a marginal increase in travel distance. In addition, the DQN-based strategy significantly suppresses fuel cell power fluctuations at the cost of only a slight increase in hydrogen consumption, thereby demonstrating the effectiveness of the path-planning-informed energy management strategy.", "keywords": [ "Energy management", "Energy consumption", "Path (computing)", "Power (physics)", "Motion planning", "Wind power", "Ant colony optimization algorithms", "Energy (signal processing)", "Fuel efficiency" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1996-1073/19/8/1854/pdf?version=1775745266" }, { "id": "https://openalex.org/W3142989865", "doi": null, "title": "Studi Komparasi Penggunaan Drone untuk LogistikLast-mile", "year": 2020, "publication_date": "2020-10-05", "type": "article", "language": "id", "authors": [ "Rizky Gilang Pratama", "Evi Fortuna Alfaridzi", "Ahmar Aji Awaluddin", "Setyo Tri Windras Mara", "Achmad Pratama Rifai" ], "first_author": "Rizky Gilang Pratama", "venue": null, "publisher": null, "abstract": "Sejak beberapa perusahaan besar menerapkan drone untuk logistik last-mile, penelitian dengan topik terkait semakin banyak dilakukan. Penggunaan drone menjadi tren dalam riset logistik karena kapabilitasnya untuk melakukan proses delivery barang dengan jarak Euclidean dan durasi waktu yang relatif lebih cepat bila dibandingkan dengan truk ataupun kendaraan darat lainnya.\r\nInisiasi perkembangan sudah dilakukan diantaranya adalah konsep mengombinasikan drone dengan truk. Beberapa model tersebut diantaranya Flying Sidekick Traveling Salesman Problem (FSTSP), Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP), Vehicle Routing Problem with Drones (VRPD), dan Multiple Flying Sidekick Traveling Salesman Problem (mFSTSP). Hanya saja, belum ada literatur yang melakukan studi komparasi untuk melihat model paling optimal untuk digunakan antar model tersebut dengan parameter delivery time. \r\nStudi komparasi tersebut untuk membandingkan hasil kalkulasi model TSP, FSTSP, dan PDSTSP. Model tersebut dibandingkan performanya menggunakan piranti lunak komersial GUROBI dan Python. Perbandingan dilakukan dengan menggunakan 15 dataset yang melingkupi beberapa titik koordinat customer di kota Yogyakarta. Dari hasil eksperimen, dapat dilihat bahwa secara keseluruhan FSTSP dan PDSTSP lebih efektif dibandingkan TSP berdasarkan total durasi penyelesaian misi pengiriman barang. Lebih jauh, FSTSP lebih unggul daripada PDSTSP saat jarak rata-rata customer lebih jauh, dan sebaliknya.", "keywords": [ "Computer science", "Drone", "Travelling salesman problem", "Humanities", "Algorithm", "Art", "Botany" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://repository.ugm.ac.id/276367/" }, { "id": "https://openalex.org/W4408708165", "doi": "10.23919/jsee.2025.000020", "title": "Vehicle and Onboard UAV Collaborative Delivery Route Planning: Considering Energy Function with Wind and Payload", "year": 2025, "publication_date": "2025-02-01", "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": "The rapid evolution of unmanned aerial vehicle (UAV) technology and autonomous capabilities has positioned UAV as promising last-mile delivery means. Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode. Spatiotemporal collaboration, along with energy consumption with payload and wind conditions play important roles in delivery route planning. This paper introduces the traveling salesman problem with time window and onboard UAV (TSP-TWOUAV) and emphasizes the consideration of real-world scenarios, focusing on time collaboration and energy consumption with wind and payload. To address this, a mixed integer linear programming (MILP) model is formulated to minimize the energy consumption costs of vehicle and UAV. Furthermore, an adaptive large neighborhood search (ALNS) algorithm is applied to identify high-quality solutions efficiently. The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.", "keywords": [ "Payload (computing)", "Function (biology)", "Computer science", "Energy (signal processing)", "Route planning", "Wind power", "Engineering", "Aerospace engineering", "Computer network", "Electrical engineering", "Transport engineering", "Physics" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/jsee.2025.000020" }, { "id": "https://openalex.org/W3025957020", "doi": "10.1109/access.2020.2994186", "title": "Design and Analysis of Novel Hybrid Multi-Objective Optimization Approach for Data-Driven Sustainable Delivery Systems", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "H. Giray Resat" ], "first_author": "H. Giray Resat", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This study presents a novel two-stage solution method designed for sustainable last-mile delivery systems in urban areas. A proposed hybrid solution methodology includes multi-criteria decision-making system to select the most efficient logistics providers by considering different performance indicators, and a mixed-integer linear optimization model for last-mile cargo distributions by drones within metropolitan areas. We present a multi-objective modeling approach by considering time windows for customer services and charging operations of drones and outline important characteristics of the mathematical programming problem to minimize transportation cost (in the meantime carbon dioxide emissions) and total sustainability score of the system by using epsilon constraint method to find out the Pareto frontiers. The main novelty of the proposed solution methodology is the inclusion of many performance indicators of last-mile delivery systems into multi-objective models for design of a sustainable city logistics. Additionally, the proposed model is applied to an illustrative case by using real-life data of one of the metropolitan in Turkey. The approach is shown as comparative analysis of proposed method with other two state-of-art solution methodologies for multi-objective problems, after defining some pre-processing, symmetry breaking steps, valid inequalities, and logic cuts.", "keywords": [ "Computer science", "Vehicle routing problem", "Pareto principle", "Sustainability", "Integer programming", "Multi-objective optimization", "Mathematical optimization", "Decision support system", "Drone", "Metropolitan area", "Linear programming", "Operations research" ], "cited_by_count": 35, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/6287639/8948470/09091793.pdf" }, { "id": "https://openalex.org/W7125513629", "doi": "10.1080/19397038.2026.2619289", "title": "How assumptions shape sustainability – a scenario-based life cycle Assessment of delivery drones", "year": 2026, "publication_date": "2026-01-23", "type": "article", "language": "en", "authors": [ "Hannah Aster", "Lukas Zeilerbauer", "Johannes Lindorfer" ], "first_author": "Hannah Aster", "venue": "International Journal of Sustainable Engineering", "publisher": "Taylor & Francis", "abstract": "Unmanned aerial vehicles (UAVs) are gaining increasing interest, among others, in parcel delivery. As this market grows, thorough analyses of their environmental impacts are essential for sustainability-oriented decisions. Life Cycle Assessment (LCA) is a commonly used tool in such analyses. In this study, we used LCA to evaluate the environmental impacts of a delivery drone across its entire life cycle for 18 impact categories. A baseline scenario was defined and an extensive sensitivity analysis was conducted to investigate more parts replacement frequencies and electricity mixes. Results show that the climate change impact is 3.08 g CO2-eq/kg*km for the baseline scenario and 8.73 g CO2-eq/kg*km for a scenario with more frequent replacements. Lower, but still significant differences appear for different electricity mixes assumed for battery charging. Additionally, the choice of the LCA system model (cut-off or consequential) significantly alters the LCA results, particularly for end-of-life battery treatment. Batteries had a high environmental impact share in all analysed scenarios. Depending on the underlying assumptions, the charging electricity, frame, and rotor blades also exhibit significant environmental impacts. These findings highlight the importance of optimising battery use, implementing sustainable materials, and considering recyclable components to reduce the environmental impact of drone deliveries.", "keywords": [ "Life-cycle assessment", "Drone", "Baseline (sea)", "Sustainability", "Environmental impact assessment", "Electricity", "Battery (electricity)", "Impact assessment" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/19397038.2026.2619289" }, { "id": "https://openalex.org/W4399990425", "doi": "10.1109/tits.2024.3415031", "title": "3D Multi-Trajectory and Pick-Up Optimization of UAV for Minimizing Delivery Time With Weight Restriction", "year": 2024, "publication_date": "2024-06-24", "type": "article", "language": "en", "authors": [ "Gitae Park", "Woongsup Lee", "Kisong Lee" ], "first_author": "Gitae Park", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In this study, we explore a three-dimensional trajectory and pick-up design of an unmanned aerial vehicle (UAV) for parcel delivery. In particular, we consider the real-world scenario in which a weight-restricted UAV cannot pick up all parcels within a single route; therefore the parcel delivery must be divided into multiple trajectories while avoiding no-fly zones. We formulate this problem mathematically as the minimization of total delivery time, which jointly optimizes the pick-up indicators, the lengths of the time slots, and the horizontal and vertical trajectories. To address the non-convexity of the formulated mixed-integer nonlinear programming, we employ a successive convex approximation to convert the problem into a convex form concerning optimization variables and utilize a penalty convex-concave procedure to preserve the binary characteristics of the pick-up indicators. Subsequently, we propose an iterative algorithm based on a block decent algorithm to efficiently identify the optimal solution by solving the relaxed convex problem. To address the problem of high computational complexity associated with the optimization-based algorithm, we also present an unsupervised deep learning (DL)-based heuristic algorithm. The simulation results confirm that the proposed schemes achieve considerably shorter delivery times than the baseline schemes in various scenarios. Furthermore, the DL-based scheme requires about 10% longer delivery time than the optimization-based scheme, but it can approximate the UAV strategy with substantially reduced computation time.", "keywords": [ "Trajectory", "Trajectory optimization", "Computer science", "Mathematical optimization", "Control theory (sociology)", "Artificial intelligence", "Optimal control", "Mathematics", "Physics", "Control (management)" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2024.3415031" }, { "id": "https://openalex.org/W4408186198", "doi": "10.3390/drones9030193", "title": "Air Route Network Planning Method of Urban Low-Altitude Logistics UAV with Double-Layer Structure", "year": 2025, "publication_date": "2025-03-06", "type": "article", "language": "en", "authors": [ "Zhendong Li", "Shan Li", "Jian John Lu", "S. Wang" ], "first_author": "Zhendong Li", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid development of e-commerce, logistics UAVs (unmanned aerial vehicles) have shown great potential in the field of urban logistics. However, the large-scale operation of logistics UAVs has brought challenges to air traffic management, and the competitiveness of UAV logistics is still weak compared with traditional ground logistics. Therefore, this paper constructs a double-layer route network structure that separates logistics transshipment from terminal delivery. In the delivery layer, a door-to-door distribution mode is adopted, and the transshipment node service location model is constructed, so as to obtain the location of the transshipment node and the service relationship. In the transshipment layer, the index of the route betweenness standard deviation (BSD) is introduced to construct the route network planning model. In order to solve the above model, a double-layer algorithm was designed. In the upper layer, the multi-objective simulated annealing algorithm (MOSA) is used to solve the transshipment node service location issue, and in the lower layer, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the network planning model. Based on real obstacle data and the demand situation, the double-layer network was constructed through simulation experiments. To verify the network rationality, actual flights were carried out on some routes, and it was found that the gap between the UAV’s autonomous flight route time and the theoretical calculations was relatively small. The simulation results show that compared with the single-layer network, the total distance with the double-layer network was reduced by 62.5% and the structural intersection was reduced by 96.9%. Compared with the minimum spanning tree (MST) algorithm, the total task flight distance obtained with the NSGA-II was reduced by 42.4%. The BSD factors can mitigate potential congestion risks. The route network proposed in this paper can effectively reduce the number of intersections and make the UAV passing volume more balanced.", "keywords": [ "Layer (electronics)", "Altitude (triangle)", "Route planning", "City logistics", "Low altitude", "Transport engineering", "Business", "Environmental science", "Computer science", "Automotive engineering", "Materials science", "Engineering" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9030193" }, { "id": "https://openalex.org/W4413192112", "doi": "10.1016/j.cie.2025.111452", "title": "Path optimization for UAV food delivery under extreme weather conditions with wind disturbance consideration", "year": 2025, "publication_date": "2025-08-09", "type": "article", "language": "en", "authors": [ "Xinhui Lu", "Qinze Wu", "Kaile Zhou" ], "first_author": "Xinhui Lu", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Disturbance (geology)", "Extreme weather", "Food delivery", "Environmental science", "Path (computing)", "Meteorology", "Computer science", "Engineering", "Ecology", "Geography", "Biology", "Climate change" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111452" }, { "id": "https://openalex.org/W3208261781", "doi": "10.3788/col202119.122702", "title": "Airborne quantum key distribution: a review [Invited]", "year": 2021, "publication_date": "2021-01-01", "type": "article", "language": "en", "authors": [ "Yang Xue", "Wei Qiu Chen", "Shuang Wang", "Zhen−Qiang Yin", "Lei Shi", "Zheng‐Fu Han" ], "first_author": "Yang Xue", "venue": "Chinese Optics Letters", "publisher": "Optica Publishing Group", "abstract": "Remarkable achievements have been witnessed in free-space quantum key distribution (QKD), which acts as an available approach to extend the transmission range of quantum communications.The feasibility of transmitting qubits through the free-space channel with the aid of moving platforms like satellites, aircraft, unmanned aerial vehicles (UAVs) has been verified.In view of the limited working time and resource consumption of the satellite-based QKD and the last-mile challenges of connecting satellite nodes with terrestrial networks, the airborne QKD is expected to provide flexible and relay links for the large-scale integrated network.This paper reviews the recent significant progress of QKD based on aircraft or UAVs, highlights their critical techniques, and prospects the future of airborne quantum communications.", "keywords": [ "Quantum key distribution", "Computer science", "Last mile (transportation)", "Transmission (telecommunications)", "Key (lock)", "Relay", "Satellite", "Qubit", "Quantum channel", "Telecommunications", "Free-space optical communication", "Remote sensing" ], "cited_by_count": 43, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researching.cn/ArticlePdf/m00005/2021/19/12/122702.pdf" }, { "id": "https://openalex.org/W4406273345", "doi": "10.48550/arxiv.2501.05200", "title": "On Coordinated Drone-Courier Logistics for Intra-city Express Services", "year": 2025, "publication_date": "2025-01-09", "type": "preprint", "language": "en", "authors": [ "Shuiwang Chen", "Kai Wang", "Lingxiao Wu", "Wei Min Qi" ], "first_author": "Shuiwang Chen", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Problem definition: Drones, despite being acknowledged as a transformative force in the city logistics sector, are unable to execute the \\textit{last-meter delivery} (unloading goods directly to customers' doorsteps) due to airspace restrictions and safety concerns. To leverage advancements and overcome the limitations of drones in providing intra-city express services, we introduce a coordinated drone-courier logistics system where drones operate within a closed network among vertiports, while couriers connect customers to the drone delivery system. This paper aims to shed light on this coordinated system in terms of system feasibility, network interactivity, and long-term sustainability. Methodology/Results: We develop an integrated optimization model to optimize the network planning of the coordinated logistics system. The interplay between network planning and tactical operations is mirrored by a queueing network model, resulting in the nonlinear and nonconvex (partially convex and partially concave) feasible region of the optimization model. An iterative exact algorithm that tightens lower and upper bounds by adaptively refining the linear approximations