[ { "id": "https://openalex.org/W2920125296", "doi": "10.32628/cseit1951138", "title": "Adaptive Autonomous Technology in Unmanned Aerial Vehicles for Parcel Delivery", "year": 2019, "publication_date": "2019-02-25", "type": "article", "language": "en", "authors": [ "Thamma Bhanu Sumanth Reddy", "Pullagura Harsha Teja", "Regalla Pavan Teja", "Thandra Praneeth" ], "first_author": "Thamma Bhanu Sumanth Reddy", "venue": "International Journal of Scientific Research in Computer Science Engineering and Information Technology", "publisher": null, "abstract": "An Unmanned Aerial Vehicle (often called a Drone) is an aircraft that is not operated by a pilot on board. Drones are rapidly growing market and increasingly leverage. The Applications of these flying robots are limitless. This paper reviews and discusses about embedded vision and sensor technology which culminates in several innovative applications over drones. It introduces an industry alliance available to help product creators incorporate robust vision capabilities into their drones designs. This paper mainly deals on Autonomous Delivery of light weighted items along with the collision avoidance technology and also tracks the whereabouts of the vehicle in a cost effective approach. This can also be used for carrying out complex assignments such as rescue operations during disasters, mines surveying.", "keywords": [ "Drone", "Leverage (statistics)", "Collision avoidance", "Robot", "Computer science", "Aeronautics", "Engineering", "Computer security", "Systems engineering", "Artificial intelligence", "Collision" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.32628/cseit1951138" }, { "id": "https://openalex.org/W3213457632", "doi": "10.23919/icac50006.2021.9594254", "title": "Challenges and Opportunities in Last Mile Delivery: A Malaysian Case Study", "year": 2021, "publication_date": "2021-09-02", "type": "conference-paper", "language": "en", "authors": [ "Satya R Shah", "Marvin Chan" ], "first_author": "Satya R Shah", "venue": null, "publisher": null, "abstract": "Last mile delivery and the concept of last mile has been experiencing major disruption from evolving business strategies while focussing on key customer demands and requirements and to have deliveries faster and easier with the advancement of technology and use of new devices such as drones and autonomous vehicles. There has been huge amount of research conducted within perceived quality, customer satisfaction and decision-making processes within the delivery aspects of ecommerce services. With the emergence of innovative technologies, the size and growth demand of the market, there is a wider need to examine the area of the impact of some of these technologies and its future development. The paper examines the concepts of last mile delivery practices, and the advancements in technology to facilitate the implementation within the concept. The study aims to bridge the gap between the commercial and academic research allowing better strategic alignment for decision making process within the global supply chain markets. The paper identifies existing research within the field of last mile solutions, with emerging and widely accepted approaches towards cost efficiency and wider performance measures within the network.", "keywords": [ "Last mile (transportation)", "Mile", "Process (computing)", "Bridge (graph theory)", "Customer satisfaction", "Business", "Quality (philosophy)", "Drone", "Supply chain", "Key (lock)", "Value proposition", "Delivery Performance" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/icac50006.2021.9594254" }, { "id": "https://openalex.org/W4387503553", "doi": "10.1007/s11590-023-02056-y", "title": "The truck–drone routing optimization problem: mathematical model and a VNS approach", "year": 2023, "publication_date": "2023-10-10", "type": "article", "language": "en", "authors": [ "Malick Ndiaye", "Ahmed H. Osman", "Saı̈d Salhi", "Batool Madani" ], "first_author": "Malick Ndiaye", "venue": "Optimization Letters", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Metaheuristic", "Computer science", "Variable neighborhood search", "Robustness (evolution)", "Vehicle routing problem", "Mathematical optimization", "Computational intelligence", "Linear programming", "Operations research", "Routing (electronic design automation)" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s11590-023-02056-y" }, { "id": "https://openalex.org/W4412040072", "doi": "10.1061/jtepbs.teeng-8741", "title": "Optimization Study on the Hybrid Scheduling of Truck–Drone Delivery System", "year": 2025, "publication_date": "2025-07-04", "type": "article", "language": "en", "authors": [ "Zaifang Zhang", "Yang Li", "Jun He", "Jiayi Chen", "Haibo Hong" ], "first_author": "Zaifang Zhang", "venue": "Journal of Transportation Engineering Part A Systems", "publisher": "American Society of Civil Engineers", "abstract": "The exigency to curtail last-mile delivery expenses is an intricate issue within the logistics industry. Owing to their flexibility, portability, and low cost, drones have great potential in the application of logistics and distribution. Drones also have short cruising ranges and small load capacity. To overcome their shortcomings, a hybrid delivery model of truck-carried drones is proposed to achieve logistics distribution. This paper comprehensively addresses pertinent constraints, including drone load capacity, flight distance, and time window, and formulates a mathematical model aimed at minimizing transportation costs. The model determines the optimal allocation between trucks and drones, the optimal path sequence for trucks to travel, and the optimal launch of drones on the truck path and recovery points. Simultaneously, we developed an improved equilibrium optimizer simulated annealing (IEOSA) algorithm to address the issue, employing both Solomon standard calculation instances and real distribution scenarios from Shanghai, China, for simulation analysis. Experimental findings demonstrate that IEOSA exhibits superior efficiency and solution quality in tackling hybrid distribution problems. Furthermore, the truck–drone hybrid distribution model proves to be more efficient in completing distribution tasks compared to relying solely on truck distribution.", "keywords": [ "Drone", "Truck", "Scheduling (production processes)", "Computer science", "Aeronautics", "Engineering", "Automotive engineering", "Operations management", "Biology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1061/jtepbs.teeng-8741" }, { "id": "https://openalex.org/W4412530850", "doi": "10.3390/drones9070512", "title": "A Bi-Layer Collaborative Planning Framework for Multi-UAV Delivery Tasks in Multi-Depot Urban Logistics", "year": 2025, "publication_date": "2025-07-21", "type": "article", "language": "en", "authors": [ "Junfu Wen", "Fei Wang", "Yebo Su" ], "first_author": "Junfu Wen", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems.", "keywords": [ "Depot", "City logistics", "Layer (electronics)", "Computer science", "Transport engineering", "Process management", "Business", "Engineering", "Materials science", "Nanotechnology", "Geography" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9070512" }, { "id": "https://openalex.org/W4411438627", "doi": "10.1016/j.tre.2025.104231", "title": "To ride or to fly: Optimal freight-on-transit operations using drones", "year": 2025, "publication_date": "2025-06-19", "type": "article", "language": "en", "authors": [ "Jesus Osorio", "Yanfeng Ouyang" ], "first_author": "Jesus Osorio", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "With the rapid growth of e-commerce, urban areas are facing increasing congestion, particularly from last-mile deliveries. This challenge has prompted the exploration of innovative solutions. One such promising approach is freight-on-transit (FoT) operations, which utilize existing high-capacity urban transit networks to transport goods to designated transit stations, where more energy-efficient and flexible modes complete the last-mile delivery. This paper proposes a modeling framework for optimizing FoT operations using drones. The proposed mixed-integer linear program formulation accounts for fixed transit vehicle schedules, capacity constraints, and drone battery limitations. The problem is decomposed into a min-cost flow and drone vehicle routing problem with time windows, and a Lagrangian relaxation with a column generation algorithm is proposed as the solution method. Numerical experiments on hypothetical networks demonstrate the system’s effectiveness, highlighting the importance of buffer capacity in transit vehicles and extended drone battery life. A real-world case study in Chicago’s Metra system reveals significant cost savings and operational benefits compared to traditional truck-based or drone-only delivery methods.", "keywords": [ "Drone", "Transit (satellite)", "Transport engineering", "Engineering", "Aeronautics", "Computer science", "Operations research", "Business", "Public transport" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104231" }, { "id": "https://openalex.org/W4313384555", "doi": "10.1007/978-3-031-09753-9_23", "title": "Effects of Variable UAV Speed on Optimization of Travelling Salesman Problem with Drone (TSP-D)", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Enes Cengiz", "Cemal Yılmaz", "Hamdi Tolga Kahraman", "Çağri Suiçmez" ], "first_author": "Enes Cengiz", "venue": "Engineering cyber-physical systems and critical infrastructures", "publisher": null, "abstract": null, "keywords": [ "Travelling salesman problem", "Drone", "Truck", "Computer science", "Real-time computing", "Routing (electronic design automation)", "Simulation", "Automotive engineering", "Engineering", "Computer network" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-09753-9_23" }, { "id": "https://openalex.org/W4402518204", "doi": "10.1016/j.aej.2024.08.114", "title": "PDAC-SL: A PUF-enabled drone access control technique for smart logistics", "year": 2024, "publication_date": "2024-09-13", "type": "article", "language": "en", "authors": [ "Abdulrahman A. Alshdadi", "Azeem Irshad" ], "first_author": "Abdulrahman A. Alshdadi", "venue": "Alexandria Engineering Journal", "publisher": "Elsevier BV", "abstract": "The conventional logistics’ paradigm is being replaced with smarter ways of supply chain management to readily serve the customers with quick modes of delivery in affordable operational costs. The weak infrastructure in rural terrain and congested roads in urban cities act as a big impediment in the successful accomplishment of last mile delivery down the lane across the complete chain of logistics. The traditional logistics has been evolved and improved in many respects, the last mile delivery is still plagued by security and privacy risks in logistics. The use of drones for package deliveries may offer promising gains in terms of efficiency. Nonetheless, drones undergo security and privacy risks besides physical threats for being deployed in public places communicating on open channels. Thus, more efforts are needed to secure the communication of inducted drones in delivery assignments. We propose a novel PUF-enabled drone access control mechanism PDAC-SL for smart logistics. By the careful employment of symmetric crypto-primitives, the contributed protocol not only supports low end devices but stands resistant to physical drone and other known threats besides preserving anonymity and untraceability features. The RoR model-based formal security analysis and performance analysis demonstrate distinct security properties and applicability of the PDAC-SL.", "keywords": [ "Drone", "Control (management)", "Computer science", "Automotive engineering", "Engineering", "Artificial intelligence" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.aej.2024.08.114" }, { "id": "https://openalex.org/W4408014712", "doi": "10.1016/j.cor.2025.107032", "title": "Autonomous delivery vehicle routing problem with drones based on multiple delivery modes", "year": 2025, "publication_date": "2025-02-27", "type": "article", "language": "en", "authors": [ "Jili Kong", "Hao Wang", "Minhui Xie" ], "first_author": "Jili Kong", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Operations research", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2025.107032" }, { "id": "https://openalex.org/W4388596637", "doi": "10.1007/s10732-023-09520-z", "title": "A hybrid variable neighborhood search heuristic for the sustainable time-dependent truck-drone routing problem with rendezvous locations", "year": 2023, "publication_date": "2023-11-11", "type": "article", "language": "en", "authors": [ "Ebrahim Teimoury", "Reza Rashid" ], "first_author": "Ebrahim Teimoury", "venue": "Journal of Heuristics", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Travelling salesman problem", "Rendezvous", "Computer science", "Routing (electronic design automation)", "Heuristic", "Vehicle routing problem", "Variable (mathematics)", "Variable neighborhood search", "Operations research", "Transport engineering" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10732-023-09520-z" }, { "id": "https://openalex.org/W7146947504", "doi": "10.1109/tits.2026.3677411", "title": "Reinforcement Learning and Exact Optimization for Traffic-Weighted Hybrid Truck-Drone Last-Mile Delivery With Deadlines", "year": 2026, "publication_date": "2026-03-31", "type": "article", "language": null, "authors": [ "Abdullahi Sani Shuaibu", "Ashraf Sharif Mahmoud", "Tarek Sheltami", "Anis Elgabli" ], "first_author": "Abdullahi Sani Shuaibu", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "We propose a Reinforcement Learning (RL) framework for hybrid truck-drone Last-Mile Delivery (LMD), where drones launch from a truck and either rendezvous with it or return to the depot after serving customers, subject to vehicle range limits and customer-defined deadlines. Deliveries completed after the latest allowable service time are treated as deadline violations (lateness) and are penalized in the objective. To provide an exact reference, a Mixed Integer Linear Programming (MILP) model is formulated. It serves both as a benchmark for evaluating the RL approach and as a practical method when the delivery set is small or when minimizing lateness is the primary objective, since the exact MILP solution is guaranteed to be optimal but become computationally expensive to solve as problem size grows due to the combinatorial increase in routing and coordination possibilities. The RL method is introduced as a scalable alternative for larger hybrid truck-drone deployments because it maintains practical computation times through iterative learning and avoids the exponential growth in solving effort associated with exact optimization. The RL framework employs an$\\epsilon $-greedy policy with decay and a reward structure that mirrors the MILP objective. Traffic congestion is incorporated through a scaling matrix that adjusts truck travel times on road segments, while drones remain unaffected due to aerial operation. RL is further benchmarked against Genetic Algorithm (GA) and Particle Swarm Optimiztion (PSO), which are also formulated to align with the same MILP objective to ensure a consistent basis for comparison. Results show that the RL component scales more efficiently with increasing delivery size while maintaining competitive solution quality.", "keywords": [ "Reinforcement learning", "Benchmark (surveying)", "Scalability", "Integer programming", "Range (aeronautics)", "Linear programming", "Set (abstract data type)", "Particle swarm optimization", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2026.3677411" }, { "id": "https://openalex.org/W4386704774", "doi": "10.2139/ssrn.4571161", "title": "Approximation Algorithms for Drone Delivery Scheduling With a Fixed Number of Drones", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Saswata Jana", "Partha Sarathi Mandal" ], "first_author": "Saswata Jana", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Scheduling (production processes)", "Computer science", "Mathematical optimization", "Algorithm", "Distributed computing", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4571161" }, { "id": "https://openalex.org/W3119167717", "doi": "10.1016/j.trc.2020.102913", "title": "A column-and-row generation approach for the flying sidekick travelling salesman problem", "year": 2021, "publication_date": "2021-01-12", "type": "article", "language": "en", "authors": [ "Maurizio Boccia", "Adriano Masone", "Antonio Sforza", "Claudio Sterle" ], "first_author": "Maurizio Boccia", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Travelling salesman problem", "Drone", "Column generation", "Computer science", "Mathematical optimization", "Truck", "Benchmark (surveying)", "Vehicle routing problem", "Representation (politics)", "Integer programming", "Operations research", "Routing (electronic design automation)" ], "cited_by_count": 122, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2020.102913" }, { "id": "https://openalex.org/W4286485293", "doi": "10.1145/3524053.3542743", "title": "Drone-Truck Cooperated Delivery Under Time Varying Dynamics", "year": 2022, "publication_date": "2022-07-21", "type": "conference-paper", "language": "en", "authors": [ "Arindam Khanda", "Federico Corò", "Sajal Kumar Das" ], "first_author": "Arindam Khanda", "venue": null, "publisher": null, "abstract": "Rapid technological developments in autonomous unmanned aerial vehicles (or drones) could soon lead to their large-scale implementation in the last-mile delivery of products. However, drones have a number of problems such as limited energy budget, limited carrying capacity, etc. On the other hand, trucks have a larger carrying capacity, but they cannot reach all the places easily. Intriguingly, last-mile delivery cooperation between drones and trucks can synergistically improve delivery efficiency.", "keywords": [ "Drone", "Truck", "Mile", "Last mile (transportation)", "Computer science", "Scale (ratio)", "Aeronautics", "Transport engineering", "Engineering", "Automotive engineering", "Geography" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3524053.3542743" }, { "id": "https://openalex.org/W4308799872", "doi": "10.21203/rs.3.rs-1871641/v1", "title": "Optimization model for designing reliable supply chain network using drones in last-mile delivery under uncertainties", "year": 2022, "publication_date": "2022-11-11", "type": "preprint", "language": "en", "authors": [ "Ali Tolooie", "Ashesh Kumar Sinha" ], "first_author": "Ali Tolooie", "venue": "Research Square", "publisher": null, "abstract": "Abstract Land transportation is generally considered one of the most expensive, polluting and least efficient parts of the logistics chain. Due to these issues, using unmanned aerial vehicles such as drones for package delivery in last-mile logistics becomes increasingly attractive. However, there are several significant obstacles in terms of technical aspects and performance capabilities of drones like limited flight coverage. In addition, supply chains are exposed to a broad range of uncertainties some of which may cause disruptions in the whole supply chain system. To hedge against these issues, a well-designed reliable network is a top priority. This research aims to construct a feasible delivery network consisting of warehouses and recharging stations through the development of a stochastic mixed-integer model, resulting in improving the coverage and reliability of the supply chain network. Due to the computational complexity of the scenario-based mixed-integer model, this research improves the performance of the genetic algorithm by considering each scenario independently in one of the steps of the algorithm to significantly improve the computational time need to find the solutions. Computational results demonstrate that the proposed algorithm is efficiently capable of solving large-scale problems. It is also demonstrated that substantial improvements in the reliability of the system can often be possible with minimal increases in facility cost.", "keywords": [ "Drone", "Supply chain", "Reliability (semiconductor)", "Computer science", "Supply chain network", "Genetic algorithm", "Construct (python library)", "Last mile (transportation)", "Range (aeronautics)", "Operations research", "Reliability engineering", "Mile" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-1871641/latest.pdf" }, { "id": "https://openalex.org/W4323309378", "doi": "10.1080/01605682.2023.2185548", "title": "Truck and rotary-wing drone routing problem considering flight-level selection", "year": 2023, "publication_date": "2023-03-06", "type": "article", "language": "en", "authors": [ "Hongqi Li", "Feilong Wang", "Zhuopeng Zhan" ], "first_author": "Hongqi Li", "venue": "Journal of the Operational Research Society", "publisher": "Palgrave Macmillan", "abstract": "Last-mile logistics is one of the most promising areas for rotary-wing drone (RWD) applications. In the literature, routing variants that use drones in collaboration with trucks assume that both drones and trucks move along arcs in two-dimensional networks. There is a need for methodological innovations to cater to more practical and general scenarios involving flight-level decisions of RWDs in the three-dimensional network. In this study, flight level is defined as the specified height at which an RWD is allowed to fly. We introduce the truck and RWD routing problem considering flight-level selection and time windows (TDRP-FT). The TDRP-FT is characterized by RWD flight-level selection, time windows, direct delivery, multiple trucks, and multiple RWDs carried by each truck. We develop a mixed-integer linear programming model and propose a hybrid algorithm based on a greedy randomized adaptive search procedure and an adaptive large neighborhood search. We experimentally evaluate the effectiveness of the TDRP-FT model and the applicability of the hybrid algorithm to small-, medium- and large-scale instances. The computational experiment results indicate that the TDRP-FT model can be used to determine how flight levels of RWDs should be selected.", "keywords": [ "Drone", "Truck", "Aeronautics", "Selection (genetic algorithm)", "Computer science", "Scheduling (production processes)", "Stall (fluid mechanics)", "Operations research", "Aerospace engineering", "Automotive engineering", "Engineering", "Operations management" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/01605682.2023.2185548" }, { "id": "https://openalex.org/W4403882632", "doi": "10.1145/3675888.3676045", "title": "Parcel Delivery Using Drones in the Presence of Obstacles", "year": 2024, "publication_date": "2024-08-08", "type": "conference-paper", "language": "en", "authors": [ "Tathagata Das", "Subhadeep Sarkar", "S. Nath", "Saurav Ghosh", "Sunirmal Khatua", "Rajib Das" ], "first_author": "Tathagata Das", "venue": null, "publisher": null, "abstract": "To tackle the challenges of last-mile logistics, drone delivery of parcels has emerged as an efficient and fast solution. However, deploying drone delivery systems in urban environments requires robust obstacle-avoidance capabilities to ensure safe and reliable operations. This study focuses on two critical aspects of parcel delivery via drones: first, how to plan the drone’s trajectory while avoiding obstacles, and second, how to find the optimal position of the source (distribution point) that minimizes the drone’s total movement while delivering parcels. We consider two versions of the problem. In one, the drone can carry only one parcel at a time, and in the other, it can carry up to k parcels. For the first version, our strategy finds the shortest paths from the source position to the customer locations or targets while avoiding obstacles. For the second version, we construct a graph representing all targets, and the edge weights are the shortest path distances considering obstacles. We divide the targets into clusters of size at most k and find a TSP tour for each cluster using Christofide’s algorithm. Experiments have been conducted on problem sets in a simulated urban environment to analyze the proposed solution and determine the effect of k on the cost of solutions.", "keywords": [ "Drone", "Computer science", "Computer security", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3675888.3676045" }, { "id": "https://openalex.org/W4411954817", "doi": "10.1016/j.compeleceng.2025.110529", "title": "Multi-Agent Reinforcement Learning for truck–drone routing in smart logistics: A comprehensive review", "year": 2025, "publication_date": "2025-07-02", "type": "article", "language": "en", "authors": [ "Ali Arishi", "Paras Ahuja" ], "first_author": "Ali Arishi", "venue": "Computers & Electrical Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Reinforcement learning", "Truck", "Routing (electronic design automation)", "Computer science", "Engineering", "Artificial intelligence", "Automotive engineering", "Computer network", "Biology" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.compeleceng.2025.110529" }, { "id": "https://openalex.org/W4415358380", "doi": "10.55845/jos-2025-1249", "title": "Environmental Impacts of Drone Delivery: A Comparative Meta-Analysis and Standardised LCA Metrics", "year": 2025, "publication_date": "2025-10-20", "type": "article", "language": "en", "authors": [ "Hannah Aster", "Lukas Zeilerbauer", "Johannes Lindorfer" ], "first_author": "Hannah Aster", "venue": "Journal of Sustainability", "publisher": null, "abstract": "The increasing demand for time-efficient and low-emission delivery solutions has brought unmanned aerial vehicles (UAVs) to the forefront of research. While numerous studies identify UAVs as a promising alternative for electrified last-mile logistics, others highlight potential ecological drawbacks. To date, the underlying factors contributing to these divergent findings have not been systematically examined. This study conducted a comprehensive meta-analysis of the environmental impacts of drone delivery systems, focusing on the life cycle assessment (LCA) methodology. It systematically reviews existing literature on climate change (CC) and other environmental impact categories associated with various UAV components and life cycle stages using the PRISMA method and searches for the main environmental hotspots associated with delivery drones across their full life cycle. Results show significant variability in the environmental burdens attributed to different drone components, particularly batteries and electronic systems, influenced by their assumed weight shares. The study found that the relative impact of drone delivery compared to ground delivery is highly dependent on the inclusion of UAV production in the environmental assessment. Furthermore, this work addresses the question of the climate change impact over an entire drone life cycle according to the reviewed studies and how modelling assumptions and system boundaries influence the results. To enable consistent comparison across studies, the study introduced the functional unit (FU) of kg CO2-eq/kg*km. Ultimately, the findings highlight the importance of standardised LCA methodologies in UAV research to ensure consistent, interpretable and comparable sustainability evaluations.", "keywords": [ "Drone", "Life-cycle assessment", "Sustainability", "Environmental impact assessment", "Climate change", "Work (physics)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.55845/jos-2025-1249" }, { "id": "https://openalex.org/W3191399245", "doi": "10.2514/6.2021-3001", "title": "Damage Severity Prediction of Helicopter Windshields Caused by a Collision with a Small Unmanned Aerial Vehicle (sUAV)", "year": 2021, "publication_date": "2021-07-28", "type": "conference-paper", "language": "en", "authors": [ "Mohd Hasrizam Che Man", "Kin Huat Low" ], "first_author": "Mohd Hasrizam Che Man", "venue": "AIAA AVIATION 2021 FORUM", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2021-3001.vid Small unmanned aerial vehicles (or drones) have recently been increased for various operations like parcel delivery, industrial inspection, aerial photography, and safety surveillance. Increased in sUAV traffic in future may lead to a higher chance of collision between sUAV with other airspace users where mid-air collision debris might fall and endanger peoples on the ground. Finite Element Method (FEM) simulation is employed to assess damage severity caused by the sUAV collision on helicopter windshield compared to bird strike, series of collision simulation has been performed to investigate the influence of different parameters like windshield thickness, windshield material type, and helicopter speed to the damage severity of the helicopter windshield. In our preliminary study, it is found the damage level in sUAV collision cause more severe damage compared to bird strike for the same weight category, i.e., 1.8 kg and 3.0 kg. Study of damage severity due to sUAV collision can also be performed in future study to have better understanding on this risk. From this study, aviation airworthiness authorities have better insight on the limitation of current airworthiness, at the same time could provide information for aviation authorities to formulate regulations for operations of sUAV, helicopter, or eVTOL.", "keywords": [ "Windshield", "Airworthiness", "Collision", "Aeronautics", "Aviation", "Derailment", "Engineering", "Marine engineering", "Computer science", "Simulation", "Aerospace engineering", "Automotive engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dr.ntu.edu.sg/bitstream/10356/152917/2/Damage%20Severity%20Prediction%20of%20Helicopter%20Windshield.pdf" }, { "id": "https://openalex.org/W4386281110", "doi": "10.1007/978-3-031-42536-3_30", "title": "An Approach of Optimisation in Last Mile Delivery", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Dragan Simić", "JOSE LUIS CALVO ROLLE", "Jose Ramon Villar", "Vladimir Ilin", "Svetislav D. Simić", "Svetlana Simić" ], "first_author": "Dragan Simić", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Truck", "Mile", "Last mile (transportation)", "Drone", "Vehicle routing problem", "Transport engineering", "Routing (electronic design automation)", "Computer science", "Product (mathematics)", "Environmental economics", "Business", "Operations research" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-42536-3_30" }, { "id": "https://openalex.org/W4412923967", "doi": "10.16980/jitc.21.3.202506.197", "title": "The Impact of Safety Factors on the Intention to Use Drone-Based UAM Last-Mile Delivery: Focusing on the TAM Model", "year": 2025, "publication_date": "2025-06-30", "type": "article", "language": "en", "authors": [ "Eunbin Kim" ], "first_author": "Eunbin Kim", "venue": "Korea International Trade Research Institute", "publisher": null, "abstract": "Purpose - This study investigates how safety factors influence user intention to adopt drone-based Urban Air Mobility (UAM) for last-mile delivery, utilizing the Technology Acceptance Model (TAM). As UAM emerges as a promising solution to urban logistics challenges, perceived safety plays a critical role in shaping user acceptance. Design/Methodology/Approach - Focusing on the three safety dimensions of product safety, personal safety, and social safety, the study conducted a structured survey and applied structural equation modeling (SEM) to examine the relationships among safety factors, perceived usefulness, perceived ease of use, and behavioral intention. Findings - The analysis revealed that product safety and social safety had significant positive effects on perceived usefulness, while product safety also influenced perceived ease of use. Both perceived usefulness and perceived ease of use were strong predictors of intention to use. However, safety factors did not show statistically significant direct effects on usage intention, highlighting the mediating role of TAM constructs. Research Implications - The findings offer practical implications for policymakers and industry stakeholders aiming to promote UAM-based logistics services. Enhancing technical safety features and social infrastructure is essential to increase user acceptance by improving perceptions of usefulness and ease of use, particularly in the early stages of UAM deployment.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Business", "Aeronautics", "Advertising", "Engineering", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.16980/jitc.21.3.202506.197" }, { "id": "https://openalex.org/W7154612589", "doi": "10.1155/atr/9985293", "title": "Designing Statewide Drone Delivery Networks in Rural Contexts: A Multiobjective Approach", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Raj Bridgelall" ], "first_author": "Raj Bridgelall", "venue": "Journal of Advanced Transportation", "publisher": "Hindawi Publishing Corporation", "abstract": "Last‐mile parcel delivery remains a major challenge in rural areas where unpaved roads, long travel distances, and sparse demand limit the efficiency of truck‐based logistics. This study evaluates the use of cargo drones as a replacement for trucks, focusing on the design of a statewide delivery network supported by multimodal hubs. It introduces an empirical multiobjective (EMO) procedure for hub placement that accounts for population density, airport proximity, and infrastructure readiness. The EMO procedure is compared to a classic single‐objective (CSO) approach that minimizes network distance, using data from North Dakota to demonstrate the models. Results show that while the CSO model minimizes travel distance, it selects hubs in low‐demand areas with limited infrastructure. In contrast, the EMO procedure increases population coverage almost ninefold, improves service equity, and enables integration with air freight systems. Cost analysis reveals that drones become more competitive than trucks as demand decreases, particularly in rural regions. Sensitivity analysis highlights strategies to achieve cost parity, including scaling drone operator batch sizes and reducing capital and insurance costs. This research provides a practical, data‐driven framework for statewide drone logistics planning and demonstrates how rural states can benefit from advanced air mobility solutions.", "keywords": [ "Drone", "Truck", "Population", "Operator (biology)", "Service (business)", "Time horizon", "Network planning and design", "Flow network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1155/atr/9985293" }, { "id": "https://openalex.org/W4384518498", "doi": "10.1109/icetci57876.2023.10176906", "title": "Urban drone delivery path optimization in the context of green carbon reduction", "year": 2023, "publication_date": "2023-05-26", "type": "conference-paper", "language": "en", "authors": [ "Hanxi Li", "Yi Gang Sun", "Song Cai" ], "first_author": "Hanxi Li", "venue": null, "publisher": null, "abstract": "With the further development of economy and technology, the problem of the last kilometre in logistics distribution has become a key issue restricting the whole logistics distribution chain. With the characteristics of low cost, high speed and high accuracy, drone delivery has become a better choice to solve the last-mile problem. This paper establishes a VRP model for drone delivery, studies the optimal solution for drone delivery under the constraints, improves the ability to solve complex problems, and introduces arithmetic examples for verification analysis to enhance the persuasive power of the model.", "keywords": [ "Drone", "Context (archaeology)", "Computer science", "Path (computing)", "Key (lock)", "Reduction (mathematics)", "Last mile (transportation)", "Mathematical optimization", "Cost reduction", "Operations research", "Simulation", "Mile" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icetci57876.2023.10176906" }, { "id": "https://openalex.org/W4399528644", "doi": "10.1109/incacct61598.2024.10551042", "title": "Assessing the feasibility of last-mile delivery alternatives using fuzzy AHP in the Indian Urban Context", "year": 2024, "publication_date": "2024-05-02", "type": "conference-paper", "language": "en", "authors": [ "Rushikesh Kanase", "Asmita Chitnis", "Rahul Priyadarshi" ], "first_author": "Rushikesh Kanase", "venue": null, "publisher": null, "abstract": "The consumer-driven e-commerce has changed the landscape of the modern-day supply chain. The last-mile delivery service facilitates the customer with a shorter delivery time. This study assesses the feasibility of last-mile delivery (LMD) alternatives in the urban Indian context, focusing on fuel-powered vehicles (FPVs), electric commercial vehicles (ECVs), and AIenabled drone technology (ADTs). Key parameters influencing the adoption and stakeholder perceptions of ecological, economic, and social sustainability aspects were evaluated using the Fuzzy Analytic Hierarchy Process (FAHP) methodology. A synthesis of expert judgements was performed to rank thirteen parameters and alternatives. The findings suggest that FPV is presently favoured due to its well-established infrastructure with desirability index $\\left(\\mathrm{DI}_{1}=0.44\\right)$. The $E C V$ s are gaining popularity as businesses and governments focus on sustainability, which will replace fuel-powered vehicles $\\left(\\mathrm{DI}_{2}=0.42\\right)$. The ADT exhibits excellent potential for the future $\\left(\\mathrm{DI}_{3}=0.14\\right)$. The study will provide significant insights to businesses, policymakers, and other stakeholders navigating the changing logistics environment while recognising the limitations and uncertainties associated with decision-making.", "keywords": [ "Mile", "Last mile (transportation)", "Context (archaeology)", "Computer science", "Fuzzy logic", "Analytic hierarchy process", "Transport engineering", "Engineering", "Operations research", "Artificial intelligence", "Geography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/incacct61598.2024.10551042" }, { "id": "https://openalex.org/W4402685933", "doi": "10.2514/6.2024-3646", "title": "Balancing Trade-Offs: The Energy Efficiency of Unmanned Aircraft Systems Integration in Last-Mile Delivery and Operational Policy Restrictions", "year": 2024, "publication_date": "2024-07-27", "type": "conference-paper", "language": "en", "authors": [ "Carrie He", "Parth Singh", "Sofiya P’yavka", "Leah Wolfe", "Kevin Caldwell", "Tasfia Mehbuba Islam", "Chris Tang", "Aws F. Mustafa" ], "first_author": "Carrie He", "venue": null, "publisher": null, "abstract": "Compared to the conventional transportation method of diesel cargo trucks, Unmanned Aircraft Systems (UAS) can potentially transform the logistics sector in the coming decade through their integration into last-mile delivery systems. Prior studies have demonstrated that UAS, given their versatility, can improve the overall energy efficiency of the system when used in conjunction with conventional delivery methods, thus offering a more environmentally sustainable approach to last-mile delivery. However, numerous challenges must be addressed for this integration to be feasible, with UAS regulations being a large limiting factor. UAS regulations are continuously evolving and oversee critical issues such as safety and privacy, but they impose many restrictions on UAS usage. This study aims to investigate this problem by examining the impacts of regulations on a heterogeneous UAS-integrated last-mile delivery. Specifically, we focus on a sustainability perspective and evaluate the effects of operational regulations on the energy efficiency of the delivery system. We develop an Ant Colony Optimization (ACO) system to simulate the truck-drone Heterogeneous Delivery Problem (HDP) with dynamic switch points. Results show that under current FAA regulations, a truck-drone hybrid delivery system saves approximately 1.60 US gallons of gasoline compared to a truck-only system. However, the energy consumption is competitive. Furthermore, we perform a sensitivity analysis to examine the effects of various flight parameters and no-fly zones on the energy consumption of the delivery operation. We find additional no-fly zones and the requirement to operate within the Visual Line-of-Sight (VLOS) of the operator to impact the allocation of the delivery system. This study serves as a reference point to guide the direction of future UAS policy-making and technological development, advocating for more sustainable forms of last-mile delivery and contributing to the realization of UAS technology.", "keywords": [ "Last mile (transportation)", "Mile", "Efficient energy use", "Computer science", "Aeronautics", "Business", "Transport engineering", "Engineering", "Electrical engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2024-3646" }, { "id": "https://openalex.org/W4399801414", "doi": "10.1109/icuas60882.2024.10556931", "title": "D2DC: Mid - Air Drone- to- Drone Charging to Enhance Flight Endurance", "year": 2024, "publication_date": "2024-06-04", "type": "conference-paper", "language": "en", "authors": [ "Archit Jaiswal", "Swarup Bhunia" ], "first_author": "Archit Jaiswal", "venue": null, "publisher": null, "abstract": "Last-mile delivery is shipping goods by ground from distribution centers to customers. It is the supply chain's most expensive, complex, and polluting phase. Companies seeking cheaper, faster, and greener logistics are looking to Unmanned Aerial Vehicles (UAVs/drones) for solutions. Several companies aspire to develop Urban Air Mobility (UAM) networks to provide on-demand mobility using an electrical Vertical Takeoff and Landing (eVTOL) aircraft. However, like ground electric vehicles (EVs), UAVs have limited flight endurance due to onboard power constraints. The conventional approach of charging a UAV requires it to halt the flight mission and remain grounded while charging at a slow rate. As a result, UAVs often need detours toward a charging station and add a time penalty on top of the time spent recharging at a station. Such challenges make drone technology less economical and impede its wide-scale adaptation. This paper proposes a novel mid-air Drone-to-Drone (D2D) energy-sharing paradigm, a scalable and cost-effective solution to increase the overall flight endurance of a UAV swarm. This framework allows the UAVs to share charge and sustain each other based on the instructions received from a centralized controller. We introduce innovative components such as multi-level battery architecture, a mid-air energy exchange mechanism, and a cloud-based control unit. We have also developed a drone traffic simulator to evaluate the efficacy of the proposed framework in package delivery tasks. After performing a numerical analysis using the developed simulator, we observed up to 28.24% improvement in the mission success rate and up to 10% increase in the overall flight range of a UAV swarm.", "keywords": [ "Drone", "Aeronautics", "Aerospace engineering", "Computer science", "Engineering", "Biology" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas60882.2024.10556931" }, { "id": "https://openalex.org/W4226048200", "doi": "10.48550/arxiv.2203.05906", "title": "Communication-aware Drone Delivery Problem", "year": 2022, "publication_date": "2022-03-11", "type": "preprint", "language": "en", "authors": [ "Cihan Tugrul Cicek", "Çağrı Koç", "Hakan Gültekin", "Güneş Erdoğan" ], "first_author": "Cihan Tugrul Cicek", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The drone delivery problem (DDP) has been introduced to include aerial vehicles in last-mile delivery operations to increase efficiency. However, the existing studies have not incorporated the communication quality requirements of such a delivery operation. This study introduces the Communication-aware DDP (C-DDP), which incorporates handover and outage constraints. In particular, any trip of a drone to deliver a customer package must require less than a certain number of handover operations and cannot exceed a predefined outage duration threshold. The authors develop a Mixed Integer Programming (MIP) model to minimize the total flight distance while satisfying communication constraints as well as the time windows of customers. We present a Genetic Algorithm (GA) that can solve large instances, and compare its performance with an off-the-shelf MIP solver. Computational results show that the GA can outperform the MIP solver for solving larger instances and is a better option.", "keywords": [ "Drone", "Solver", "Computer science", "Handover", "Integer programming", "Duration (music)", "Genetic algorithm", "Mathematical optimization", "Real-time computing", "Computer network", "Algorithm", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2203.05906" }, { "id": "https://openalex.org/W4387870866", "doi": "10.1109/icc45041.2023.10279648", "title": "Q-Learning-Based Time-Adapted Early Arrival Path Algorithm for Drone Delivery Using Public Transport", "year": 2023, "publication_date": "2023-05-28", "type": "conference-paper", "language": "en", "authors": [ "Mohammed Rahmani", "Florian Delavernhe", "Sidi‐Mohammed Senouci", "Marion Berbineau" ], "first_author": "Mohammed Rahmani", "venue": null, "publisher": null, "abstract": "Drone-based delivery has gained popularity in recent years, and many different delivery systems and schemes have been proposed. One of the most promising scheme concepts is based on the collaboration between a drone and a public transportation network to expand the delivery range while conserving drone battery energy and reducing delivery costs. Path planning is the main problem with this design, as the public transportation network is stochastic and time-dependent. In this paper, an inspection time-adapted early arrival path problem is formulated, which seeks the path for a drone that ensures: (i) reaching the customer as soon as possible, (ii) adapting to random fluctuations in public transportation schedules, and (iii) taking into account the battery life of the drone. To achieve these requirements, a Q-learning-based planning method is proposed. The simulation results validate the effectiveness and feasibility of Q-learning on the planning path for parcel deliveries: at any departure instant, the arrival of the drones at the customer's location was guaranteed, i.e., the resulting path is 100% reliable. In addition, the convergence of the Q-Learning algorithm was reached after only 1000 learning epochs. Furthermore, the experimental results show that the Q-Learning solution can achieve a lower early arrival time and lower power consumption compared to a random algorithm.", "keywords": [ "Drone", "Computer science", "Path (computing)", "Motion planning", "Public transport", "Reinforcement learning", "Real-time computing", "Mathematical optimization", "Algorithm", "Operations research", "Artificial intelligence", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icc45041.2023.10279648" }, { "id": "https://openalex.org/W3037769134", "doi": null, "title": "Carbon Emissions Reductions in Last Mile and Grocery Deliveries Utilizing Autonomous Vehicles", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Miguel Figliozzi" ], "first_author": "Miguel Figliozzi", "venue": "PDXScholar (Portland State University)", "publisher": "Portland State University", "abstract": "New driverless air and ground vehicles are being launched and tested to deliver products or services in the areas of retail, groceries, and healthcare. This research focuses on the efficiency of autonomous (driverless) delivery vehicles in terms of vehicle-miles, energy consumption, and carbon emissions. Drones or UAVs, sidewalk autonomous delivery robots (SADRs), and road autonomous delivery robots (RADRs) vehicles carbon emissions are compared against emissions from an electric van (e-van), a conventional internal combustion engine van, and driving to a store utilizing electric and conventional vehicles. The impacts of vehicle capacity, range, and time constraints are analyzed as well as the impacts of number of deliveries, service time, area of service, and depot-service area distance. Novel results are found regarding the efficiency of each vehicle type and tradeoffs between driving to a store and store delivery as a function of order size and type of vehicle driven by consumers.", "keywords": [ "Mile", "Greenhouse gas", "Business", "Last mile (transportation)", "Environmental science", "Waste management", "Engineering", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://pdxscholar.library.pdx.edu/cengin_fac/552" }, { "id": "https://openalex.org/W7134063757", "doi": "10.1142/s2301385028300016", "title": "eVTOL Aircraft for Last-Mile Delivery: Opportunities, Challenges, and Climate Considerations", "year": 2026, "publication_date": "2026-03-06", "type": "article", "language": "en", "authors": [ "Badar Ali", "Mingkai Wang", "Mohamed Elhesasy", "Yahya Khurshid", "Mohamed M. Kamra", "Tarek Naem Dief" ], "first_author": "Badar Ali", "venue": "Unmanned Systems", "publisher": "World Scientific", "abstract": "With the increasing demand for sustainable and efficient last-mile delivery, electric vertical take-off and landing (eVTOL) aircraft are emerging as a groundbreaking solution. By bypassing the challenges of ground congestion, eVTOL systems can reach hard-to-access urban and rural areas, transforming delivery networks. This paper presents a structured and systematic review of eVTOL technologies across five domains: design and aerodynamics, control strategies, energy systems, last-mile delivery integration, and climate-related performance constraints. Unlike descriptive surveys, the review synthesizes evidence across studies to identify cross-domain trade-offs, performance limitations, and operational implications for short-range, hover-dominant delivery missions. To make the role of atmospheric conditions explicit, we introduce a compact climate–performance interaction framework that links key climate variables (wind, temperature, density-altitude, and precipitation) to aerodynamic, control, and energy metrics relevant for VTOL last-mile operations. Building on this framework, we examine how operational weather conditions such as wind, extreme temperatures, and precipitation impact eVTOL performance and mission reliability. Particular emphasis is placed on climate influences, which remain underexamined yet critically shape operational feasibility in densely built environments. Using a transparent inclusion and classification framework, the study evaluates how eVTOL performance, autonomy, and energy efficiency interact with logistics demands and urban constraints. The paper identifies critical gaps in research: (1) improving the ability of eVTOLs to adapt to unpredictable weather, (2) enhancing energy management and advancing battery technologies, (3) increasing the role of autonomy, artificial intelligence (AI), and machine learning in navigation, (4) seamlessly integrating eVTOLs into larger logistics networks, and (5) exploring the potential of multi-drone swarming. Overall, this review demonstrates that the successful deployment of eVTOLs for last-mile delivery depends on coordinated advances in aircraft design, autonomy, energy systems, infrastructure, and climate-aware planning. The synthesis presented herein provides a foundation for future research and the development of scalable, resilient, and sustainable eVTOL delivery ecosystems.", "keywords": [ "Efficient energy use", "Key (lock)", "Climate change", "Adaptation (eye)", "Energy (signal processing)", "Extreme weather" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s2301385028300016" }, { "id": "https://openalex.org/W4285170492", "doi": "10.1007/978-3-030-95380-5_13", "title": "Truck-and-Drone Parcel Delivery in the Alps", "year": 2022, "publication_date": "2022-01-01", "type": "book-chapter", "language": "en", "authors": [ "Olivier Gallay", "Marc‐Antoine Coindreau", "Nicolas Zufferey" ], "first_author": "Olivier Gallay", "venue": "AIRO Springer series/AIRO Springer Series", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Truck", "Context (archaeology)", "Computer science", "Integer programming", "Geography", "Relevance (law)", "Transport engineering", "Operations research", "Engineering", "Archaeology", "Aerospace engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-95380-5_13" }, { "id": "https://openalex.org/W4290660010", "doi": "10.1038/d41586-022-02101-3", "title": "Drones bearing parcels deliver big carbon savings", "year": 2022, "publication_date": "2022-08-05", "type": "other", "language": "en", "authors": [ "Freda Kreier" ], "first_author": "Freda Kreier", "venue": "Nature", "publisher": "Nature Portfolio", "abstract": null, "keywords": [ "Drone", "Truck", "Mile", "Diesel fuel", "Last mile (transportation)", "Food delivery", "Environmental science", "Aeronautics", "Business", "Engineering", "Automotive engineering", "Geography" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1038/d41586-022-02101-3" }, { "id": "https://openalex.org/W7117143853", "doi": "10.3390/drones10010007", "title": "A Hybrid Strategy-Assisted Cooperative Vehicles–Drone Multi-Objective Routing Optimization Method for Last-Mile Delivery", "year": 2025, "publication_date": "2025-12-23", "type": "article", "language": "en", "authors": [ "Mingyuan Yang", "Bing Xue", "Rui Zhang", "Fuwang Dong" ], "first_author": "Mingyuan Yang", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Drones have emerged as critical infrastructure for enhancing logistics efficiency in the emerging low-altitude economy, particularly in collaborative vehicle–drone research. However, existing research often neglects the impact of fair task allocation on workload balance among formations in large-scale routing scenarios, which compromises service quality. To address this gap, we introduce the Multi-vehicle with drones Collaborative Routing Problem with Large-scale Packages (MCRPLP), formulated as a bi-objective model aiming to minimize both operational cost and workload imbalance. A Hybrid Strategy-assisted Multi-objective Optimization Algorithm (HSMOA) is developed to overcome the limitations of existing methods, which struggle with balancing solution quality and computational efficiency in solving large-scale routing. Based on a Non-dominated Sorting Genetic Algorithm (NSGA-II), the HSMOA integrates a heuristic task assignment strategy that greedily reassigns packages between adjacent clusters. Then, by integrating a Pareto-front superiority evaluation model, an elite individual supplement strategy is designed to dynamically prune sub-optimal solution subspaces while enhancing the search within high-quality Pareto-front subspaces in HSMOA. Extensive experiments demonstrate the effectiveness of HSMOA in terms of solution quality and computational efficiency compared to multiple state-of-the-art methods. Further sensitivity analysis and managerial insights derived from a real-world case are also provided to support practical logistics implementation.", "keywords": [ "Sorting", "Workload", "Task (project management)", "Genetic algorithm", "Routing (electronic design automation)", "Multi-objective optimization", "Heuristic", "Vehicle routing problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10010007" }, { "id": "https://openalex.org/W4413009044", "doi": "10.1016/j.tre.2025.104347", "title": "The traveling salesman problem with drone based on vehicle mobile parking for customer self-pickup", "year": 2025, "publication_date": "2025-08-06", "type": "article", "language": "en", "authors": [ "Lingrui Hong", "Lin Zhou", "Roberto Baldacci" ], "first_author": "Lingrui Hong", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Pickup", "Travelling salesman problem", "Computer science", "Engineering", "Transport engineering", "Artificial intelligence" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S1366554525003886?via=ihub" }, { "id": "https://openalex.org/W2998124486", "doi": null, "title": "Assessment of Parcel Delivery Systems Using Unmanned Aerial Vehicles", "year": 2019, "publication_date": "2019-09-01", "type": "article", "language": "en", "authors": [ "Stephen D. Boyles", "Cesar N. Yahia" ], "first_author": "Stephen D. Boyles", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Computer science", "Drone", "Aeronautics", "Environmental science", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://trid.trb.org/view/1669961" }, { "id": "https://openalex.org/W4399376605", "doi": "10.1109/iciprob62548.2024.10543428", "title": "Visible Light Communication Based User Verification System for Drone Delivery", "year": 2024, "publication_date": "2024-03-09", "type": "conference-paper", "language": "en", "authors": [ "A.M.N.N. Senevirathna", "Chinthaka Premachandra" ], "first_author": "A.M.N.N. Senevirathna", "venue": null, "publisher": null, "abstract": "The realm of goods transportation has witnessed a paradigm shift with the advent of modern technological advancements, notably the utilization of drones for efficient and innovative movement of goods. This paper explores the challenges and advancements in drone delivery systems, emphasizing the critical need for secure and accurate parcel handover methods. We introduce a novel drone delivery LED array-based light communication system that addresses the drawbacks associated with traditional verification processes. The system employs pattern recognition technology, facilitated by a Raspberry Pi single-board computing unit and integrated with a smartphone application for seamless interaction. Through comprehensive testing and analysis, our research showcases the effectiveness and potential of the proposed system in revolutionizing parcel delivery services.", "keywords": [ "Drone", "Computer science", "Raspberry pi", "Handover", "Communications system", "Computer security", "Systems engineering", "Human–computer interaction", "Embedded system", "Telecommunications", "Engineering", "Internet of Things" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/iciprob62548.2024.10543428" }, { "id": "https://openalex.org/W3161967852", "doi": "10.2139/ssrn.3767870", "title": "The Time-Dependent Multiple Flying Sidekicks Traveling Salesman Problem: Parcel Delivery with Traffic Congestion", "year": 2021, "publication_date": "2021-01-01", "type": "preprint", "language": "en", "authors": [ "Ritwik Raj", "Chase C. Murray" ], "first_author": "Ritwik Raj", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Travelling salesman problem", "Traffic congestion", "Computer science", "Transport engineering", "Simulation", "Engineering", "Algorithm" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.3767870" }, { "id": "https://openalex.org/W4407783498", "doi": "10.1109/ispa63168.2024.00125", "title": "Dynamic Task Scheduling for Coordinated Truck-Drone Parcel Delivery: A Hybrid Genetic Tabu Algorithm Approach", "year": 2024, "publication_date": "2024-10-30", "type": "conference-paper", "language": "en", "authors": [ "Ruitai Li", "Quan Yuan", "Tongxin Liao", "Lijun Luo", "Menglan Hu", "Pan Lai", "Xiao Zhang" ], "first_author": "Ruitai Li", "venue": null, "publisher": null, "abstract": "With the rapid growth of the on-demand economy, logistics companies and merchants increasingly struggle to meet customer demands in dynamic and uncertain conditions. This paper studies the coordinated delivery of parcels by trucks and drones under such demands, proposing a Dynamic Task Scheduling Algorithm based on Hybrid Genetic Tabu algorithm (DTSAGT) for route optimization. Simulating dynamic customer demands with a Poisson distribution and statistical methods, the algorithm addresses timeliness issues due to variations in customer needs. It optimizes drone path planning and task allocation considering drone endurance and payload limits to minimize total delivery time. The algorithm includes three steps: initial solution construction, iterative optimization, and dynamic operations. Experimental results show that DTSAGT reduces the total service time by 15.05%, 34.13%, and 35.71% on average compared to the baseline algorithms. This paper’s contribution is the combination of hybrid genetic and tabu search algorithms applied to dynamic task scheduling in truck-drone delivery, enhancing logistics efficiency.", "keywords": [ "Tabu search", "Drone", "Computer science", "Genetic algorithm", "Scheduling (production processes)", "Job shop scheduling", "Truck", "Task (project management)", "Dynamic priority scheduling", "Mathematical optimization", "Distributed computing", "Algorithm" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ispa63168.2024.00125" }, { "id": "https://openalex.org/W4416850776", "doi": "10.3390/app152312619", "title": "Balancing Efficiency and Sustainability in Last-Mile Logistics: A Novel Multi-Truck Multi-Drone Collaborative Framework with Bi-Objective Optimization", "year": 2025, "publication_date": "2025-11-28", "type": "article", "language": "en", "authors": [ "Yong Chen", "Weimin Sheng", "Wenchao Yi" ], "first_author": "Yong Chen", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The increasing variety in last-mile delivery demands requires diverse vehicle-drone collaboration models to meet various scenarios. Meanwhile, growing environmental concerns demand that we optimize not just delivery efficiency but also sustainability. This study thus proposes a unified multi-mode framework for collaborative multi-vehicle, multi-drone delivery networks to enable fair model comparisons. We introduce a hybrid metaheuristic algorithm combining NSGA-II and VND using specialized encoding and neighborhood structures to handle complex constraints, thereby comprehensively enhancing both efficiency and sustainability. Experiments on nine benchmark instances across three models reveal a nonlinear trade-off between efficiency and sustainability, with our migratory-relay model consistently outperforming others in terms of the Pareto front across multiple comparisons. Sensitivity analysis shows diminishing returns from adding more drones; while the first drone can cut emissions by up to 23.1%, additional drones bring progressively smaller reductions. These findings provide a strong framework and practical insights for designing sustainable urban logistics systems.", "keywords": [ "Drone", "Benchmark (surveying)", "Variety (cybernetics)", "Sustainability", "Pareto principle", "Multi-objective optimization", "Metaheuristic" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/15/23/12619/pdf?version=1764334096" }, { "id": "https://openalex.org/W4309129973", "doi": "10.48550/arxiv.2211.06636", "title": "Approximation Algorithms for Drone Delivery Scheduling Problem", "year": 2022, "publication_date": "2022-11-12", "type": "preprint", "language": "en", "authors": [ "Saswata Jana", "Partha Sarathi Mandal" ], "first_author": "Saswata Jana", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The coordination among drones and ground vehicles for last-mile delivery has gained significant interest in recent years. In this paper, we study \\textit{multiple drone delivery scheduling problem(MDSP) \\cite{Betti_ICDCN22} for last-mile delivery, where we have a set of drones with an identical battery budget and a set of delivery locations, along with reward or profit for delivery, cost and delivery time intervals. The objective of the MDSP is to find a collection of conflict-free schedules for each drone such that the total profit for delivery is maximum subject to the battery constraint of the drones. Here we propose a fully polynomial time approximation scheme (FPTAS) for the single drone delivery scheduling problem (SDSP) and a $\\frac{1}{4}$-approximation algorithm for MDSP with a constraint on the number of drones.", "keywords": [ "Drone", "Scheduling (production processes)", "Computer science", "Mathematical optimization", "Job shop scheduling", "Approximation algorithm", "Mathematics", "Algorithm", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2211.06636" }, { "id": "https://openalex.org/W4409456794", "doi": "10.1016/j.aei.2025.103324", "title": "A cost-efficiency analysis of drones in revolutionizing intra-city express services", "year": 2025, "publication_date": "2025-04-15", "type": "article", "language": "en", "authors": [ "Xiao Chu", "Shuiwang Chen", "Kai Wang", "Lingxiao Wu", "Gangyan Xu" ], "first_author": "Xiao Chu", "venue": "Advanced Engineering Informatics", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Engineering", "Computer security", "Biology" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.aei.2025.103324" }, { "id": "https://openalex.org/W4407561019", "doi": "10.3390/app15042007", "title": "Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing", "year": 2025, "publication_date": "2025-02-14", "type": "article", "language": "en", "authors": [ "Aigerim Bogyrbayeva", "Bissenbay Dauletbayev", "Meraryslan Meraliyev" ], "first_author": "Aigerim Bogyrbayeva", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Many exact algorithms, heuristics, and metaheuristics have been proposed to solve the Vehicle Routing Problem with Drones, which involves using a fleet of trucks and drones to fulfil customer orders in last-mile delivery. In this study, the problem is formulated using the Markov Decision Process, and a Reinforcement Learning (RL) based solution is proposed. The proposed RL model is based on an attention-encoder and a recurrent neural network-decoder architecture. This approach enhances coordination by determining which vehicles should visit specific customers and where vehicles can rendezvous, effectively leveraging drones and reducing the overall completion time. The RL model has demonstrated competitive performance compared to benchmark algorithms through extensive experiments.", "keywords": [ "Reinforcement learning", "Computer science", "Drone", "Artificial intelligence", "Biology" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/15/4/2007/pdf?version=1739532726" }, { "id": "https://openalex.org/W3000796525", "doi": "10.1155/2020/4053983", "title": "Inverting the Truck-Drone Network Problem to Find Best Case Configuration", "year": 2020, "publication_date": "2020-01-22", "type": "article", "language": "en", "authors": [ "Robert Rich" ], "first_author": "Robert Rich", "venue": "Advances in Operations Research", "publisher": "Hindawi Publishing Corporation", "abstract": "Many industries are looking for ways to economically use truck/rail/ship fitted with drone technologies to augment the “last mile” delivery effort. While drone technologies abound, few, if any studies look at the proper configuration of the drone based on significant features of the problem: delivery density, operating area, drone range, and speed. Here, we first present the truck-drone problem and then invert the network routing problem such that the best case drone speed and range are fitted to the truck for a given scenario based on the network delivery density. By inverting the problem, a business can quickly determine the drone configuration (proper drone range and speed) necessary to optimize the delivery system. Additionally, we provide a more usable version of the truck-drone routing problem as a mixed integer program that can be easily adopted with standardized software used to solve linear programming. Furthermore, our computational metaheuristics and experiments conducted in support of this work are available for download. The metaheuristics used herein surpass current best-in-class algorithms found in literature.", "keywords": [ "Drone", "Truck", "Computer science", "USable", "Routing (electronic design automation)", "Last mile (transportation)", "Metaheuristic", "Integer programming", "Range (aeronautics)", "Vehicle routing problem", "Software", "Operations research" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/aor/2020/4053983.pdf" }, { "id": "https://openalex.org/W4400655340", "doi": "10.1016/j.trpro.2024.03.003", "title": "Recent developments in urban freight analytics for collaborative city logistics", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Eiichi Taniguchi", "Russell George Thompson", "Ali Gul Qureshi" ], "first_author": "Eiichi Taniguchi", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "This paper presents recent developments in urban freight analytics for collaborative city logistics. City logistics provides innovative solutions using emerging technologies such as Information and Communication Technologies (ICT), Intelligent Transport Systems (ITS), Internet of Things (IoT), big data, Artificial Intelligence (AI), autonomous vehicles and robots. Sharing data between public and private entities is important for modelling, planning and evaluating policy measures related to city logistics. Mathematical models such as multi-agent models can replicate the behaviour of stakeholders in city logistics to help make a better decision considering logistics costs, the environment and energy consumption. Autonomous robots and drones can be used for urban deliveries to minimise the risk of infections during the pandemic. Hyperconnected city logistics combines the concepts of the Physical Internet and city logistics to create more sustainable urban distribution systems by promoting more collaborative, integrated and open networks. Digital twins aim to provide a virtual system for replicating physical systems and their processes that allow the dynamic status of the environment and physical elements in the real world to be updated using various sources of information. Warehouse sharing systems are logistics platforms where the available capacity of warehouses can be matched with the shippers/factories looking to temporarily store their goods before further distribution.", "keywords": [ "Big data", "City logistics", "Humanitarian Logistics", "Information sharing", "Drone", "Sustainable city", "Analytics", "The Internet", "Supply chain", "Computer science", "Traffic management", "Urban planning" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2024.03.003" }, { "id": "https://openalex.org/W4403302138", "doi": "10.1177/03611981241278351", "title": "Plug-In Hybrid Electric Vehicle Traveling-Salesman Problem with Drone", "year": 2024, "publication_date": "2024-10-10", "type": "article", "language": "en", "authors": [ "Tengkuo Zhu", "Stephen D. Boyles", "Avinash Unnikrishnan" ], "first_author": "Tengkuo Zhu", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "Drones and electric vehicles (EVs) represent promising technologies for enhancing the efficiency and sustainability of last-mile delivery services. This paper focuses on the optimization of customer deliveries through the integration of a plug-in hybrid electric vehicle (PHEV) and a drone. Our model, named the plug-in hybrid electric vehicle traveling-salesman problem with drone (PHEVTSPD), assumes the PHEV can be recharged, either fully or partially, at charging stations, while the drone can be launched or retrieved from the EV. Both the EV and drone are capable of independently serving the customer. In comparison with traditional truck-only or drone-only delivery models, the hybrid EV–drone model overcomes the limitations of drone payload capacity and EV service area, thereby significantly improving delivery efficiency and reducing greenhouse-gas emissions. This research presents a three-index mixed-integer linear program (MILP) formulation of PHEVTSPD. Additionally, a linear or piecewise linear approximation of the concave time–state-of-charge (SoC) function is adopted in the model. To solve the proposed problem, we introduce an adaptive large-neighborhood search (ALNS) metaheuristic. Numerical analysis results reveal that the proposed ALNS method outperforms variable neighborhood search (VNS) with an average optimality gap of approximately 3% when solving instances with 10 nodes. Furthermore, a piecewise linear function with a six-line-segment approximation demonstrates an average of 10.8% lower cost compared with a linear approximation.", "keywords": [ "Drone", "Travelling salesman problem", "Linear programming", "Mathematical optimization", "Computer science", "Payload (computing)", "Piecewise linear function", "Electric vehicle", "Piecewise", "Simulation", "Mathematics", "Network