of nonlinear constraints is developed to provide optimality-guaranteed solutions with finite convergence. The computational experiments demonstrate the scalability and robustness of our algorithm across various network configurations and scenarios.Managerial implications: The case study, based on a real-world dataset from SF Express, a logistics giant in China, validates that the coordinated logistics system efficiently attains cost and time savings by leveraging the effective turnover of drones and the coordination between drones and couriers. The optimal network design features a concentrated structure, streamlining demand consolidation and reducing deadhead repositioning.", "keywords": [ "Drone", "Business", "Humanitarian Logistics", "Process management" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2501.05200" }, { "id": "https://openalex.org/W4415760861", "doi": "10.1016/j.seta.2025.104644", "title": "Energetic, comparative analysis of package delivery by drones and e-vehicles", "year": 2025, "publication_date": "2025-11-01", "type": "article", "language": "en", "authors": [ "Dániel Rohács", "Utku Kale", "Artūras Kilikevičius", "József Rohács" ], "first_author": "Dániel Rohács", "venue": "Sustainable Energy Technologies and Assessments", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Payload (computing)", "Energy consumption", "Key (lock)", "Efficient energy use", "Work (physics)", "Energy conservation" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.seta.2025.104644" }, { "id": "https://openalex.org/W7116701459", "doi": "10.1007/s10696-025-09648-z", "title": "Combining multiple delivery systems for urban logistics by ALNS-based route planning", "year": 2025, "publication_date": "2025-12-22", "type": "article", "language": "en", "authors": [ "Barbara Himstedt", "Frank Meisel" ], "first_author": "Barbara Himstedt", "venue": "Flexible Services and Manufacturing Journal", "publisher": "Springer Science+Business Media", "abstract": "Abstract Diesel-powered delivery vans still dominate the parcel delivery sector. However, due to their negative environmental impact, delivery service providers are urged to switch to more innovative and eco-friendly delivery vehicles like cargo bikes, delivery robots, or drones. Unfortunately, relying on only one type of delivery system is not always feasible, either because the customer’s site lacks the requirements or because the parcel size is too large. A possible solution is to combine multiple such delivery systems. In this context, this paper focuses on a problem in city logistics based on the two-echelon vehicle routing problem. To accommodate a diverse range of delivery vehicles, it involves deploying various vehicle types in the second echelon, direct delivery by first-echelon vehicles, transfer of second-echelon vehicles and parcels at satellite locations, and preference constraints for customers and vehicle types. To solve the problem, we propose an Adaptive Large Neighborhood Search (ALNS) heuristic that features problem-specific local search and destroy operators. Experiments are conducted for an urban delivery area in Hamburg, Germany. They show that the ALNS can handle various fleet compositions and instance sizes. Our findings indicate that the combination of delivery systems can lead to significant cost savings compared to traditional van delivery and that customer preferences heavily influence the optimal fleet composition.", "keywords": [ "Routing (electronic design automation)", "Vehicle routing problem", "Heuristic", "Service delivery framework", "Delivery Performance", "Service (business)", "Delivery system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10696-025-09648-z.pdf" }, { "id": "https://openalex.org/W4293167759", "doi": "10.5038/cutr-nicr-rr-2021-1-1", "title": "Demand-Side Management of Auto Traffic for Urban Parcel Delivery", "year": 2021, "publication_date": "2021-08-01", "type": "report", "language": "en", "authors": [ "Joan L. Walker", "Mark M. Hansen", "Pei-Sung Lin", "Zhenyu Wang", "Ang Li", "Ivan Tereshchenko", "Teddy Forscher", "Mingchen Li", "Runan Yang" ], "first_author": "Joan L. Walker", "venue": null, "publisher": null, "abstract": "Fueled by burgeoning e-commerce, urban parcel delivery (UPD) has emerged as a high growth market that is undergoing rapid technological change, particularly in the business-to-consumer segment. New classes of vehicles such as drones, droids, and autonomous ground vehicles, combined with new delivery models featuring crowdsourcing, parcel lockers, and mobile lockers, will enable a significant shift away from the conventional model of a dedicated delivery person operating a van. To reach the full potential of these changes to reduce costs and increase convenience, it is necessary to develop a complementary set of demand management strategies that will enable the next-generation parcel delivery system to mitigate current traffic congestion problems and avoid creating new ones. The project aims to (1) quantify the current and anticipated future contributions of UPD to urban congestion and related problems, such as traffic accidents and (2) identify opportunities for incentivizing consumers and delivery services to modify their behaviors to reduce the congestion impacts of UPD. To accomplish these objectives, the focus is on (1) demand models of e-commerce behaviors, (2) measuring the impact of delivery service operations on urban congestion using macroscopic fundamental diagrams, and (3) urban operations of drone deliveries to assess their potential for removing parcel delivery demand on roads. The modeling system will be used to assess the congestion reduction benefits of a range of policies geared toward encouraging consumers and service providers to adopt behaviors that reduce the congestion caused by urban delivery. In addition, an analytical framework for assessing the safety impacts, including non-recurring congestion reductions, of innovative UPD technologies is proposed. The method for identifying UPD crashes and statistical models for estimating UPD crash risks at TAZ levels by given demographic, roadway, and traffic conditions.", "keywords": [ "Traffic congestion", "Demand management", "Transport engineering", "Service (business)", "Service provider", "Business", "Drone", "Computer science", "Marketing", "Engineering", "Economics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5038/cutr-nicr-rr-2021-1-1" }, { "id": "https://openalex.org/W4407157315", "doi": "10.3390/drones9020118", "title": "A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning", "year": 2025, "publication_date": "2025-02-05", "type": "article", "language": "en", "authors": [ "X.B. Liu", "Fufu Wang", "Yu Liu", "Long Li" ], "first_author": "X.B. Liu", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimization challenge. To evaluate the efficacy of NSBKA, it was benchmarked against other algorithms using the Zitzler, Deb, and Thiele (ZDT) test problems, Deb, Thiele, Laumanns, and Zitzler (DTLZ) test problems, and test functions from the conference on evolutionary computation 2009 (CEC2009) for three types of multi-objective problems. Comparative analyses and statistical results indicate that the proposed algorithm outperforms on all 22 test functions. To verify the capability of NSBKA in addressing the multi-UAV cooperative problem model, the algorithm is applied to solve the problem. Simulation experiments for three UAVs and five UAVs show that the proposed algorithm can obtain a more reasonable collaborative path solution set for UAVs. Moreover, path planning based on NSBKA is generally superior to other algorithms in terms of energy saving, safety, and computing efficiency during planning. This affirms the effectiveness of the meta-heuristic algorithm in dealing with multiple objective multi-UAV cooperation problems and further enhances the robustness and competitiveness of NSBKA.", "keywords": [ "Kite", "Computer science", "Path (computing)", "Motion planning", "Algorithm", "Mathematical optimization", "Artificial intelligence", "Mathematics", "Computer network", "Geometry" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9020118" }, { "id": "https://openalex.org/W4401809224", "doi": "10.1109/tvt.2024.3448494", "title": "FSO-Based HAP-Assisted Multi-UAV Backhauling Over $\\mathcal {F}$ Channels With Imperfect CSI", "year": 2024, "publication_date": "2024-08-23", "type": "article", "language": "en", "authors": [ "Hoang D. Le", "Tinh V. Nguyen", "Vuong V. Mai", "Anh T. Pham" ], "first_author": "Hoang D. Le", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Non-terrestrial Network (NTN), utilizing high-altitude platforms (HAP)-based free-space optical (FSO) backhaul and unmanned aerial vehicles (UAV) for last-mile access, is a feasible and promising architecture to achieve high data rate and seamless network coverage in the future 6G era. Effective resource allocation emerges as a pivotal concern for such networks. This paper addresses the data allocation issue for FSO backhaul from the HAP to multiple UAV-mounted base stations (BSs) under the constraints of ground users' requested data rates. We introduce frame allocation schemes (FAS), including rate adaptation with constraints (RAC)- and rate/power adaptation (RPA)-aided FAS. The key idea of these schemes is to allocate data frames effectively based on UAV's turbulence channel conditions, which aims to (i) guarantee the quality of services (QoS), (ii) retain both latency and throughput fairness, and (iii) minimize the transmitted power. Furthermore, the performance of these schemes is also analyzed under the impact of imperfect channel state information (CSI). We newly derive the channel probability density function (PDF) and the cumulative density function (CDF), considering the imperfect CSI due to channel estimation and quantization errors. Capitalizing on the derived PDF and CDF, different performance metrics are analytically obtained, incorporating combined effects of cloud coverage, transceiver misalignment, Fisher-Snedecor$\\mathcal {F}$turbulence, and angle-of-arrival (AoA) fluctuations. Numerical results demonstrate the effectiveness of our design proposals over the state-of-the-art. Finally, Monte Carlo simulations are employed to validate the analysis.", "keywords": [ "Computer science", "Computer network", "Electronic engineering", "Engineering" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10454/19999" }, { "id": "https://openalex.org/W7161238863", "doi": "10.3390/su18104861", "title": "Joint Scheduling and Route Optimization for Bus–Heterogeneous Drone Collaborative Delivery Systems Under Spatiotemporal Synchronization Constraints", "year": 2026, "publication_date": "2026-05-13", "type": "article", "language": "en", "authors": [ "Chennan Gou", "Lei Wang", "Mayila Aizezi", "Zhenzhen Chen", "Xiyangzi Yang" ], "first_author": "Chennan Gou", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Rural logistics faces persistent challenges such as high distribution costs, dispersed demand, and limited transport infrastructure, which hinder efficient last-mile delivery. To address these issues, this study proposes a bus–heterogeneous drone collaborative delivery system that integrates the fixed-route coverage of rural buses with the flexibility of multiple types of drones. The proposed system enables synchronized operations between buses and drones, where buses serve as mobile depots for drone launching and recovery along predefined routes. A mixed-integer programming (MIP) model is developed to jointly optimize bus schedules and drone routing under spatiotemporal synchronization constraints, considering drone endurance, payload capacity, energy consumption, and bus departure times. Due to the NP-hard nature of the problem, an Improved Genetic Algorithm (IGA) is designed, incorporating a three-layer encoding scheme, adaptive crossover and mutation operators, and a local search repair mechanism to enhance convergence and solution feasibility. A real-world case study from Baihe County, Shaanxi Province, China, is conducted to evaluate the performance of the proposed model and algorithm. Comparative experiments under the reported case-study setting show that the proposed bus–heterogeneous drone system achieves notable cost reduction and improved overall delivery performance. Sensitivity analyses further confirm the robustness of the model with respect to drone endurance, drone payload capacity, and bus stop quantity. This research contributes to the literature by bridging the methodological gap between truck–drone coordination and bus-based collaborative delivery, offering an innovative framework for sustainable rural logistics and multi-modal last-mile optimization.", "keywords": [ "Drone", "Payload (computing)", "Robustness (evolution)", "Scheduling (production processes)", "Bridging (networking)", "Synchronization (alternating current)", "Flexibility (engineering)", "Crossover" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/su18104861" }, { "id": "https://openalex.org/W4406209149", "doi": "10.2139/ssrn.5023766", "title": "On Coordinated Drone-Courier Logistics for Intra-city Express Services", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Shuiwang Chen", "Kai Wang", "Lingxiao Wu", "Wei Min Qi" ], "first_author": "Shuiwang Chen", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Business", "Operations management", "Transport engineering", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5023766" }, { "id": "https://openalex.org/W7126167674", "doi": "10.1016/j.trpro.2026.01.027", "title": "Assessing Privacy Concerns in Urban Air Mobility: A Parcel Delivery Case Study in Madrid", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Emir Ganić", "Marta Alonso", "Miguel Baena", "Tatjana Krstić Simić", "Jovana G. Kuljanin", "Cristina Barrado" ], "first_author": "Emir Ganić", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "Urban Air Mobility (UAM) offers new opportunities for parcel delivery in metropolitan areas, but its success will depend on public acceptance. Among the societal challenges, privacy concerns represent a particularly sensitive issue that has received limited attention compared to noise or visual pollution. This paper applies privacy-related performance indicators, developed within the MUSE (Measuring U-space Social and Environmental Impact) project, to a simulated case study of parcel delivery in Madrid. Two operational concepts are analysed: free-route airspace and a grid-based network. The indicators capture the population potentially exposed to drone operations and provide a structured way to assess how different airspace designs influence privacy. The results show that the grid-based network consistently leads to higher exposure compared to the free-route concept. For individual trajectories, the number of affected people is more than doubled in the grid-based case. For aggregated traffic, the grid-based network yields approximately double the values during morning operations, while the difference is less pronounced during afternoon peaks. Given that results show grid-based networks may double privacy exposure, these findings highlight policy implications, calling for integration of privacy indicators, balanced altitude rules, and continuous monitoring in U-space regulation to ensure socially acceptable and sustainable UAM deployment.", "keywords": [ "Metropolitan area", "Drone", "Population", "Information privacy", "Sustainability", "Social network (sociolinguistics)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2026.01.027" }, { "id": "https://openalex.org/W4367018342", "doi": "10.2139/ssrn.4429473", "title": "Multi-Depot Vehicle Routing Problem with Drones: Mathematical Formulation, Solution Algorithm and Experiments", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Petr Stodola", "Libor Kutěj" ], "first_author": "Petr Stodola", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Vehicle routing problem", "Drone", "Mathematical optimization", "Computer science", "Routing (electronic design automation)", "Depot", "Algorithm", "Mathematics", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4429473" }, { "id": "https://openalex.org/W4362583657", "doi": "10.58837/chula.the.2020.112", "title": "Lagrangian relaxation method for integrated vehicles and drones routing model", "year": 2020, "publication_date": "2020-01-01", "type": "dissertation", "language": "en", "authors": [ "Perawit Charoenwut", "Manoj Lohatepanont", "Shin-ei Takano" ], "first_author": "Perawit Charoenwut", "venue": null, "publisher": null, "abstract": "Nowadays, e-commerce increases parcel transportation demand year by year. Many companies are seeking the way to improve performance of delivery service and cost-efficiency. One among many solutions is using a drone attached with a truck for the last mile delivery. A drone can be launched from either depot or truck and returned to the depot or wait for the truck to take them back from the docking station around the delivery area. The vehicle routing problem with drones considers truck- and drone-route simultaneously. It is an enormous problem that cannot be solved by a normal linear programming method. This research proposes the Lagrangian relaxation technique, in conjunction with the Branch-and-Price technique, to estimate the lower bound of the solution in each iteration of the Branch-and-Price. The lower bound from the Lagrangian relaxation is tighter than the solution from the Column generation which can improve the total solution time. The bound use in Branch-and-Price as a fathom