packet" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981241278351" }, { "id": "https://openalex.org/W4226129103", "doi": "10.2139/ssrn.4091250", "title": "A Model for Public Attitude Towards Drone Delivery", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Robin Kellermann", "Tobias Biehle", "Hamid Mostofi" ], "first_author": "Robin Kellermann", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Business", "Computer science", "Computer security", "Internet privacy" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4091250" }, { "id": "https://openalex.org/W4404822118", "doi": "10.1049/cim2.70013", "title": "RETRACTION: A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India", "year": 2024, "publication_date": "2024-11-28", "type": "retraction", "language": "en", "authors": [], "first_author": null, "venue": "IET Collaborative Intelligent Manufacturing", "publisher": "Institution of Engineering and Technology", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "City logistics", "Business", "Transport engineering", "Geography", "Engineering" ], "cited_by_count": 1, "is_retracted": true, "is_paratext": false, "open_access_url": "https://doi.org/10.1049/cim2.70013" }, { "id": "https://openalex.org/W4391578106", "doi": "10.1016/j.cie.2024.109963", "title": "Pseudo node insertion method for synchronization in drone–truck combined operations", "year": 2024, "publication_date": "2024-02-06", "type": "article", "language": "en", "authors": [ "Sung Hoon Chung", "Bhawesh Sah", "Jinkun Lee" ], "first_author": "Sung Hoon Chung", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Node (physics)", "Computer science", "Synchronization (alternating current)", "Point (geometry)", "Routing (electronic design automation)", "Real-time computing", "Simulation", "Engineering", "Computer network", "Automotive engineering" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2024.109963" }, { "id": "https://openalex.org/W4367178550", "doi": "10.4324/9781003241478-8", "title": "Drones", "year": 2023, "publication_date": "2023-04-27", "type": "book-chapter", "language": "en", "authors": [ "Matt Grote", "Andy Oakey", "Aliaksei Pilko", "Angela Smith", "Tom Cherrett" ], "first_author": "Matt Grote", "venue": null, "publisher": null, "abstract": "This chapter discusses the practical realities of integrating drones into existing urban logistics supply chain infrastructures, and focuses on medical use cases and using first-hand experience of operating Beyond-Visual-Line-of-Sight flights. The majority of logistics studies have reflected on drones being used for the carriage of consumer goods parcels, replacing or supporting traditional delivery vehicles (e.g., vans/trucks/cargo bikes) that will have payload capacities far in excess of those typical for drones. In developed nations, the reliability of traditional logistics services using vans/trucks is one of their key strengths. Cost estimates were based on a future scenario where drone operations are routinely automated, removing the need for safety pilots for take-off/landing and enabling mission commanders to manage multiple drones simultaneously. Ultimately, drones must be able to deliver an urban logistics service that is reliable and, maybe most importantly for commercial drone operators, cost-effective.", "keywords": [ "Drone", "Computer science", "Biology", "Botany" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4324/9781003241478-8" }, { "id": "https://openalex.org/W4393128414", "doi": "10.1016/j.ejtl.2024.100127", "title": "Modeling optimal drone fleet size considering stochastic demand", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Yuval Hadas", "Miguel Figliozzi" ], "first_author": "Yuval Hadas", "venue": "EURO Journal on Transportation and Logistics", "publisher": "Springer Science+Business Media", "abstract": "The last mile delivery is particularly challenging for stochastic deliveries with narrow time windows. This topic is timely due to the rise of e-commerce and courier type services and the impacts of fleet size and vehicle type on delivery costs. A novel contribution of this research is to provide an optimization approach, extending the newsvendor model, to provide an optimal drone fleet sizing solution with stochastic demand in terms of number of deliveries and deliveries weight or payload from one central depot. The solutions obtained are robust, as shown in a comprehensive sensitivity analysis.", "keywords": [ "Drone", "Computer science", "Mathematical optimization", "Operations research", "Mathematics", "Biology" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejtl.2024.100127" }, { "id": "https://openalex.org/W4391329887", "doi": "10.2514/6.2024-2510", "title": "Wind Tunnel Measurement of the Urban Wind Field for Flight Path Planning of Unmanned Aerial Vehicles", "year": 2024, "publication_date": "2024-01-04", "type": "conference-paper", "language": "en", "authors": [ "Juergen Frey", "Hannes Rienecker", "Sebastian Schubert", "Veit Hildebrand", "Harald Pfifer" ], "first_author": "Juergen Frey", "venue": null, "publisher": null, "abstract": "The focus of the present paper is the investigation of the wind field along the flight path of a small fixed-wing unmanned aerial vehicle (UAV) in an urban environment. The considered mission is a goods delivery as part of a last-mile logistics system. A representative downtown environment of a typical European city is studied. As was recently shown, the local wind field between the buildings can be exploited to obtain energy optimal flight paths for a UAV. So far these results are solely based on numerical simulations. In this paper, the numerical simulations of the wind field are supplemented and verified by wind tunnel experiments. For the wind tunnel tests a 1:100-scale model of the benchmark environment is used. Velocity profiles are measured with hot-wire probes and a five-hole pneumatic probe at several positions along the flight path. The latter is used to detect the flow direction. A combination of aerodynamic alignment and multi-zone approach for calibration is used to determine the local wind vectors. Further attention is paid to the turbulence and gustiness of the local flow. The present work constitutes an important step in the evaluation of the exploitation potential of local wind effects on UAV flight path planning.", "keywords": [ "Wind tunnel", "Meteorology", "Motion planning", "Environmental science", "Wind speed", "Aerospace engineering", "Field (mathematics)", "Remote sensing", "Wind power", "Aeronautics", "Marine engineering", "Computer science" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.2514/6.2024-2510" }, { "id": "https://openalex.org/W4389321430", "doi": "10.1016/j.tre.2023.103314", "title": "Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning", "year": 2023, "publication_date": "2023-12-03", "type": "article", "language": "en", "authors": [ "Zhangchen Hu", "Heng Chen", "Eric Lyons", "Senay Solak", "Michael Zink" ], "first_author": "Zhangchen Hu", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Social cost", "Minification", "Stochastic optimization", "Mathematical optimization", "Stochastic programming", "Discretization", "Motion planning", "Economic cost", "Operations research", "Engineering", "Economics" ], "cited_by_count": 45, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2023.103314" }, { "id": "https://openalex.org/W4320917459", "doi": "10.3397/in_2022_0721", "title": "Drone noise in my backyard: the challenges for public acceptability", "year": 2023, "publication_date": "2023-02-01", "type": "conference-paper", "language": "en", "authors": [ "Roalt Aalmoes", "Marta Tojal Castro", "Naomi Sieben", "Rui Roosien" ], "first_author": "Roalt Aalmoes", "venue": "NOISE-CON proceedings", "publisher": null, "abstract": "The introduction of drones in urban areas for surveillance, parcel delivery services, air taxis, or other services have raised the issue of public acceptability. How can this concept known as Urban Air Mobility (UAM) be successfully introduced in an area without upsetting communities? And how can not yet known benefits of these services be compared to feared drawbacks? Noise impact is already considered to be one of the main concerns for successful introduction of UAM, but focusing on noise levels exclusively may not be enough. Based on recent research on noise annoyance and how it affects individuals and communities, a holistic approach, including noise impact, as well as non-acoustical factors, is presented to address the annoyance towards these disruptive air vehicles. Subjective measures should be considered including demographic factors, as well as perceptional factors, such as the visual environment where these vehicles operate, and emotional factors such as attitude towards drones and air taxis. Using this approach, studies on noise impact of UAM will be able to evaluate the use cases in their intended setting and with the appropriate target communities to assess the true impact and define the real challenges to overcome for noise research in the coming decade.", "keywords": [ "Annoyance", "Taxis", "Drone", "Noise (video)", "Noise control", "Business", "Noise pollution", "Transport engineering", "Environmental planning", "Computer science", "Engineering", "Geography" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3397/in_2022_0721" }, { "id": "https://openalex.org/W3194393741", "doi": "10.1145/3469213.3470360", "title": "Research on Distribution Route Planning Model and Algorithm of Unmanned Aerial Vehicle (UAV) based on Improved Multi-objective Genetic Algorithm", "year": 2021, "publication_date": "2021-05-28", "type": "conference-paper", "language": "en", "authors": [ "Gao Changjiang", "Chen Yinan", "Tang Xiaohai" ], "first_author": "Gao Changjiang", "venue": null, "publisher": null, "abstract": "As an automatic and efficient delivery approach, the unmanned aerial vehicle (UAV) has received much attention in the field of electronic commerce and urban logistics for its natural ability to avoid traffic congestion. Regarding the arrangement of UAV delivery paths as a multi-objective path planning problem, we propose a novel genetic method based on the Non-Dominant Sorting Genetic Algorithm II (NSGA-II) with constraints to optimize the multi-UAV path planning problem, which is to minimize the total cost while maximizing customer satisfaction. In this algorithm, decision vectors are represented as 1-dimensional chromosomes, and two specialized variation operators are employed. These designs, as well as the properties of NSGA-II, enable us to find more non-crowding Pareto optimal solutions with less calculation. In a case study on part of the Solomon dataset, this algorithm found three sparsely distributed non-dominant solutions in one Pareto frontier within a relatively small population size and a short time.", "keywords": [ "Sorting", "Genetic algorithm", "Computer science", "Pareto principle", "Motion planning", "Population", "Multi-objective optimization", "Path (computing)", "Mathematical optimization", "Algorithm", "Artificial intelligence", "Machine learning" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3469213.3470360" }, { "id": "https://openalex.org/W4392169926", "doi": "10.58956/liikenne.143000", "title": "Zero emission freight transport and impacts on last mile delivery", "year": 2024, "publication_date": "2024-02-09", "type": "article", "language": "en", "authors": [ "Samuel Babjak", "Chirs Yung" ], "first_author": "Samuel Babjak", "venue": "Liikenne-vuosikirja", "publisher": null, "abstract": "This literature review explores mainly the challenges and potential solutions in urban freight transportation, for last-mile logistics. The growing number of delivery vehicles in urban areas has led to problems such as traffic congestion, competition for parking spaces, noise, and air pollution. To address these issues, the paper focuses on the last mile problem in transportation logistics and the potential of disruptive innovations, a potential of disruptive innovations such as Unmanned Aerial Vehicles (UAVs) and Autonomous Delivery Robots (ADRs) as alternatives to traditional Internal Combustion Engine (ICE) vehicles. It highlights the drastic operational differences between aerial drones and ADRs and the need for changes in operational procedures to fully leverage these technologies. The study also discusses routing strategies and delivery routes, emphasizing the shift from one-route delivery to a point-to- point model with the advent of autonomous vehicle technologies. The paper concludes that while these technologies can reduce costs and emissions, their effectiveness depends on the specific urban setting and operational factors.", "keywords": [ "Mile", "Last mile (transportation)", "Zero (linguistics)", "Zero emission", "Business", "Transport engineering", "Environmental science", "Engineering", "Geography", "Waste management", "Geodesy" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://liikenne.journal.fi/article/download/143000/90357" }, { "id": "https://openalex.org/W4391889573", "doi": "10.1109/comsnets59351.2024.10427308", "title": "Greedy Algorithms for Heterogeneous Drone Delivery Packing in the Cloud-assisted Intelligent Transportation System", "year": 2024, "publication_date": "2024-01-03", "type": "conference-paper", "language": "en", "authors": [ "Satyajit Mohapatra", "Sanjeet Kumar Nayak" ], "first_author": "Satyajit Mohapatra", "venue": null, "publisher": null, "abstract": "Intelligent Transportation Systems (ITS) paves the way towards a futuristic world. A systematic, quick and trouble-free transportation system is in demand for last-mile deliveries to support the logistic companies. With the help of drones, last-mile delivery becomes quite viable for ensuring deliveries through difficult landscapes and heavy traffic. However, as the number of warehouses with multiple drones increase, it becomes challenging for the logistic companies to efficiently manage these drones with limited battery and weight carrying capacity. The warehouses thus require an efficient governance to ensure deliveries with least number of drones, known as drone delivery packing problem. To address the issue of drone delivery packing with variable battery and weight lifting capacity, in this work, we present the heterogeneous drone delivery packing problem (HDPP) for a cloud-assisted ITS. The objective of HDPP is to minimise the number of heterogeneous drones for a warehouse. Further, we prove that HDPP is NP-hard problem and present an Integer Linear Programming (ILP) based modeling technique for finding optimal solution. Subsequently, we devise two heuristic methods (CD-MaxB and CD-MinB) which provide us near optimal results quickly. Finally, we perform a comparison of our heuristic algorithms with baseline approaches and show their competency in solving large sized instances quickly. The deviation of performance of CD-MaxB and CD-MinB are less than 12% when compared to optimal solution.", "keywords": [ "Drone", "Cloud computing", "Computer science", "Greedy algorithm", "Distributed computing", "Algorithm", "Operating system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/comsnets59351.2024.10427308" }, { "id": "https://openalex.org/W4403835060", "doi": "10.3390/vehicles6040088", "title": "Multi-Depot Electric Vehicle–Drone Collaborative-Delivery Routing Optimization with Time-Varying Vehicle Travel Time", "year": 2024, "publication_date": "2024-10-27", "type": "article", "language": "en", "authors": [ "Yong Peng", "Wenjing Zhu", "Dennis Z. Yu", "Song Liu", "Yali Zhang" ], "first_author": "Yong Peng", "venue": "Vehicles", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This paper presents an electric vehicle-drone (EV–drone) collaborative-delivery routing optimization model that leverages the time-varying characteristics of electric vehicles and drones across multiple distribution centers (i.e., central depots) to address the logistics industry’s low-carbon transformation in the last-mile delivery. The model aims to minimize total delivery costs by formulating a mixed-integer programming (MIP) model that accounts for essential constraints such as nonlinear charging time, time-varying EV travel time, delivery time window, payload capacity, and maximum range. An improved adaptive large-neighborhood search (ALNS) algorithm is developed to solve the model. Experimental results validate the effectiveness of the proposed algorithm and highlight the impact of EV and drone technology parameters, along with the time-varying EV travel times, on the economic efficiency of delivery distribution and route planning.", "keywords": [ "Drone", "Vehicle routing problem", "Depot", "Computer science", "Electric vehicle", "Routing (electronic design automation)", "Travel time", "Transport engineering", "Automotive engineering", "Engineering", "Computer network", "Geography" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/vehicles6040088" }, { "id": "https://openalex.org/W3211300636", "doi": "10.1007/978-3-030-83144-8_9", "title": "Cargo Handling, Transport and Logistics Processes in the Context of Drone Operation", "year": 2021, "publication_date": "2021-11-02", "type": "book-chapter", "language": "en", "authors": [ "Peter Meincke" ], "first_author": "Peter Meincke", "venue": "Research topics in aerospace", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Payload (computing)", "Supply chain", "Last mile (transportation)", "Context (archaeology)", "Aerospace", "Transport engineering", "Container (type theory)", "Automation", "Order (exchange)", "Air cargo", "Process (computing)" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-83144-8_9" }, { "id": "https://openalex.org/W4416971586", "doi": "10.20944/preprints202512.0417.v1", "title": "GIS-Enabled Truck–Drone Hybrid Systems for Agricultural Last-Mile Delivery: A Multidisciplinary Review with Insights from a Rural Region", "year": 2025, "publication_date": "2025-12-04", "type": "preprint", "language": null, "authors": [ "Imran Badshah", "Raj Bridgelall", "Emmanuel Anu Thompson" ], "first_author": "Imran Badshah", "venue": "Preprints.org", "publisher": null, "abstract": "Efficient last-mile delivery remains a critical challenge for rural agricultural logistics, globally, particularly in cold-climate regions with dispersed agricultural operations. This review evaluates the potential of GIS-enabled truck–drone hybrid systems to overcome infrastructural, environmental, and operational barriers in such settings. This study uses North Dakota, USA as a representative case alongside insights from similar rural regions worldwide. The study conducts a systematic review of 82 high-quality publications. It identifies seven interconnected research domains: GIS analytics, truck–drone coordination, smart agriculture integration, rural implementation, sustainability assessment, strategic design, and data security. The findings stipulate that GIS enhances hybrid logistics through route optimization, launch site planning, and real-time monitoring. Additionally, this study emphasizes the rural, low-density context and identifies specific gaps related to cold-weather performance, restrictions to line-of-sight operations, and economic feasibility in ultra-low-density delivery networks. The study concludes with a roadmap for research and policy development to enable practical deployment in cold-climate agricultural regions.", "keywords": [ "Sustainability", "Multidisciplinary approach", "Agriculture", "Context (archaeology)", "Software deployment", "Hybrid system", "Rural area", "Rural development" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/frontend/manuscript/663b7033baad86623447f39ef81a277c/download_pub" }, { "id": "https://openalex.org/W4412580498", "doi": "10.1080/00207543.2025.2536193", "title": "Optimising vertical deliveries with integrated hybrid drone-truck systems", "year": 2025, "publication_date": "2025-07-22", "type": "article", "language": "en", "authors": [ "Ryan O’Neil", "Abdelhakim Khatab", "Uday Venkatadri", "Claver Diallo", "Nidhal Rezg" ], "first_author": "Ryan O’Neil", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "Last-mile delivery (LMD) constitutes a significant portion of contemporary distribution networks, accounting for up to 75% of total supply chain costs. Drones offer numerous advantages over conventional delivery systems, including reduced energy consumption, enhanced flexibility, and lower carbon emissions. However, drone delivery faces limitations such as range, payload carrying capacity, battery life, and regulatory hurdles. This paper presents a novel optimisation model for a 3D multi-visit multi-launch integrated hybrid drone–truck delivery system capable of delivering to multiple customers at various altitudes. Drones can launch multiple times from a truck stop and rendezvous with the truck at a subsequent truck stop. A formulation is proposed that incorporates real-time payload, payload-carrying capacity, battery capacity, and energy consumption profiles to determine optimal truck and drone routes. Small-scale instances are solved to optimality using Gurobi, while large-scale instances are addressed through a solution approach integrating a Relax-&-Fix heuristic and column generation. Experiments are conducted to demonstrate the model's key features and emphasise the significance of incorporating multi-drone launches and their energy consumption function into the delivery decision process. Numerical results indicate trade-offs between drone and truck travel time, customer altitude, and drone energy consumption.", "keywords": [ "Drone", "Truck", "Computer science", "Engineering", "Automotive engineering", "Aeronautics", "Operations management" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2025.2536193" }, { "id": "https://openalex.org/W2922023509", "doi": "10.1007/978-3-030-12450-2_53", "title": "Techniques for Smart Urban Logistics Solutions’ Simulation: A Systematic Review", "year": 2019, "publication_date": "2019-01-01", "type": "conference-paper", "language": "en", "authors": [ "Ioannis D. Karakikes", "Eftihia Nathanail", "Mihails Savrasovs" ], "first_author": "Ioannis D. Karakikes", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "City logistics", "Context (archaeology)", "Transport engineering", "Process (computing)", "Traffic management", "Sustainability", "Plan (archaeology)", "Smart city", "Computer science", "Operations research", "Business", "Engineering" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-12450-2_53" }, { "id": "https://openalex.org/W4233106111", "doi": "10.1145/3477090", "title": "Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond", "year": 2021, "publication_date": "2021-10-21", "type": "paratext", "language": "en", "authors": [], "first_author": null, "venue": null, "publisher": null, "abstract": "UAVs mounted with millimeter wave base stations will enable last-mile high bandwidth access, as well as help in rapidly deploying point to point aerial backhaul links. Because such transmitters use directional beamforming to increase capacity, UAV ...", "keywords": [ "Backhaul (telecommunications)", "Drone", "Base station", "Beamforming", "Computer science", "Last mile (transportation)", "Wireless", "Telecommunications", "Bandwidth (computing)", "Computer network", "Extremely high frequency", "Mile" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": true, "open_access_url": "https://doi.org/10.1145/3477090" }, { "id": "https://openalex.org/W2796596851", "doi": "10.1016/j.endm.2018.03.013", "title": "A Randomized Variable Neighborhood Descent Heuristic to Solve the Flying Sidekick Traveling Salesman Problem", "year": 2018, "publication_date": "2018-04-01", "type": "article", "language": "en", "authors": [ "Júlia Cária de Freitas", "Puca Huachi Vaz Penna" ], "first_author": "Júlia Cária de Freitas", "venue": "Electronic Notes in Discrete Mathematics", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Descent (aeronautics)", "Heuristic", "Benchmark (surveying)", "Payload (computing)", "Solver", "Computer science", "Mathematical optimization", "2-opt", "Variable neighborhood search", "Variable (mathematics)" ], "cited_by_count": 78, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S1571065318300593>." }, { "id": "https://openalex.org/W4416808804", "doi": "10.1016/j.seps.2025.102393", "title": "Evaluating sustainable last-mile delivery modes using IF-MAIRCA: The perspective of local authorities", "year": 2025, "publication_date": "2025-11-29", "type": "article", "language": "en", "authors": [ "Fulya Zaralı", "Şaban Fatih Yılmaz", "Neslihan Demirel", "Akram Elomiya", "Stefan Jovčić" ], "first_author": "Fulya Zaralı", "venue": "Socio-Economic Planning Sciences", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Incentive", "Variety (cybernetics)", "Intuition", "Set (abstract data type)", "Perspective (graphical)", "Energy consumption", "Consumption (sociology)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.seps.2025.102393" }, { "id": "https://openalex.org/W4403063408", "doi": "10.1016/j.asoc.2024.112290", "title": "A reinforcement learning-based multiobjective heuristic algorithm for multiple-truck routing problems with heterogeneous drones", "year": 2024, "publication_date": "2024-10-02", "type": "article", "language": "en", "authors": [ "Gülçin Bektur" ], "first_author": "Gülçin Bektur", "venue": "Applied Soft Computing", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Reinforcement learning", "Computer science", "Drone", "Heuristic", "Truck", "Routing (electronic design automation)", "Mathematical optimization", "Vehicle routing problem", "Algorithm", "Routing algorithm", "Artificial intelligence", "Mathematics" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.asoc.2024.112290" }, { "id": "https://openalex.org/W4403016347", "doi": "10.32956/kopoms.2024.35.3.337", "title": "A Study on the Acceptance of Urban Drone Delivery Services Using Text Mining: Utilizing YouTube Comments", "year": 2024, "publication_date": "2024-08-31", "type": "article", "language": "en", "authors": [ "The Korean Production And Operations Management Society", "Boyoung Yoon", "Donghyun Choi" ], "first_author": "The Korean Production And Operations Management Society", "venue": "한국생산관리학회지", "publisher": null, "abstract": "In modern society, logistics systems need new innovations due to urban traffic congestion and environmental issues. Drones, or unmanned aerial vehicles, are gaining attention for improving urban logistics efficiency. However, drone delivery faces issues such as costs, regulations, and noise. This study analyzes consumer perceptions of drone delivery services using text mining technology. A total of 16,695 comments from 587 YouTube videos containing the keyword ‘drone delivery’ were analyzed through frequency analysis, topic modeling, and sentiment analysis. The results provided insights into consumer opinions, attitudes, concerns, and preferences regarding drone logistics. Major issues identified include potential accidents, noise, and high costs. Proposed solutions include enhancing safety, developing noise reduction technologies, reducing costs, and achieving social consensus. This study offers insights for improving drone delivery services and developing strategies that reflect consumer demands for future implementations.", "keywords": [ "Drone", "Computer science", "World Wide Web", "Data science", "Biology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.32956/kopoms.2024.35.3.337" }, { "id": "https://openalex.org/W4414520614", "doi": "10.1016/j.tre.2025.104425", "title": "Optimal design for an urban truck-drone collaborative delivery system enhanced with relay points", "year": 2025, "publication_date": "2025-09-25", "type": "article", "language": "en", "authors": [ "Hongtai Yang", "Jianzhang Wu", "Zhaolin Zhang", "Xiaobo Liu", "Andrea D’Ariano" ], "first_author": "Hongtai Yang", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Leverage (statistics)", "Truck", "Relay", "Heuristic", "Key (lock)", "Drone", "Linear programming", "Routing (electronic design automation)", "Vehicle routing problem" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104425" }, { "id": "https://openalex.org/W3023463309", "doi": "10.5075/epfl-thesis-10007", "title": "Human-friendly solutions for last-centimeter drone delivery", "year": 2020, "publication_date": "2020-01-01", "type": "dissertation", "language": "en", "authors": [ "Przemyslaw Kornatowski" ], "first_author": "Przemyslaw Kornatowski", "venue": "Infoscience (Ecole Polytechnique Fédérale de Lausanne)", "publisher": "École Polytechnique Fédérale de Lausanne", "abstract": "The popularity of delivery drones is constantly increasing due to their ability to deliver packages more rapidly and economically than current transportation methods. Consequently, drones have been employed by logistics companies for delivering payloads to hard-to-reach places and for daily door-to-door deliveries. However, logistic companies focus mainly on large platforms for long-range (<150km) and last-mile delivery (<20km), not designed to deliver directly to individual consumers. The drones suffer four major limitations: limited safety, difficulty for storage and poor portability, poor adaptability to different sized parcels and lack of user-friendliness. Safety limitations arise from the unshielded spinning propellers. Drone operators have to utilize secure take-off and landing locations, away from people e.g. on runways, rooftops and fenced-off areas. Unshielded propellers and large-sized platforms limit the drone's capability to deliver parcels in crowded urban environments. Moreover, landing in remote locations is challenging due to dense vegetation or inclined surfaces such as mountains. A common solution to shield propellers is to adopt a protective cage to enclose the drone. However, this approach generates a large amount of drag on the drone that reduces its aerodynamic efficiency. Presently, storage and transportation of drones to the deployment area are inconvenient, especially for large flying platforms needed for the delivery of large or heavy parcels. Similarly, the use of large platforms and their software is limited to trained operators. Finally, sizes of delivered parcels vary significantly and the adaptability of current drones is limited; most drones can only deliver parcels of a predefined size. This thesis focuses on the design process of parcel delivery solutions that addresses the abovementioned limitations for person-to-person and short-range delivery services. These solutions are integrated into a single aerial delivery system known as Dronistics (\"Drone\" and \"logistics\"). This new system consists of the hardware component, which includes two drones code-named PackDrone and GearQuad, and the software component called SimplyFly. The integrated parcel delivery solution comprises the following features: * Transportation of parcels of variable sizes * Enhanced safety for people, the drone and the transported items * Easier storage and transportation of the drone * Usability of the delivery drone - software interface for inexperienced users * An energy-efficient system Transporting parcels of variable size was realized using a new design approach of placing the parcel above the propellers in both drone platforms. This allows transportation of super-sized parcels larger than the drone while maintaining the vehicleâ s small footprint. Moreover, PackDrone addresses the safety, storage and transportation concerns by integrating a quadcopter within an origami-inspired foldable cage. The cage separates the propellers from the external environment, which includes people, obstacles and the parcel as well. For easier storage and transportation, PackDrone folds in a single hand movement, reducing storage volume by 92%. GearQuad on the other hand, overcomes the above limitations and improves safety and aerodynamic efficiency. The very dense cage of GearQuad allows it to fly near children and land in the hands of the consumer (last-centimeter delivery) or in hard-to-land environments. Its morphing", "keywords": [ "Centimeter", "Drone", "Computer science", "Medicine", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://infoscience.epfl.ch/handle/20.500.14299/164965" }, { "id": "https://openalex.org/W7134815526", "doi": "10.3390/su18062679", "title": "Optimization of Last-Mile Logistics Delivery Routes for Ground-Vehicle and Drone Parallel Distribution from Pre-Warehouses Considering Customer Priorities", "year": 2026, "publication_date": "2026-03-10", "type": "article", "language": "en", "authors": [ "Hui Wang", "Zuning Zhang", "Manzhi Liu", "Lingxuan Liu", "Zhongjin Wang", "Shuyu Long", "Li Huang", "Xiaohan Liu", "Jie Tian", "Sen Yan" ], "first_author": "Hui Wang", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Pre-warehouse last-mile delivery is currently constrained by service radiuses and intense delivery pressures. Meanwhile, national policies are increasingly promoting a transition toward green logistics. By undertaking deliveries to remote or dispersed locations, UAVs can streamline truck routes and minimize the fuel consumption and emissions typically exacerbated by urban traffic congestion. Accordingly, this paper establishes a Ground-Vehicle and Drone Parallel Distribution Model with Priorities (PW-PDSVRP-P), quantifying customer priorities via delivery delay functions to align efficiency with social service requirements. A master–slave hybrid Large Neighborhood Search algorithm is developed and validated through a Hema Fresh case study in Xuzhou. Results define a clear “economic advantage zone” for drone adoption and reveal an adaptive assignment strategy: drones serve as mass-delivery tools in low-cost scenarios but act as “surgical tools” to prune inefficient truck segments in high-cost environments. These findings confirm that air–ground collaboration fosters a more resilient urban distribution system by balancing operational costs with environmental and social sustainability goals.", "keywords": [ "Drone", "Truck", "Sustainability", "Service (business)", "Fuel efficiency", "Operational efficiency", "Distribution (mathematics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/18/6/2679/pdf?version=1773130843" }, { "id": "https://openalex.org/W4387645704", "doi": "10.36676/irt.2023-v9i5-002", "title": "Reinforcement Learning for Autonomous Drone Navigation", "year": 2023, "publication_date": "2023-10-14", "type": "article", "language": "en", "authors": [ "Myra Gupta" ], "first_author": "Myra Gupta", "venue": "Innovative Research Thoughts", "publisher": null, "abstract": "Drone navigation involves the process of controlling the movement and flight path of unmanned aerial vehicles (UAVs). It encompasses both the hardware and software systems that enable drones to navigate and maneuver autonomously or under the guidance of a human operator. The utility of drone navigation is vast and varied, making it a critical component in numerous industries and applications. Firstly, drone navigation plays a crucial role in aerial surveillance and reconnaissance. Drones equipped with advanced navigation systems can efficiently patrol large areas, monitor activities, and gather real-time data from various perspectives. This capability is particularly valuable in security and law enforcement operations, disaster response, and environmental monitoring, where access and visibility might be limited.Drone navigation is critical in aerial mapping and surveying. Drones outfitted with GPS and other positioning technologies may record photographs and collect data with pinpoint accuracy, allowing for the creation of comprehensive 3D maps, topographic models, and land surveys. This allows for more precise, quicker, and cost-effective data collecting for urban planning, agricultural, infrastructure inspection, and building projects. Furthermore, drone navigation is critical in the transportation and logistics industries. Autonomous drones can fly predetermined paths to rapidly and effectively transfer commodities, medical supplies, and other payloads to distant or inaccessible regions. This technology has the potential to transform last-mile distribution, especially in rural areas or during emergencies when regular transportation routes may be hampered.", "keywords": [ "Drone", "Computer science", "Visibility", "Reinforcement learning", "Law enforcement", "Process (computing)", "Component (thermodynamics)", "Software", "Real-time computing", "Aeronautics", "Computer security", "Artificial intelligence" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.36676/irt.2023-v9i5-002" }, { "id": "https://openalex.org/W4410252316", "doi": "10.1016/j.cor.2025.107104", "title": "A branch-price-and-cut algorithm for the time-dependent multiple truck–drone routing problem", "year": 2025, "publication_date": "2025-05-10", "type": "article", "language": "en", "authors": [ "Xiaoning Zang", "Li Jiang", "Changyong Liang", "Xiang Fang" ], "first_author": "Xiaoning Zang", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Branch and price", "Branch and cut", "Routing algorithm", "Mathematical optimization", "Algorithm", "Integer programming", "Mathematics" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2025.107104" }, { "id": "https://openalex.org/W4410065606", "doi": "10.1186/s43093-025-00513-x", "title": "Aerial pathways to resilience: the acceptance of drones in logistics transformation", "year": 2025, "publication_date": "2025-05-04", "type": "article", "language": "en", "authors": [ "Sofia Gomes", "João Morais Lopes", "Tiago Trancoso" ], "first_author": "Sofia Gomes", "venue": "Future Business Journal", "publisher": "Springer Science+Business Media", "abstract": "Abstract Amidst the tumultuous backdrop of the COVID-19 pandemic and the ensuing lockdowns, traditional parcel delivery services faced substantial disruptions, grappling with challenges such as delays, heightened demand, and the inherent risk of contact transmission. In contrast, this paper explores the promising prospect of drone delivery services as a resilient and efficient alternative during crises, offering a contactless delivery solution to circumvent the limitations endured by traditional services in times of unprecedented upheaval. This study examines the determinants of consumer acceptance of drone delivery services, focusing on the roles of perceived usefulness, ease of use, and privacy concerns. We collected data from 1,108 Portuguese consumers through an online survey to assess how these factors influence consumer attitudes and intentions toward adopting drone delivery services. Our findings confirm that perceived usefulness is the most significant predictor of both positive attitudes toward and intentions to adopt drone delivery services, underscoring the importance of practical benefits in technology acceptance. Perceived ease of use also positively impacts consumer attitudes and intentions, highlighting the role of usability in facilitating adoption. Conversely, perceived privacy concerns, while negatively affecting attitudes and intentions, do so marginally, suggesting that privacy issues are secondary to the perceived benefits and usability of the service. The study further reveals that the mediation effect of consumer attitudes weakens the relationship between these antecedents and adoption intentions, particularly diminishing the negative impact of privacy concerns. This research not only provides valuable insights for businesses seeking to implement drone package delivery services but also offers a theoretical foundation for future studies in the burgeoning field of innovative logistics solutions. As industries evolve, it is imperative to unravel the complexities of consumer perceptions to foster successful integration and acceptance of cutting-edge technologies.", "keywords": [ "Drone", "Resilience (materials science)", "Transformation (genetics)", "Aeronautics", "Business", "Engineering", "Biology", "Materials science", "Genetics", "Gene" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://fbj.springeropen.com/counter/pdf/10.1186/s43093-025-00513-x" }, { "id": "https://openalex.org/W4386220236", "doi": "10.3390/s23177472", "title": "A Real-Time Path Planning Method for Urban Low-Altitude Logistics UAVs", "year": 2023, "publication_date": "2023-08-28", "type": "article", "language": "en", "authors": [ "Minyi Deng", "Qingqing Yang", "Yi Peng" ], "first_author": "Minyi Deng", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "To solve the problem of poor real-time performance in path planning algorithms for unmanned aerial vehicles (UAVs) in low-altitude urban logistics, a path planning method combining modified Beetle Antennae Search (BAS) with the Simulated Annealing (SA) algorithm is proposed. Firstly, based on the requirements of task execution and constraints of UAV flight, a fitness function for real-time search of waypoints is designed while ensuring the safety and obstacle avoidance of the UAV. Then, to improve the search accuracy and real-time performance, determining the initial search direction in the BAS algorithm is improved, while the search step size and antennae sensing length are updated in real-time according to the distance between the UAV and the obstacle. Finally, the SA algorithm is combined with the BAS algorithm to update the waypoints, expanding the search range of each waypoint, avoiding the process of updating the waypoints from becoming trapped in the local optimal waypoints. Meanwhile, the effectiveness of the next waypoint is evaluated based on the Metropolis criterion. This paper generates a virtual urban logistics distribution environment based on the density and distribution of urban buildings, and compares the performance of algorithms in obstacle-sparse, obstacle-moderate, and obstacle-dense environments. The simulation results demonstrate that the improved method in this paper has a more significant capacity for environmental adaptation. In terms of the path length, waypoints, safety obstacle avoidance, and smoothness, the planned path outperforms the original BAS method. It satisfies the needs of real-time path planning for UAVs involved in urban low-altitude logistics.", "keywords": [ "Waypoint", "Obstacle", "Motion planning", "Obstacle avoidance", "Computer science", "Real-time computing", "Simulated annealing", "Path (computing)", "Smoothness", "Mathematical optimization", "Simulation", "Algorithm" ], "cited_by_count": 40, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/23/17/7472/pdf?version=1693216997" }, { "id": "https://openalex.org/W7120001054", "doi": "10.1016/j.elerap.2026.101573", "title": "Integrating customer preferences into operational decision-making for prioritizing emerging technologies in last-mile delivery", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Ahmet Çalık", "Esra Boz", "Sinan Çizmecioğlu", "Erfan Babaee Tirkolaee" ], "first_author": "Ahmet Çalık", "venue": "Electronic Commerce Research and Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Robustness (evolution)", "Customer satisfaction", "Service quality", "Drone", "Operational efficiency", "Work (physics)", "Operational costs", "Vehicle routing problem" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.elerap.2026.101573" }, { "id": "https://openalex.org/W3162612265", "doi": "10.1142/s2301385022500017", "title": "A Novel Mini Jet Engine Powered Unmanned Aerial Vehicle: Modeling and Control", "year": 2021, "publication_date": "2021-05-14", "type": "article", "language": "en", "authors": [ "Erdinç Altuğ", "Abdullah Türkmen" ], "first_author": "Erdinç Altuğ", "venue": "Unmanned Systems", "publisher": "World Scientific", "abstract": "Significant progress has been made in recent years on personal air vehicles (PAVs), which offer independent and autonomous urban transportation. On-demand parcel delivery drones and heavy-lift drones are gaining serious attention. Although various designs for these systems have been put forward, they still have not reached sufficient maturity. The current systems provide somehow satisfactory operation, but many of these systems are limited in payload capacity and flight duration, and not suitable for future operations. In this paper, we propose a novel thrust system that uses multiple mini jet engines. Unlike electric motors, the jet engine thrust cannot vary rapidly. This led us to design and develop a thrust vectoring system for each jet engine. This proposed system has the potential to enable drones to carry more payload and achieve longer flight times. This paper discusses the design and modeling of the system as well as the stabilization algorithms that satisfactorily stabilize the proposed system. We presented that due to motor lag, thrust variations cannot stabilize the vehicle. We showed that the use of a thrust vectoring mechanism with LQR-based controller can overcome the effects of motor lag and stabilize the vehicle, successfully.", "keywords": [ "Thrust vectoring", "Thrust", "Payload (computing)", "Drone", "Automotive engineering", "Jet engine", "Lift (data mining)", "Lag", "Controller (irrigation)", "Aerospace engineering", "Computer science", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s2301385022500017" }, { "id": "https://openalex.org/W3044840095", "doi": "10.1007/978-3-662-61755-7_22", "title": "Localization and grasping of small load carriers with autonomous unmanned aerial vehicles", "year": 2020, "publication_date": "2020-01-01", "type": "book-chapter", "language": "en", "authors": [ "Markus Lieret", "M. Lallinger", "Markus Tauscher", "Jörg E. Franke" ], "first_author": "Markus Lieret", "venue": null, "publisher": null, "abstract": "The application of unmanned aerial vehicles (UAV) in the area of inspection, survey or urban logistics has become a rapidly developing research domain. While the feasibility of material transports with UAVs has already been shown in the scope of different projects, the payload is thereby usually transferred manually into the UAV’s load handling device. A decisive factor for the economic usability of UAVs for aerial transportation, however, is a fully automated system including the autonomous recognition and pick-up of the cargo. We therefore present a solution for the automated detection, localization and grasping of small load carriers with UAVs. The system includes a specialized load handing device, a camera-based real-time tracking solution for small load carriers and a fusion of the global and relative position measurements to achieve the in-flight positioning accuracy required for the autonomous cargo pick-up.", "keywords": [ "Payload (computing)", "Drone", "Usability", "Scope (computer science)", "Computer science", "Sensor fusion", "Real-time computing", "Engineering", "Simulation", "Artificial intelligence", "Human–computer interaction", "Computer security" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007%2F978-3-662-61755-7_22.pdf" }, { "id": "https://openalex.org/W4382313337", "doi": "10.30518/jav.1277694", "title": "Drone Technology in Transportation Management: A Systematic Review and Framework for Future Research", "year": 2023, "publication_date": "2023-05-11", "type": "review", "language": "en", "authors": [ "Muhammed Turğut", "Bilal ŞEKER" ], "first_author": "Muhammed Turğut", "venue": "Journal of Aviation", "publisher": "Süleyman Demirel University", "abstract": "This study examines the current state of the drone technology literature in detail, both academically and industrially. It describes the advantages and disadvantages of drones in the use of logistics activities, the methods used in the studies and the gaps in the literature. In addition, this study aims to reveal the academic efforts about drone-based transportation systems that will have a say in the future and which areas require effort in the future. In this article, a systematic literature review (SLR) about the use of drones in the transportation industry has been carried out. In total, 56 articles published in Elsevier's Scopus, Thomson Reuter's Web of Science, IEEE Xplore and ScienceDirect (Elsevier) databases were examined in detail. The findings show that the use of drones in transportation activities is an effective method. However, the fact that it is a technology that has just been integrated into business processes reveals that there are aspects that need to be developed. Especially in the last ten years, they found that businesses have benefited from drone technology in their transportation activities that increased their last mile delivery speed, efficiency, accessibility and customer satisfaction.", "keywords": [ "Drone", "Scopus", "Web of science", "Systematic review", "Engineering management", "Business", "Computer science", "Knowledge management", "Transport engineering", "Engineering", "Political science" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.30518/jav.1277694" }, { "id": "https://openalex.org/W4403261800", "doi": "10.1016/j.cie.2024.110641", "title": "Traveling salesman problem with time windows and a drone-utilizing intermediate points (TSPTWD-IP)", "year": 2024, "publication_date": "2024-10-09", "type": "article", "language": "en", "authors": [ "Bo Lan", "Yoshinori Suzuki" ], "first_author": "Bo Lan", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Computer science", "Engineering", "Algorithm", "Biology", "Botany" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2024.110641" }, { "id": "https://openalex.org/W4323782864", "doi": "10.1007/978-3-031-25891-6_17", "title": "A Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Nikolaos A. Kyriakakis", "Themistoklis Stamadianos", "Magdalene Marinaki", "Nikolaos F. Matsatsinis", "Yannis Marinakis" ], "first_author": "Nikolaos A. Kyriakakis", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Swarm intelligence", "Artificial bee colony algorithm", "Metaheuristic", "Routing (electronic design automation)", "Variable neighborhood search", "Mathematical optimization", "Swarm behaviour", "Electric vehicle", "Vehicle routing problem", "Optimization problem" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-25891-6_17" }, { "id": "https://openalex.org/W4415044666", "doi": "10.20944/preprints202510.0347.v1", "title": "GIS-Enabled Truck–Drone Hybrid Systems for Agricultural Last-Mile Delivery: A Multidisciplinary Review with Insights from Rural North Dakota", "year": 2025, "publication_date": "2025-10-07", "type": "preprint", "language": "en", "authors": [ "Imran Badshah", "Raj Bridgelall", "Emmanuel Anu Thompson" ], "first_author": "Imran Badshah", "venue": "Preprints.org", "publisher": null, "abstract": "Efficient last-mile delivery remains a major challenge for agriculture in rural regions such as the state of North Dakota in the United States. In these regions, farms are large, dispersed, and dependent on timely access to inputs. Truck–drone hybrid systems offer a potential solution by combining the long-haul capacity of trucks with the speed and flexibility of drones. Economic studies indicate that such proposed hybrid systems can enable faster, lower-cost, and more sustainable delivery of small, time-critical packages. This research further reviews the role of geographic information systems (GIS) in enabling these systems. A combined systematic and thematic review of 82 high-quality publications identifies five domains: GIS applications, truck–drone coordination, smart agriculture integration, rural implementation, and sustainability impacts. The findings show that GIS supports route optimization, drone launch-site selection, and real-time monitoring. Beyond the capacity of drones to extend reach and reduce delays, integrating IoT and AI platforms enhances decision-making and improves efficiency. However, constraints include federal regulations, payload limits, harsh weather (especially in rural areas), and cybersecurity risks. This review concludes that GIS-enabled truck–drone systems can transform agricultural logistics and rural resilience if providers can address regulatory, technical, and security challenges through coordinated innovation.", "keywords": [ "Agriculture", "Sustainability", "Drone", "Geographic information system", "Flexibility (engineering)", "Resilience (materials science)", "Food security", "Hybrid system", "Multidisciplinary approach" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/frontend/manuscript/5fe2a6a7ab3b655d9123d6ef3f1704ca/download_pub" }, { "id": "https://openalex.org/W4409388867", "doi": "10.54660/.ijmrge.2022.3.1.887-892", "title": "Innovating Last-Mile Delivery Post-Pandemic: A Dual-Continent Framework for Leveraging Robotics and AI", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Ogechi Thelma Uzozie", "Osazee Onaghinor", "Oluwafunmilayo Janet Esan" ], "first_author": "Ogechi Thelma Uzozie", "venue": "International Journal of Multidisciplinary Research and Growth Evaluation", "publisher": null, "abstract": "The COVID-19 pandemic significantly disrupted traditional logistics and delivery models, prompting a shift toward more innovative and efficient solutions in the last-mile delivery sector. This paper examines the role of robotics and artificial intelligence (AI) in transforming last-mile delivery systems, focusing on a dual-continent framework that explores their adoption in both developed and emerging economies. The literature review synthesizes key findings regarding the impact of the pandemic on delivery systems, highlighting how robotics, such as drones and autonomous vehicles, alongside AI technologies like route optimization and demand forecasting, have been leveraged to enhance delivery efficiency. The review also delves into regional differences in technology adoption, analyzing challenges and opportunities specific to advanced and developing markets. The paper concludes by discussing the implications for business practices and policymaking, emphasizing the need for clear regulatory frameworks and infrastructure development. It identifies gaps in the current research, particularly concerning the long-term sustainability, employment impact, and potential for further technological innovation in last-mile delivery. This paper contributes valuable insights into the future trajectory of last-mile delivery systems and provides directions for further research to address existing gaps.", "keywords": [ "Mile", "Dual (grammatical number)", "Pandemic", "Robotics", "Coronavirus disease 2019 (COVID-19)", "Artificial intelligence", "2019-20 coronavirus outbreak", "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)", "Drone", "Last mile (transportation)", "Computer science", "Aeronautics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.allmultidisciplinaryjournal.com/uploads/archives/20250412183515_MGE-2025-2-260.1.pdf" }, { "id": "https://openalex.org/W2788226543", "doi": "10.1051/matecconf/201815104011", "title": "UAV Delivery Monitoring System", "year": 2018, "publication_date": "2018-01-01", "type": "conference-paper", "language": "en", "authors": [ "Khin Thida San", "Sun Ju Mun", "Yeong Hun Choe", "Yoon‐Seok Chang" ], "first_author": "Khin Thida San", "venue": "MATEC Web of Conferences", "publisher": "EDP Sciences", "abstract": "UAV-based delivery systems are increasingly being used in the logistics field, particularly to achieve faster last-mile delivery. This study develops a UAV delivery system that manages delivery order assignments, autonomous flight operation, real time control for UAV flights, and delivery status tracking. To manage the delivery item assignments, we apply the concurrent scheduler approach with a genetic algorithm. The present paper describes real time flight data based on a micro air vehicle communication protocol (MAVLink). It also presents the detailed hardware components used for the field tests. Finally, we provide UAV component analysis to choose the suitable components for delivery in terms of battery capacity, flight time, payload weight and motor thrust ratio.", "keywords": [ "Payload (computing)", "Computer science", "Real-time computing", "Component (thermodynamics)", "Embedded system", "Delivery Performance", "Simulation", "Engineering", "Computer network" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1051/matecconf/201815104011" }, { "id": "https://openalex.org/W3212925408", "doi": "10.18690/978-961-286-538-2.4", "title": "Slovenia on the Way to Drone Delivery - What More Needs to be Done", "year": 2021, "publication_date": "2021-11-10", "type": "conference-paper", "language": "en", "authors": [ "Kristijan Perčič", "Branka Leskovšek" ], "first_author": "Kristijan Perčič", "venue": null, "publisher": null, "abstract": "Nowadays, it is impossible to bypass the fact that digitization, robotics and automation of work are becoming an increasingly important part of our living and business. It becomes crucial, especially in urban areas, to identify new operational models that could be applied for last mile deliveries, where increasing of city logistics sustainability is being the main goal. Drones have been widely acknowledged as a promising technology in many fields and industries, especially for the delivery of medical and aid packages in humanitarian and healthcare logistics. In this study, we present the project of Post of Slovenia, which aimed to implement first delivery drone to the fleet to access hard-to-access locations. Slovenian Post aims to create innovative, cost-efficient and market-led business environment for the development and take-up of new drone services and technologies within the Slovenian’s internal market. As the national legislation in this area is still relatively unregulated, Post of Slovenia has actively contacted with the national authorities, which are the drafters of the relevant legislation, in order to accelerate the introduction of delivery drones into Slovenian airspace.", "keywords": [ "Drone", "Legislation", "Digitization", "Business", "Work (physics)", "Last mile (transportation)", "Sustainability", "Order (exchange)", "Automation", "Engineering management", "Computer science", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.18690/978-961-286-538-2.4" }, { "id": "https://openalex.org/W4403486022", "doi": "10.1007/978-981-97-4988-1_15", "title": "Last-Mile Delivery Solutions in Urban Areas: Comparative Analysis", "year": 2024, "publication_date": "2024-10-17", "type": "conference-paper", "language": "en", "authors": [ "Kanwar Dimple Singh", "Rashmi Ashtt" ], "first_author": "Kanwar Dimple Singh", "venue": "Lecture notes in civil engineering", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Mile", "Last mile (transportation)", "Geography", "Geodesy" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-97-4988-1_15" }, { "id": "https://openalex.org/W4391769280", "doi": "10.1109/itsc57777.2023.10422505", "title": "Electric Vehicle-Drone Routing Problem with Optional Drone Availability", "year": 2023, "publication_date": "2023-09-24", "type": "conference-paper", "language": "en", "authors": [ "Setyo Tri Windras Mara", "Ruhul Amin Sarker", "Daryl Essam", "Saber Mohammed Elsayed" ], "first_author": "Setyo Tri Windras Mara", "venue": null, "publisher": null, "abstract": "This paper considers a last-mile distribution system consisting of a fleet of electric vehicles, where each vehicle has the option to be equipped with a single drone. We formulate the system as a mixed-fleet electric vehicle-drone routing problem, with the objective of minimizing the total cost of the system. Subsequently, an adaptive memetic algorithm with a Q-learning-based operator selection mechanism is employed to solve the model. A set of extensive numerical experiments show that the proposed algorithm performs better than the state-of-the-art approaches. Further, we also demonstrate the importance of economies of scale in drone-aided routing problems.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Aeronautics", "Computer network", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itsc57777.2023.10422505" }, { "id": "https://openalex.org/W4388910192", "doi": "10.1016/j.ifacol.2023.10.036", "title": "Accurate Wind Observation and Robust Control for Drones in the Field of Micro-meteorology⋆", "year": 2023, "publication_date": "2023-01-01", "type": "article", "language": "en", "authors": [ "Abner Asignacion", "Ryusuke Noda", "Toshiyuki Nakata", "Daisuke Tsubakino", "Hao Liu", "Satoshi Suzuki" ], "first_author": "Abner Asignacion", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "Urban logistics has been the primary target for recent drone applications. However, these drones face varying weather conditions in their flight horizon that can limit their performance. From another perspective, this flight horizon is the focus of micro-meteorology data gathering for a city-wide accurate weather prediction system. Hence, these drones can carry out logistics while gathering micro-meteorology data. This paper focuses on the latter and aims to realize the drone capabilities required for forecasting micro-meteorology, especially observing highly accurate wind data and robust control capability against strong wind disturbances. The generation of wind maps using a 2D wind sensor mounted on a drone and wind gust estimation using a nonlinear disturbance observer (NDOB) are proposed as the capability for accurate wind observation. Next, the position and velocity control of the drone using super twisting sliding mode control (STSMC) and NDOB are designed for robust control. Lastly, numerical simulation and real-world experiments verify the effectiveness of the whole system proposed in this paper.", "keywords": [ "Drone", "Computer science", "Wind speed", "Meteorology", "Geography" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2023.10.036" }, { "id": "https://openalex.org/W4392966818", "doi": "10.20944/preprints202403.1028.v1", "title": "An Energy-Efficient Logistic Drone Routing Method considering Dynamic Drone Speed and Payload", "year": 2024, "publication_date": "2024-03-18", "type": "preprint", "language": "en", "authors": [ "Kunpeng Wu", "Shaofeng Lu", "Haoqin Chen", "Minling Feng", "Zenghao Lu" ], "first_author": "Kunpeng Wu", "venue": "Preprints.org", "publisher": null, "abstract": "Unmanned aerial vehicle (UAV), or drone is recognized for its potential to improve efficiency and address last-mile delivery issues. As a result, there has been a lot of activity in recent years in the field of drone scheduling and routing. Unlike the vehicle routing problem, drone route design is difficult due to several operational characteristics, such as speed optimization, multitrip operation, and energy consumption estimation. On the one hand, drone energy consumption is a complex nonlinear function of both speed and payload in practice. On the other hand, the high operating speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. Most of the existing drone delivery models either assume constant drone speed or do not consider the effect of drone speed and parcel weight on energy consumption, leading to costly or energy-infeasible routes. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP-VFS), in which a team of homogeneous drones is employed for delivery services. We propose a new model to particularly consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables so that various drone speeds can be adopted in applications. The DRP-VFS is initially formulated as mixed-integer linear programming (MILP) to minimize total energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm. The experimental results demonstrate that suboptimal solutions can be found effectively in practical scenarios using the proposed method. Furthermore, results indicate that operating drones at variable speeds leads to about 21% of energy savings compared to fixed speeds, boasting advantages in cost-savings and range extensions.", "keywords": [ "Drone", "Payload (computing)", "Routing (electronic design automation)", "Computer science", "Energy (signal processing)", "Aerospace engineering", "Engineering", "Mathematics", "Embedded system", "Computer network", "Statistics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/frontend/manuscript/e7c3dd482d61986708a36f0bd5a9a5fc/download_pub" }, { "id": "https://openalex.org/W4407506210", "doi": "10.1007/978-3-031-80775-6_1", "title": "Last-Mile Delivery Optimization Using Mixed Electric Vehicles, UAVs and Full Truck Delivery Based on Artificial Intelligence Algorithms", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Bouhia Hanaa", "Jamal Benhra", "Wissal Ed-dehbi" ], "first_author": "Bouhia Hanaa", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Truck", "Mile", "Computer science", "Last mile (transportation)", "Algorithm", "Artificial intelligence", "Automotive engineering", "Engineering", "Physics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-80775-6_1" }, { "id": "https://openalex.org/W4403279964", "doi": "10.1016/j.ifacol.2024.09.255", "title": "Analyzing Forklift and Drone Applications in Sustainable Logistics: A Bibliometric Review", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Saverio Ferraro", "Leonardo Leoni", "Alessandra Cantini", "Filippo De Carlo" ], "first_author": "Saverio Ferraro", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "Nowadays organizations are under increasing pressure to adopt sustainable solutions for material handling and management due to growing environmental awareness. This drive has spurred advancements in innovative technologies, optimizing logistic processes to reduce energy usage and emissions, and enhancing overall supply chain sustainability. This approach extends to internal material handling with forklifts and the emerging use of drones for last-mile operations. Despite the importance of sustainable logistic practices, the literature lacks a comprehensive review of research on forklifts and drones in logistic activities. Through a Systematic Literature Network Analysis (SLNA), this paper addresses this gap by conducting a comparative analysis of two bibliometric reviews: one on forklifts’ sustainable logistic applications and the other on drones’. This analysis aims to identify key contributors, countries, journals, and research themes in both areas. The SLNA results seek to highlight existing literature, productive countries, influential authors, and research themes. This paper provides theoretical contributions by identifying gaps in the current literature and highlighting key research themes related to sustainable logistic practices. Furthermore, it offers practical insights to industry stakeholders on integrating these practices using forklifts and drones, while also laying the groundwork for future research through a comparative analysis.", "keywords": [ "Drone", "Computer science", "Business" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2024.09.255" }, { "id": "https://openalex.org/W7104022509", "doi": "10.1016/j.aei.2025.104008", "title": "Multi-depot electric vehicle routing problem with drones considering time-dependent traffic", "year": 2025, "publication_date": "2025-11-05", "type": "article", "language": "en", "authors": [ "Hao Fan", "Kai Liu", "Jiangbo Wang", "Jian Zhao" ], "first_author": "Hao Fan", "venue": "Advanced Engineering Informatics", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Initialization", "Vehicle routing problem", "Population", "Particle swarm optimization", "Genetic algorithm", "Crossover", "Electric vehicle" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.aei.2025.104008" }, { "id": "https://openalex.org/W4399970211", "doi": "10.4018/979-8-3693-2093-8.ch003", "title": "Drone Swarms in Industry 5.0", "year": 2024, "publication_date": "2024-05-31", "type": "book-chapter", "language": "en", "authors": [ "Chikesh Ranjan", "Bipllab Chakraborty", "J. Srinivas", "Kaushik Kumar" ], "first_author": "Chikesh Ranjan", "venue": "Advances in computational intelligence and robotics book series", "publisher": "IGI Global", "abstract": "The integration of drone swarms in Industry 5.0 marks a significant advancement in industrial automation and optimization. This chapter explores the typical applications of drone swarms within Industry 5.0, emphasizing their transformative impact across various sectors. In manufacturing, drone swarms facilitate efficient inventory management, autonomous inspections, and streamlined logistics, enhancing operational efficiency and productivity. In agriculture, they enable precision farming practices through aerial imaging, crop monitoring, and targeted interventions. In construction, drone swarms play a crucial role in site mapping, progress monitoring, and safety inspections, revolutionizing project management and ensuring worker safety. Moreover, in transportation and logistics, drone swarms offer innovative solutions for last-mile delivery, warehouse management, and traffic monitoring, contributing to the seamless integration of Industry 5.0 principles.", "keywords": [ "Drone", "Computer science", "Aeronautics", "Engineering", "Biology" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3693-2093-8.ch003" }, { "id": "https://openalex.org/W4399094528", "doi": "10.1177/03611981241248439", "title": "Why and Why Not? Systematic Review of the Willingness to Accept Drone and Robot Deliveries", "year": 2024, "publication_date": "2024-05-28", "type": "review", "language": "en", "authors": [ "Woojung Kim", "Sung Ho Hur" ], "first_author": "Woojung Kim", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "Last-mile deliveries using drones and robots have recently emerged as alternatives to deliveries using conventional trucks. Because of the newness of the topic, there has been no synthesized summary of the acceptance of the emerging delivery modes, although the importance of the topic has increased with the growth of home deliveries. To fill the knowledge gap, this research systematically reviewed the literature on the willingness to accept drone and robot deliveries using the two largest publication databases: Web of Science and SCOPUS. A total of 46 academic studies were reviewed with respect to their trends, study scope (i.e., type of mode, type of products, modeling framework, and background theories), and factors that positively and negatively affect the willingness. Based on the review, knowledge gaps and the corresponding future research directions were derived: (a) robot-related investigations have been understudied compared to drones; (b) research from the shipper perspective is necessary; (c) transportation and logistics planning-related factors (e.g., socio-economic factors, trip and shipment characteristics) should be investigated; (d) an investigation of users’ behaviors based on actual usage (rather than relying solely on hypothetical scenarios) is necessary; (e) regulatory aspects should be taken into account; and (f) the heterogeneity of products needs to be taken into consideration.", "keywords": [ "Drone", "Willingness to accept", "Willingness to pay", "Engineering", "Transport engineering", "Computer science", "Business", "Aeronautics", "Economics", "Microeconomics" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981241248439" }, { "id": "https://openalex.org/W7171356737", "doi": "10.1016/j.trip.2026.102158", "title": "A scientometric review of unmanned aerial vehicles (UAVs) and transportation science", "year": 2026, "publication_date": "2026-07-27", "type": "review", "language": "en", "authors": [ "Tony H. Grubesic" ], "first_author": "Tony H. Grubesic", "venue": "Transportation Research Interdisciplinary Perspectives", "publisher": "Elsevier BV", "abstract": "The rapid evolution of unmanned aerial vehicles (UAVs) is transforming modern transportation ecosystems. From the development of mathematical models and heuristics to improve UAV-assisted last-mile parcel deliveries to real-time traffic monitoring efforts, UAV technology now permeates nearly every subdomain of transportation science. However, because these innovations are developing both quickly and concurrently, many potential synergies between research fields go unnoticed. This paper leverages a suite of scientometric approaches to provide a comprehensive