condition, these help the search tree not to explore unnecessary nodes. The computational experiment includes randomly generated instances and real-world instances. Moreover, the operation cost of truck-based and truck-drone-based are compared to point out the benefit of using drones.", "keywords": [ "Lagrangian relaxation", "Drone", "Truck", "Column generation", "Vehicle routing problem", "Branch and price", "Routing (electronic design automation)", "Computer science", "Branch and bound", "Lagrangian", "Upper and lower bounds", "Operations research" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://cuir.car.chula.ac.th/bitstream/123456789/77162/1/6170231921.pdf" }, { "id": "https://openalex.org/W4415820524", "doi": "10.3390/drones9110759", "title": "Operational Performance of a 3D Urban Aerial Network and Agent-Distributed Architecture for Freight Delivery by Drones", "year": 2025, "publication_date": "2025-11-01", "type": "article", "language": "en", "authors": [ "Maria Nadia Postorino", "Giuseppe M. L. Sarnè" ], "first_author": "Maria Nadia Postorino", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The growing demand for fast and sustainable urban deliveries has accelerated exploration of the use of Unmanned Aerial Vehicles as viable logistics solutions for the last mile. This study investigates the integration of a distributed multi-agent system with a structured three-dimensional Urban Aerial Network (3D-UAN) for drone delivery operations. The proposed architecture models each drone as an autonomous agent operating within predefined air corridors and communication protocols. Unlike traditional approaches, which rely on simplified 2D models or centralized control systems, this research exploits a multi-layered 3D network structure combined with decentralized decision-making for improving scalability, safety, and responsiveness in complex environments. Through agent-based simulations, this study evaluates the operational performance of the proposed system under varying fleet size conditions, focusing on travel times and system scalability. Preliminary results demonstrate that the potential of this approach in supporting efficient, adaptive, resilient logistics within Urban Air Mobility frameworks depends on both the size of the fleet operating in the 3D-UAN and constraints linked to the current regulations and technological properties, such as the maximum allowed operational height. These findings contribute to ongoing efforts to define robust operational architectures and simulation methodologies for next-generation urban freight transport systems.", "keywords": [ "Drone", "Exploit", "Architecture", "Systems architecture", "Control (management)", "Network architecture", "Operational efficiency" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9110759" }, { "id": "https://openalex.org/W4403534494", "doi": "10.1109/codit62066.2024.10708243", "title": "A Survey of AI-based Models for UAVs’ Intelligent Control for Deconfliction", "year": 2024, "publication_date": "2024-07-01", "type": "conference-paper", "language": "en", "authors": [ "Xuan-Phuc Phan-Nguyen", "Neno Ruseno", "Fábio Suim Chagas", "Aurelie Aurilla Arntzen Bechina" ], "first_author": "Xuan-Phuc Phan-Nguyen", "venue": null, "publisher": null, "abstract": "The growing potential for Unmanned Aerial vehicles (UAVs) is creating new business opportunities. Drones, U-space, UTM, and their application to Air Mobility are fostering new applications that evolve quicker than the regulatory framework. For instance, the number of domains of applications is increasing, ranging from infrastructure inspection to parcel deliveries in urban settings. Thus, the number of drones flying simultaneously in the same geographical area is expected to grow over the next few years. It will soon pose safety issues as it might become more and more challenging to ensure safe control of drones so that they are separated from each other and with manned flight operations. The deconfliction or separation management problem, a pressing issue, has been tackled in several research projects. However, there is still an urgent need for a better approach to automate deconfliction at the strategic and tactical levels. Our SESAR-funded project (AI4HyDrop) aims to explore the use of machine learning to develop an intelligent control system to resolve drone deconfliction. To this purpose, we have conducted an extensive literature review, as outlined in this paper. This research was needed to understand better how AI has been explored in the field of UAV’s deconfliction and thus will pave the way for basic concepts that contribute to the requirement elicitation for an AI model-based UAV’s deconfliction.", "keywords": [ "Computer science", "Control (management)", "Artificial intelligence" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/codit62066.2024.10708243" }, { "id": "https://openalex.org/W7161123084", "doi": "10.3399/bjgp26x745245", "title": "Clearing for takeoff: exploring barriers and enablers to medical drone integration", "year": 2026, "publication_date": "2026-05-14", "type": "conference-abstract", "language": "en", "authors": [ "Aleksandra Tanaka", "Alasdair Purves", "Elinor Thomas", "Rebecca Payne" ], "first_author": "Aleksandra Tanaka", "venue": "British Journal of General Practice", "publisher": "Royal College of General Practitioners", "abstract": "BACKGROUND: Over recent decades, drones have gained prominence in discussions surrounding health innovation, demonstrating the potential to overcome logistical barriers in 'last-mile' medical deliveries. Global case studies have highlighted their ability to enhance accessibility and support sustainable healthcare delivery. Yet, despite this promise, widespread integration of drones into routine health systems lags behind research and remains limited. AIM: To identify key enabling and limiting factors influencing the integration of medical drones into healthcare delivery systems. METHOD: Semi-structured interviews with key stakeholders involved in medical drone projects were analysed using Greenhalgh's NASSS framework to identify contextual, technological, and organisational factors affecting drone adoption into healthcare systems. RESULTS: Preliminary findings indicate that airspace regulation represents one of the most significant barriers to medical drone implementation across the globe. In particular, the absence of permanent supportive policies for Beyond Visual Line of Sight (BVLOS) operations restricts scalability and integration into real-world health systems. Nevertheless, BVLOS is widely considered as the next logical regulatory step alongside the maturation of drone technologies. Following on, limitations such as battery performance, payload safety standards, and operational risks also significantly influence widespread adoption. Again, these are seen to potentially diminish with technological progression and progressive policy-making. Additionally, community engagement and education were found to underpin public acceptability, particularly in rural or technologically underserved regions. CONCLUSION: Medical drone integration is currently constrained by primarily regulatory and technological factors. Addressing these remains fundamental for drone adoption, which presents a sustainable and equitable solution for healthcare logistics and in strengthening accessibility.", "keywords": [ "Drone", "Health care", "Clearing", "Limiting", "Payload (computing)", "Public health", "Key (lock)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3399/bjgp26x745245" }, { "id": "https://openalex.org/W3212497734", "doi": "10.2139/ssrn.3913875", "title": "Multi-Depot Vehicle Fleet Planning with Trucks and Aerial Drones", "year": 2021, "publication_date": "2021-01-01", "type": "preprint", "language": "en", "authors": [ "Alexander Rave", "Pirmin Fontaine", "Heinrich C. Kuhn" ], "first_author": "Alexander Rave", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Truck", "Drone", "Depot", "Aeronautics", "Fleet management", "Transport engineering", "Engineering", "Automotive engineering", "Geography", "Archaeology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.3913875" }, { "id": "https://openalex.org/W4206294383", "doi": "10.1109/mspec.2022.9676362", "title": "Flying Pallets Without Pilots: A drone startup will test a radical new vision of long-range cargo transport in Europe", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Philip E. Ross" ], "first_author": "Philip E. Ross", "venue": "IEEE Spectrum", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Delivering things by drone began as a stunt in 2012, when a model airplane dropped a burrito by parachute to a hungry customer waiting below. The concept then graduated, first to a proof-of-principle venture in Iceland using multicopters, then to a well-funded Amazon project in the United Kingdom. But these and similar attempts to solve the last-mile problem―the mile leading to the customer―have largely been disappointing. Amazon recently scaled back its drone-based delivery project in the U.K.", "keywords": [ "Drone", "Aeronautics", "Mile", "Engineering", "Airplane", "Pallet", "Last mile (transportation)", "Launched", "Test (biology)", "Range (aeronautics)", "Transport engineering", "Operations research" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mspec.2022.9676362" }, { "id": "https://openalex.org/W3162839318", "doi": "10.1016/j.omega.2021.102493", "title": "Algorithms based on branch and bound for the flying sidekick traveling salesman problem", "year": 2021, "publication_date": "2021-05-18", "type": "article", "language": "en", "authors": [ "Mauro Dell’Amico", "Roberto Montemanni", "Stefano Novellani" ], "first_author": "Mauro Dell’Amico", "venue": "Omega", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Travelling salesman problem", "Drone", "Branch and bound", "Computer science", "Solver", "Heuristic", "Traveling purchaser problem", "Mathematical optimization", "Service (business)", "2-opt", "Upper and lower bounds", "Nearest neighbour algorithm" ], "cited_by_count": 100, "is_retracted": false, "is_paratext": false, "open_access_url": "https://cris.unibo.it/bitstream/11585/898008/3/DMN_BB.pdf" }, { "id": "https://openalex.org/W4411534582", "doi": "10.62311/nesx/rphcrefra5", "title": "Edge AI in Real-Time Path Planning for Autonomous Delivery Drones", "year": 2025, "publication_date": "2025-06-23", "type": "article", "language": "en", "authors": [ "Murali Krishna Pasupuleti" ], "first_author": "Murali Krishna Pasupuleti", "venue": "International Journal of Academic and Industrial Research Innovations(IJAIRI)", "publisher": null, "abstract": "This study evaluates the integration of Edge AI in real-time path planning for autonomous delivery drones to enhance responsiveness, safety, and computational efficiency in urban logistics. Traditional cloud-based systems suffer from latency and connectivity constraints, limiting decision-making speed in dynamic environments. This research proposes a hybrid framework leveraging Edge AI for local decision-making while maintaining synchronization with cloud systems for global updates. A reinforcement learning-based algorithm was deployed on NVIDIA Jetson Nano and tested in real-time navigation scenarios using simulated urban obstacle courses. The results demonstrate a significant reduction in path replanning time, improved obstacle avoidance accuracy, and enhanced delivery reliability. Regression analysis confirmed the inverse correlation between processing latency and task completion efficiency. Predictive models revealed optimal energy allocation patterns under Edge AI scenarios. This paper concludes that Edge AI is a viable enabler for scalable, responsive, and secure autonomous drone delivery systems. Keywords: Edge AI, autonomous drones, real-time path planning, reinforcement learning, delivery systems, obstacle avoidance", "keywords": [ "Computer science", "Reinforcement learning", "Obstacle avoidance", "Motion planning", "Drone", "Latency (audio)", "Enhanced Data Rates for GSM Evolution", "Obstacle", "Cloud computing", "Scalability", "Real-time computing", "Upstream (networking)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.nationaleducationservices.org/file-download/1426766/Download Full-Text Research Paper PDF Pp 103-113.pdf" }, { "id": "https://openalex.org/W4391096859", "doi": "10.1145/3631461.3631479", "title": "Re-Max: A Reward Maximization Approach for Heterogeneous Drone Scheduling Problem", "year": 2024, "publication_date": "2024-01-04", "type": "conference-paper", "language": "en", "authors": [ "Satyajit Mohapatra", "Sanjeet Kumar Nayak" ], "first_author": "Satyajit Mohapatra", "venue": null, "publisher": null, "abstract": "Over the decade, drones have paved the way into a variety fields including research and commercial applications. A captivating scenario of application is found in the logistics industry, where the collaboration of trucks and drones is seen for boosting last-mile deliveries. In this work, we devise a heterogeneous drone scheduling problem (HDSP), for maximization of rewards or profit. A truck starts from a warehouse with a fleet of heterogeneous drones (varying battery and weight lifting capacity) and packages for delivering to customers. The objective of HDSP is to maximize the reward while also taking into account multiple deliveries can be accomplished using the same drone. We then establish that HDSP is NP-hard problem and formulate an Integer Linear Programming (ILP) for finding the optimal solution. Initial results obtained demonstrate the increase in execution time for higher problem size, for which we further plan to provide heuristic solutions.", "keywords": [ "Drone", "Computer science", "Maximization", "Scheduling (production processes)", "Integer programming", "Profit maximization", "Truck", "Mathematical optimization", "Operations research", "Job shop scheduling", "Profit (economics)", "Schedule" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dl.acm.org/doi/pdf/10.1145/3631461.3631479" }, { "id": "https://openalex.org/W7128630581", "doi": "10.1109/ictmod66732.2025.11371786", "title": "Optimization and Energy Efficiency of Ground Autonomous Vehicles and Drones Systems: Current Approaches and Future Perspectives", "year": 2025, "publication_date": "2025-10-20", "type": "conference-paper", "language": null, "authors": [ "Martina Luzzi", "Khaoula Kharfati", "Luigi Di Puglia Pugliese", "Giusy Macrina", "Francesca Guerriero" ], "first_author": "Martina Luzzi", "venue": null, "publisher": null, "abstract": "This study presents a comprehensive review of recent research addressing routing, scheduling, and operational challenges in autonomous electric vehicles and unmanned aerial vehicles within logistics and mobility systems. Unlike previous surveys that provided broad overviews of vehicle routing problem variants and electric vehicle sharing systems, our review focuses specifically on the integration of autonomy and electrification, with particular attention to collaborative ground and air delivery and shared autonomous mobility. The surveyed works highlight three main directions: the incorporation of realistic energy-related constraints such as nonlinear battery consumption and partial recharging; the diversification of applications ranging from last-mile delivery and warehouse stocktaking to ride-sharing and large-scale shared mobility; and the methodological variety spanning exact optimization, heuristics, metaheuristics, and hybrid approaches. These advances mark a shift toward models that better balance realism and efficiency in autonomous logistics. As a result, the study outlines several promising directions for future research in this area.", "keywords": [ "Drone", "Diversification (marketing strategy)", "Efficient energy use", "Variety (cybernetics)", "Autonomy", "Electric vehicle", "Energy consumption" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ictmod66732.2025.11371786" }, { "id": "https://openalex.org/W4292070481", "doi": "10.23919/ecc55457.2022.9838052", "title": "Congestion-aware Bi-modal Delivery Systems Utilizing Drones", "year": 2022, "publication_date": "2022-07-12", "type": "conference-paper", "language": "en", "authors": [ "Mark Beliaev", "Negar Mehr", "Ramtin Pedarsani" ], "first_author": "Mark Beliaev", "venue": "2022 European Control Conference (ECC)", "publisher": null, "abstract": "Bi-modal delivery systems are a promising solution to the challenges posed by the increasing demand of e-commerce. Due to the potential benefit drones can have on logistics networks such as delivery systems, some countries have taken steps towards integrating drones into their airspace. In this paper we aim to quantify this potential by developing a mathematical model for a Bi-modal delivery system composed of trucks and drones. We propose an optimization formulation that can be efficiently solved in order to design socially-optimal routing and allocation policies. We incorporate both societal cost in terms of road congestion and parcel delivery latency in our formulation. Our model is able to quantify the effect drones have on mitigating road congestion, and can solve for the path routing needed to minimize the chosen objective. To accurately capture the effect of stopping trucks on road latency, we model it using SUMO by simulating roads shared by trucks and cars. Based on this, we show that the proposed framework is computationally feasible to scale due to its reliance on convex quadratic optimization techniques.", "keywords": [ "Drone", "Computer science", "Truck", "Routing (electronic