mapping of the primary subdomains shaping the intersection of unmanned aerial systems and transportation research. The results reveal that while hybrid truck-drone logistics operations remain a focal area for research, urban infrastructure inspection and surveillance applications are also growing. However, these applications are strongly dependent upon advances in localization processes developed in allied fields. The paper concludes with a prospective look at the future of UAVs and transportation research.", "keywords": [ "Suite", "Intersection (aeronautics)", "Drone", "Heuristics", "Transportation infrastructure", "Intelligent transportation system", "Aerial survey", "Field (mathematics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trip.2026.102158" }, { "id": "https://openalex.org/W4385482058", "doi": "10.1109/cscloud-edgecom58631.2023.00013", "title": "A Short Review of Truck and Drone Collaborative Delivery Problem", "year": 2023, "publication_date": "2023-07-01", "type": "conference-paper", "language": "en", "authors": [ "Yena Zhu", "Min Liu", "D. Jin" ], "first_author": "Yena Zhu", "venue": null, "publisher": null, "abstract": "With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented.", "keywords": [ "Drone", "Truck", "Computer science", "Aeronautics", "Engineering", "Aerospace engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/cscloud-edgecom58631.2023.00013" }, { "id": "https://openalex.org/W4400277005", "doi": "10.1109/wcnc57260.2024.10571229", "title": "UAV-Assisted Routing Algorithm for Truck Parcel Delivery", "year": 2024, "publication_date": "2024-04-21", "type": "conference-paper", "language": "en", "authors": [ "Chung-Yi Tsai", "Jang‐Ping Sheu", "Pi-Yu Yi" ], "first_author": "Chung-Yi Tsai", "venue": null, "publisher": null, "abstract": "This paper studies how UAVs can efficiently deliver parcels with trucks in rural areas. Due to the limited payload of the UAVs, they can only serve the parcels under the pay-load limitation, and the other parcels are delivered by truck. Furthermore, the battery capacities of the UAVs are limited, and they cannot deliver all parcels at once. The UAV will take off from the truck to deliver parcels and then meet with the truck to load new parcels and change the new battery for the next trip. For UAV safety considerations, the truck must wait at the rendezvous node for the UAV to land. Our paper aims to deliver all the parcels as quickly as possible. Since the problem is NP-hard, we proposed a three-stage heuristic algorithm to solve this problem. The simulation results show that our proposed algorithm outperforms the candidate algorithms in minimizing task completion time.", "keywords": [ "Truck", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Algorithm", "Real-time computing", "Simulation", "Computer network", "Engineering", "Automotive engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/wcnc57260.2024.10571229" }, { "id": "https://openalex.org/W4288739946", "doi": "10.1016/j.rtbm.2022.100865", "title": "Experts' opinions about lasting innovative technologies in City Logistics", "year": 2022, "publication_date": "2022-07-30", "type": "article", "language": "en", "authors": [ "Giovanni Zenezini", "Giulio Mangano", "Alberto De Marco" ], "first_author": "Giovanni Zenezini", "venue": "Research in Transportation Business & Management", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "City logistics", "Maturity (psychological)", "Delphi method", "Relevance (law)", "Drone", "Process (computing)", "Business", "Emerging technologies", "Field (mathematics)", "Coronavirus disease 2019 (COVID-19)", "Set (abstract data type)", "Value (mathematics)" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/9743804" }, { "id": "https://openalex.org/W4417057004", "doi": "10.1109/ojits.2025.3638659", "title": "Multi-Modal Last-Mile Delivery Optimization With Smart Micro-Hubs: A Branch-and-Price Approach With Valid Inequalities", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Kassem Danach", "Ali Raad", "Abbass Nasser" ], "first_author": "Kassem Danach", "venue": "IEEE Open Journal of Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The rapid growth of e-commerce and urban logistics has intensified the challenges associated with last-mile delivery, necessitating the integration of autonomous delivery vehicles (ADVs), drones, and smart micro-hubs for cost-effective and sustainable operations. This study introduces a novel Multi-Depot Capacitated Vehicle Routing Problem with Time Windows and Multi-Modal Transportation (MD-CVRPTW-MM), where deliveries are dynamically assigned to either ADVs or drones while optimizing hub locations and vehicle routing. The objective is to minimize total operational costs, including transportation, hub activation, and delivery mode selection, while ensuring service-level constraints such as time windows and capacity limits. To efficiently solve this complex combinatorial problem, we propose a mathematical formulation enhanced with valid inequalities for computational efficiency. Furthermore, a Branch-and-Price algorithm is developed, where column generation is employed to iteratively refine delivery routes while enforcing depot assignments and mode selection constraints. The solution approach integrates dual stabilization techniques and path-based heuristics to accelerate convergence. Experimental evaluations on benchmark datasets and real-world logistics scenarios demonstrate the effectiveness of the proposed method in reducing last-mile delivery costs, optimizing fleet utilization, and enhancing service reliability. The results highlight the significance of multi-modal last-mile delivery optimization in improving urban logistics and sustainability. This research contributes a new mathematical model, efficient optimization techniques, and practical insights for intelligent last-mile delivery planning in smart cities.", "keywords": [ "Vehicle routing problem", "Heuristics", "Benchmark (surveying)", "Column generation", "Optimization problem", "Routing (electronic design automation)", "Dual (grammatical number)", "Service (business)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ojits.2025.3638659" }, { "id": "https://openalex.org/W4385062483", "doi": "10.2139/ssrn.4516190", "title": "The Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Roberto Montemanni", "Mauro Dell'Amico", "Andrea Corsini" ], "first_author": "Roberto Montemanni", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Travelling salesman problem", "Vehicle routing problem", "Computer science", "Scheduling (production processes)", "Integer programming", "Constraint programming", "Routing (electronic design automation)", "Operations research", "Mathematical optimization", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4516190" }, { "id": "https://openalex.org/W4395960245", "doi": "10.4018/979-8-3693-1447-0.ch002", "title": "Comparative Analysis of Sustainable Urban Last-Mile Delivery Modes", "year": 2024, "publication_date": "2024-04-05", "type": "book-chapter", "language": "en", "authors": [ "Hasibul Islam Lingkon", "Syed Imran Ali" ], "first_author": "Hasibul Islam Lingkon", "venue": "Advances in logistics, operations, and management science book series", "publisher": "Routledge", "abstract": "Sustainability has become more critical in logistics and transportation, especially in last-mile delivery (LMD). Electronic vehicles (EVs), electronic(E)-bicycles, cargo bikes, and drones are alternative solutions to the existing internal combustion engine (ICE) vehicles that enable low environmental and economic impact. However, they may require a higher investment and have constraints like battery autonomy, small loading capacity, and higher electricity production to meet the demand. This chapter aims to explore the environmental and economic impact of electronic vehicles, as well as the operational challenges and barriers to achieving sustainability. Hence, a thorough literature review is necessary to understand the scholarly work in the area. The study employs the systematic literature review (SLR) to extract data from the Scopus database from 2019-2023. Thirty-two articles were reviewed. The descriptive and thematic analyses have identified the environmental, economic, and social components related to the distribution methods, delivery methods, and perceived sustainability.", "keywords": [ "Mile", "Last mile (transportation)", "Geography", "Geodesy" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3693-1447-0.ch002" }, { "id": "https://openalex.org/W4413178967", "doi": "10.1109/netcrypt65877.2025.11102782", "title": "Autonomous Delivery Drones Powered by AI for Urban Logistics Using a Pathfinding and Simultaneous Localization and Mapping", "year": 2025, "publication_date": "2025-05-29", "type": "conference-paper", "language": "en", "authors": [ "I. Sudha", "T. Vinoth", "Prashant Johri", "Omon Matqurbonov", "Shokir Ataev", "J. G. Rana" ], "first_author": "I. Sudha", "venue": null, "publisher": null, "abstract": "Modern urban logistics is experiencing significant development, and solving the key questions like traffic problems, delivery time, and emissions became crucial. Self-driving drones that employ AI technology can become a perfect solution for solving the problems of efficient city logistics. The principal area of interest in this research is to employ AI-based path planning algorithms and incorporate SLAM techniques into delivery drones to optimize their performance within UAS's environments. Using path planning techniques propelled by Artificial Intelligence, drones can modify their operating pathways depending on real challenges that may include physical obstacles, weather disturbances or even traffic jams in recognition of an optimum safe path of operation. While, SLAM also helps the drones to provide self-localization and mapping in the urban environments without relying on GPS signal and increase the level of accuracy and operation freedom. This paper focuses on the combined use of path planning algorithms and SLAM in autonomous delivery drones and work towards understanding the capability that may reshape the future of logistics. Also included in this work is the strategy of bringing the focus to the question of how this system can be scaled while looking at best practices in terms of its operation, for instance how efficient the use of energy is, how much the system costs and how existing legislation will impact the system. These findings prove the effectiveness of enabling autonomous drones for last-mile delivery and add to the concept of urban fulfillment and smart city infrastructure.", "keywords": [ "Pathfinding", "Drone", "Computer science", "Simultaneous localization and mapping", "Human–computer interaction", "Artificial intelligence", "Computer vision", "Mobile robot", "Robot", "Shortest path problem" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/netcrypt65877.2025.11102782" }, { "id": "https://openalex.org/W4366226280", "doi": "10.30880/paat.2022.02.01.006", "title": "The Structural Design and Aerodynamics Analysis for a Hybrid VTOL Fixed-Wing Drone for Parcel Delivery Applications", "year": 2022, "publication_date": "2022-08-28", "type": "article", "language": "en", "authors": [ "Universiti Tun Hussein Onn Malaysia (UTHM)", "Seyhrvin Kumar Chakraverty", "Mohd Fadhli Zulkafli", "Universiti Tun Hussein Onn Malaysia (UTHM)", "Mohammad Fahmi Pairan", "Universiti Tun Hussein Onn Malaysia (UTHM)", "Mohamad Nadjmi Mohd Shakir See", "Universiti Tun Hussein Onn Malaysia (UTHM)", "Izzat Khair Mahmud", "Universiti Tun Hussein Onn Malaysia (UTHM)" ], "first_author": "Universiti Tun Hussein Onn Malaysia (UTHM)", "venue": "Progress in Aerospace and Aviation Technology", "publisher": "Penerbit UTHM", "abstract": "A number of companies are experimenting with multicopter drones to deliver items to clients. Because electric planes have a restricted range, their flight range is usually limited. However, if propelled by gasoline, electric multicopter drones can only travel a short distance because of high power consumption and noise difficulties. Despite their lower aerodynamic efficiency than fixed-wing aircraft, multicopters' ability to perform vertical take-off and landing (VTOL) makes them an ideal delivery vehicles. A hybrid fixed-wing VTOL system with a tilting system that alters the flight mode could be an upgradeto the current design of hybrid fixed wing VTOL. The goal is to effectively manufacture a fixed-wing drone with an appropriate structural design and a functional tilting mechanism that can take off vertically. SolidWorks and SIMNET aero were the two approaches used throughout the design software. The drone's aerodynamic qualities were investigated in order to better understand its behaviour, such as range of flight at a given altitude, stall speed, and maximum lift created, in order to determine the maximum parcel weight the drone can carry. The drone was built using SolidWorks 3D-Solid modelling and SIMNET aero design software. The tilting mechanism is 3D printed with Polylactic Acid (PLA) material since it is both light and strong. The structural strength can also be altered by changing the in-fill. After the drone was manufactured,numerous test flights were made to examine the drone's actual behaviour and enhance its functionality. The drone's theoretical stall speed was determined to be 12.74 m/s with a maximum payload of 500g and 11.43 m/s at no load. The maximum glide distance was estimated to be 1.2 kilometres. The drone yaws to the left during test flights at a rate of 63.43 degrees per second and 4.879 degrees per second at 50% throttle. It slanted to the front, nose down, with a weight of 516 g while support was given at the tip of the left wing. The pitch rate was 2.5 degrees per second without a payload and 3.12 degrees per second with the 516g payload. With further design and calibration advancements, experimental findings that are comparable to theoretical outcomes might be possible.", "keywords": [ "Drone", "Stall (fluid mechanics)", "Aerodynamics", "Aerospace engineering", "Wing", "Wing loading", "Airplane", "Computer science", "Engineering", "Automotive engineering", "Simulation", "Angle of attack" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://publisher.uthm.edu.my/ojs/index.php/paat/article/download/10983/5177/52977" }, { "id": "https://openalex.org/W4399704353", "doi": "10.1007/s40747-024-01472-6", "title": "An iterative two-phase optimization method for heterogeneous multi-drone routing problem considering differentiated demands", "year": 2024, "publication_date": "2024-06-15", "type": "article", "language": "en", "authors": [ "Huan Liu", "Guohua Wu", "Yufei Yuan", "Dezhi Wang", "Long Gen Zheng", "Wei Zhou" ], "first_author": "Huan Liu", "venue": "Complex & Intelligent Systems", "publisher": "Springer Science+Business Media", "abstract": "Abstract Owing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of “last-mile” delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a pioneering heterogeneous multi-drone delivery system. In this system, the mother drone handles the delivery of large and heavy parcels, releasing small drones to manage the delivery of smaller and lighter parcels. To address the complexities of this multi-drone delivery system, we introduce a divide-and-conquer framework consisting of two integral phases. The first phase, the task allocation phase, generates multiple task allocation schemes, while the second phase, the single-drone route planning phase, produces high-quality routes for each individual drone. Two phases are performed in an iterative manner until the predefined stopping criteria are satisfied. In the task allocation phase, we propose a simulated annealing algorithm (SA) to facilitate task allocation among multiple drones, utilizing transfer and recombination operators to generate high-quality solutions. After obtaining the task allocation scheme, a satisfactory route of a mother drone is generated by a variable neighborhood descent algorithm (VND). A desirable route for each single small drone is produced by dynamic programming (DP).Extensive experiments are conducted, demonstrating the outstanding optimization and time efficiency of the proposed two-phase optimization method by the fact that it obtains within a 4.89% gap from the optimal solution generated by CPLEX in 15.48 s for instance up to 125 nodes.", "keywords": [ "Drone", "Computer science", "Simulated annealing", "Task (project management)", "Mathematical optimization", "Payload (computing)", "Routing (electronic design automation)", "Optimization problem", "Distributed computing", "Network packet", "Computer network", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s40747-024-01472-6" }, { "id": "https://openalex.org/W4376874676", "doi": "10.3390/drones7050320", "title": "An Integer Programming Based Approach to Delivery Drone Routing under Load-Dependent Flight Speed", "year": 2023, "publication_date": "2023-05-16", "type": "article", "language": "en", "authors": [ "Mao Nishira", "Satoshi Ito", "Hiroki Nishikawa", "Xiangbo Kong", "Hiroyuki Tomiyama" ], "first_author": "Mao Nishira", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Delivery drones have been attracting attention as a means of solving recent logistics issues, and many companies are focusing on their practical applications. Many research studies on delivery drones have been active for several decades. Among them, extended routing problems for drones have been proposed based on the Traveling Salesman Problem (TSP), which is used, for example, in truck vehicle routing problems. In parcel delivery by drones, additional constraints such as battery capacity, payload, and weather conditions need to be considered. This study addresses the routing problem for delivery drones. Most existing studies assume that the drone’s flight speed is constant regardless of the load. On the other hand, some studies assume that the flight speed varies with the load. This routing problem is called the Flight Speed-Aware Traveling Salesman Problem (FSTSP). The complexity of the drone flight speed function in this problem makes it difficult to solve the routing problem using general-purpose mathematical optimization solvers. In this study, the routing problem is reduced to an integer programming problem by using linear and quadratic approximations of the flight speed function. This enables us to solve the problem using general-purpose mathematical optimization solvers. In experiments, we compared the existing and proposed methods in terms of solving time and total flight time. The experimental results show that the proposed method with multiple threads has a shorter solving time than the state-of-the-art method when the number of customers is 17 or more. In terms of total flight time, the proposed methods deteriorate by an average of 0.4% for integer quadratic programming and an average of 1.9% for integer cubic programming compared to state-of-the-art methods. These experimental results show that the quadratic and cubic approximations of the problem have almost no degradation of the solution.", "keywords": [ "Drone", "Travelling salesman problem", "Payload (computing)", "Routing (electronic design automation)", "Computer science", "Mathematical optimization", "Vehicle routing problem", "Integer programming", "Network packet", "Mathematics", "Computer network" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/7/5/320/pdf?version=1684238185" }, { "id": "https://openalex.org/W4220763957", "doi": "10.1108/aeat-07-2020-0138", "title": "Drone-based delivery: a concurrent heuristic approach using a genetic algorithm", "year": 2022, "publication_date": "2022-04-01", "type": "article", "language": "en", "authors": [ "Khin Thida San", "Yoon‐Seok Chang" ], "first_author": "Khin Thida San", "venue": "Aircraft Engineering and Aerospace Technology", "publisher": "Emerald Publishing Limited", "abstract": "Purpose The purpose of this study is to solve NP-Hard drone routing problem for the last-mile distribution. This is suitable for the multi-drones parcel delivery for the various items from a warehouse to many locations. Design/methodology/approach This study conducts as a mission assignment of the single location per flight with the constraint satisfactions such as various payloads in weight, drone speeds, flight times and coverage distances. A genetic algorithm is modified as the concurrent heuristics approach (GCH), which has the knapsack problem dealing initialization, gene elitism (crossover) and gene replacement (mutation). Those proposed operators can reduce the execution time consuming and enhance the routing assignment of multiple drones. The evaluation value of the routing assignment can be calculated from the chromosome/individual representation by applying the proposed concurrent fitness. Findings This study optimizes the total traveling time to accomplish the distribution. GCH is flexible and can provide a result according to the first-come-first-served, demanded weight or distance priority. Originality/value GCH is an alternative option, which differs from conventional vehicle routing researches. Such researches (traveling time optimization) attempt to minimize the total traveling time, distance or the number of vehicles by assuming all vehicles have the same traveling speed; therefore, a specific vehicle assignment to a location is neglected. Moreover, the main drawback is those concepts can lead the repeated selection of best quality vehicles concerning the speed without considering the vehicle fleet size and coverage distance while this study defines the various speeds for the vehicles. Unlike those, the concurrent concept ensures a faster delivery accomplishment by sharing the work load with all participant vehicles concerning to their different capabilities. If the concurrent assignment is applied to the drone delivery effectively, the entire delivery can be accomplished relatively faster than the traveling time optimization.", "keywords": [ "Knapsack problem", "Vehicle routing problem", "Drone", "Heuristic", "Computer science", "Mathematical optimization", "Genetic algorithm", "Routing (electronic design automation)", "Crossover", "Heuristics", "Initialization", "Algorithm" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1108/aeat-07-2020-0138" }, { "id": "https://openalex.org/W4386410340", "doi": "10.3390/vehicles5030058", "title": "Planning Integrated Unmanned Aerial Vehicle and Conventional Vehicle Delivery Operations under Restricted Airspace: A Mixed Nested Genetic Algorithm and Geographic Information System-Assisted Optimization Approach", "year": 2023, "publication_date": "2023-09-02", "type": "article", "language": "en", "authors": [ "Κωνσταντίνος Κουρέτας", "Konstantinos L. Kepaptsoglou" ], "first_author": "Κωνσταντίνος Κουρέτας", "venue": "Vehicles", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Using Unmanned Aerial Vehicles (UAVs), commonly referred to as “drones”, as a supplementary mode for last-mile deliveries has been a research focus for some years now. Motivation lies in the reduced dependency on Conventional Vehicles (CVs) and fossil fuels and in serving remote areas and underprivileged populations. We are building a flexible, modular framework for integrated CV-UAV parcel delivery operations planning that is responsive to infrastructure and demand and offers an open and practical tool for future adaptations. The entire model and solution methodology are practical tools for decision making and strategic planning, with novelties such as the variable Launch Site types for Launch and Recovery Operations (LAROs), the tailored Assignment and Routing Optimization nested GA, the consideration of airspace restrictions of any shape and size, the inclusion of GIS tools in the process, the modularity of the platform, and most importantly, the inclusion of all the above in a single, comprehensive, and holistic approach. Because of the need for safe UAV deployment sites and the high presence of restricted airspace zones in urban environments, the intended field of application is assumed to be the delivery of small packages in rural and under-connected areas, the execution of inter-city deliveries, and the expansion of a city’s original service range. A single CV is equipped onboard with UAVs, while special locations, such as Remote Depots (RDs) with UAVs and Virtual Hubs (VHs) for UAV deployment facilitation, are introduced. The framework considers the presence of Restricted Zones (RZs) for UAV flights. Part of the methodology is implemented in a GIS environment, taking advantage of modern tools for spatial analysis and optimal path planning. We have designed a tailored nested GA method for solving the occurring mode assignment and vehicle routing optimization problems and have implemented our workflow on a devised case study with benchmark characteristics. Our model responds well to unfavorable network types and demand locations, while the presence of RZs notably affects the expected solution and should be considered in the decision-making process.", "keywords": [ "Software deployment", "Computer science", "Modular design", "Process (computing)", "Vehicle routing problem", "Drone", "Systems engineering", "Operations research", "Engineering", "Routing (electronic design automation)", "Computer network", "Software engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2624-8921/5/3/58/pdf?version=1693880370" }, { "id": "https://openalex.org/W3005982104", "doi": "10.1109/icus48101.2019.8996012", "title": "An Airport Airspace Flow Control Method for Drones", "year": 2019, "publication_date": "2019-10-01", "type": "conference-paper", "language": "en", "authors": [ "Guoqi Zeng", "Kai Cui", "Quan Quan", "Wei Lin", "Yuehan Lei" ], "first_author": "Guoqi Zeng", "venue": "2019 IEEE International Conference on Unmanned Systems (ICUS)", "publisher": null, "abstract": "This paper proposes an airport airspace flow control method based on graph theory for a kind of airport model designed for urban logistics drones. According to the flight status of drones in the airport, arrival and departure times are controlled, routes inside the airport are planned to ensure the safety of drones, and realized the simultaneous take-off and landing of multiple drones. The simulation results show that the flow control method can ensure safe and orderly flight of UAVs in the airport airspace. The more the number of tarmac, but the smaller the operating capacity, and therefore should be appropriately designed to reduce the number of airport tarmacs.", "keywords": [ "Drone", "Aeronautics", "Computer science", "Control (management)", "Air traffic control", "Transport engineering", "Engineering", "Aerospace engineering", "Artificial intelligence" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icus48101.2019.8996012" }, { "id": "https://openalex.org/W4412987370", "doi": "10.1109/dcoss-iot65416.2025.00101", "title": "Integrated Planning of Infrastructure and Drone Delivery Operations with Multiple Cycles", "year": 2025, "publication_date": "2025-06-09", "type": "conference-paper", "language": "en", "authors": [ "Carlos A. F. Teodoro", "Cristiano M. Silva", "Fernanda Sumika Hojo de Souza" ], "first_author": "Carlos A. F. Teodoro", "venue": null, "publisher": null, "abstract": "Using aerial deliveries by drones has been considered a promising option, given the growing demand for last-mile deliveries or emergency scenarios. This work proposes an optimization model for infrastructure planning and drone deliveries in multiple operating cycles. The optimization problem under study aims to determine the location of facilities and the allocation of demand from customers served using drones with payload and range capabilities over a set of periods. Each period represents an operating cycle of the drones, which can have their batteries recharged between cycles. The objective is to minimize the energy consumption of the drones while meeting the maximum coverage of the customers' demand, subject to operational feasibility in an urban scenario. The results demonstrated that the multi-period model could provide up to 29% wider demand coverage than the single-period model. The analysis of the number of customers being served showed that an additional up to 36% benefited from the introduction of multiple periods, indicating that lower-priority customers are more likely to be served. In cases where both models achieve full customer coverage, the multi-period model provided lower energy consumption of drones due to its planning flexibility. The contributions of this work indicate that it is possible to propose optimized logistics solutions that are more appropriate for the smart city scenario.", "keywords": [ "Drone", "Computer science", "Systems engineering", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dcoss-iot65416.2025.00101" }, { "id": "https://openalex.org/W4411357755", "doi": "10.20944/preprints202506.1002.v1", "title": "A Bi-Layer Collaborative Planning Framework for Multi-UAV Delivery Tasks in Multi-Depot Urban Logistics", "year": 2025, "publication_date": "2025-06-12", "type": "preprint", "language": "en", "authors": [ "Junfu Wen", "Fei Wang", "Yebo Su" ], "first_author": "Junfu Wen", "venue": "Preprints.org", "publisher": null, "abstract": "To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple Unmanned Aerial Vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic cost, delivery delay, and the number of UAVs deployed. An Enhanced Non-dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved Particle Swarm Optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic cost and 71.4% in UAV deployment quantity, while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average Inverted Generational Distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems.", "keywords": [ "Depot", "Layer (electronics)", "Computer science", "Process management", "Transport engineering", "Business", "Engineering", "Geography", "Materials science", "Nanotechnology" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.20944/preprints202506.1002.v1" }, { "id": "https://openalex.org/W3186862030", "doi": "10.2514/6.2021-2577", "title": "Flow Profiling in a WindShaper for Testing Free-Flying Drones in Adverse Winds", "year": 2021, "publication_date": "2021-07-28", "type": "conference-paper", "language": "en", "authors": [ "Flavio Noca", "Thierry Bujard", "Geshanth Visvaratnam", "Guillaume Catry", "Nicolas Bosson" ], "first_author": "Flavio Noca", "venue": "AIAA AVIATION 2021 FORUM", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2021-2577.vid Drones will soon fill our aerial ecosystem in the field of imaging/cartography, parcel delivery, and passenger transport, and will need to operate around the clock in arbitrary atmospheric conditions, especially in adverse weather conditions during emergency situations. Drones are much smaller than conventional aircraft and are thus more sensitive to weather conditions. In order to test drones in various and controllable atmospheric conditions, a real weather simulator was developed. The wind and weather facility (windshaper) consists of an array of a large number of fans that may be arranged in various patterns on demand. It subjects drones to winds of variable intensity and direction as well as various weather conditions (such as rain, snow, hail, fog etc.) that reflect real world situations. These tests can rate drones according to their capacity in maintaining a proper flight attitude and tackling flight perturbations in an urban, countryside, or high altitude environment. For this work, various time-independent flow morphologies are generated with a windshaper and measurements are performed at a certain distance in order to see whether the output at the measurement station matches the input at the surface plane of the windshaper.", "keywords": [ "Drone", "Meteorology", "Adverse weather", "Environmental science", "Snow", "Wind speed", "Aeronautics", "Computer science", "Simulation", "Aerospace engineering", "Engineering", "Geography" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2021-2577" }, { "id": "https://openalex.org/W4400238202", "doi": "10.3390/app14135712", "title": "Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction", "year": 2024, "publication_date": "2024-06-29", "type": "article", "language": "en", "authors": [ "Jian Zhang", "Zongxiao Li", "Xinyue Luo", "Yifei Zhao", "Fei Lu" ], "first_author": "Jian Zhang", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs.", "keywords": [ "Separation (statistics)", "Track (disk drive)", "Computer science", "Position (finance)", "Machine learning" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/app14135712" }, { "id": "https://openalex.org/W4413212007", "doi": "10.1109/access.2025.3597781", "title": "Genetic Algorithms for a Scheduling Problem of Parcel Delivery and Pickup by Drones", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Yohei Hazama", "Hitoshi Iima", "Yoshiyuki Karuno" ], "first_author": "Yohei Hazama", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Recent studies have been conducted actively on scheduling in a parcel delivery system using a truck and drones to deliver parcels more effectively. In logistics, it is necessary to provide not only delivery service but also pickup service, and drones can be used for the pickup service. This paper extends our previous delivery system to a new one handling both delivery and pickup and then formulates a scheduling problem for the new system. A challenge in this problem is to consider how to appropriately select one of three options: whether a drone only delivers a parcel, only picks up, or both delivers and picks up. This paper proposes two methods based on a genetic algorithm (GA) introducing procedures to select an appropriate option for solving the problem. The first method uses a maximum weight matching procedure before executing a GA, and the procedure is applied to a bipartite graph of customers whose parcels are delivered and customers whose parcels are picked up. The second uses a greedy pairing procedure to reduce drone flight time in evaluating each individual of a GA. Experimental results show that the two proposed methods can successfully find good feasible solutions. The second method can often find better solutions but consumes longer computation time than the first.", "keywords": [ "Pickup", "Drone", "Computer science", "Genetic algorithm", "Scheduling (production processes)", "Job shop scheduling", "Algorithm", "Mathematical optimization", "Artificial intelligence", "Embedded system", "Mathematics", "Machine learning" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2025.3597781" }, { "id": "https://openalex.org/W7117325606", "doi": "10.61782/fa.2025.0609", "title": "Noise impact of drone parcel delivery scenario over Madrid", "year": 2025, "publication_date": "2025-12-25", "type": "conference-paper", "language": "en", "authors": [ "Ingrid Legriffon", "Elise Ruaud", "Jovana G. Kuljanin", "Emir Ganić" ], "first_author": "Ingrid Legriffon", "venue": null, "publisher": null, "abstract": "To effectively integrate drones into densely populated urban infrastructures, it is crucial to assess their impact on the population's quality of life and mitigate any adverse effects.As noise pollution from drone overflights is a top concern, enhancing our numerical capabilities for predicting VTOL aircraft noise in urban environments is essential.To achieve this goal, we have developed an integrated toolchain comprising three key components: Flight Mechanics, to accurately model aircraft behavior; Noise Emission at the Source: to quantify the noise generated by the drones and finally Urban Environment Noise Propagation using the open-source tool NoiseModelling.This comprehensive toolchain enables the creation of detailed noise maps for specific drone scenario definitions.This paper presents an application of our toolchain in a parcel delivery scenario, featuring DJI Matrice 600 drones operating over a 10 km² sector of Madrid in Spain within a day period, incorporating five distribution centers with varying hourly capacities.The results include a range of informative noise indicators, taking into account noise level, number of flyovers and background noise levels.", "keywords": [ "Drone", "Noise (video)", "Aircraft noise", "Noise pollution" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dael.euracoustics.org/confs/fa2025/data/articles/000609.pdf" }, { "id": "https://openalex.org/W4295837866", "doi": "10.1007/978-3-031-16579-5_18", "title": "The Dynamic Drone Scheduling Delivery Problem", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Giovanni Campuzano", "Eduardo Lalla‐Ruiz", "Martijn Mes" ], "first_author": "Giovanni Campuzano", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Markov decision process", "Reinforcement learning", "Computer science", "Operations research", "Scheduling (production processes)", "Dynamic programming", "Dynamic priority scheduling", "Heuristic", "Mathematical optimization", "Markov process", "Operations management" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ris.utwente.nl/ws/files/286497207/978_3_031_16579_5_18.pdf" }, { "id": "https://openalex.org/W3045669725", "doi": "10.1007/978-3-658-30166-8_19", "title": "Last Mile Logistics Reloaded – Automation and Personalization in Electronic Commerce Fulfillment", "year": 2020, "publication_date": "2020-01-01", "type": "book-chapter", "language": "en", "authors": [ "Maria Madlberger" ], "first_author": "Maria Madlberger", "venue": "Forum Dienstleistungsmanagement", "publisher": null, "abstract": null, "keywords": [ "Automation", "Personalization", "Mile", "Last mile (transportation)", "Business", "Engineering", "Computer science", "Transport engineering", "World Wide Web", "Geography", "Mechanical engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-658-30166-8_19" }, { "id": "https://openalex.org/W4415243154", "doi": "10.63544/ijss.v4i4.183", "title": "Logistics Entrepreneurship in the Digital Era: Opportunities, Challenges, and Growth Models in Smart Warehousing and Last-Mile Delivery", "year": 2025, "publication_date": "2025-10-14", "type": "article", "language": "en", "authors": [ "Asad A. Ali", "Sikandar Nawab Khattak", "Akbar Ali Rabbani", "Maisam Hussian", "Abdul Mateen" ], "first_author": "Asad A. Ali", "venue": "Inverge Journal of Social Sciences", "publisher": null, "abstract": "The study explored the evolving dynamics of logistics entrepreneurship in the digital era, emphasizing how deep technological integration has fundamentally reshaped smart warehousing and last-mile delivery systems. The primary aim was to examine how digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and automation, contributed to operational efficiency, innovation, and business growth among logistics enterprises. Using a mixed-method approach combining quantitative surveys and qualitative interviews, data were collected from a diverse sample of logistics firms and technology-driven entrepreneurs across multiple regions. The results revealed that digital transformation significantly enhanced productivity, supply chain transparency, and customer satisfaction, with predictive analytics enabling more proactive decision-making. Moreover, innovation capability and technology adoption served as key mediating factors linking robust digital infrastructure to superior entrepreneurial performance. However, significant challenges such as high implementation costs, data privacy risks, and limited technical expertise constrained widespread adoption, particularly among small and medium enterprises. The study concluded that successful logistics entrepreneurship in the digital era depended on a balanced strategy of targeted technological investment, continuous human capital development, and a strong sustainability orientation. Recommendations included promoting public-private partnerships for digital literacy, establishing robust cybersecurity frameworks, and incentivizing eco-efficient logistics practices. 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The integration of AI and IoT in optimizing warehouse and delivery systems. International Journal of Production Economics, 267, 109022. https://doi.org/10.1016/j.ijpe.2023.109022 Malik, S., Khonji, M., Elbassioni, K., & Dias, J. (2025). Collaborative last-mile delivery: A multi-platform vehicle routing problem with en-route charging. arXiv. https://doi.org/10.48550/arXiv.2505.23584 Mandal, M. P., & Archetti, C. (2023). A decomposition approach to last mile delivery using public transportation systems. arXiv. https://doi.org/10.48550/arXiv.2306.04219 Moradi, N., Mirzavand Boroujeni, N., Aftabi, N., & Aslani, A. (2024). Two-echelon electric vehicle routing problem in parcel delivery: A literature review. arXiv. Müller, J. M., Buliga, O., & Voigt, K. I. (2023). The role of digital transformation in logistics entrepreneurship: Implications for performance and competitiveness. Industrial Marketing Management, 112, 125–138. https://doi.org/10.1016/j.indmarman.2023.01.009 Pereira, C. R., Da Silva, A. L., & Maffei, M. J. (2024). Predictive analytics and automation for smart logistics performance: Evidence from global logistics firms. Journal of Business Research, 172, 114093. https://doi.org/10.1016/j.jbusres.2023.114093 Rafiq-uz-Zaman, M. (2025). Beyond the blackboards: Building a micro-edtech economy through teacher-led innovation in low-income schools. Journal of Business Insight and Innovation, 4(1), 46–52. https://doi.org/10.5281/zenodo.16875721 Rafiq-uz-Zaman, M. (2025). Bridging the skills divide: A comparative study of skill-base", "keywords": [ "Digital transformation", "Agile software development", "Entrepreneurship", "Supply chain", "Digital strategy", "Ambidexterity", "Big data", "Business model", "Sample (material)", "Dynamic capabilities" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://invergejournals.com/index.php/ijss/article/download/183/338" }, { "id": "https://openalex.org/W4412446950", "doi": "10.1016/j.trc.2025.105255", "title": "Benders decomposition for traveling salesman problem with drone: A space-time-state network perspective", "year": 2025, "publication_date": "2025-07-15", "type": "article", "language": "en", "authors": [ "Chenhao Zhang", "Yaoxin Wu", "Tao Feng", "Yibei Zhang", "Maocan Song", "Lin Cheng" ], "first_author": "Chenhao Zhang", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Travelling salesman problem", "Drone", "Perspective (graphical)", "Decomposition", "2-opt", "Traveling purchaser problem", "State space", "State (computer science)", "Computer science", "Mathematical optimization", "Operations research", "Flow network" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://research.tue.nl/en/publications/482fdb14-e5f6-4c0b-b288-72e388abcd0a" }, { "id": "https://openalex.org/W4411711965", "doi": "10.1016/j.trpro.2025.06.003", "title": "Evaluating barriers to drone application in rural areas", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Snežana Radoman Tadić", "Ljubica Radovanović", "Mladen Dragan Krstić", "Olja Čokorilo", "Miloš Veljović" ], "first_author": "Snežana Radoman Tadić", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "New technologies, such as drone delivery (DD), have the potential to transform rural goods delivery and support the achievement of sustainable development goals, particularly regarding health and well-being. While previous research highlights the advantages (fast delivery, high responsiveness, cost-effectiveness, etc.) and applicability of drones in last-mile delivery, especially in e-commerce and urban areas, their applicability in rural areas (RA) remains underexplored. This study analyzes the barriers to adopting DD of goods in RA using a hybrid Grey Delphi-BWM model. Research results identify lack of aviation regulations, drone owner liability, identity theft, unauthorized data usage, cultural perceptions, unauthorized photography and filming without consent, and insurance obligations as major obstacles, providing insights to decision-makers and policymakers to support successful adoption of DD in RA. Considering the identified barriers, this research suggests the need for regulatory adaptation and policy development to facilitate the implementation of DD in rural environments.", "keywords": [ "Drone", "Transport engineering", "Computer science", "Environmental planning", "Business", "Engineering", "Computer security", "Geography", "Biology" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2025.06.003" }, { "id": "https://openalex.org/W4306362530", "doi": "10.3390/app122010427", "title": "A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies", "year": 2022, "publication_date": "2022-10-16", "type": "article", "language": "en", "authors": [ "Daiki Hachiya", "Erick Mas", "Shunichi Koshimura" ], "first_author": "Daiki Hachiya", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In large-scale disasters, such as earthquakes and tsunamis, quick and sufficient transportation of emergency relief supplies is required. Logistics activities conducted to quickly provide appropriate aid supplies (relief goods) to people affected by disasters are known as humanitarian logistics (HL), and play an important role in terms of saving the lives of those affected. In the previous last-mile distribution of HL, supplies are transported by trucks and helicopters, but these transport methods are sometimes not feasible. Therefore, the use of unmanned aerial vehicles (UAVs) to transport supplies is attracting attention due to their convenience regardless of the disaster conditions. However, existing transportation planning that utilizes UAVs may not meet some of the requirements for post-disaster transport of supplies. Equitable distribution of supplies among affected shelters is particularly important in a crisis situation, but it has not been a major consideration in the logistics of UAVs in the existing study. Therefore, this study proposes transportation planning by introducing three crucial performance metrics: (1) the rapidity of supplies, (2) the urgency of supplies, and (3) the equity of supply amounts. We formulated the routing problem of UAVs as the multi-objective, multi-trip, multi-item, and multi-UAV problem, and optimize the problem with Q-learning (QL), one of the reinforcement learning methods. We performed reinforcement learning for multiple cases with different rewards and quantitatively evaluated the transportation of each countermeasure by comparing them. The results suggest that the model improved the stability of the supply of emergency relief supplies to all evacuation centers when compared to other models.", "keywords": [ "Truck", "Transport engineering", "Countermeasure", "Reinforcement learning", "Emergency management", "Computer science", "Business", "Operations research", "Engineering" ], "cited_by_count": 29, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/12/20/10427/pdf?version=1666353228" }, { "id": "https://openalex.org/W4225156081", "doi": "10.1149/10701.0341ecst", "title": "A Comprehensive Review on Involvement of Drones in Covid-19 Pandemics", "year": 2022, "publication_date": "2022-04-24", "type": "conference-paper", "language": "en", "authors": [ "Ruby Mishra" ], "first_author": "Ruby Mishra", "venue": "ECS Transactions", "publisher": "Institute of Physics", "abstract": "The core idea of the paper is to create a multi-purpose drone/unmanned aerial vehicle which are used to counter challenges faced by COVID-19. Our work is structured to give insights into our theory method with a constructive form of qualitative research. This work's main contribution lies in the comparative study of many research articles based on the use of drone in different fields specially in sanitization of a large area in a stipulated period, thermal screening of people and storing their temperature recorded data in cloud, AI implementation in order to detect abnormal pattern of coughing, heart rate, etc., and delivery of medicines and necessary essential to quarantined people and migrants this will cause a large-scale implementation of drone delivery may drastically change the business, substituting hundreds of workers, fossil-fuel-powered delivery vehicles, traffic congestion, and centralized distribution centers of drone, which performs point A to point B parcel delivery. The challenges faced by drone and its operators during active mission are studied. This study will help others who are new to drones in the COVID and medical field.", "keywords": [ "Drone", "Coronavirus disease 2019 (COVID-19)", "Constructive", "Pandemic", "Work (physics)", "Computer science", "Computer security", "Field (mathematics)", "2019-20 coronavirus outbreak", "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)", "Operations research", "Aeronautics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1149/10701.0341ecst" }, { "id": "https://openalex.org/W4321479853", "doi": "10.20944/preprints202302.0381.v1", "title": "The Use of Drones to Delivery Rift Valley Fever Vaccines in Rwanda: Perceptions and Recommendations", "year": 2023, "publication_date": "2023-02-22", "type": "preprint", "language": "en", "authors": [ "Evan F. Griffith", "Janna M. Schurer", "Billy Mawindo", "Rita Kwibuka", "Thierry Turibyarive", "Hellen J. Amuguni" ], "first_author": "Evan F. Griffith", "venue": "Preprints.org", "publisher": null, "abstract": "Given the recent emergence of Rift Valley Fever (RVF) in Rwanda and its profound impact on livelihoods and health, improving RVF prevention and control strategies is crucial. Vaccinating livestock is one of the most sustainable strategies to mitigate the impact of RVF on health and livelihoods, yet vaccine supply chain constraints severely limit the effectiveness of vaccination programs. In the human health sector, unmanned aerial vehicles, i.e., drones, are increasingly being used to improve supply chains and last-mile vaccine delivery. We investigated perceptions on whether delivering RVF vaccines by drone in Rwanda might help to overcome logistical constraints in the vaccine supply chain. We conducted semi-structured interviews with stakeholders in the animal health sector and Zipline employees in Nyagatare District in the Eastern Province of Rwanda and used content analysis to identify key themes. We found that stakeholders in the animal health sector and Zipline employees believe that drones could improve RVF vaccination in Nyagatare. The main benefits study participants identified included decreased transportation time, improved cold chain maintenance, and cost savings.", "keywords": [ "Rift Valley fever", "Livelihood", "Business", "Drone", "Vaccination", "Supply chain", "Geography", "Medicine", "Marketing", "Agriculture", "Virology", "Outbreak" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/manuscript/202302.0381/v1/download" }, { "id": "https://openalex.org/W4405414913", "doi": "10.1007/978-3-031-75593-4_26", "title": "Efficient Approaches for Drone-Assisted Vehicle Parcel Delivery Routing Problems in IoT Logistics Ensuring Optimized Energy Consumption", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Nguyen Thi My Binh", "Thi-Hoa-Hue Nguyen", "Do Thi Ngoc Huyen", "Nguyen Ngoc Quang" ], "first_author": "Nguyen Thi My Binh", "venue": "Lecture notes on data engineering and communications technologies", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Energy consumption", "Internet of Things", "Computer science", "Routing (electronic design automation)", "Consumption (sociology)", "Oil consumption", "Vehicle routing problem", "Computer network", "Operations research", "Engineering", "Computer security" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-75593-4_26" }, { "id": "https://openalex.org/W2989577570", "doi": null, "title": "Drone Acceptance and Noise Concerns - Some Findings", "year": 2019, "publication_date": "2019-05-10", "type": "conference-paper", "language": "en", "authors": [ "Hinnerk Eißfeldt", "Verena Vogelpohl" ], "first_author": "Hinnerk Eißfeldt", "venue": "elib (German Aerospace Center)", "publisher": "Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)", "abstract": "Drone acceptance and noise concerns – some findings Drones – understood here as unmanned aerial vehicles (UAV) in civil context - are becoming more and more present in public perception. Reaching from parcel delivery to wildlife protection, from precision farming to law enforcement, and from industrial inspection to digital fireworks, many applications are said to have market-changing potential, and nations and institutions around the world are trying to keep up with the dynamic development concerning rules and regulations. 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. Widespread public acceptance can promote the successful dissemination of new technologies. But as shown with the planning of airport extensions for example lacking acceptance in the public can be a limit to aviation growth in general. Likewise, concerns of citizens about the use of drones in their daily environment could be potential barriers to the further proliferation of civil drones, especially in urban areas. The psychoacoustic properties of drones have repeatedly been discussed as being one such limiting factor. This paper reports findings of a study about the acceptance of civil drones in Germany. 832 respondents participated in a phone interview of about 20 minutes duration. This paper will describe factors of drone acceptance with a special reference to noise concerns as stated by about half of the study participants.", "keywords": [ "Drone", "Phone", "Context (archaeology)", "Business", "Enforcement", "Clothing", "Internet privacy", "Public relations", "Advertising", "Marketing", "Computer security", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://elib.dlr.de/127712/1/Ei%C3%9Ffeldt%20%26%20Vogelpohl%20%282019%29.%20Drone%20Acceptance%20and%20Noise%20Concerns%20-%20Some%20Findings.%2020thISAP%2C%20Dayton%2COH.pdf" }, { "id": "https://openalex.org/W4362696338", "doi": "10.3233/jifs-224307", "title": "A hybrid large-neighborhood search for a truck and drone delivery system with stochastic customer existence and time windows", "year": 2023, "publication_date": "2023-04-07", "type": "article", "language": "en", "authors": [ "Ebrahim Teimoury", "Reza Rashid" ], "first_author": "Ebrahim Teimoury", "venue": "Journal of Intelligent & Fuzzy Systems", "publisher": "IOS Press", "abstract": "In recent years, e-commerce has become increasingly popular, and consumers expect quick and affordable delivery, placing additional pressure on city logistics activities. An innovative approach is proposed to coordinate ground vehicles and drones for delivery services, which has gained tremendous attention from academia and logistic service providers. This paper introduces a variant of this problem: the two-echelon truck and drone routing problem, characterized by stochastic demand existence and soft time windows. A Markov chain is used to model the problem, and a linear mathematical model is presented. This work employs a hybrid large-neighborhood search approach. Numerous computational experiments are conducted to evaluate the performance of the proposed solution method, and the results demonstrate its efficacy.", "keywords": [ "Drone", "Truck", "Computer science", "Markov chain", "Vehicle routing problem", "Service (business)", "Operations research", "Mathematical optimization", "Routing (electronic design automation)", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3233/jifs-224307" }, { "id": "https://openalex.org/W4407930565", "doi": "10.1080/21681015.2025.2468201", "title": "Optimizing multi-objective instant logistics with trucks and drones for the quick commerce order fulfilment", "year": 2025, "publication_date": "2025-02-24", "type": "article", "language": "en", "authors": [ "Haoran Ma", "Yung Po Tsang", "C.K.M. Lee" ], "first_author": "Haoran Ma", "venue": "Journal of Industrial and Production Engineering", "publisher": "Taylor & Francis", "abstract": "Quick commerce (Q-commerce) is an emerging online retail model that demands near-instant order fulfillment and adapts to delivering goods with varying environmental requirements, including temperature-sensitive items. Addressing the complex optimization challenges in distribution, a gap in effective delivery route planning and tool utilization has been identified. This study introduces three innovations: (1) a formulation that reduces drone sub-route path computations for real-time decision-making, (2) a multi-objective instant delivery model employing both vehicles and drones, and (3) a multi-objective memetic algorithm that accounts for time windows and temperature variations. Computational experiments demonstrate that this approach outperforms traditional models, significantly enhancing delivery time satisfaction, product quality, and overall service efficiency. The model scales effectively to large order quantities while maintaining a high customer satisfaction rate of 97%. This research contributes to the industrial engineering literature by presenting a novel Q-commerce logistics model and offering insights into multi-objective optimization for last-mile delivery.", "keywords": [ "Instant", "Drone", "Order (exchange)", "Truck", "Computer science", "Operations management", "Operations research", "Business", "Computer security", "Economics", "Automotive engineering", "Mathematics" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/21681015.2025.2468201" }, { "id": "https://openalex.org/W4399337907", "doi": "10.11591/ijece.v14i4.pp4154-4166", "title": "An autopilot-based method for unmanned aerial vehicles trajectories control and adjustment", "year": 2024, "publication_date": "2024-06-04", "type": "article", "language": "en", "authors": [ "Lesia Mochurad", "Jamil Abedalrahim Jamil Alsayaydeh", "Mohd Faizal Yusof" ], "first_author": "Lesia Mochurad", "venue": "International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering", "publisher": "Institute of Advanced Engineering and Science (IAES)", "abstract": "In today's world, the rapid development of aviation technologies, particularly unmanned aerial vehicles (UAVs), presents new challenges and opportunities. UAVs are utilized across various industries, including scientific research, military, robotics, surveying, logistics, and postal delivery. However, to ensure efficient and safe operation, UAVs require a reliable autopilot system that delivers precise navigation control and flight stability. This paper introduces a method for controlling and adjusting UAV trajectories, which enhances accuracy in environments and tasks corresponding to the first or second level of autonomy. It outperforms the linear-quadratic method and the unmodified predictive control method by 43% and 74%, respectively. The findings of this study can be applied to the development and modernization of new UAV, as well as the advancement of new UAV motion control systems, thereby enhancing their quality and efficiency.", "keywords": [ "Autopilot", "Aeronautics", "Computer science", "Control theory (sociology)", "Control (management)", "Control engineering", "Engineering", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ijece.iaescore.com/index.php/IJECE/article/download/34665/17533" }, { "id": "https://openalex.org/W3157090301", "doi": "10.3390/su13095228", "title": "Reactive UAV Fleet’s Mission Planning in Highly Dynamic and Unpredictable Environments", "year": 2021, "publication_date": "2021-05-07", "type": "article", "language": "en", "authors": [ "Grzegorz Radzki", "Izabela Ewa Nielsen", "Paulina Golińska-Dawson", "Grzegorz Bocewicz", "Zbigniew Antoni Banaszak" ], "first_author": "Grzegorz Radzki", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicles (UAVs) create an interesting alternative for establishing more sustainable urban freight deliveries. The substitution of traditional trucks in the last-mile distribution by a UAV fleet can contribute to urban sustainability by reducing air pollution and increasing urban freight efficiency. This paper presents a novel approach to the joint proactive and reactive planning of deliveries by a UAV fleet. We develop a receding horizon-based approach to reactive, online planning for the UAV fleet’s mission. We considered the delivery of goods to spatially dispersed customers over an assumed time horizon. Forecasted weather changes affect the energy consumption of UAVs and limit their range. Therefore, consideration should be given to plans for follow-up tasks, previously unmet needs, and predictions of disturbances over a moving time horizon. We propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. We implement a constraint programming paradigm, which is well suited to cope with the nonlinearity of the system’s characteristics. The proposed approach to online reactive UAV routing is evaluated in several instances. The computational experiments have shown that the developed model is capable of providing feasible plans for a UAV fleet’s mission that are robust to changes in weather and customer’s orders.", "keywords": [ "Truck", "Time horizon", "Computer science", "Fuel efficiency", "Constraint (computer-aided design)", "Routing (electronic design automation)", "Transport engineering", "Operations research", "Engineering", "Business", "Automotive engineering" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/13/9/5228/pdf?version=1620474506" }, { "id": "https://openalex.org/W4410088841", "doi": "10.1111/itor.70045", "title": "Collaborative delivery problem of trucks and multiple drones: an en‐route operation with flexible launch positions", "year": 2025, "publication_date": "2025-05-05", "type": "article", "language": "en", "authors": [ "Bing Han", "Tianze Chi", "Zhengyi Sun" ], "first_author": "Bing Han", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Abstract Drones are gradually being used in logistics, and collaborative systems combining trucks and drones are proposed for last‐mile delivery. Therefore, it is a meaningful study to improve the efficiency of collaborative delivery of truck–drone system. Based on the truck–drone collaborative delivery problem with en‐route operations, this paper introduces a new concept of flexible launch point, which is to set up multiple non‐customer stops in the truck's route for launching drones. In light of this concept, an en‐route operation with flexible positions in the truck–drone collaborative delivery problem is proposed. The problem is formulated as a mixed integer linear program that aims to minimize customer waiting times, determine the optimal route for the truck with multi‐drones, and establish the optimal flexible launch positions for the drones. We developed a variable neighborhood search algorithm and conducted large‐scale experiments. The experiments have demonstrated that the new collaborative approach maximizes drone utilization, expands the range of drone services, and ensures flexible launch. The en‐route operation with flexible launch positions presented in this paper provides innovative ideas for further application of trucks with multi‐drones in last‐mile delivery. This is also relevant for other scenarios, such as emergency management and routine inspections, which involve collaboration between trucks and drones.", "keywords": [ "Drone", "Truck", "Computer science", "Aeronautics", "Operations research", "Aerospace engineering", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.70045" }, { "id": "https://openalex.org/W4393968387", "doi": "10.48550/arxiv.2404.02442", "title": "Data-driven Optimization for Drone Delivery Service Planning with Online Demand", "year": 2024, "publication_date": "2024-04-03", "type": "preprint", "language": "en", "authors": [ "Aditya Paul", "Michael W. Levin", "Steven Travis Waller", "David Rey" ], "first_author": "Aditya Paul", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In this study, we develop an innovative data-driven optimization approach to solve the drone delivery service planning problem with online demand. Drone-based logistics are expected to improve operations by enhancing flexibility and reducing congestion effects induced by last-mile deliveries. With rising digitalization and urbanization, however, logistics service providers are constantly grappling with the challenge of uncertain real-time demand. This study investigates the problem of planning drone delivery service through an urban air traffic network to fulfil online and stochastic demand. Customer requests, if accepted, generate profit and are serviced by individual drone flights as per request origins, destinations and time windows. We cast this stochastic optimization problem as a Markov decision process. We present a novel data-driven optimization approach which generates predictive prescriptions of parameters of a surrogate optimization formulation. Our solution method consists of synthesizing training data via lookahead simulations to train a supervised machine learning model for predicting relative link priority based on the state of the network. This knowledge is then leveraged to selectively create weighted reserve capacity in the network and via a surrogate objective function that controls the trade-off between reserve capacity and profit maximization to maximize the cumulative profit earned. Using numerical experiments based on benchmarking transportation networks, the resulting data-driven optimization policy is shown to outperform a myopic policy. Sensitivity analyses on learning parameters reveal insights into the design of efficient policies for drone delivery service planning with online demand.", "keywords": [ "Drone", "Business", "Computer science", "Service (business)", "On demand", "Operations research", "Marketing", "Engineering", "Multimedia" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2404.02442" }, { "id": "https://openalex.org/W4415424785", "doi": "10.1016/j.cie.2025.111624", "title": "Deep reinforcement learning for drone-assisted truck delivery route optimization in a time-dependent network with time windows", "year": 2025, "publication_date": "2025-10-22", "type": "article", "language": "en", "authors": [ "Yong Peng", "Jing Huang", "Dennis Z. Yu", "Ajinkya N. Tanksale", "Pengfei Gu" ], "first_author": "Yong Peng", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Reinforcement learning", "Truck", "Flexibility (engineering)", "Traffic congestion", "Markov decision process", "Vehicle routing problem", "Routing (electronic design automation)", "Drone", "Variable neighborhood search" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111624" }, { "id": "https://openalex.org/W2606990251", "doi": "10.7737/jkorms.2016.41.3.075", "title": "Usefulness of Drones in the Urban Delivery System: Solving the Vehicle and Drone Routing Problem with Time Window", "year": 2016, "publication_date": "2016-08-31", "type": "article", "language": "en", "authors": [ "Yerim Chung", "Taejoon Park", "Yunhong Min" ], "first_author": "Yerim Chung", "venue": "한국경영과학회지", "publisher": null, "abstract": "This paper investigates the usefulness of drones in an urban delivery system. We define the vehicle and drone routing problem with time window (VDRPTW) and present a model that can describe a dual mode delivery system consisting of drones and vehicles in the metropolitan area. Drones are relatively free from traffic congestion but have limited flight range and capacity. Vehicles are not free from traffic congestion, and the complexity of urban road network reduces the efficiency of vehicles. Using drones and vehicles together can reduce inefficiency of the urban delivery system because of their complementary cooperation. In this paper, we assume that drones operate in a point-to-point manner between the depot and customers, and that customers in the need of fast delivery are willing to pay additional charges. For the experiment datasets, we use instances of Solomon (1987), which are well known in the Vehicle Routing Problem society. Moreover, to mirror the urban logistics demand trend, customers who want fast delivery are added to the Solomon's instances. We propose a hybrid evolutionary algorithm for solving VDRPTW. The experiment results provide different useful insights according to the geographical distributions of customers. In the instances where customers are randomly located and in instances where some customers are randomly located while others form some clusters, the dual mode delivery system displays lower total cost and higher customer satisfaction. In instances with clustered customers, the dual mode delivery system exhibits narrow competition for the total cost with the delivery system that uses only vehicles. In this case, using drones and vehicles together can reduce the level of dissatisfaction of customers who take their cargo over the time-window. From the view point of strategic flexibility, the dual mode delivery system appears to be more interesting. In meeting the objective of maximizing customer satisfaction, the use of drones and vehicles incurs less cost and requires fewer resources.", "keywords": [ "Drone", "Computer science", "Metropolitan area", "Vehicle routing problem", "Routing (electronic design automation)", "Traffic congestion", "Inefficiency", "Mode (computer interface)", "Operations research", "Transport engineering", "Computer network", "Economics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://koreascience.or.kr:80/article/JAKO201609633505758.pdf" }, { "id": "https://openalex.org/W3206747120", "doi": "10.1016/j.techsoc.2021.101789", "title": "Consumer's usage of logistics technologies: Integration of habit into the unified theory of acceptance and use of technology", "year": 2021, "publication_date": "2021-10-18", "type": "article", "language": "en", "authors": [ "Lanhui Cai", "Kum Fai Yuen", "Diancen Xie", "Mingjie Fang", "Xueqin Wang" ], "first_author": "Lanhui Cai", "venue": "Technology in Society", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Unified theory of acceptance and use of technology", "Expectancy theory", "Habit", "Structural equation modeling", "Marketing", "Business", "Service provider", "Consumer behaviour", "Service (business)", "Technology acceptance model", "Integrated logistics support", "Process management" ], "cited_by_count": 120, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.techsoc.2021.101789" }, { "id": "https://openalex.org/W4287869667", "doi": "10.48550/arxiv.2002.03118", "title": "Shipper Cooperation in Stochastic Drone Delivery: A Dynamic Bayesian\\n Game Approach", "year": 2020, "publication_date": "2020-02-08", "type": "preprint", "language": null, "authors": [ "Suttinee Sawadsitang", "Dusit Niyato", "Tan Puay Siew", "Ping Wang", "Sarana Nutanong" ], "first_author": "Suttinee Sawadsitang", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "With the recent technological innovation, unmanned aerial vehicles, known as\\ndrones, have found numerous applications including package and parcel delivery\\nfor shippers. Drone delivery offers benefits over conventional ground-based\\nvehicle delivery in terms of faster speed, lower cost, more\\nenvironment-friendly, and less manpower needed. However, most of existing\\nstudies on drone delivery planning and scheduling focus on a single shipper and\\nignore uncertainty factors. As such, in this paper, we consider a scenario that\\nmultiple shippers can cooperate to minimize their drone delivery cost. We\\npropose the Bayesian Shipper Cooperation in Stochastic Drone Delivery (BCoSDD)\\nframework. The framework is composed of three functions, i.e., package\\nassignment, shipper cooperation formation and cost management. The\\nuncertainties of drone breakdown and misbehavior of cooperative shippers are\\ntaken into account by using multistage stochastic programming optimization and\\ndynamic Bayesian coalition formation game. We conduct extensive performance\\nevaluation of the BCoSDD framework by using customer locations from Solomon\\nbenchmark suite and a real Singapore logistics industry. As a result, the\\nframework can help the shippers plan and schedule their drone delivery\\neffectively.