design automation)", "Modal", "Traffic congestion", "Vehicle routing problem", "Latency (audio)", "Mathematical optimization", "Transport engineering", "Computer network", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/ecc55457.2022.9838052" }, { "id": "https://openalex.org/W4206192110", "doi": "10.1109/icid54526.2021.00049", "title": "Research on the delivery mode of rural e-commerce logistics drones under the background of \"5G+AI\"", "year": 2021, "publication_date": "2021-10-01", "type": "conference-paper", "language": "en", "authors": [ "Ying Zhou", "Zhilan Song", "SongHong Yan" ], "first_author": "Ying Zhou", "venue": "2021 2nd International Conference on Intelligent Design (ICID)", "publisher": null, "abstract": "Aiming at the \"last mile\" distribution problem of rural e-commerce logistics, field surveys are conducted for data collection and analysis, and the analytic hierarchy process is used to study the factors affecting the quality of rural e-commerce terminal logistics distribution. We proposed the use of the \"5G+AI\" drone delivery model to solve the \"final\" in rural logistics distribution. The one-kilometer problem has promoted the transformation of the logistics industry to \"smart technology\", while responding to the national \"rural revitalization\" strategic call to promote rural economic development.", "keywords": [ "Drone", "Distribution (mathematics)", "Analytic hierarchy process", "Business", "Humanitarian Logistics", "E-commerce", "Mode (computer interface)", "Data collection", "Last mile (transportation)", "Hierarchy", "Quality (philosophy)", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icid54526.2021.00049" }, { "id": "https://openalex.org/W4409507664", "doi": "10.30574/wjaets.2025.15.1.0307", "title": "Autonomous vehicles, drones, and AI: Transforming modern supply chain management", "year": 2025, "publication_date": "2025-01-30", "type": "article", "language": "en", "authors": [ "Tushar Dasgupta" ], "first_author": "Tushar Dasgupta", "venue": "World Journal of Advanced Engineering Technology and Sciences", "publisher": "GSC Online Press", "abstract": "This article examines the transformative impact of autonomous vehicles, drones, and artificial intelligence/machine learning technologies on modern supply chain management. It explores how these technologies are revolutionizing traditional logistics operations through enhanced efficiency, cost reduction, and sustainability. The article provides a comprehensive analysis of the technical foundations, economic impacts, and operational benefits of autonomous vehicles in transportation, the applications of drone technology in warehouse operations and last-mile delivery, and the role of AI/ML as the cognitive backbone of supply chain automation. Furthermore, it addresses integration challenges, including technological barriers, workforce transformation requirements, and ethical considerations, while offering strategic recommendations for organizations and identifying research gaps for future scholarly inquiry. The findings suggest that these technologies collectively enable fundamental operational transformations, shifting supply chains from linear to networked structures, from reactive to predictive decision-making, and from fragmented to integrated information flows, ultimately leading to more resilient, sustainable, and intelligent supply networks.", "keywords": [ "Drone", "Supply chain", "Supply chain management", "Business", "Aeronautics", "Engineering", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.30574/wjaets.2025.15.1.0307" }, { "id": "https://openalex.org/W7161152286", "doi": "10.3390/drones10050376", "title": "Digital Twin-Driven Trajectory and Resource Optimization for UAV Swarms in Low-Altitude Urban Logistics and Communication Environments", "year": 2026, "publication_date": "2026-05-14", "type": "article", "language": "en", "authors": [ "Hanyang Tong", "Ziyang Song", "Zhenyan Zhu", "Jinlong Sun" ], "first_author": "Hanyang Tong", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicles (UAVs) serve as both communication relays and aerial couriers in modern urban logistics networks. Conventional trajectory optimization methods assume perfect localization and isotropic free-space tracking signal propagation, which limits their effectiveness in urban canyons. To address the positional uncertainty and signal blockage from buildings, we propose a digital twin-driven framework for continuous trajectory and resource optimization in UAV swarms. We model an urban environment containing random high-rise structures, applying a non-line-of-sight (NLoS) uncertainty to reflect realistic communication degradation. The digital twin (DT) architecture utilizes a dual-layer spatial representation that captures a dynamically decaying positional uncertainty radius of the recipient. We define a strict visual localization boundary that initiates deterministic target tracking with a state transition mechanism. To manage the complexity of swarm routing, we apply Density-Based Spatial Clustering of Applications with Noise (DBSCAN), assigning one UAV courier and one logistics transfer station to each cluster. The system executes a continuous re-optimization loop using an adaptive multi-objective Genetic Algorithm. This framework jointly minimizes cumulative outage probability and total flight time while enforcing a signal-to-noise ratio threshold and throughput constraints. This continuous adaptation mechanism mitigates NLoS blockage risks, supporting reliable communication and efficient delivery in Global Navigation Satellite System (GNSS)-degraded and obstacle-dense urban environments.", "keywords": [ "Trajectory", "Trajectory optimization", "Particle swarm optimization", "Optimization problem", "Cluster analysis", "Probabilistic logic", "Throughput", "Communications system", "Robust optimization" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10050376" }, { "id": "https://openalex.org/W4416785228", "doi": "10.4103/jfmpc.jfmpc_1994_24", "title": "Leveraging drone technology in health systems strengthening: A cost-effectiveness analysis from Northeast India", "year": 2025, "publication_date": "2025-11-01", "type": "article", "language": "en", "authors": [ "Prakamya Gupta", "Ragini Bhatia", "Ranjan Kumar Choudhury", "Atul Kotwal" ], "first_author": "Prakamya Gupta", "venue": "Journal of Family Medicine and Primary Care", "publisher": "Medknow", "abstract": "A BSTRACT Introduction: Unmanned aerial vehicles (UAVs) or drones have the potential to revolutionize healthcare logistics last-mile delivery. The use of drone technology in healthcare is relatively a new domain with a few sites undertaking pilot trials resulting in a lack of data for analysis, particularly in the healthcare sector. The present study aims to explore the longitudinal analysis and cost-effectiveness assessment of the implementation of drone technology in the public healthcare system. Methods: Retrospective data from December 2022 to January 2024 were obtained from the World Bank sponsored Meghalaya Drone Delivery Network under the Meghalaya Health Systems Strengthening Project. An economic evaluation of the drones versus vehicle transportation of medical logistics from hub to spoke was conducted. Carbon emissions from vehicles and drones for transportation were also compared. Results: A total of 214 flights were conducted, transporting 199·05 kg of payload to 11 spoke sites (primary healthcare centers) during 7 months period. The average cost by road transportation for one round trip was INR 2902 (US$ 34·80), whereas by drone, it was INR 3743 (US$ 44·87). The Incremental Cost-Effectiveness Ratio (ICER) for drone transportation is INR 12 (US$ 0·14) per Disability Adjusted Life Years (DALY) averted as compared to vehicle transportation of medical logistics. The healthcare facility with drone services was able to avert 106 DALYs compared to 36 DALYs per month by the healthcare facility with a conventional mode of transportation. The drone services would require an additional INR 56 (US$ 0·67) to save 1 minute to transport medical logistics. The CO 2 emission per payload delivered by drones was up to 99% less than the conventional mode of transportation. Conclusion: Drone technology in healthcare complements the existing supply chain routes and is a value addition for saving lives in unforeseen medical emergencies. Further trials are required for firming up use cases to understand the challenges associated.", "keywords": [ "Drone", "Healthcare system", "Value (mathematics)", "Health care", "Supply chain", "Health technology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4103/jfmpc.jfmpc_1994_24" }, { "id": "https://openalex.org/W4411634772", "doi": "10.2139/ssrn.5320948", "title": "Reliable Design of Hub-Based Truck-Drone Relay Delivery System Under Uncertainty", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Bo Zhang", "Meng Zhao", "Jiateng Yin", "Lixing Yang", "Xiangpei Hu" ], "first_author": "Bo Zhang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Relay", "Truck", "Computer science", "Delivery system", "Engineering", "Automotive engineering", "Physics", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5320948" }, { "id": "https://openalex.org/W4294691833", "doi": "10.23919/acc53348.2022.9867361", "title": "Extension of Traveling Salesperson Problem with Drone for Manufacturing Digital Twin Data Collection", "year": 2022, "publication_date": "2022-06-08", "type": "conference-paper", "language": "en", "authors": [ "Sarah G. Garrow", "Shankar Narayan Mohan" ], "first_author": "Sarah G. Garrow", "venue": "2022 American Control Conference (ACC)", "publisher": null, "abstract": "This work investigates dual-agent path planning for marsupial robots with different energy time scales. We extend upon the studied Traveling Salesman Problem with Drone (TSP-D) originally formulated for last-mile package delivery. We contribute a drone energy model and constraints to allow for drone dock charging in a variation we refer to as the TSP-D with Energy Management. A moving-horizon heuristic method is also proposed to reduce the computational load and allow for on-line system feedback. We demonstrate that this heuristic method is computationally more efficient and provides significant path time savings as compared to a single agent solution. Furthermore, it enables the desired system feedback.", "keywords": [ "Drone", "Travelling salesman problem", "Computer science", "Heuristic", "2-opt", "Motion planning", "Mathematical optimization", "Extension (predicate logic)", "Robot", "Path (computing)", "Real-time computing", "Distributed computing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/acc53348.2022.9867361" }, { "id": "https://openalex.org/W3203598252", "doi": "10.1109/jiot.2021.3115346", "title": "Energy-Aware Computation Management Strategy for Smart Logistic System With MEC", "year": 2021, "publication_date": "2021-09-24", "type": "article", "language": "en", "authors": [ "Xu Jia", "Xiao Liu", "Xuejun Li", "Lei Zhang", "Jiong Jin", "Yun Jin Yang" ], "first_author": "Xu Jia", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "As the most important part of a smart city and long-standing challenging issue, a highly efficient smart logistic system has attracted a great deal of attention in recent years. In particular, unmanned aerial vehicles (UAVs) are ideal solutions for last-mile delivery scenarios in recent years due to their fast speed and easy deployment. However, because of the highly automatic delivery process, UAVs are still constrained by the limited payload, battery, and computing capacity for complex computational tasks. With the aid of the mobile edge computing (MEC) technology, UAVs can offload computational tasks to the MEC computational resources in various types of IoT environments. In spite of the task offloading which can enhance their task process capability, it also brings extra overhead, such as data transfer time and energy consumption. These extra overheads may significantly impact the efficiency and payload of UAV-based delivery systems. Therefore, taking the UAV last-mile delivery system with MEC as an example, this article investigates the energy-aware multi-UAV task computation management problem according to a realistic autonomous delivery network (ADNET). Specifically, we propose a computation management strategy, namely, the MEC-based task offloading and scheduling strategy (TOSS), to provide an integral approach covering both the static task offloading and scheduling algorithm, as well as the dynamic resource conflict resolution algorithm. Grounded on real-world scenarios, our experimental results show that TOSS can achieve a higher payload for UAVs by using minimum energy consumption and task makespan within the given constraints of the deadline compared to the state-of-the-art methods.", "keywords": [ "Computer science", "Distributed computing", "Energy consumption", "Payload (computing)", "Scheduling (production processes)", "Mobile edge computing", "Computation offloading", "Software deployment", "Embedded system", "Edge computing", "Real-time computing", "Computer network" ], "cited_by_count": 42, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/journal_contribution/Energy_aware_Computation_Management_Strategy_for_Smart_Logistic_System_with_MEC/20645187" }, { "id": "https://openalex.org/W2996171288", "doi": null, "title": "Analysis of supply chain operational performances using vehicle routing with UAV delivery in city logistics", "year": 2019, "publication_date": "2019-09-02", "type": "article", "language": "en", "authors": [ "Alexey Solomasov" ], "first_author": "Alexey Solomasov", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Vehicle routing problem", "Supply chain", "Routing (electronic design automation)", "Transport engineering", "Computer science", "Business", "Operations management", "Operations research", "Automotive engineering", "Engineering", "Computer network", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://matheo.uliege.be/bitstream/2268.2/8463/4/Solomasov_MT_Analysis_SCOP_UAV.pdf" }, { "id": "https://openalex.org/W7128516104", "doi": "10.1109/tai.2026.3662657", "title": "Collaborative Truck-Drone Routing With Multivisit via Surrogate-Assisted Bi-Level Optimization", "year": 2026, "publication_date": "2026-02-10", "type": "article", "language": null, "authors": [ "Ruonan Zhai", "Xi Zhang", "Yi Mei", "Wenbo Du", "Tong Guo", "Tao Song" ], "first_author": "Ruonan Zhai", "venue": "IEEE Transactions on Artificial Intelligence", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The development of drone techniques has extensive applications to fields like last-mile parcel delivery. For economic and efficiency advantages, collaborative truck-drone systems have attracted great attention and formulated as truck-drone routing problems. In this paper, a multi-visit traveling salesman problem with drones is investigated. We decompose it into a two-layer structure and formulate it as a new bi-level model (Bi-MTSPD). The major advantage of the bi-level model is its ability to represent the complex interactions between truck and drone routes by dispatching the drones under the premise of truck routes. To solvethe bi-level model, we propose a novel surrogate-assisted bi-level optimization method. The upper-level subproblem can be treated as a traveling salesman problem with customer assignment, and high-quality truck routes are identified by the K-nearest neighbor-based surrogate model with newly proposed features in order to allocate more computational resources to promising upper-level solutions. For the lower-level drone location routing problem (DLR), a customized memetic algorithm is developed to optimize the drone routes for the corresponding truck route. Comprehensive experimental results show that the proposed algorithm significantly outperforms the existing state-of-the-art algorithms across most benchmark instances. Further analysis verifies the congruence of the bi-level optimization for MTSP-D and the effectiveness of upper-level and lower-level solvers.", "keywords": [ "Drone", "Travelling salesman problem", "Vehicle routing problem", "Routing (electronic design automation)", "Benchmark (surveying)", "Truck", "2-opt" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tai.2026.3662657" }, { "id": "https://openalex.org/W3165417815", "doi": "10.3389/ffutr.2021.626935", "title": "A Survey on Operation Concept, Advancements, and Challenging Issues of Urban Air Traffic Management", "year": 2021, "publication_date": "2021-04-26", "type": "article", "language": "en", "authors": [ "Rakesh Shrestha", "Inseon Oh", "Shiho Kim" ], "first_author": "Rakesh Shrestha", "venue": "Frontiers in Future Transportation", "publisher": "Frontiers Media", "abstract": "The recent growth and adoption of unmanned aerial vehicles (UAVs) is due to their low development cost, high aerial mobility, advanced battery technology, rotors, gyroscopes, GPS, cameras, sensors, and wide range of applications. The UAV offers new possibilities for business in civil and noncivil applications such as parcel delivery, aerial mapping, agriculture, wildlife conservation, surveillance, and search and rescue. However, considering the exponential growth of various types of aerial vehicles, it is clear that advanced air traffic management services are urgently needed to control and handle the ever-increasing air traffics. In this paper, we first present the two major low-altitude UAV traffic management systems in the United States and Europe and compare them