\\n", "keywords": [ "Drone", "Operations research", "Computer science", "Benchmark (surveying)", "Scheduling (production processes)", "Schedule", "Stochastic programming", "Engineering", "Mathematical optimization", "Operations management" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2002.03118" }, { "id": "https://openalex.org/W3126211537", "doi": "10.1109/robosoft51838.2021.9479353", "title": "DroneTrap: Drone Catching in Midair by Soft Robotic Hand with Color-Based Force Detection and Hand Gesture Recognition", "year": 2021, "publication_date": "2021-04-12", "type": "conference-paper", "language": "en", "authors": [ "Aleksey Fedoseev", "Valerii Serpiva", "Ekaterina Karmanova", "Miguel Altamirano Cabrera", "Vladimir Shirokun", "Iakov Vasilev", "Stanislav Savushkin", "Dzmitry Tsetserukou" ], "first_author": "Aleksey Fedoseev", "venue": null, "publisher": null, "abstract": "The paper proposes a novel concept of docking drones to make this process as safe and fast as possible. The idea behind the project is that a robot with a soft gripper grasps the drone in midair. The human operator navigates the robotic arm with the ML-based gesture recognition interface. The 3-finger robot hand with soft fingers is equipped with touch sensors, making it possible to achieve safe drone catching and avoid inadvertent damage to the drone's propellers and motors. Additionally, the soft hand is featured with a unique color-based force estimation technology based on a computer vision (CV) system. Moreover, the visual color-changing system makes it easier for the human operator to interpret the applied forces.Without any additional programming, the operator has full real-time control of robot's motion and task execution by wearing a mocap glove with gesture recognition, which was developed and applied for the high-level control of DroneTrap.The experimental results revealed that the developed color-based force estimation can be applied for rigid object capturing with high precision (95.3%). The proposed technology can potentially revolutionize the landing and deployment of drones for parcel delivery on uneven ground, structure maintenance and inspection, risque operations, and etc.", "keywords": [ "Drone", "Gesture", "Robot", "Gesture recognition", "Process (computing)", "Task (project management)", "Wired glove", "Operator (biology)", "Software deployment" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2102.03923" }, { "id": "https://openalex.org/W4409229887", "doi": "10.1016/j.rtbm.2025.101365", "title": "Delivering tomorrow today: The role of cutting-edge technologies such as Artificial Intelligence in drones in shaping new business models", "year": 2025, "publication_date": "2025-04-08", "type": "article", "language": "en", "authors": [ "Sofia Gomes", "João Morais Lopes", "Bruno Barbosa Sousa" ], "first_author": "Sofia Gomes", "venue": "Research in Transportation Business & Management", "publisher": "Elsevier BV", "abstract": "Drone delivery services, wich enable faster and more efficient deliveries, reduce costs, minimize environmental impacts and increase satisfaction, have revolutionized global logistics services. Despite these advantages, there are still obstacles to consumers' acceptance of these service, so it is necessary to understand their motivations. This study evaluates how acceptance of the technology influences the perceived usefulness of drone delivery services and the intention to adopt them, considering consumer attitude as a mediating factor. A total of 1108 answers were collected from Portuguese citizens and the Partial Least Square method was applied to a proposed research model. The drone delivery service in Portugal is not yet available in logistics chains. As such, the participants in this study do not yet have an opinion based on experience already carried out. The results indicate that the perceived usefulness of drone delivery services influences consumer attitudes and intention to use them. The technological and task characteristics of drone parcel delivery services positively influence consumers' perceived usefulness of this service. It was also found that consumers are more willing to adopt these services if they are perceived as useful. From the perspective of technology acceptance model (TAM) theory, this study brings empirical insights to extend the literature to drone parcel delivery service technology, and a new model for consumer acceptance of this service is proposed. This study is pioneering in the Portuguese context. Some practical implications are left to help companies and policymakers in faster introducing these services worldwide, namely in Portugal.", "keywords": [ "Drone", "Enhanced Data Rates for GSM Evolution", "Engineering", "Business intelligence", "Computer science", "Artificial intelligence", "Aerospace engineering", "Knowledge management", "Biology" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.rtbm.2025.101365" }, { "id": "https://openalex.org/W3147539621", "doi": "10.1109/it51528.2021.9390121", "title": "Collaborative Truck-Drone Routing Optimization Using Quantum-Inspired Genetic Algorithms", "year": 2021, "publication_date": "2021-02-16", "type": "conference-paper", "language": "en", "authors": [ "Hasan Yetış", "Mehmet Karaköse" ], "first_author": "Hasan Yetış", "venue": null, "publisher": null, "abstract": "Today, e-commerce has become more common than ever. The time for customers to receive the product they ordered is directly dependent on the speed of the shipment companies. In addition to operating and operator costs, traditional deliveries also increase delivery times. That's why the distribution with truck-drone cooperation method emerged. In this study, it is aimed to determine the less costly route for parcel delivery with the cooperation of truck-drone. Quantum-inspired genetic algorithms are used to optimize the route. The proposed method is tested with truck-drone simulation developed with Python programming language. Simulation results are obtained and compared exclusively for truck-based distribution and distribution with truck-drone collaboration. A cost calculation is made by taking into account factors such as the total distance traveled, the waiting time of the customers, and the working time of the staff. The simulation results show that despite the increasing total traveling distance in the truck-drone collaboration, cost savings are achieved. As a result of the tests carried out, it is seen that 4-8% cost savings are achieved from 3 different scenarios created for 100 customers.", "keywords": [ "Drone", "Truck", "Python (programming language)", "Computer science", "Routing (electronic design automation)", "Genetic algorithm", "Vehicle routing problem", "Simulation", "Automotive engineering", "Engineering", "Computer network", "Operating system" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/it51528.2021.9390121" }, { "id": "https://openalex.org/W4406436359", "doi": "10.30574/ijsra.2025.14.1.0117", "title": "The future of autonomous vehicles and drones in supply chain delivery", "year": 2025, "publication_date": "2025-01-16", "type": "article", "language": "en", "authors": [ "Oluwole Raphael Odumbo", "Enyinnaya Prince Onuma" ], "first_author": "Oluwole Raphael Odumbo", "venue": "International Journal of Science and Research Archive", "publisher": "GSC Online Press", "abstract": "The integration of autonomous vehicles and drones into supply chain delivery systems is transforming logistics and redefining last-mile delivery. With advancements in artificial intelligence, robotics, and Internet of Things (IoT) technologies, these innovations promise significant cost savings, enhanced efficiency, and reduced environmental impact. Autonomous vehicles, including self-driving trucks, are reshaping long-haul transportation by optimizing routes, minimizing fuel consumption, and mitigating driver shortages. Simultaneously, drones are revolutionizing urban and rural delivery by enabling rapid, contactless transportation of goods in challenging terrains and congested areas. Together, these technologies represent a paradigm shift toward a more efficient and sustainable supply chain ecosystem. However, the adoption of autonomous vehicles and drones raises significant challenges. Regulatory hurdles, infrastructure limitations, and safety concerns remain key barriers to widespread implementation. Additionally, issues of cybersecurity, privacy, and public acceptance must be addressed to ensure successful deployment. From an operational perspective, integrating autonomous technologies with existing supply chain systems requires careful coordination and investment in infrastructure and workforce reskilling. This study examines the future trajectory of autonomous vehicles and drones in supply chain delivery, focusing on their transformative potential and the obstacles they face. By analysing case studies and industry trends, the research identifies best practices and strategic frameworks for leveraging these technologies. The findings highlight the importance of collaborative efforts between policymakers, technology developers, and industry stakeholders in creating a regulatory and operational environment conducive to innovation. Ultimately, this research underscores the need for a balanced approach to harness the benefits of autonomy while addressing its associated challenges.", "keywords": [ "Drone", "Supply chain", "Business", "Chain (unit)", "Computer science", "Marketing", "Biology", "Physics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-0117.pdf" }, { "id": "https://openalex.org/W4362686433", "doi": "10.1504/ijmor.2023.130109", "title": "Determining the optimal parcel delivery method using one drone and one truck", "year": 2023, "publication_date": "2023-01-01", "type": "article", "language": "en", "authors": [ "Kazuki Satoh", "Hiroshi Morita" ], "first_author": "Kazuki Satoh", "venue": "International Journal of Mathematics in Operational Research", "publisher": "Inderscience Publishers", "abstract": "Delivering parcels using a combination of drones and trucks represents a promising new package delivery method. In previous studies, several truck/drone delivery planning problems and their solutions have been proposed. However, little research has been done to determine which delivery method is most appropriate. The reason for this is that it is difficult to obtain an exact solution for such problems, and thus the accuracy of the solutions is an issue. In this study, we propose an accurate solution procedure for the flying sidekick travelling salesman problem (FSTSP) and the parallel drone scheduling travelling salesman problem (PDSTSP), and establish which delivery method is most suitable based on the solutions obtained.", "keywords": [ "Drone", "Travelling salesman problem", "Truck", "Computer science", "Scheduling (production processes)", "Mathematical optimization", "Operations research", "Mathematics", "Algorithm", "Aerospace engineering", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijmor.2023.130109" }, { "id": "https://openalex.org/W4411377229", "doi": "10.1504/ijlsm.2025.146707", "title": "Green vehicles adoption for last-mile delivery: a systematic literature review and current trend", "year": 2025, "publication_date": "2025-01-01", "type": "review", "language": "en", "authors": [ "Rezli Ainur Savira Lubis", "Yuliani Dwi Lestari" ], "first_author": "Rezli Ainur Savira Lubis", "venue": "International Journal of Logistics Systems and Management", "publisher": "Inderscience Publishers", "abstract": "The final step in the e-commerce supply chain, last-mile delivery, is an innovative approach to urban logistics issues, including lowering carbon emissions through alternative vehicles. This research intends to update and identify green vehicles adoption based on the type of last-mile delivery and the type of commodities transported by a systematic literature review method to enhance the sustainability of urban logistics. The findings show that collaboration between conventional vehicles (fossil fuels) and active transportation (bicycles, walking), public transportation, electric vehicles, drones, and robots is the most widely adopted. From an environmental standpoint, carbon emissions can be significantly reduced with this alternative vehicle when making deliveries. In addition, the result shows that this environmentally friendly vehicle mostly serves general goods delivery to the consumer's location. Meanwhile, crowdshipping is an urban logistics trend that has emerged recently by utilising individuals to make deliveries when traveling in certain areas.", "keywords": [ "Last mile (transportation)", "Mile", "Current (fluid)", "Business", "Operations management", "Economics", "Engineering", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijlsm.2025.146707" }, { "id": "https://openalex.org/W4404411596", "doi": "10.1109/dasc62030.2024.10749661", "title": "Comparison of Strategic Conflict Prevention Methods for Departure Planning in Drone Delivery", "year": 2024, "publication_date": "2024-09-29", "type": "conference-paper", "language": "en", "authors": [ "Sasha Vlaskin", "Emmanuel Sunil", "Dennis Nieuwenhuisen", "Joost Ellerbroek", "Jacco M. Hoekstra" ], "first_author": "Sasha Vlaskin", "venue": null, "publisher": null, "abstract": "U-Space drone operations are expected to be a driver for further urban development, especially through use cases such as medical and commercial parcel delivery. In particular, package delivery using small drones shows great promise, with e-commerce giants such as Amazon deploying limited-scale drone delivery trials in rural areas. As the technology matures, large-scale operations will take place in constrained urban areas, leading to high airborne traffic densities. It is necessary to develop a robust automated separation management system that actively ensures safe separation of drones both in the air and on the ground. This paper focuses on the Strategic element, more specifically on pre-departure planning. The aim of this is to reduce the chance of conflicts around vertiports, where spatial and environmental constraints make tactical resolutions difficult. This work focuses on two scenarios: a single pad for both takeoffs and landings (in a spatially constrained urban area) and 4 takeoff-landing pad pairs (for a distribution center). Several methods are compared for this takeoff sequencing task, coupled with a conflict detection algorithm: A First-Come First-Served method that applied delay to conflicting flights, a Mixed-Integer Programming approach, a Genetic Algorithm, Particle Swarm Algorithm and Simulated Annealing were used. For a single-pad approach, first-come first-served works best in terms of computation time and total deployment time (or makespan). For the multi-pad approach however, changing the flight sequence through metaheurisitic methods and mixed-integer linear programming show a reduction in total deployment time.", "keywords": [ "Drone", "Computer science", "Strategic planning", "Computer security", "Business", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dasc62030.2024.10749661" }, { "id": "https://openalex.org/W7130641597", "doi": "10.1016/j.sftr.2026.101758", "title": "Aerial innovations in healthcare: A systematic review of drone-based logistics and regulatory barriers", "year": 2026, "publication_date": "2026-02-20", "type": "review", "language": "en", "authors": [ "Tejinder Singh Lakhwani", "Yerasani Sinjana", "Anuj Pal Kapoor" ], "first_author": "Tejinder Singh Lakhwani", "venue": "Sustainable Futures", "publisher": "Elsevier BV", "abstract": "This paper presents a PRISMA-guided systematic literature review synthesizing peer-reviewed evidence on the use of unmanned aerial vehicles (UAVs) in healthcare logistics, with emphasis on operational performance and regulatory barriers. The final corpus comprises 54 studies (53 published during 2018–2025 and one pre-2018 conceptual baseline retained solely for historical framing). Evidence is synthesized across four themes: UAV logistics applications, healthcare logistics use cases, emergency deployments, and regulatory/ethical constraints. Findings indicate that UAV-enabled delivery can improve timeliness and last-mile access for selected time-critical, lightweight medical payloads in infrastructure-constrained settings, provided that services are integrated into routine workflows and cold-chain handling is validated. Recurrent limitations include battery endurance and payload constraints, weather and connectivity sensitivity, cold-chain risk for temperature-sensitive products, fragmented BVLOS and airspace approval pathways, cybersecurity and privacy vulnerabilities, and unresolved liability and accountability arrangements. Emerging enablers—AI-assisted routing and dispatch, IoT-based condition monitoring, blockchain-supported traceability, and 5G-enabled communications—are frequently discussed as mitigations, but system-scale validation remains limited. Using TCMEOD-based evidence mapping, the literature is shown to focus on technological and operational dimensions, with comparatively weaker coverage of ethical and developmental readiness, indicating that governance and institutional capacity remain persistent bottlenecks to sustainable scale-up. The review concludes with implications for regulatory harmonization, privacy-preserving digital infrastructure, and interoperable operational standards to support equitable and resilient UAV adoption in healthcare supply chains.", "keywords": [ "Interoperability", "Workflow", "Health care", "Corporate governance", "Supply chain", "Drone", "Liability", "Accountability", "Sustainability" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.sftr.2026.101758" }, { "id": "https://openalex.org/W7142212703", "doi": "10.1016/j.trc.2026.105660", "title": "Multi-service network design with multi-function drones: Urban monitoring, data collection, and parcel delivery services", "year": 2026, "publication_date": "2026-03-28", "type": "article", "language": "en", "authors": [ "Mingzhi Lyu", "Lishuai Li", "Wei Liu" ], "first_author": "Mingzhi Lyu", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": "• Joint optimisation for routing and data transmission under three heterogeneous drone demands. • Synchronised data transmission between drones when direct depot connection is lost. • A MIQCP model formulation is proposed and then solved by ALNS algorithm. • Case study shows cost savings under certain drone and cost configurations. Drones are emerging as a promising tool in urban areas for tasks like monitoring, data collection, and deliveries. While each of these tasks has been studied independently in previous research, there have been very few studies on the possible combination of multiple types of services and their joint optimisation. Since different services can potentially occur within the same geographical area, and drones can be equipped with various tools to simultaneously handle different tasks, joint optimisation has the potential to reduce costs while maintaining service quality. This study proposes and examines the joint optimisation of multi-service capabilities within urban applications using multi-function drones. Our approach includes equipping drones with urban monitoring, wireless communication, data collection and storage systems, as well as parcel mounting frames. These drones can undertake (i) Real-time urban monitoring, (ii) Data collection, and (iii) One-to-one pickup and delivery parcel services. We consider constraints like battery, payload capacity, service time windows, data storage capacity, wireless connectivity, and synchronised data inter-transmission. The problem is modelled as a Mixed Integer Quadratically Constrained Program to optimise drone routes and data transmission strategies for cost minimisation. We also introduce an Adaptive Large Neighbourhood Search heuristic algorithm for solving large-scale problems. Numerical studies are conducted on a toy network to evaluate the algorithm and on a regional network in Hong Kong to showcase the efficacy of utilising multi-function drones.", "keywords": [ "Drone", "Payload (computing)", "Heuristic", "Service (business)", "Data transmission", "Routing (electronic design automation)", "Backhaul (telecommunications)", "Data collection", "Data center", "Network planning and design" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2026.105660" }, { "id": "https://openalex.org/W4205655597", "doi": "10.1007/978-3-030-86841-3_13", "title": "A Two-Echelon Truck-and-Drone Distribution System: Formulation and Heuristic Approach", "year": 2021, "publication_date": "2021-01-01", "type": "book-chapter", "language": "en", "authors": [ "Maurizio Boccia", "Andrea Mancuso", "Adriano Masone", "Antonio Sforza", "Claudio Sterle" ], "first_author": "Maurizio Boccia", "venue": "AIRO Springer series/AIRO Springer Series", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Truck", "Context (archaeology)", "Heuristic", "Computer science", "Integer programming", "Linear programming", "Parking lot", "TRIPS architecture", "Exploit", "Operations research", "Operator (biology)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-86841-3_13" }, { "id": "https://openalex.org/W7126158577", "doi": "10.1016/j.tre.2026.104678", "title": "Enabling the multi-LR ability of drones in the multi-visit truck-drone routing problem with pickup and delivery", "year": 2026, "publication_date": "2026-01-31", "type": "article", "language": "en", "authors": [ "Sepehr Pasha", "S. Mehdi Sajadifar" ], "first_author": "Sepehr Pasha", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "The use of drones alongside trucks for parcel delivery has received considerable research attention, further stimulated by advancements in drone capacity and range that enhance operational viability relative to traditional methods. In this paper, we address a variant of the combined truck-drone routing problem, entailing multiple trucks collaborating with drones to meet the pickup and delivery demands of customers. In the proposed problem, drone energy consumption depends on the carried load; drones may serve multiple customers per flight, and each truck can launch and retrieve its drone multiple times at each customer node (multi-LR) to enhance overall utilization. We propose a mixed-integer linear programming model to minimize total cost, enhanced with problem-specific cuts, which are demonstrated through extensive computational experiments to effectively reduce runtime. The model includes flexible features that allow it to handle diverse operational constraints, such as restrictions on the number of flights performed and high-traffic areas. Given the complexity of the model, we develop an adapted algorithm from the literature, incorporating significant modifications along with a new acceleration strategy. The approach combines a maximum payload method in the first stage with an improved simulated annealing algorithm using problem-specific neighborhood operators in the second stage. Although our findings show that the multi-LR feature increases the number of flights performed, both the model and the adapted algorithm demonstrate its cost efficiency, achieving average transportation cost reductions of 14.51% compared to the system without multi-LR and 45.62% compared to the traditional truck-only system.", "keywords": [ "Drone", "Payload (computing)", "Pickup", "Linear programming", "Routing (electronic design automation)", "Truck", "Energy consumption", "Node (physics)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104678" }, { "id": "https://openalex.org/W4413349674", "doi": "10.1109/logistiqua66323.2025.11122674", "title": "Urban Last-Mile Delivery: Environmental Challenges and Expertise - A Comprehensive Literature Revie", "year": 2025, "publication_date": "2025-05-28", "type": "conference-paper", "language": "en", "authors": [ "Jihad Mkhalfi", "Fouad Jawab", "Abderrahman El Mhamedi", "Imane Moufad" ], "first_author": "Jihad Mkhalfi", "venue": null, "publisher": null, "abstract": "In this review, we examine the environmental challenges of urban last-mile delivery and explore sustainable solutions. We analyze studies from 2015 to 2025 using a systematic approach based on the PRISMA methodology, ensuring a rigorous and reproducible selection of relevant academic works. Additionally, we conduct a bibliometric analysis with VOSviewer, allowing us to identify key research trends and gaps in the field. Our study focuses on alternative delivery solutions such as electric vehicles, drones, cargo bikes, and smart lockers, as well as innovative technologies like AI-based route optimization and crowdshipping. While these solutions show great potential in reducing emissions and improving efficiency, large-scale adoption remains hindered by regulatory barriers, infrastructure limitations, and high costs. By combining PRISMA and VOSviewer, this review provides a comprehensive mapping of the field, offering valuable insights for policymakers and logistics providers while emphasizing the need for collaborative efforts to support greener and more sustainable delivery models.", "keywords": [ "Mile", "Last mile (transportation)", "Computer science", "Architectural engineering", "Engineering", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/logistiqua66323.2025.11122674" }, { "id": "https://openalex.org/W4407376974", "doi": "10.1111/itor.13590", "title": "Integrated speed optimization and multivisit drone routing: a mathematical programming approach", "year": 2025, "publication_date": "2025-02-07", "type": "article", "language": "en", "authors": [ "Mahla Meskar", "Amir Ahmadi Javid" ], "first_author": "Mahla Meskar", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Abstract This paper considers a last‐mile delivery system in which drones are used to deliver parcels to customers within a predefined delivery‐time limit, while drones can perform multiple multivisit trips. The effects of both drone speed and load weight are considered on energy consumption in all flight phases, including takeoff, cruise, hovering, and landing phases, using a realistic nonconvex formula. The problem determines the routes of the drones and their speeds during the cruise flight phases, with the aim of minimizing the total consumed energy. The paper initially examines the problem for the discrete case where the speeds are selected from a finite list of options. The initial formulation of the problem is a mixed‐integer nonlinear program that cannot be solved efficiently. Using a novel approach that changes the problem's input space, a mixed‐integer linear program is proposed and enhanced by valid inequalities. Utilizing an efficient preprocessing algorithm, the proposed linear formulation is able to solve problem instances with up to 100 customers. Next, the paper studies the problem for the continuous case in which the speeds can take values from a specified interval. It is first demonstrated how the continuous case can be solved optimally when no delivery‐time limit is imposed. Then, it is shown that the continuous case can be solved approximately using the discrete case. A method for computing the approximation error is also presented, which can be used to determine an appropriate level of discretization.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1111/itor.13590" }, { "id": "https://openalex.org/W4391381669", "doi": "10.1109/wsc60868.2023.10407713", "title": "A Two-Stage Stochastic Model For Drone Delivery System With Uncertainty In Customer Demands", "year": 2023, "publication_date": "2023-12-10", "type": "conference-paper", "language": "en", "authors": [ "Xudong Wang", "Gerald Jones", "Xueping Li" ], "first_author": "Xudong Wang", "venue": null, "publisher": null, "abstract": "Drone delivery is a popular logistics method for e-commerce businesses due to its efficiency and convenience, especially for last-mile delivery and emergency situations in areas with poor infrastructure. However, the uncertainty of customer demands can affect transportation costs in the long run, making it vital to design an effective delivery system. To tackle this issue, we propose a two-stage stochastic model that minimizes the sum of fixed and expected operating costs. The first stage minimizes the total cost of the delivery system, including the facilities fixed costs and expected operating costs, while the second stage arranges drones’ routes according to simulated demands to estimate the minimal expected transportation cost and penalty cost. Since this stochastic programming has infinite scenarios, we deploy a sample average approximation method to estimate its bounds. Additionally, we use a heuristic simulation framework to find a satisfactory solution in an acceptable time.", "keywords": [ "Drone", "Stage (stratigraphy)", "Computer science", "Geology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/wsc60868.2023.10407713" }, { "id": "https://openalex.org/W4408408487", "doi": "10.7307/ptt.v37i2.634", "title": "Location-Route Planning for VTOL Airport and UAV Urban Logistics Network – A Case Study of Tianjin", "year": 2025, "publication_date": "2025-03-13", "type": "article", "language": "en", "authors": [ "Hong JIANG", "Jiaxue WANG", "Xinhui Ren" ], "first_author": "Hong JIANG", "venue": "PROMET - Traffic&Transportation", "publisher": "University of Zagreb, Faculty of Transport and Traffic Sciences", "abstract": "With the potential for fast, contactless and environmentally friendly delivery, unmanned aerial vehicles (UAVs) have gained increasing attention and application due to their cost-effectiveness and convenient and rapid delivery operations. In future cities, a multi-level airport that supports vertical take-off and landing (VTOL) of UAVs and forming a delivery network is necessary to improve delivery efficiency and provide a competitive advantage. This paper proposes a multi-level airport location-routing problem for UAVs that considers UAV flight energy consumption and operational costs. The goal is to minimise the number of locations and minimise delivery path planning while meeting delivery demands within the service range. Based on the traditional distribution centre site-path problem, the UAV distribution network is constructed to solve the problem of airport location and flight path planning, and the two-layer genetic algorithm is used to solve it. Based on this, the validity of the model and algorithm is verified using the urban area of Tianjin as an example. The experimental results show that the constructed model can be used for UAV airport layout planning, which is applicable to large-scale, multi-aircraft-type and multi-level airport layout planning. Data analysis results indicate that when the location layout of the vertical hub airport is on the edge of the VTOL points, both the flight distance and the total cost of the delivery network relatively increase. Increasing the payload capacity will reduce the number of UAV operations, but the total cost shows a decreasing-then-increasing trend. This study can provide a theoretical basis for the selection of airport sites and UAV types in future UAV urban delivery networks.", "keywords": [ "Transport engineering", "City logistics", "Route planning", "Environmental planning", "Business", "Geography", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7307/ptt.v37i2.634" }, { "id": "https://openalex.org/W4412873654", "doi": "10.36713/epra23176", "title": "A STUDY ON CHALLENGES FACED BY LAST MILE DELIVERY SERVICE PROVIDERS WITH A FOCUS ON INNOVATION", "year": 2025, "publication_date": "2025-07-19", "type": "article", "language": "en", "authors": [ "P.V. Yaswanth Reddy", "Dr Jahnavi" ], "first_author": "P.V. Yaswanth Reddy", "venue": "EPRA International Journal of Multidisciplinary Research (IJMR)", "publisher": null, "abstract": "The rapid expansion of e-commerce and shifts in consumer preferences have significantly increased the need for effective last-mile delivery solutions. This paper explores the obstacles that last-mile delivery service providers encounter while highlighting innovative strategies to overcome them. The study identifies major challenges, including elevated costs, inefficient routing, sustainability issues, and rising customer demands. It analyzes the role of emerging technologies such as automation, AI-powered logistics, and alternative delivery methods like drones and autonomous vehicles. Furthermore, it discusses approaches for optimizing delivery routes, improving workforce management, and ensuring scalability in diverse environments. This research offers practical recommendations for boosting delivery efficiency, minimizing costs, and meeting customer expectations, thereby contributing to advancements in logistics and delivery systems. Keywords: : Last-Mile Delivery, Challenges, Innovation, Logistics, Automation, Route Optimization, Customer Satisfaction, Efficiency.", "keywords": [ "Business", "Focus (optics)", "Service provider", "Service delivery framework", "Mile", "Last mile (transportation)", "Service (business)", "Marketing", "Public relations", "Political science", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.36713/epra23176" }, { "id": "https://openalex.org/W4414116315", "doi": "10.2139/ssrn.5447074", "title": "Adoption Barriers in the Last-Mile Ecosystem: Robotic Delivery vs. Electrified Modes", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Tho V. Le", "Yuchen Du", "Abel Kebede Reda", "Hoang T. Hua", "Trang Huong Pham", "Long T. Truong" ], "first_author": "Tho V. Le", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Corporate governance", "Autonomy", "Automation", "Emerging technologies", "Information technology", "Space (punctuation)", "Perception" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5447074" }, { "id": "https://openalex.org/W4313680058", "doi": "10.48550/arxiv.2301.01755", "title": "Autonomous Drone Racing: A Survey", "year": 2023, "publication_date": "2023-01-04", "type": "preprint", "language": "en", "authors": [ "Drew Hanover", "Antonio Loquercio", "Leonard Bauersfeld", "Ángel Romero", "Robert Pěnička", "Yunlong Song", "Giovanni Cioffi", "Elia Kaufmann", "Davide Scaramuzza" ], "first_author": "Drew Hanover", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms which can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground which in turn increases productivity and further strengthens their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future.", "keywords": [ "Drone", "Computer science", "Aeronautics", "Artificial intelligence", "Real-time computing", "Simulation", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2301.01755" }, { "id": "https://openalex.org/W4415926225", "doi": "10.4324/9781003563532-9", "title": "Understanding Uncertainties for the Future of Drone Deliveries through Stakeholder Engagement and Co-design", "year": 2025, "publication_date": "2025-11-05", "type": "book-chapter", "language": "en", "authors": [ "Daniela Paddeu", "Eda Beyazıt" ], "first_author": "Daniela Paddeu", "venue": null, "publisher": null, "abstract": "Participation in policy and planning is crucial for a democratic and inclusive society. Co-designing policies with stakeholders increases the impacts of policy interventions while enhancing their credibility. Stakeholder engagement and participation is key to dealing with complex policy and long-term planning, especially under uncertainty. Participatory methods for policymaking engage stakeholders with different (sometimes conflicting) needs and expectations. Effective policies should align with stakeholders’ perceptions of the underlying problem that the solutions aim to solve. Stakeholder engagement becomes particularly important when exploring potential implications for policy and planning, as well as regulations and operational requirements due to the introduction of disruptive technologies. Therefore, this chapter identifies critical uncertainties for drone last-mile deliveries