thoroughly. We present the global vision and advancement of UAV traffic management system. To understand the complexity of future air transportation system, we discuss the necessary elements required for an urban air traffic management system. We provide key strategic elements for an advanced urban traffic management through connectivity, open source, safety enhancement, and automation to maximize its impacts on the innovative future air traffic and mobility system. In the end, we discuss the challenging security issues in urban traffic management system.", "keywords": [ "Global Positioning System", "Advanced Traffic Management System", "Computer science", "Air traffic control", "Management system", "Transport engineering", "Drone", "Automation", "Traffic congestion", "Intelligent transportation system", "Engineering", "Telecommunications" ], "cited_by_count": 73, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.frontiersin.org/articles/10.3389/ffutr.2021.626935/pdf" }, { "id": "https://openalex.org/W7112667669", "doi": null, "title": "Last mile drone delivery: complexity and research challenges", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Xhafa Xhafa, Fatos", "Domínguez, Cristian", "Juan, Angel A." ], "first_author": "Xhafa Xhafa, Fatos", "venue": "UPCommons institutional repository (Universitat Politècnica de Catalunya)", "publisher": "Universitat Politècnica de Catalunya", "abstract": null, "keywords": [ "Drone", "Last mile (transportation)", "Mile", "Truck", "Pace", "Product (mathematics)", "Process (computing)", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/2117/423621" }, { "id": "https://openalex.org/W4402627982", "doi": "10.1109/apwcs61586.2024.10679285", "title": "Deep Reinforcement Learning for UAV Placement Over Mixed FSO/RF-Based Non-Terrestrial Networks", "year": 2024, "publication_date": "2024-08-21", "type": "conference-paper", "language": "en", "authors": [ "Tinh V. Nguyen", "Hoang D. Le", "Vuong V. Mai", "R. Swaminathan", "Anh T. Pham" ], "first_author": "Tinh V. Nguyen", "venue": null, "publisher": null, "abstract": "Non-terrestrial Network (NTN) architecture, leveraging high-altitude platforms (HAP)-based free-space optical (FSO) backhaul and unmanned aerial vehicles (UAV) for radio frequency (RF) last-mile access, is a promising solution for the future 6G era. Nevertheless, the mobility of the end-users, together with time-varying turbulence/clouds in backhaul links, poses significant challenges in deploying UAVs to maximize the end-to-end network performance. This paper introduces a framework that utilizes deep reinforcement learning (DRL) to optimize UAV placement, considering both dynamic backhaul and access constraints. The results indicate that the trained agent can effectively learn from the environment, confirming its effectiveness in maintaining a relatively high end-to-end throughput performance.", "keywords": [ "Reinforcement learning", "Computer science", "Environmental science", "Artificial intelligence" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/apwcs61586.2024.10679285" }, { "id": "https://openalex.org/W4412954658", "doi": "10.3390/drones9080538", "title": "Optimized Collaborative Routing for UAVs and Ground Vehicles in Integrated Logistics Systems", "year": 2025, "publication_date": "2025-07-30", "type": "article", "language": "en", "authors": [ "Hafiz Muhammad Rashid Nazir", "Yanming Sun", "Yongjun Hu" ], "first_author": "Hafiz Muhammad Rashid Nazir", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This study investigates the optimization of urban parcel delivery by integrating logistics vehicles and onboard drones within a static road network. A centralized delivery hub is responsible for coordinating both modes of transport to minimize total vehicle operation costs and customer waiting times. A simulation-based framework is developed to accurately model the delivery process. An enhanced Ant Colony Optimization (ACO) algorithm is proposed, incorporating a multi-objective formulation to improve route planning efficiency. Additionally, a scheduling algorithm is designed to synchronize the operations of multiple delivery bikes and drones, ensuring coordinated execution. The proposed integrated approach yields substantial improvements in both cost and service efficiency. Simulation results demonstrate a 16% reduction in vehicle operation costs and an 8% decrease in average customer waiting times relative to benchmark methods, indicating the practical applicability of the approach in urban logistics scenarios.", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Ground transportation", "Transport engineering", "Computer science", "Engineering", "Embedded system" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/8/538/pdf?version=1753891793" }, { "id": "https://openalex.org/W7159966771", "doi": "10.3390/drones10050334", "title": "Sustainability of Drone-Based Urban Air Mobility: A Systematic Review of Consensus and Controversies", "year": 2026, "publication_date": "2026-04-29", "type": "review", "language": "en", "authors": [ "Yuchen Guo", "Junming Zhao", "Mingbo Wu", "Xiangguo Peng", "Yu Xia", "Yankai Yu" ], "first_author": "Yuchen Guo", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Drone-based Urban Air Mobility (UAM) shows immense potential in urban logistics and emergency response; however, evidence regarding its systemic sustainability remains fragmented. In a systematic review using the PRISMA methodology, this study analyzes 301 core articles to construct an evaluation framework spanning environmental, economic, social, and systemic effectiveness dimensions. Given technical similarities, electric Vertical Take-off and Landing (eVTOL) findings are integrated to anticipate operational challenges. Results highlight a clear consensus: drone delivery is time-efficient in high-sensitivity scenarios, though noise, equity, and safety remain critical bottlenecks. Meanwhile, deep controversies persist across some dimensions. Environmental benefits are highly context-dependent, contingent on operating models, battery life cycles, and clean energy proportions from a Life Cycle Assessment (LCA) perspective. Economically, a mismatch between high costs and low willingness to pay (WTP) necessitates optimized pricing strategies. Socially, public acceptance is sensitive to the balance between perceived benefits and risks. Furthermore, systemic effectiveness depends on the coupling between vertiports and ground infrastructure. Concluding that sustainable drone-based UAM is a multistakeholder systemic endeavor, we urge future research to prioritize LCA, pricing strategies, public acceptance surveys, and integrated air-ground coordination to resolve controversies and foster sustainable systems.", "keywords": [ "Sustainability", "Construct (python library)", "Sustainable city", "Systematic review", "Willingness to pay", "Balance (ability)", "Drone", "Sustainable transport" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/5/334/pdf?version=1778835890" }, { "id": "https://openalex.org/W3171814612", "doi": "10.2139/ssrn.3561793", "title": "Which States Are Prepared for the Drone Industry? A 50-State Report Card", "year": 2020, "publication_date": "2020-01-01", "type": "preprint", "language": "en", "authors": [ "Brent Skorup" ], "first_author": "Brent Skorup", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "State (computer science)", "Business", "Computer security", "Aeronautics", "Advertising", "Engineering", "Computer science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.3561793" }, { "id": "https://openalex.org/W7148299182", "doi": "10.3390/a19040278", "title": "A Comprehensive Review of Metaheuristics for the Modern Traveling Salesman Problem and Drone-Assisted Delivery", "year": 2026, "publication_date": "2026-04-02", "type": "article", "language": "en", "authors": [ "Alessio Mezzina", "Mario Francesco Pavone" ], "first_author": "Alessio Mezzina", "venue": "Algorithms", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The Traveling Salesman Problem (TSP) is a fundamental challenge in combinatorial optimization, with wide-ranging applications in logistics, manufacturing, and network design. In addition to the classical formulation, recent years have witnessed the emergence of complex variants, such as the TSP with Drones (TSP-D), TSP with Time Windows, and Prize-Collecting TSP, that incorporate novel constraints reflecting real-world requirements like last-mile delivery and multimodal logistics. This review presents a comprehensive survey of metaheuristic approaches for solving both the classical TSP and its emerging extensions, with particular emphasis on metaheuristic, hybrid methods, and machine learning-enhanced strategies. Recent algorithmic developments, benchmark datasets, and evaluation metrics are investigated, and critical challenges in addressing drone coordination, synchronization, and uncertainty are identified, as well. Bibliometric analysis is further provided to map research trends and the evolution of the field. By synthesizing foundational techniques and state-of-the-art innovations, this review outlines current progress and proposes future directions for metaheuristic optimization in increasingly dynamic and heterogeneous routing scenarios.", "keywords": [ "Metaheuristic", "Travelling salesman problem", "Drone", "Benchmark (surveying)", "Parallel metaheuristic", "Combinatorial optimization", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1999-4893/19/4/278/pdf?version=1775135163" }, { "id": "https://openalex.org/W4412792092", "doi": "10.1080/10106049.2025.2538099", "title": "Hybrid metaheuristic-driven 3D path planning for UAVs in complex urban environments: a multi-objective fusion framework", "year": 2025, "publication_date": "2025-07-31", "type": "article", "language": "en", "authors": [ "Qing Cheng", "Zhengyuan Zhang", "Yunfei Du", "Xi Zhao" ], "first_author": "Qing Cheng", "venue": "Geocarto International", "publisher": "Taylor & Francis", "abstract": "Unmanned Aerial Vehicles (UAVs) offer transformative potential for urban logistics, but their deployment is challenged by complex 3D path planning in constrained environments. This study introduces GS-PSO, a novel multi-stage hybrid framework designed to address this challenge by sequentially integrating Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), and a Genetic Algorithm (GA). Key innovations include dynamic chaotic inertia weights in PSO for enhanced exploration and specialized GA operators for fine-grained refinement. The efficacy of GS-PSO was validated on the CEC2020 benchmarks, where it found the global optimum in 8/10 functions, showing superior accuracy and stability over standard PSO. In high-fidelity UAV simulations, GS-PSO generated paths up to 22.9% shorter than the AVOA, significantly outperforming competitors. Statistical tests (p < 0.05) confirmed its superior solution quality and robustness. This work provides an effective, reliable solution for complex UAV trajectory planning.", "keywords": [ "Motion planning", "Metaheuristic", "Path (computing)", "Computer science", "Fusion", "Urban planning", "Geography", "Artificial intelligence", "Engineering", "Civil engineering", "Robot", "Computer network" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.tandfonline.com/doi/pdf/10.1080/10106049.2025.2538099?needAccess=true" }, { "id": "https://openalex.org/W2945785916", "doi": "10.1145/3331053.3335032", "title": "Multiservice UAVs for Emergency Tasks in Post-disaster Scenarios", "year": 2019, "publication_date": "2019-06-10", "type": "conference-paper", "language": "en", "authors": [ "Francesco Malandrino", "Cristina Rottondi", "Carla Fabiana Chiasserini", "Andrea G. Bianco", "Ioannis Stavrakakis" ], "first_author": "Francesco Malandrino", "venue": null, "publisher": null, "abstract": "UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication-support and other specific tasks. In certain cases, the same geographical area may be in need of multiple services, which can be carried out by the same drones. In this paper, we propose and investigate a joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, a heuristic solution is developed for large-scale environments to cope with the increased complexity of the optimization framework. The developed joint planning of multitask missions is applied to a specific post-disaster recovery scenario of a flooding in the San Francisco area. The results show the effectiveness of the proposed solutions and the potential savings in the number of UAVs needed to carry out all the tasks with the required level of quality.", "keywords": [ "Computer science", "Emergency management", "Computer security", "Aeronautics", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3331053.3335032" }, { "id": "https://openalex.org/W4295860426", "doi": "10.2139/ssrn.4211063", "title": "A Collaborative Truck Multi-Drone Routing Problem with Flexible Launch and Recovery Operations", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Liu Changshi", "Wu Zhang", "Gang Kou", "Feng Xiao", "Xiancheng Zhou", "Lijun Fan" ], "first_author": "Liu Changshi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Aeronautics", "Routing (electronic design automation)", "Vehicle routing problem", "Computer science", "Operations research", "Engineering", "Business", "Aerospace engineering", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4211063" }, { "id": "https://openalex.org/W7165685507", "doi": "10.1016/j.tre.2026.105034", "title": "Two-phase truck-van-drone collaborative transportation with transshipment: a two-stage heuristic approach enhanced with reinforcement learning", "year": 2026, "publication_date": "2026-06-23", "type": "article", "language": "en", "authors": [ "Haipeng Cui", "Zhixin Tan", "Xin Jiang", "H T Wang", "Xi Zhang" ], "first_author": "Haipeng Cui", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Reinforcement learning", "Heuristic", "Intelligent transportation system", "Minification", "Mathematical model" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105034" }, { "id": "https://openalex.org/W4417509453", "doi": "10.1109/leficlu65987.2025.11297266", "title": "Location Method of Logistics Drone Vertiport Based on Urban Spatial Accessibility", "year": 2025, "publication_date": "2025-09-26", "type": "conference-paper", "language": null, "authors": [ "Zhendong Li", "Jian Lü", "Shan Li", "Xuerui Wang" ], "first_author": "Zhendong Li", "venue": null, "publisher": null, "abstract": "Under the background of the rapid development of urban low-altitude logistics, there are some problems such as insufficient accessibility and weak market competitiveness at the drone vertiport, which seriously restricts the efficiency and service quality of urban logistics distribution. In this paper, a location method of logistics drone vertiport based on urban spatial accessibility is proposed. Combined with the urban environmental characteristics and the distribution of users’ needs, the accessibility evaluation framework of vertiports based on cumulative opportunity model is constructed, and the vertiport location model is established, which is solved by self-organizing map algorithm to realize the optimal location of vertiports and the optimization of service relationship. An empirical analysis is made on an area in Nanjing. The results show that the layout scheme of 20 vertiports obtained by this method can ensure that residents can enjoy at least one vertiport service within a five-minute walking life circle. The proposed method realizes the optimal allocation of drone vertiport resources and promotes the development of low-altitude logistics industry.", "keywords": [ "Drone", "Service (business)", "Location-allocation", "Scheme (mathematics)", "City logistics", "Facility location problem", "Empirical research", "Distribution (mathematics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/leficlu65987.2025.11297266" }, { "id": "https://openalex.org/W4388562190", "doi": "10.1109/dasc58513.2023.10311219", "title": "A UAV Path Planning Method in Three-Dimensional Urban Airspace based on Safe Reinforcement Learning", "year": 2023, "publication_date": "2023-10-01", "type": "conference-paper", "language": "en", "authors": [ "Yan Li", "Xuejun Zhang", "Yuanjun Zhu", "Ziang Gao" ], "first_author": "Yan Li", "venue": null, "publisher": null, "abstract": "Under the demand of urban terminal \"Last Mile Delivery\" scenario, finding a safe and efficient UAV path planning method is a crucial issue of current research. Nowadays, reinforcement learning is widely used in UAV path planning, but it is difficult to ensure the safety of the learning or execution phases due to the lack of hard constraints. Aiming at the constraints above, this paper studies how to combine safety properties with RL algorithm to find a safe path and proposes a safe reinforcement learning method called Shield-DDPG for UAV path planning. In the method, a protection mechanism Shield is mainly introduced to prevent the algorithm from outputting unsafe actions. Further, the state space, action space, and reward function are specifically improved for efficiency and safety. Then we compare the Shield-DDPG algorithm with the DDPG and RRT algorithm in some different scenarios, and the results show that the proposed algorithm has a better performance. With the proposed path planning method, UAV can learn well to efficiently and safely reach the destination via calling the trained policy. This research is of great importance to UAV operations and practical applications in complex urban airspace.", "keywords": [ "Reinforcement learning", "Computer science", "Motion planning", "Path (computing)", "Function (biology)", "Mathematical optimization", "Simulation", "Real-time computing", "Artificial intelligence", "Mathematics", "Computer network" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dasc58513.2023.10311219" }, { "id": "https://openalex.org/W3082190938", "doi": "10.1109/fuzz48607.2020.9177642", "title": "Decentralized Distribution of UAV Fleets Based on Fuzzy Clustering for Demand-driven Aerial Services", "year": 2020, "publication_date": "2020-07-01", "type": "conference-paper", "language": "en", "authors": [ "Maria João Sousa", "Alexandra Moutinho", "Miguel Almeida" ], "first_author": "Maria João Sousa", "venue": null, "publisher": null, "abstract": "Unmanned aerial vehicles provide a flexible solution for emerging civilian applications such as last-mile delivery, remote environmental monitoring or hazard detection. However, since the demand for this type of applications is sparsely distributed and comprises spatiotemporal uncertainty, prior to the deployment of aerial means, the dimension and configuration of multi-UAV systems has to be addressed. This paper proposes a clustering-based dimensioning strategy for networks of aerial vehicles, that incorporates heterogeneous vehicle characteristics and energy-related autonomy constraints to determine the type and number of vehicles required to meet a given demand. The approach follows a fuzzy data-driven method which enables generating coverage regions with elastic boundaries that allows for cooperation between neighboring regions to manage high and low demand scenarios, providing improved resilience to demand fluctuations and guaranteeing a well-conditioned formulation for the optimal resource allocation problem.", "keywords": [ "Computer science", "Cluster analysis", "Software deployment", "Resilience (materials science)", "Fuzzy logic", "Distributed computing", "Real-time computing", "Artificial intelligence" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/fuzz48607.2020.9177642" }, { "id": "https://openalex.org/W2807670213", "doi": "10.1109/ictai.2017.00174", "title": "A Language for UAV Traffic Rules in an Urban Environment and Decentralized Scenario", "year": 2017, "publication_date": "2017-11-01", "type": "conference-paper", "language": "en", "authors": [ "Giuseppe Lombardi", "Eric Medvet", "Alberto Bartoli" ], "first_author": "Giuseppe Lombardi", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular and the amount of UAV traffic in urban environments will largely increase in the future, due to profitable tasks which are particularly suited to UAVs, e.g., parcel delivery and surveillance, in particular in the context of smart cities. Trying to ensure the traffic safety and efficiency by acting on the UAV controller alone might be challenging, since the set of involved players (regulators, manufacturers, business users) is large and diversified. In this work, we address this problem by proposing a language for defining rules suitable for UAV traffic which can be enforced in a decentralized way by the UAVs themselves, without any need for communication and regardless of the UAV navigation algorithm. The language allows to express realistic rules, such as ``when cruising, keep a minimum altitude'', concisely and such that they can be processed online by each single UAV basing on its perception of the nearby environment. We experimentally validate the ability of our proposal to impact on the UAV traffic efficiency and safety by performing a large number of simulations with and without a set of realistic rules.", "keywords": [ "Computer science", "Set (abstract data type)", "Context (archaeology)", "Air traffic control", "Perception", "Controller (irrigation)", "Computer security", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arts.units.it/bitstream/11368/2926465/2/2017-ICTAI-UAVsLanguage-preprint.pdf" }, { "id": "https://openalex.org/W4410623858", "doi": "10.1007/s00291-025-00818-x", "title": "Collision-free trajectory planning for drones with velocity dependent energy consumption and moving piggyback vehicles", "year": 2025, "publication_date": "2025-05-23", "type": "article", "language": "en", "authors": [ "Alf Kimms", "Christin Münch" ], "first_author": "Alf Kimms", "venue": "OR Spectrum", "publisher": "Springer Science+Business Media", "abstract": "Abstract In this work, we challenge the common assumption in truck-drone last-mile delivery that trucks and drones operate on a graph. Instead, we adopt a geometric approach, allowing the vehicles to operate in Euclidean space. Our proposed mixed-integer linear program determines a collision-free trajectory for a drone in the Euclidean space with obstacles, where the drone is launched and recovered by moving vehicles. Compared to graph-based approaches, the advantages are: (a) Flexible launching and recovery: The drone can be launched and recovered from any position along the streets, rather than being limited to discrete points (i.e., nodes of a graph). (b) Continuous drone velocity: The drone velocity is a continuous variable, and the drone energy consumption depends directly on its velocity. (c) No-fly zones and obstacles: Trajectory planning is incorporated to ensure a collision-free flight (rather than considering detours as a predefined parameter). To the best of our knowledge, such a geometric approach is new in truck-drone last-mile delivery. In our computational study we prove the usefulness of our geometric approach, as even large instances with up to 300 obstacles can be solved within reasonable computation time. In comparison to graph-based approaches, our geometric approach yields faster delivery times and enables the drone to reach customers that are out of its reach in graph-based approaches.", "keywords": [ "Drone", "Trajectory", "Computer science", "Collision", "Energy consumption", "Aeronautics", "Simulation", "Computer security", "Physics", "Electrical engineering", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s00291-025-00818-x.pdf" }, { "id": "https://openalex.org/W7127966341", "doi": "10.1080/23302674.2026.2626506", "title": "Dynamic medical logistics with drone-truck collaboration and pheromone decay in ant colony optimization", "year": 2026, "publication_date": "2026-02-05", "type": "article", "language": "en", "authors": [ "Tejinder Singh Lakhwani", "Yerasani Sinjana", "Anuj Pal Kapoor" ], "first_author": "Tejinder Singh Lakhwani", "venue": "International Journal of Systems Science Operations & Logistics", "publisher": "Taylor & Francis", "abstract": "The timely and reliable delivery of medical supplies is vital in emergencies, where even minor delays can endanger lives. This study proposes a real-time optimisation framework for healthcare logistics that incorporates an urgency-weighted pheromone decay mechanism into an Ant Colony Optimization (ACO) algorithm. The method dynamically adjusts routing priorities according to medical urgency, enabling adaptive path selection under fluctuating traffic, battery, and weather conditions. A hybrid drone–truck collaboration system is developed, combining the endurance of trucks for long-range transport with the agility of drones for last-mile, time-critical deliveries. Simulation experiments using operational data from 40 healthcare facilities in Delhi demonstrate an approximately 30% reduction in average delivery time, a 15% decrease in fuel consumption, and an approximately 30% improvement in overall energy efficiency compared to conventional static routing models. These findings indicate that the proposed framework offers a scalable, energy-aware, and urgency-responsive solution for dynamic healthcare supply chains, with potential applicability to other critical logistics domains.", "keywords": [ "Ant colony optimization algorithms", "Pheromone", "Ant colony", "ANT" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/23302674.2026.2626506" }, { "id": "https://openalex.org/W4414603255", "doi": "10.1109/jiot.2025.3615410", "title": "LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-Assisted Sensor Networks", "year": 2025, "publication_date": "2025-09-29", "type": "article", "language": "en", "authors": [ "Yousef Emami", "Hao Zhou", "Seyedsina Nabavirazavi", "Lúıs Almeida" ], "first_author": "Yousef Emami", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various private and commercial applications, e.g., traffic control, parcel delivery, and Search and Rescue (SAR) missions. Machine Learning (ML) methods used in UAV-Assisted Sensor Networks (UASNETs) and, especially, in Deep Reinforcement Learning (DRL) face challenges such as complex and lengthy model training, gaps between simulation and reality, and low sampling efficiency, which conflict with the urgency of emergencies, such as SAR missions. In this paper, an In-Context Learning (ICL)-Data Collection Scheduling (ICLDC) system is proposed as an alternative to DRL in emergencies. The UAV collects sensory data and transmits it to a Large Language Model (LLM), which creates a task description in natural language. From this description, the UAV receives a data collection schedule that must be executed. A verifier ensures safe UAV operations by evaluating the schedules generated by the LLM and overriding unsafe schedules based on predefined rules. The system continuously adapts by incorporating feedback into the task descriptions and using this for future decisions. This method is tested against jailbreaking attacks, where the task description is manipulated to undermine network performance, highlighting the vulnerability of LLMs to such attacks. The proposed ICLDC significantly reduces cumulative packet loss compared to both the DQN and Maximum Channel Gain baselines. ICLDC presents a promising direction for intelligent scheduling and control in UASNETs.", "keywords": [ "Scheduling (production processes)", "Reinforcement learning", "Wireless sensor network", "Data collection", "Schedule", "Network packet", "Drone", "Task analysis" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2504.14556" }, { "id": "https://openalex.org/W4247249550", "doi": "10.1504/ijpqm.2019.10020995", "title": "Replication of a Six Sigma black belt case study: GEP box's paper helicopter experiment in a drone logistics scenario", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "Kevin P. McCarron", "Oliver M. Strong", "Sean P. Goffnett", "Andrew N. Paquet" ], "first_author": "Kevin P. McCarron", "venue": "International Journal of Productivity and Quality Management", "publisher": "Inderscience Publishers", "abstract": "Unmanned aerial vehicles (drones) have gained attention in business in recent years. Drones have demonstrated potential use in logistics operations, such as surveillance, inventory counting, order picking, and 'last-mile' delivery. Given the proliferation in potential uses of drones, it is no surprise companies are testing drone capabilities. This article presents a theoretical business case describing the Six Sigma DMAIC model applied in a logistics context involving drone capacity and reliability. Define, measure, analyse, improve and control are presented. This article builds on earlier studies on structuring Six Sigma projects. This service-related business case can be used for training and education purposes.", "keywords": [ "Drone", "Six Sigma", "DMAIC", "Context (archaeology)", "Aeronautics", "Reliability (semiconductor)", "Replication (statistics)", "Engineering", "Surprise", "Computer science", "Operations management", "Operations research" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijpqm.2019.10020995" }, { "id": "https://openalex.org/W7124752390", "doi": "10.3934/electreng.2026005", "title": "Electronic components and key algorithms for a prototype drone: Economic and sustainability advantages", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Luigi Bibbò", "Emanuela Genovese", "Clemente Maesano", "Sonia Calluso", "Gabriele Barrile", "Giuseppe Maria Meduri", "Giuliana Bilotta", "Vincenzo Barrile" ], "first_author": "Luigi Bibbò", "venue": "AIMS Electronics and Electrical Engineering", "publisher": "American Institute of Mathematical Sciences", "abstract": "In recent years, the use of drones for cargo transport has experienced rapid development, driven by the need to reduce the environmental impact of traditional delivery systems and improve access to essential services, particularly in complex urban environments. In this study, we aimed to present the first experimental results for the development of a drone prototype characterized by low energy consumption and the integration of advanced technologies, with attention to the electronic components and embedded algorithms. The combination of cutting-edge technologies with such advanced algorithms enabled us to witness a reduction in the carbon footprint in terms of resource optimization, due especially to the neural network algorithms suitable for path analysis and obstacle detection, and the cutting-edge processor that enables us to manage and analyze the data derived from the different sensors. The design of this drone focuses on minimizing environmental impact through optimized energy resources by using high-efficiency components, including brushless DC motors and lithium polymer (LiPo) batteries integrated with perovskite photovoltaic cells that extend flight autonomy while reducing dependence on traditional energy sources. The drone's body, made of lightweight and durable materials, improves aerodynamic efficiency without compromising structural strength. Advanced artificial intelligence algorithms optimize flight paths, prevent collisions, and adapt dynamically to environmental conditions. Thanks to computing platforms such as the NVIDIA Jetson Nano and equipped with advanced sensors like LiDAR, accelerometers, and gyroscopes, the drone processes data in real-time, enhancing its autonomous operation and ensuring precise and stable navigation. The proposed prototype represents a significant step toward a future where technology supports sustainability, providing innovative solutions for modern society's needs aiming to reduce urban traffic and pollutant emissions, offering an efficient and ecofriendly solution for urban logistics.", "keywords": [ "Drone", "Energy consumption", "Carbon footprint", "Key (lock)", "Photovoltaic system", "Efficient energy use", "Resource (disambiguation)", "Obstacle", "Electronics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.aimspress.com/aimspress-data/electreng/2026/1/PDF/electreng-10-01-005.pdf" }, { "id": "https://openalex.org/W7161945748", "doi": "10.1016/j.tre.2026.104951", "title": "Physical internet drone logistics model: Optimizing cost and energy efficiency for urban delivery systems", "year": 2026, "publication_date": "2026-05-21", "type": "article", "language": "en", "authors": [ "Murugaiyan Pachayappan", "Tanmoy Kundu", "Rohit Kapoor", "R. Sundar" ], "first_author": "Murugaiyan Pachayappan", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Efficient energy use", "The Internet", "Operational efficiency", "Economic efficiency", "Cost efficiency" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104951" }, { "id": "https://openalex.org/W4415744419", "doi": "10.1109/ictis68762.2025.11215034", "title": "Enhancing the Resilience of Emergency Food Supply Chains: Dehydration Solutions and the Role of Drones in Humanitarian Logistics", "year": 2025, "publication_date": "2025-07-16", "type": "conference-paper", "language": null, "authors": [ "Ana I. García López", "Pedro Antonio García López" ], "first_author": "Ana I. García López", "venue": null, "publisher": null, "abstract": "Within the REMESH project, one of the lines of work focuses on strengthening emergency supply chains and enhancing their resilience in disaster situations. This involves developing innovative solutions and fostering collaboration among key stakeholders to optimize humanitarian response. In this context, this research focuses on cost-benefit analysis and, more specifically, on efficient supply management, exploring ways to ensure food security during crises. It emphasizes innovative approaches to extending food shelf life through dehydration techniques, thereby reducing the need for refrigeration and improving logistical efficiency.In parallel, this research looks into how drones can improve last-mile delivery, tackling the challenges of reaching isolated communities. By optimizing delivery routes and using autonomous UAV systems, it explores ways to make distribution more efficient. A key part of this effort is developing an open-source software tool to better manage drone fleets and plan emergency food rations. This tool aims to ensure cost-effective, nutritionally balanced food distribution, tailored to the realities of crisis situations.The findings contribute to improving the resilience of humanitarian supply chains, providing a data-driven framework for integrating emerging technologies into emergency response efforts. By combining dehydration solutions with drone-based logistics, this research offers practical strategies to enhance the efficiency, reach, and sustainability of food distribution in disaster scenarios.", "keywords": [ "Resilience (materials science)", "Drone", "Supply chain", "Humanitarian Logistics", "Sustainability", "Key (lock)", "Food security", "Work (physics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ictis68762.2025.11215034" }, { "id": "https://openalex.org/W4413371312", "doi": "10.1109/tccn.2025.3601015", "title": "Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions", "year": 2025, "publication_date": "2025-08-20", "type": "article", "language": "en", "authors": [ "Yixian Wang", "Geng Sun", "Zemin Sun", "Jiacheng Wang", "Jiahui Li", "Changyuan Zhao", "Jing Wu", "Shuang Liang", "Minghao Yin", "Pengfei Wang", "Dusit Tao Niyato", "Sumei Sun", "Dong In Kim" ], "first_author": "Yixian Wang", "venue": "IEEE Transactions on Cognitive Communications and Networking", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The rise of the low-altitude economy (LAE) is propelling urban development and emerging industries by integrating advanced technologies to enhance efficiency, safety, and sustainability in low-altitude operations. The widespread adoption of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft plays a crucial role in enabling key applications within LAE, such as urban logistics, emergency rescue, and aerial mobility. However, unlike traditional UAV networks, LAE networks encounter increased airspace management demands due to dense flying nodes and potential interference with ground communication systems. In addition, there are heightened and extended security risks in real-time operations, particularly the vulnerability of low-altitude aircraft to cyberattacks from ground-based threats. To address these, this paper first explores related standards and core architecture that support the development of LAE networks. Subsequently, we highlight the integration of technologies such as communication, sensing, computing, positioning, navigation, surveillance, flight control, and airspace management. We also analyze how generative artificial intelligence (GAI) contributes to this integration by enabling intelligent adaptation, uncertainty modeling, and real-time optimization. This synergy of multi-technology drives the advancement of real-world LAE applications, particularly in improving operational efficiency, optimizing airspace usage, and ensuring safety. Finally, we outline future research directions for LAE networks, such as intelligent and adaptive optimization, security and privacy protection, sustainable energy and power management, quantum-driven coordination, generative governance, and three-dimensional (3D) airspace coverage, which collectively underscore the potential of collaborative technologies to advance LAE networks.", "keywords": [ "Realization (probability)", "Computer science", "Architecture", "Computer architecture", "Telecommunications", "Core (optical fiber)" ], "cited_by_count": 106, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tccn.2025.3601015" }, { "id": "https://openalex.org/W4412958415", "doi": "10.4018/979-8-3693-8462-6.ch005", "title": "Autonomous UAV Swarms", "year": 2025, "publication_date": "2025-04-09", "type": "book-chapter", "language": "en", "authors": [ "S. Suresh", "D. Velmurugan", "N. Vasudevan", "D. Surekha Rathi Samundi", "R. Elayaraja" ], "first_author": "S. Suresh", "venue": null, "publisher": null, "abstract": "Autonomous UAV swarms are transforming automation, collaboration, and data-driven decision-making in Industry 5.0. This chapter explores their innovative applications, underscoring their impact on efficiency, adaptability, and safety across sectors. In manufacturing, UAV swarms streamline inventory management, conduct inspections, and optimize supply chains, boosting productivity. In agriculture, they enable precision farming through real-time crop monitoring, soil analysis, and pest control. In construction, UAV swarms facilitate site mapping, progress tracking, and safety inspections. In logistics, they enhance last-mile delivery, warehouse operations, and traffic monitoring. These applications illustrate how UAV swarms advance Industry 5.0's vision of seamless, intelligent systems, fostering interconnected and adaptive industrial ecosystems.", "keywords": [ "Computer science", "Artificial intelligence", "Computer vision" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3693-8462-6.ch005" }, { "id": "https://openalex.org/W3126899716", "doi": "10.1016/j.trpro.2021.01.008", "title": "An Exact Approach for a Variant of the FS-TSP", "year": 2021, "publication_date": "2021-01-01", "type": "article", "language": "en", "authors": [ "Maurizio Boccia", "Adriano Masone", "Antonio Sforza", "Claudio Sterle" ], "first_author": "Maurizio Boccia", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "In this work we focus on the flying sidekick traveling salesman problem (FS-TSP). The FS-TSP arises in the last-mile distribution context and it is a variant of the TSP aimed at determining the distribution plan of a driver-operated truck assisted by a drone (unmanned aerial vehicle, UAV), where the synchronization between the two vehicles allows to parallelize the delivery operations, so providing a reduction of the overall completion time. Several variants of the FS-TSP have been considered in literature, most of them sharing the assumption that launching and rendezvous position of a drone sortie must be different. In this work, we relax this assumption allowing the truck to wait for the drone at the launching position (FS-TSP*). This introduces an important flexibility element to deal with deliveries in rural or in urban areas with no-fly zones. We propose an original and compact integer linear programming formulation which allows to exactly solve the FS-TSP* and consequently also the FS-TSP. The results on several test instances show that our method can effectively solve to optimality instances with up to 20 customers and quantify the advantages of the FS-TSP* with respect to FS-TSP in terms of overall completion time.", "keywords": [ "Travelling salesman problem", "Drone", "Computer science", "Rendezvous", "Context (archaeology)", "Flexibility (engineering)", "Truck", "Position (finance)", "Heuristics", "Mathematical optimization", "Integer programming", "Focus (optics)" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S2352146521000120/pdf" }, { "id": "https://openalex.org/W7138183648", "doi": "10.1016/j.trpro.2026.02.070", "title": "Optimizing Drone Operations for Middle-mile Deliveries: Are We Ready for Real-World Deployment?", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Nicolò Avogadro", "Chiara Morlotti", "Renato Redondi" ], "first_author": "Nicolò Avogadro", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "The use of drones for freight delivery has been extensively investigated in the context of last-mile logistics, with existing literature underlining potential benefits across several dimensions, from economic efficiency to reductions in CO 2 emissions. However, it is not clear if these advantages also extend to longer routes and apply to middle-mile delivery (MMD). By relying on real-world data on freight transported between distribution and sorting centers of one of Italy’s major freight operators and proposing an optimization model dealing with drone location and flight scheduling, this study evaluates the operational feasibility of using drones for MMD. We consider three different variants of the model —namely, maximization of freight transported, maximization of the ground movements replaced by drones, and minimization of overall CO 2 emissions— to evaluate the applicability of drones based on different objective functions. The results reveal that, while the current technical characteristics of drones are not yet sufficient to fully replace ground-based freight movements, they already offer opportunities for integration in specific segments. By applying the proposed model to a real-world MMD network in Central Italy, we found that drones can handle up to 31.6% of the total daily package demand, contributing to a reduction of up to 3.5% in current emission levels. These findings provide a preliminary instrument for operators and policymakers, highlighting the potential role of drones as a complementary solution within MMD, while also emphasizing the need for continued technological and regulatory advancements to fully realize their potential.", "keywords": [ "Drone", "Key (lock)", "Poison control" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2026.02.070" }, { "id": "https://openalex.org/W4200387446", "doi": "10.35219/mms.2021.4.05", "title": "Current Challenges and Prospective Benefits of Using UAVs", "year": 2021, "publication_date": "2021-12-15", "type": "article", "language": "en", "authors": [ "Carmelia Mariana DRAGOMIR BĂLĂNICĂ", "Adrian Leopa" ], "first_author": "Carmelia Mariana DRAGOMIR BĂLĂNICĂ", "venue": "The Annals of “Dunarea de Jos” University of Galati Fascicle IX Metallurgy and Materials Science", "publisher": "University of Galați", "abstract": "The modern economy completely changed by offering to industry groups the capability to reorganize their operating processes. Nowadays we become observers of similar in scale revolutionary transformations: technologies for the use of unmanned aerial vehicles substantially modified business models and new operating conditions have been developed in various sectors from industries, agriculture to the emergency, parcel delivery or movie industry. In the very near future, clients’ enterprises from multiple sectors of the economy will recognize the first effect of the use of unmanned aerial vehicles (UAVs) in diverse fields - from commercial sectors to examining and exploring the environment or to reduce the impacts of unexpected disasters. The aim of the present review paper is to analyse the opportunities and advantages of using the application of UAVs technologies. On the other hand, not only the devices (drones) themselves are of importance, but also their wider utilization to acquire extraordinary volumes of data.", "keywords": [ "Drone", "Scale (ratio)", "Business", "Industrial organization", "Computer science", "Risk analysis (engineering)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.gup.ugal.ro/ugaljournals/index.php/mms/article/download/4973/4386" }, { "id": "https://openalex.org/W7118009505", "doi": "10.1007/978-981-96-6351-4_16", "title": "Flight Path Constrained Last-Mile Parcel Delivery Using Drone", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Preetam Kumar Sur", "Sunirmal Khatua", "Rajib Das" ], "first_author": "Preetam Kumar Sur", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Path (computing)", "Context (archaeology)", "Solver", "Tree traversal", "Traverse", "Integer programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-96-6351-4_16" }, { "id": "https://openalex.org/W7162093547", "doi": "10.1007/s13198-026-03349-y", "title": "IoT-Drone synergy in healthcare logistics: a framework for real-time decision-making and remote access", "year": 2026, "publication_date": "2026-05-22", "type": "article", "language": "en", "authors": [ "Tejinder Singh Lakhwani", "Yerasani Sinjana", "Anuj Pal Kapoor" ], "first_author": "Tejinder Singh Lakhwani", "venue": "International Journal of Systems Assurance Engineering and Management", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Software deployment", "Drone", "Health care", "Scalability", "Work (physics)", "Transformative learning", "Humanitarian Logistics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s13198-026-03349-y" }, { "id": "https://openalex.org/W3161079322", "doi": "10.1016/j.compeleceng.2021.107197", "title": "Delivery path planning of heterogeneous robot system under road network constraints", "year": 2021, "publication_date": "2021-05-16", "type": "article", "language": "en", "authors": [ "Yang Chen", "Mengqing Chen", "Zhihuan Chen", "Lei Cheng", "Yanhua Yang", "Hui Li" ], "first_author": "Yang Chen", "venue": "Computers & Electrical Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Unmanned ground vehicle", "Computer science", "Motion planning", "Path (computing)", "Genetic algorithm", "Real-time computing", "Ant colony optimization algorithms", "Robot", "Artificial intelligence", "Simulation", "Computer network", "Machine learning" ], "cited_by_count": 45, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.compeleceng.2021.107197" }, { "id": "https://openalex.org/W4214720713", "doi": "10.1002/net.22093", "title": "Preface: Special issue on the future of city logistics and urban mobility", "year": 2022, "publication_date": "2022-02-26", "type": "editorial", "language": "en", "authors": [ "Mor Kaspi", "Tal Raviv", "Marlin Wolf Ulmer" ], "first_author": "Mor Kaspi", "venue": "Networks", "publisher": "Wiley", "abstract": "This special issue of Networks focuses on recent and forthcoming trends in city logistics and urban mobility. Specifically, new business models and services are enabled due to technological advancements in information technology, vehicle autonomy and connectivity, vehicle electrification, payment methodologies, and clearing solutions. Notable trending topics include: drones and autonomous ground vehicles, physical internet for parcel and cargo delivery, electric vehicles and micromobility, ride-sharing and ride-hailing, crowd shipping and the gig economy, same-day delivery of goods and meals, collaborated transportation, and brokering. We have invited and received a broad range of operational research papers that study these topics for this special issue. In what follows, we briefly describe the seven research papers included in this issue, covering a wide range of trending topics. To these papers, we have added a survey paper that provides an overview of future directions for research in urban mobility and city logistics 4. In particular, the survey focuses on three main developments: vehicle autonomy, crowdsourced logistics, and urban micro-consolidation centers. Abbaas and Ventura 1 study the continuous deviation-flow refueling station location problem on a general network that arises in the context of alternative fuels. They propose an exact algorithm that determines the endpoints of all refueling segments on each edge of the network that cover the corresponding origin-destination flows. This set of endpoints is used in a set covering model to locate multiple refueling stations under the assumption that every vehicle only refuels once on each way of its round trip. A numerical experiment illustrates the performance of the proposed methodology. Arrieta–Prieto et al. 2 formulate the last-mile delivery problem assisted by urban micro-consolidation centers as a mixed-integer quadratically-constrained program (MIQCP) and develop a greedy heuristic solution, inspired by decomposition algorithms for large-scale optimization. Simulation results and a case study in Manhattan (NY, USA) demonstrate that the proposed heuristic can provide good results with respect to the exact solution and can be used for freight-efficient urban design and policy planning. Haferkamp and Ehmke 3 study demand and fulfillment control policies in ride-sharing systems. They classify the existing policies in the literature and explore the effectiveness of such policies under varying conditions in order to identify benefits and risks for ride-sharing systems. For this purpose, the authors define policies that differ in the optimization of demand and/or fulfillment control through the exploitation of either confirmed or complete information. Experimental results demonstrate that demand and fulfillment control have different effects on ride-sharing systems' performance and service quality. Le et al. 5 analyze the impact of optimized coordination between autonomous vehicles and traffic-light controlled intersections. To this end, they develop a mixed-integer linear programming (MILP) model based on a microscopic traffic model with centrally controlled autonomous vehicles and they incorporate traffic-light switching regulations. Their model allows an estimation of the maximum performance gains due to improved communication and serves as a benchmark for decentralized approaches. An evaluation of the numerical results with a traffic simulation tool shows that performance indicators such as time, energy, and emissions can be reduced significantly compared to simulated real-world traffic. In addition, the authors evaluate the performance of a moving horizon approach which exposes the trade-off between performance and real-time feasibility. Ostermeier et al. 6 present an approach to cost-optimal routing of a truck-and-robot system for last-mile deliveries with time windows. Their solution algorithm is based on a combination of a neighborhood search with cost-specific priority rules and search operators for the truck routing. An exact solution approach and a heuristic approach are proposed for the robot scheduling sub-problem. The numerical experiments show that the approach can reduce last-mile delivery costs significantly. In particular, a case study indicates that the truck-and-robot concept reduces last-mile costs by up to 68%, compared to truck-only delivery. Finally, a sensitivity analysis provides managerial insights regarding the setting in which truck-and-robot deliveries can efficiently be used in the delivery industry. Rosenfeld 7 studies a retrieval problem arising in puzzle-based storage systems. The problem consists of a general number of loads to be retrieved and arbitrary numbers of I/O points and empty locations on a two-dimensional lattice graph. The author analyzes the theoretical characteristics of the problem and proposes a set of graph search algorithms to tackle these characteristics. Numerical results demonstrate that the proposed algorithms can optimally solve moderate-sized puzzle-based storage problems using a limited amount of memory and in a reasonable amount of time. Voigt and Kuhn 8 consider a parcel delivery service that ships parcels from pickup to delivery points using regular drivers and occasional drivers where drivers can hand over or collect parcels at predefined transshipment points. The company seeks to minimize the overall costs that arise from the total distance traveled by regular drivers and the compensation paid to occasional drivers. The authors model the pickup and delivery problem with transshipments and occasional drivers (PDPTOD) as a mixed-integer programming (MIP) problem. They develop a specialized heuristic approach based on an adaptive large neighborhood search (ALNS). The numerical study reveals the impact of the number and location of transshipment points on the cost advantages achieved by integrating occasional drivers into the delivery process. It also shows that the cos", "keywords": [ "Context (archaeology)", "Computer science", "Operations research", "Transport engineering", "Payment", "The Internet", "Engineering", "Geography", "World Wide Web" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/net.22093" }, { "id": "https://openalex.org/W7117587058", "doi": "10.1016/j.rtbm.2025.101585", "title": "Machine learning-enabled real-time risk prediction and mitigation in drone-based hazmat delivery", "year": 2025, "publication_date": "2025-12-30", "type": "article", "language": "en", "authors": [ "Ahmed Moussa", "Elkafi Hassini", "Mohamed Ezzeldin", "Wael W. El-Dakhakhni" ], "first_author": "Ahmed Moussa", "venue": "Research in Transportation Business & Management", "publisher": "Elsevier BV", "abstract": "Hazardous material (hazmat) transportation presents considerable public safety and regulatory challenges, especially in dense urban environments. While drones are gaining traction as a viable solution for last-mile hazmat delivery, the literature has yet to present a comprehensive framework that integrates real-time risk mitigation with multi-stakeholder consequence assessment. This study addresses this critical gap by introducing a novel drone-based hazmat transportation framework built on two core innovations: the Accident Prediction and Mitigation System (APRiMS) and a multi-dimensional risk prediction suite. APRiMS functions as an autonomous decision-support engine, continuously processing real-time flight, shipment, and environmental data to estimate accident likelihood and initiate mitigation measures. In parallel, the framework incorporates four machine learning models that predict the potential impacts of drone-related incidents on the public, businesses, and customers. These components operate within a closed-loop architecture, wherein risk predictions dynamically inform APRiMS decisions, enabling real-time, consequence-aware operational responses. The proposed framework offers a scalable and intelligent deployment of drones for high-risk logistics.", "keywords": [ "Risk assessment", "Hazardous waste", "Risk management", "Production (economics)", "Work (physics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.rtbm.2025.101585" }, { "id": "https://openalex.org/W4411190129", "doi": "10.1108/aeat-04-2024-0114", "title": "Rotor position aerodynamics research of a hybrid configuration electric UAV", "year": 2025, "publication_date": "2025-06-10", "type": "article", "language": "en", "authors": [ "Xiaolu Wang", "Wei-Wei Liu", "Changning Chen", "Yingyu Liu" ], "first_author": "Xiaolu Wang", "venue": "Aircraft Engineering and Aerospace Technology", "publisher": "Emerald Publishing Limited", "abstract": "Purpose This study aims to conduct computational fluid dynamics analysis and design work on the aerodynamics of urban logistics hybrid configuration unmanned aerial vehicles (UAVs). The primary focus is on examining how the placement of the rotor in terms of chord, span and vertical positioning affects the aerodynamic properties of a hybrid configuration UAV. Design/methodology/approach A numerical approach is developed for analyzing the combined flow field of the rotor and airframe, based on the RANS equations. The aerodynamic performance of the hybrid configuration UAV was designed and analyzed using ANSYS Fluent software. Findings The findings of this study indicated that the placement of the rotor significantly affects the aerodynamic properties of the hybrid configuration UAV. Properly positioning the rotor can enhance the aerodynamic performance and the longitudinal static stability. Practical implications Conventional hybrid configuration UAVs exhibit numerous aerodynamic shortcomings during the hovering, forward flight, vertical take-off and landing and transition phases. This research paper presents and examines a novel hybrid configuration UAV that incorporates rotors into the hybrid configuration UAV airframe. This innovative aerodynamic arrangement mitigates or reduces the design defects observed in the hybrid configuration UAVs, thus laying the foundation for their development of hybrid configuration UAVs. Originality/value This study addresses the effect mechanism of rotor position on the aerodynamic interaction between rotor and airframe of a hybrid configuration UAV, in terms of the aerodynamic design of vertical take-off and landing UAV. The research presented a practical case and furtherly provided an insight into the aerodynamic design of a hybrid configuration vertical take-off and landing UAV.", "keywords": [ "Aerodynamics", "Rotor (electric)", "Position (finance)", "Aerospace engineering", "Engineering", "Computer science", "Mechanical engineering", "Aeronautics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1108/aeat-04-2024-0114" }, { "id": "https://openalex.org/W4287009827", "doi": "10.3390/ijgi11080419", "title": "Exploring the Spatial Heterogeneity and Driving Factors of UAV Logistics Network: Case Study of Hangzhou, China", "year": 2022, "publication_date": "2022-07-23", "type": "article", "language": "en", "authors": [ "Hongbo He", "Huping Ye", "Chenchen Xu", "Xiaohan Liao" ], "first_author": "Hongbo He", "venue": "ISPRS International Journal of Geo-Information", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Urban logistics is an important research topic in human and economic geography; unmanned aerial vehicles (UAVs) are an emerging technology that has huge potential in the field of logistics with the release of control restrictions on low-altitude airspace. The scientific identification of the spatial pattern and impact factors of UAV logistics networks is greatly significant in regards to UAV logistics planning and scheduling. This study considered the urban logistics network of Hangzhou in 2020 as the research topic and used kernel density estimation, a geodetector, and geographic information system (GIS) spatial analysis technology to systematically analyze the spatial patterns and influencing factors at the city and district scales. The study found that a significant spatial pattern was revealed in the UAV logistics network in Hangzhou, and the logistics nodes showed an obvious “core-edge” structure. The urban population, market scale and logistics infrastructure jointly shaped the structure and function of the UAV logistics network, and logistics nodes had a strong coupling relationship with the urban spatial structure. Through interaction detectors, the technical route of urban UAV logistics network construction was analyzed and summarized, and results can provide a scientific basis and case reference for other cities to build and plan UAV logistics networks.", "keywords": [ "Transport engineering", "Computer science", "Engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2220-9964/11/8/419/pdf?version=1659677213" }, { "id": "https://openalex.org/W7161292891", "doi": "10.2139/ssrn.6773309", "title": "Routing, Scheduling, and Synchronization of Unmanned Aerial Vehicles (UAVs) with Bus Hitchhiking for Last-mile Parcel Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Yizhi Zhen", "Fangni Zhang", "Yong‐Hong Kuo", "Kaifeng Ji" ], "first_author": "Yizhi Zhen", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Scalability", "Synchronization (alternating current)", "Simulated annealing", "Linear programming", "Scheduling (production processes)", "Range (aeronautics)", "Cost reduction" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6773309" }, { "id": "https://openalex.org/W4411080376", "doi": "10.1016/j.trd.2025.104851", "title": "Knowledge-Based city logistics: Do technology and people’s attitude go hand in hand?", "year": 2025, "publication_date": "2025-06-06", "type": "article", "language": "en", "authors": [ "Natalia Wagner", "Stanisław Iwan", "Kinga Kijewska", "Sidsel Ahlmann Jensen" ], "first_author": "Natalia Wagner", "venue": "Transportation Research Part D Transport and Environment", "publisher": "Elsevier BV", "abstract": "Initiatives taken in the area of city logistics are still not effective enough. A solution to this problem may be more intensive development and wide application of the knowledge-based city logistics approach. This paper aims to evaluate the current development trends and challenges in city logistics, focusing on emerging technologies and knowledge-based processes. The study was conducted using the Delphi method. The findings show an expert consensus on the future common use of several technologies such as ICT, IoT, AI, big data, and drones. Autonomous vehicles constitute an exception here, as the experts do not expect them to be in common use in the future. It can be expected that city logistics projects and activities will be more data-driven, whereas processes necessary for effective knowledge management, which to a large extent depend on people’s attitude, will not be strengthened. Identifying these processes as unlikely indicates the necessary way forward.", "keywords": [ "Psychology", "Advertising", "Business" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trd.2025.104851" }, { "id": "https://openalex.org/W2969999343", "doi": "10.1016/j.trip.2019.100024", "title": "Innovative scenarios for modeling intra-city freight delivery", "year": 2019, "publication_date": "2019-08-24", "type": "article", "language": "en", "authors": [ "Amy M. Moore" ], "first_author": "Amy M. Moore", "venue": "Transportation Research Interdisciplinary Perspectives", "publisher": "Elsevier BV", "abstract": "There has been a shift in the focus of freight modeling to the short-haul (or last-mile) due to an increase in online shopping. This study considers innovative freight delivery modes and multi-modal shifts, especially for the last-mile portion of intra-city freight delivery. For this study, GPS data were obtained from a truck fleet from a major parcel delivery company's depot near Columbus, Ohio and used to develop a freight delivery demand estimation model. Freight delivery tours were modeled in TransCAD and used to develop scenarios to incorporate various modal shifts to compare energy usage in kilowatt-hour estimates. Innovative modes of freight delivery were considered for the scenarios and were compared to a class six truck: electric class six trucks, electric delivery vans, parcel delivery lockers, drones, and electric passenger vehicles. Initial findings suggest that electric trucks reduce energy usage when the majority of miles traveled are in the long-haul, or stem portion of the route. Parcel delivery lockers reduced energy usage in suburban areas, especially those with large neighborhoods with cul-de-sacs. The findings from this study were intended to provide decision makers, both in government and industry, with information to consider when determining suitable alternatives for energy-efficient intra-city freight transport.", "keywords": [ "Truck", "Transport engineering", "City logistics", "Last mile (transportation)", "Mile", "Traffic management", "Engineering", "Geography", "Automotive engineering" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trip.2019.100024" }, { "id": "https://openalex.org/W4396957651", "doi": "10.1007/s13132-024-01984-z", "title": "RETRACTED ARTICLE: Knowledge-Driven Logistics Transformation: Complex Networks and UAVs in Distribution", "year": 2024, "publication_date": "2024-05-16", "type": "article", "language": "en", "authors": [ "Longyang Huang", "Siyi Li", "Xiang Zou", "Bozhi Zhao", "Cheng-Long Li" ], "first_author": "Longyang Huang", "venue": "Journal of the Knowledge Economy", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Transformation (genetics)", "Entrepreneurship", "Distribution (mathematics)", "Business", "Economic geography", "Complex network", "Industrial organization", "Computer science", "Knowledge management", "Geography", "Mathematics", "World Wide Web" ], "cited_by_count": 9, "is_retracted": true, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s13132-024-01984-z" }, { "id": "https://openalex.org/W4410294091", "doi": "10.2139/ssrn.5199779", "title": "Real-Time Order Dispatch for On-Demand Food-Delivery Platforms with a Mixed Fleet of Drones and Couriers", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Tingting Dong", "Yitong Shang", "Zhijie Lai", "Sen Li" ], "first_author": "Tingting Dong", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Order (exchange)", "Food delivery", "Business", "Computer science", "On demand", "Operations research", "Advertising", "Engineering", "Commerce", "Finance", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5199779" }, { "id": "https://openalex.org/W4400412187", "doi": "10.1145/3638529.3654106", "title": "An Order-aware Adaptive Iterative Local Search Metaheuristic for Multi-depot UAV Pickup and Delivery Problem", "year": 2024, "publication_date": "2024-07-08", "type": "conference-paper", "language": "en", "authors": [ "Xiangling Chen", "Xiao-Cheng Liao", "Feng-Feng Wei", "Wei–Neng Chen" ], "first_author": "Xiangling Chen", "venue": "Proceedings of the Genetic and Evolutionary Computation Conference", "publisher": null, "abstract": "The emergence of the last-mile delivery by unmanned aerial vehicles (UAVs) has gained widespread attention in both scientific and industrial communities in recent years. The problem can be modeled as a mixed linear integer programming problem to minimize the routing cost to serve all customers and the number of UAV launches. Considering the complexity of the multi-depot, multi-UAV, and multi-customer pickup and delivery integrated scheduling problem, this paper proposes a novel two-stage order-aware adaptive iterative local search metaheuristic algorithm to solve this problem. In the first stage, tasks are assigned to different depots, transforming the complex original problem into multiple single depot scheduling problem. In the second stage, an order-aware adaptive iterative local search (OAILS) metaheuristic is designed to optimize the route planning for each depot's UAVs. In AILS, we propose a novel order-based adaptive operator selection named (OAOS) to select the appropriate operator based on the recent performance of operator and the order relationships of operators. Finally, a series of experiments were conducted to verify the effectiveness of the proposed OAOS and OAILS methods.", "keywords": [ "Pickup", "Metaheuristic", "Computer science", "Depot", "Guided Local Search", "Local search (optimization)", "Mathematical optimization", "Artificial intelligence", "Mathematics", "Geography" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3638529.3654106" }, { "id": "https://openalex.org/W3163495724", "doi": null, "title": "Adaptive Control of the Transition from Vertical to Horizontal Flight Regime of a Quad-Tailsitter UAV", "year": 2021, "publication_date": "2021-05-19", "type": "dissertation", "language": "en", "authors": [ "Grant Inman Carter" ], "first_author": "Grant Inman Carter", "venue": "VTechWorks (Virginia Tech)", "publisher": "Virginia Tech", "abstract": "Tailsitter UAVs (Unmanned Aerial Vehicles) are a type of VTOL (Vertical Take off and Landing) aircraft that combines the agility of a quadrotor drone with the endurance and speed of a fixed-wing aircraft. For this reason, they have become popular in a wide range of applications from tactical surveillance to parcel delivery. This thesis details a clean sheet design process for a tailsitter UAV that includes the dynamic modeling, control design, simulation, vehicle design, vehicle prototype fabrication, and testing of a tailsitter UAV. The goal of this process was to design a robust controller that is able to handle uncertainties in the system's parameters and external disturbances and subsequently can control the vehicle through the transition between vertical and horizontal flight regimes. It is evident in the literature that most researchers choose to model and control tailsitter UAVs using separate methods for the vertical and horizontal flight regimes and combine them into one control architecture. The novelty of this thesis is the use of a single dynamical model for all flight regimes and the robust control technique used. The control algorithm used for this vehicle is a MRAC (Model Reference Adaptive Control) law, which relies on reference models and gains that adapt according to the vehicle's response in all flight regimes. To validate this controller, numerical simulations in Matlab and flight tests were conducted. The combination of these validation methods confirms our adaptive controller's ability to control the transition between the vertical and horizontal flight regimes when faced with both parametric uncertainties and external disturbances.", "keywords": [ "Aerospace engineering", "Transition (genetics)", "Engineering", "Meteorology", "Computer science", "Aeronautics", "Geology", "Physics", "Chemistry" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10919/103385" } ]