governance considering a multiple stakeholder perspective. Using the UK air mobility ecosystem as a case study, our methodological approach integrates stakeholder engagement and co-design with Foresight methods, which allow stakeholders to think ahead and consider, model, create, and respond to future possibilities. Our research findings are expected to have a significant impact on the development of national regulations and local policies and planning related to drone technology in the UK. While this project focuses primarily on the UK, the results will be potentially transferable to other countries.", "keywords": [ "Stakeholder", "Stakeholder engagement", "Stakeholder analysis", "Drone", "Corporate governance", "Futures studies", "Citizen journalism", "Public engagement", "Participatory planning" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://uwe-repository.worktribe.com/15580319/1/Understanding%20uncertainties%20for%20the%20future%20of%20drone%20deliveries%20through%20stakeholder%20engagement%20and%20co-design" }, { "id": "https://openalex.org/W3048363878", "doi": "10.1155/2020/9437605", "title": "Impact of UAV Delivery on Sustainability and Costs under Traffic Restrictions", "year": 2020, "publication_date": "2020-08-08", "type": "article", "language": "en", "authors": [ "Yuyu Li", "Wei Yang", "Bo Yuan Huang" ], "first_author": "Yuyu Li", "venue": "Mathematical Problems in Engineering", "publisher": "Hindawi Publishing Corporation", "abstract": "Compared with traditional vehicles delivery, unmanned aerial vehicle (UAV) delivery can reduce energy consumption and greenhouse gas emissions, which benefits environmental sustainability. Besides, UAVs can overcome traffic restrictions, which are the big obstacle in parcel delivery. In reality, there are two kinds of most popular traffic restrictions, vehicle-type restriction, and half-side traffic. We propose a mixed-integer (0-1 linear) green routing model with these two kinds of traffic restrictions for UAVs to exploit the environmental aspects of the use of UAVs in logistics. A genetic algorithm is proposed to efficiently solve the complex routing problem, and an experimental analysis is made to illustrate and validate our model and the algorithm. We found that, under both these two traffic restrictions, UAV delivery can accomplish deliveries that cannot be carried out or are carried out at much higher costs by vehicles only and can always effectively save costs and cut CO2 emissions, which is environmentally friendly. Furthermore, UAV delivery saves more cost and cuts more CO2 emission under the first kind of traffic restriction than that under the second.", "keywords": [ "Obstacle", "Greenhouse gas", "Exploit", "Sustainability", "Energy consumption", "Routing (electronic design automation)", "Transport engineering", "Vehicle routing problem", "Computer science", "Genetic algorithm", "Engineering", "Computer network" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/mpe/2020/9437605.pdf" }, { "id": "https://openalex.org/W2947709395", "doi": null, "title": "Modeling the Sustainability of Small Unmanned Aerial Vehicles Technologies: Final Report", "year": 2018, "publication_date": "2018-01-01", "type": "article", "language": "en", "authors": [ "Miguel Figliozzi" ], "first_author": "Miguel Figliozzi", "venue": "PDXScholar (Portland State University)", "publisher": "Portland State University", "abstract": "In the past decade, unmanned aerial vehicles (UAV) have become increasingly more popular in the commercial sector. Drones are being used for all kinds of purposes, such as surveillance, inspecting architecture, filming, wildlife research, and more. Freight delivery is a potential application that is getting lots of attention from large companies. This research presented novel data, relationship, and models for deliveries utilizing small UAVs. Small UAVs were defined as aircrafts with a tare of up to 15 kilograms (kg) and a potential payload of up to 15 kg. Since the weight of the UAVs is capped, only drones with engines that are electric were included; noise and pollution problems are likely to hinder urban deployments of internal combustion engines. Internal combustion engines are mostly used in larger UAVs. The scope of the search was limited to multicopter drones that can potentially deliver in both urban and rural areas. Fixed-wing drones were excluded from the search because currently only copters have the capability of hovering and delivering products in tight spaces (required in urban areas); fixed-wing UAVs typically cannot land or take off vertically. Single copters can hover similarly to helicopters, but were not included in the search because these aircrafts tend to be larger, and the size of the propeller and blade made them unsafe for areas without a large. Multicopters or multi-rotor drones can hover but also have higher stability and maneuverability, which makes them more suitable for navigating tight spaces or flying near humans and/or valuable property. The survey of currently available UAVs shows that payload, size, energy consumption, and cost are positively correlated and tend to increase together. Unfortunately, potential safety, noise, and last-yard constraints also increase as drone capabilities and size increase. Cost metrics such as cost per flying hour (CPFH) are the most relevant for small UAVs since they readily take into account the impact of operator labor cost and utilization, clearly the largest cost components. The economic analysis indicates that labor/staff costs can range between 30% and 85% of UAV costs per flying hour. The impact of labor costs will be highly dependent on future regulations and the level of automation of the last-mile delivery process. A novel analysis of lifecycle UAV and ground commercial vehicles’ CO2e emissions is presented. Different route and customer configurations are modeled analytically. Utilizing real-word data, tradeoffs and comparative advantages of UAVs are discussed. Breakeven points for operational emissions are obtained and the results clearly indicate that UAVs are more CO2e efficient for small payloads than conventional diesel vans on a per-distance basis. Drastically different results are obtained when customers can be grouped in a delivery route. UAV deliveries are not more CO2e efficient than tricycle or electric van delivery services if a few customers can be grouped in a route. Vehicle phase CO2e emissions for UAVs are significant and must be taken into account. Ground vehicles are more efficient when comparing vehicles’ production and disposal emissions per delivery. Currently available UAV technology can fill a delivery service niche in sparsely populated areas with low numbers of customers and density. In rural areas, the regulatory landscape and last-yard delivery constraints are also more relaxed. In rural areas, the economic benefit brought about by reducing the cost of a driver to visit remote customers are obvious, but in this environment, UAV range is a key consideration. In dense urban areas, several first- and last-mile service, privacy, regulatory, and security issues must be addressed before UAV services are feasible. UAVs are likely to have an edge regarding speed delivery if they are operated in uncongested skies where they can outperform slower ground vehicles delayed by conditions of the congested ground road network. On the other hand, drones may not be able to compete solely in terms of costs with a delivery truck that can deliver hundreds of packages to one location in an urban setting. The urban landscape is a place where larger payload capacity may be more beneficial than flight distance for some delivery types. The future of UAV deliveries will also depend on other factors such as UAV noise levels, regulations and safety concerns, and last-yard delivery configurations.", "keywords": [ "Sustainability", "Business", "Remote sensing", "Computer science", "Environmental science", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1488&context=cengin_fac" }, { "id": "https://openalex.org/W4400157684", "doi": "10.1142/s0217595924400165", "title": "Collaborative Routing Optimization for Heterogeneous Trucks–Drones Under Urban Regional Restrictions", "year": 2024, "publication_date": "2024-06-29", "type": "article", "language": "en", "authors": [ "Jiaojiao Gao", "Xiuping Guo" ], "first_author": "Jiaojiao Gao", "venue": "Asia Pacific Journal of Operational Research", "publisher": "World Scientific", "abstract": "Collaborative truck–drone delivery is a crucial model of drone involvement in urban logistics, addressing drone limitations in load capacity and endurance. However, regional constraints, including damage, blockades, pollution, and epidemics, pose routing challenges for trucks and drones. This study integrates regional restrictions into the heterogeneous truck–drone routing problem, presenting a mixed-integer programming model for cost minimization. To tackle complexity, we introduce an enhanced gray wolf optimization algorithm (EGWO), which improves the initial solution through partition scanning and a heuristic insertion algorithm. EGWO effectiveness is confirmed through enhancements in the standard test library. On average, the heterogeneous truck–drone model achieves a 28.31% cost reduction compared to the single-type truck delivery model. Moreover, deep insights into the impacts of multi-type trucks, the number of no-fly zones and the number of restricted traffic zones on the performance of the heterogeneous truck–drone system are discussed.", "keywords": [ "Truck", "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Transport engineering", "Computer science", "Business", "Computer network", "Engineering", "Biology", "Automotive engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s0217595924400165" }, { "id": "https://openalex.org/W4399374167", "doi": "10.1109/access.2024.3409780", "title": "Autonomous UAV Visual Navigation Using an Improved Deep Reinforcement Learning", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Hussein Samma", "Sami El Ferik" ], "first_author": "Hussein Samma", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In recent years, unmanned aerial vehicles (UAVs) have grown in popularity for a variety of purposes, including parcel delivery, search operations for missing persons, and surveillance. However, autonomously navigating UAVs in dynamic environments is a challenging task due to the presence of moving objects like pedestrians. In addition, traditional deep reinforcement learning approaches suffer from slow learning rates in dynamic situations and they need substantial training data. To improve learning performance, the present study proposed an enhanced deep reinforcement learning approach that encompasses two distinct learning stages namely the reinforced and self-supervised. In the reinforced learning stage, the deep Q-learning network (DQN) has been implemented and trained guided by the loss in the bellmen equation. On the other hand, the self-supervised stage is responsible for fine-tuning the backbone layers of DQN and it was directed by the contrastive loss function. The main benefit of incorporating the self-supervised stage is to speed up the encoding of the input scene captured by the UAV camera. To further enhance the navigation performances, an obstacle detection model was embedded to reduce UAV collisions. For experimental analysis, we have utilized an outdoor UAV-simulated environment called Blocks. This environment contains stationary objects that mimic buildings, as well as moving pedestrians. The study undertaken indicates that the implementation of the self-supervised stage led to significant improvements in navigation performance. Specifically, the simulated UAV was able to navigate longer distances in the correct direction toward the goal point. Moreover, the conducted analysis shows a significant navigation performance as compared with other DQN-based approaches like double DQN and dueling DQN.", "keywords": [ "Computer science", "Reinforcement learning", "Artificial intelligence", "Computer vision", "Human–computer interaction" ], "cited_by_count": 29, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2024.3409780" }, { "id": "https://openalex.org/W7161445604", "doi": "10.1016/j.urbmob.2026.100231", "title": "Noise-aware urban drone delivery: Integrating demand modeling, routing optimization and spatial noise exposure assessment", "year": 2026, "publication_date": "2026-05-16", "type": "article", "language": "en", "authors": [ "Ei Phyu Kyi", "Tao FENG" ], "first_author": "Ei Phyu Kyi", "venue": "Journal of Urban Mobility", "publisher": "Elsevier BV", "abstract": "While drone delivery is increasingly discussed for urban logistics, concerns regarding resident noise exposure potentially limit its large-scale deployment. In this study, we attempt to evaluate noise-aware urban delivery strategies by proposing an integrated modeling framework that links demand estimation with spatially explicit drone routing and noise simulation. Consumer choice of drone delivery is predicted based on a multinomial logit model, and the resulting distance-based delivery demand is translated into daily order volumes. Drone operations are then simulated under four scenarios combining direct-to-home delivery versus neighborhood-scale micro-hub-based (convenience-store-based) delivery and distance-optimal versus noise-aware routing. A modified A* algorithm incorporating altitude-dependent sound propagation and population-weighted noise penalties is applied to generate flight paths. Results based on the assessment of grid-based sound exposure indicate that noise-aware routing and micro-hub delivery reshape the spatial distribution of noise exposure, with mixed effects across exposure thresholds, while higher flight altitudes further mitigate peak noise levels. These findings offer important insights for urban planners and logistics providers by demonstrating that drone-related noise is not an avoidable externality, but a controllable outcome shaped by noise-aware integrated routing frameworks. The proposed framework provides a practical approach for assessing noise-aware urban drone deployment strategies in dense city environments.", "keywords": [ "Drone", "Noise (video)", "Routing (electronic design automation)", "Software deployment", "Noise control", "Vehicle routing problem", "Multinomial logistic regression" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.urbmob.2026.100231" }, { "id": "https://openalex.org/W4399768847", "doi": "10.1109/balkancom61808.2024.10557193", "title": "Energy-aware UAV Parcel Delivery Assignment", "year": 2024, "publication_date": "2024-06-03", "type": "conference-paper", "language": "en", "authors": [ "Viviana Arrigoni", "Giulio Attenni", "Novella Bartolini", "Matteo Finelli" ], "first_author": "Viviana Arrigoni", "venue": null, "publisher": null, "abstract": "Unmanned aerial vehicle (UAV) networks represent an ecological alternative to truck-based delivery systems, especially in urban areas prone to traffic congestion. In this paper, we envision a delivery system composed of a fleet of UAVs relying on a decentralized infrastructure that includes several service stations providing parcel storage and battery replacement. This innovative delivery system introduces a problem of coordination in task scheduling as drones have limited battery capacities.", "keywords": [ "Computer science", "Energy (signal processing)", "Physics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/balkancom61808.2024.10557193" }, { "id": "https://openalex.org/W7160854349", "doi": "10.3390/drones10050364", "title": "Assessing the Carbon Mitigation Potential of UAV-Based Last-Mile Delivery Using 3D Path Planning: A Case Study of Shanghai", "year": 2026, "publication_date": "2026-05-11", "type": "article", "language": "en", "authors": [ "Ruiqi Wang", "Ying Liu" ], "first_author": "Ruiqi Wang", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Urban last-mile delivery is an increasingly important source of transport-related emissions, yet evidence on low-altitude logistics under real-order demand and urban spatial constraints remains limited. Taking Shanghai as a representative megacity, this study integrates 185,673 real parcel orders with 3D urban spatial data to develop a unified unmanned aerial vehicle (UAV)–courier carbon accounting framework. The framework combines 3D UAV route-planning algorithms, UAV energy-consumption models, electric courier-vehicle energy models, and grid emission factors to compare carbon emissions between UAV and conventional delivery modes. The results show that, under the modeled operating assumptions, UAV delivery tends to provide lower per-delivery carbon emissions under lightweight and high-speed operating conditions. Scenario analysis further suggests that UAV deployment in Shanghai could reduce carbon emissions by approximately 343,300 t CO2 annually by 2030. These findings provide quantitative support for urban low-altitude logistics planning, infrastructure deployment, and policy design for low-carbon last-mile delivery. The framework is transferable to other Chinese cities with similar urban conditions, but the numerical results require local recalibration of parcel demand, urban morphology, airspace constraints, and electricity-related carbon factors.", "keywords": [ "Software deployment", "Greenhouse gas", "Grid", "Carbon fibers", "Scenario analysis", "Efficient energy use", "Geographic information system", "Urban area" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10050364" }, { "id": "https://openalex.org/W4415672020", "doi": "10.1371/journal.pone.0335614", "title": "Research on the optimization of delivery routes for vehicles with drones under no-fly zone restrictions", "year": 2025, "publication_date": "2025-10-29", "type": "article", "language": "en", "authors": [ "Yingbo Mao", "Guiqiong Jia" ], "first_author": "Yingbo Mao", "venue": "PLoS ONE", "publisher": "Public Library of Science", "abstract": "With the rapid development of e-commerce, logistics and distribution systems face the dual pressures of efficiency improvement and cost control. Unmanned Aerial Vehicle (UAV) delivery, featuring flexibility, high efficiency, and low carbon emissions, has become an effective means to solve the \"last-mile\" problem. However, the widespread no-fly zones in urban environments (e.g., airports, government agencies, and high-voltage power lines) severely limit the application scope of UAVs and increase the complexity of path planning. Against this backdrop, the vehicle-assisted UAV collaborative delivery model has emerged: through the division of labor and collaboration between ground vehicles and UAVs, it not only expands the service radius of UAVs but also overcomes the constraints of no-fly zones, achieving dual improvements in delivery efficiency and service quality.This study focuses on the optimization of vehicle-assisted UAV delivery paths under no-fly zone constraints, aiming to construct a multi-objective optimization model that balances delivery costs, carbon emissions, and customer satisfaction, and to design an efficient solution algorithm for providing scientific decision support to logistics enterprises. First, the paper systematically sorts out the classification and definition of no-fly zones as well as their impact mechanisms on UAV path planning, and elaborates on the theoretical basis of vehicle-UAV collaborative delivery, including the constituent elements of the problem, methods for quantifying customer satisfaction, and the application framework of heuristic algorithms. On this basis, a mixed-integer programming model is built with the objectives of minimizing total cost, minimizing carbon emissions, and maximizing customer satisfaction. Given that this model falls into the category of NP-hard problems, we have designed a four-stage heuristic solution. First, an improved K-means algorithm (IKM) is used to cluster customer points under the constraint of the UAV's maximum flight radius, so as to determine vehicle parking points. Second, a multi-objective genetic algorithm is applied to plan UAV delivery routes for customers in open areas. Next, the multi-objective genetic algorithm is continued to design initial routes for vehicles between parking points. Finally, the multi-objective genetic algorithm is utilized again to plan delivery routes for customers in no-fly zones, ultimately forming a complete collaborative \"vehicle-UAV\" delivery scheme.To verify the effectiveness of the model and algorithm, simulation experiments are conducted using two sets of cases: 30 customer points in a local area of Harbin and the large-scale R201 case from the Solomon dataset. The results show that compared with traditional vehicle-only or UAV-only delivery models, the vehicle-UAV collaborative delivery model exhibits significant advantages in total cost, carbon emissions, and customer satisfaction; the model maintains good robustness in stability tests under different no-fly zone settings; and parameter sensitivity analysis further reveals the impact of key parameters (e.g., UAV load capacity, endurance, and vehicle load capacity) on delivery performance, providing practical references for logistics enterprises in equipment selection and operation strategy formulation.", "keywords": [ "Scope (computer science)", "Drone", "Dual (grammatical number)", "Service (business)", "Heuristic", "Customer satisfaction", "Linear programming", "Path (computing)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0335614&type=printable" }, { "id": "https://openalex.org/W3191336037", "doi": null, "title": "Last mile logistics applied to the distribution of COVID-19 vaccines: A prospection of good practices", "year": 2021, "publication_date": "2021-07-01", "type": "preprint", "language": "fr", "authors": [ "Badr Bentalha", "Aziz Hmioui", "Lhoussaine Alla" ], "first_author": "Badr Bentalha", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "International audience", "keywords": [ "Prospection", "Mile", "Coronavirus disease 2019 (COVID-19)", "Distribution (mathematics)", "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)", "2019-20 coronavirus outbreak", "Geography", "Virology", "Medicine", "Archaeology", "Outbreak", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.archives-ouvertes.fr/hal-03581626" }, { "id": "https://openalex.org/W4200587457", "doi": "10.1007/978-981-16-5559-3_19", "title": "A Parallel Genetic Algorithm for Solving the Vehicle Routing Problem with Drone Medication Delivery", "year": 2021, "publication_date": "2021-12-09", "type": "book-chapter", "language": "en", "authors": [ "Mustapha Ouiss", "Abdelaziz Ettaoufik", "Abdelaziz Marzak", "Abderrahim Tragha" ], "first_author": "Mustapha Ouiss", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Domain (mathematical analysis)", "Vehicle routing problem", "Genetic algorithm", "Routing algorithm", "Computer network", "Machine learning", "Routing protocol", "Mathematics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-16-5559-3_19" }, { "id": "https://openalex.org/W4416862874", "doi": "10.1121/10.0039953", "title": "Directional active noise control for drone noise reduction", "year": 2025, "publication_date": "2025-12-01", "type": "article", "language": "en", "authors": [ "Hanwen Bi", "Yile Zhang", "Thushara Dheemantha Abhayapala", "Prasanga N. Samarasinghe" ], "first_author": "Hanwen Bi", "venue": "JASA Express Letters", "publisher": "Acoustical Society of America", "abstract": "Drones have become essential tools for applications ranging from aerial surveillance to last-mile delivery, raising concerns about noise pollution in populated environments. This paper presents a directional active noise control (ANC) framework that targets far-field noise reduction, rather than local suppression. A virtual microphone-based ANC algorithm is employed, using a near-drone microphone array to attenuate noise in a specific region. Experiments in a semi-anechoic chamber show an average reduction of 4.78 dB in the 1500-2400 Hz band and up to 10 dB at harmonic frequencies, highlighting the promise of directional ANC for quieter drone operations in sensitive settings.", "keywords": [ "Drone", "Active noise control", "Noise reduction", "Noise (video)", "Noise control", "Microphone", "Ranging", "Noise pollution" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://pubs.aip.org/asa/jel/article-pdf/doi/10.1121/10.0039953/20817863/124801_1_10.0039953.pdf" }, { "id": "https://openalex.org/W2622216637", "doi": "10.2514/6.2017-3072", "title": "UAV Trajectory Modeling Using Neural Networks", "year": 2017, "publication_date": "2017-06-05", "type": "conference-paper", "language": "en", "authors": [ "Min Xue" ], "first_author": "Min Xue", "venue": "17th AIAA Aviation Technology, Integration, and Operations Conference", "publisher": null, "abstract": "Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural network should be able to predict the vehicle's future states at next time step. A complete 4-D trajectory are then generated step by step using the trained neural network. Experiments in this work show that the neural network can approximate the sUAV's model and predict the trajectory accurately.", "keywords": [ "Trajectory", "Computer science", "Artificial neural network", "Artificial intelligence", "Physics" ], "cited_by_count": 23, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ntrs.nasa.gov/api/citations/20170009832/downloads/20170009832.pdf" }, { "id": "https://openalex.org/W2921867328", "doi": "10.20944/preprints201902.0110.v1", "title": "Cooperated Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance and Reconnaissance Missions", "year": 2019, "publication_date": "2019-02-13", "type": "preprint", "language": "en", "authors": [ "Yao Liu", "Zhihao Luo", "Zhong Liu", "Jianmai Shi", "Guangquan Cheng" ], "first_author": "Yao Liu", "venue": "Preprints.org", "publisher": null, "abstract": "In this paper, we present a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP). The 2E-GUCRP arises in the field of Unmanned Aerial Vehicle (UAV) Routing Problem such as those encountered in the context of city logistics. In a typical cooperated system, the UAV is launched from the Ground Vehicle (GV) and automatically flies to the designated target. Meanwhile, acting as a mobile base station, the GV can charge or change the UAV’s battery on the designated landing points to enable the UAV to continue its mission. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) model, which could be solved by commercial software, is constructed to describe this problem. In order to quickly solve the medium-scale problems, two existing heuristics to solve 2E-VRP are improved. The computational experiments are set up to compare our model with the 2E-VRP. The results indicate that the 2E-GUCRP obtains a better efficiency. Further discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.", "keywords": [ "Heuristics", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Context (archaeology)", "Flight plan", "Integer programming", "Real-time computing", "Embedded system", "Geography", "Algorithm" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/manuscript/201902.0110/v1/download" }, { "id": "https://openalex.org/W7154060935", "doi": "10.3390/logistics10040086", "title": "A Refined Kano Model Approach to Sustainable Last-Mile Convenience Services and Customer Satisfaction", "year": 2026, "publication_date": "2026-04-13", "type": "article", "language": "en", "authors": [ "Balázs Gyenge", "Viktor Póka", "Kornélia Mészáros" ], "first_author": "Balázs Gyenge", "venue": "Logistics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Background: Last-mile logistics is one of the most complex and cost-intensive segments of supply chains, particularly in densely populated urban environments where rising customer expectations, sustainability requirements, and operational constraints increasingly intersect. Despite growing academic interest, empirical evidence remains limited regarding how convenience-related last-mile service attributes influence customer satisfaction, while the sector is undergoing a revolutionary transformation. Methods: This study applies a refined Kano model to classify last-mile convenience services according to their differentiated effects on customer satisfaction. Data were collected through a structured questionnaire administered to active e-commerce users in a metropolitan area. The methodological approach modifies and extends the traditional Kano framework. Results: The findings reveal clear patterns among last-mile service attributes. Online tracking and preferred payment options function as One-dimensional attributes, proportionally influencing customer satisfaction. Time-based delivery, flexible pickup options, and sustainability-oriented service features appear as Attractive attributes, generating additional increases in service value. In contrast, advanced technological solutions such as drone or autonomous vehicle delivery were perceived as Indifferent attributes. These interpretations are further nuanced by the fuzzy approach. Conclusions: The results provide important insights and validation for consumer-centered service design and support the prioritization of investments aimed at developing sustainable and customer-oriented last-mile logistics systems.", "keywords": [ "Kano model", "Customer satisfaction", "Service (business)", "Service quality", "Sustainability", "Metropolitan area", "Service delivery framework", "Customer engagement", "Customer advocacy", "Customer retention" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2305-6290/10/4/86/pdf?version=1776070256" }, { "id": "https://openalex.org/W3035279594", "doi": "10.1016/j.trb.2020.05.010", "title": "Two-echelon vehicle routing problem with time windows and mobile satellites", "year": 2020, "publication_date": "2020-06-10", "type": "article", "language": "en", "authors": [ "Hongqi Li", "Haotian Wang", "Jun Chen", "Ming Bai" ], "first_author": "Hongqi Li", "venue": "Transportation Research Part B Methodological", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Heuristic", "Cruise", "Computer science", "Operations research", "Routing (electronic design automation)", "Real-time computing", "Engineering", "Computer network", "Artificial intelligence", "Aerospace engineering" ], "cited_by_count": 170, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trb.2020.05.010" }, { "id": "https://openalex.org/W4200305114", "doi": "10.31618/esu.2413-9335.2021.2.92.1516", "title": "PROSPECTS AND PROBLEMS OF ROBOTIC DELIVERY OF THE LAST MILE. THE RELEVANCE OF THE PROBLEM AND THE CONCEPT OF THE SOLUTION. PART I.", "year": 2021, "publication_date": "2021-12-16", "type": "article", "language": "en", "authors": [ "Yu.Kh. Khamukov", "М. А. Kanokova" ], "first_author": "Yu.Kh. Khamukov", "venue": "EurasianUnionofScientists", "publisher": "Science and Education", "abstract": "The express delivery market in recent years has been growing at the level of 3-4%, and even in these conditions, not only is it not saturated, but the demand for it is growing. According to Oxford Economics, the growth of the air cargo market, which determines the volume of the express delivery market, accelerated at times up to 7% per year from 2013 to 2018 [1]. The biggest changes took place in 2016-17 due to a technological breakthrough in the field of logistics with the introduction of services such as drone delivery, processing orders on the blockchain, calculation of the delivery mode using artificial intelligence, etc. It was expected that due to the growing demand on fast delivery guaranteed, the number of express delivery employees worldwide will grow to 4.5 million over the next few years. But the coronavirus pandemic has accelerated this process. In the study “The Future of Freight Transportation. How new technologies and new thinking can change the movement of goods”, presented by the international network of consulting companies Deloitte in 2017, states that carriers have already solved many of the problems associated with the transportation of goods. But the “last mile delivery” stage has remained limiting the development of the delivery service. At this stage, companies suffer losses due to the concentration of logistics, algorithmic and kinematic tasks that cannot be automated with modern means and technologies for replacing human labor. Consequently, the use of alternative, unconventional technologies at this stage is a key condition for the mass development of delivery.", "keywords": [ "Last mile (transportation)", "Relevance (law)", "Service (business)", "Business", "Operations research", "Computer science", "Operations management", "Economics", "Marketing", "Engineering", "Mile" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://econ-law.euroasia-science.ru/index.php/Euroasia/article/download/771/684" }, { "id": "https://openalex.org/W2969381273", "doi": "10.1109/dcoss.2019.00103", "title": "Emerging Urban Challenge: RPAS/UAVs in Cities", "year": 2019, "publication_date": "2019-05-01", "type": "conference-paper", "language": "en", "authors": [ "Luke Russell", "Rafik A Goubran", "Felix Kwamena" ], "first_author": "Luke Russell", "venue": null, "publisher": null, "abstract": "An emerging urban challenge will be the proliferation of Remotely Piloted Aircraft Systems (RPAS) or drones, as their usage grows and drones fill the urban skies. Urban airspace will include many more Uninhabited Aerial Vehicles (UAVs), and related accidents and mishaps will also increase. Applications of UAVs in urban environments include photography and film-making, security monitoring, real estate, construction, property and infrastructure inspections, leisure, public safety (fires, natural disasters, investigations), traffic, and much more. Emerging UAV applications include last-mile drone delivery services. This paper discusses urban scenarios where drones are more ubiquitous. Monitoring and related safety of these UAVs will be increasingly important. Though multi-modal purpose-built drone tracking and monitoring systems will be the most effective solution for detection and tracking of these RPAS, during the transition to more regular drone use in urban areas and in everyday urban applications, a more rapid-deployment and agile detection system is needed that does not require installation of hardware. In this paper, sensory substitution is presented as an approach to use ambient and pre-existing microphones and AI techniques to detect the presence of RPAS. Preliminary results show promise for this agile IoT method as a key solution to this emerging urban challenge.", "keywords": [ "Drone", "Agile software development", "Computer science", "Computer security", "Software deployment", "Transport engineering", "Engineering" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dcoss.2019.00103" }, { "id": "https://openalex.org/W4410469213", "doi": "10.1016/j.swevo.2025.101968", "title": "A multi-objective benchmark for UAV path planning with baseline results", "year": 2025, "publication_date": "2025-05-18", "type": "article", "language": "en", "authors": [ "Daison Darlan", "Oladayo Solomon Ajani", "Anand Paul", "Rammohan Mallipeddi" ], "first_author": "Daison Darlan", "venue": "Swarm and Evolutionary Computation", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Baseline (sea)", "Benchmark (surveying)", "Motion planning", "Path (computing)", "Artificial intelligence", "Real-time computing", "Robot", "Computer network" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.swevo.2025.101968" }, { "id": "https://openalex.org/W4391979512", "doi": "10.1145/3638550.3643631", "title": "Demo: Towards Autonomous Drone Delivery to Your Door Over House-Aware Semantics", "year": 2024, "publication_date": "2024-02-20", "type": "conference-paper", "language": "en", "authors": [ "Shengqing Xia", "Junpeng Guo", "Chen Peng", "Chunyi Peng" ], "first_author": "Shengqing Xia", "venue": null, "publisher": null, "abstract": "Drone delivery is swiftly on the rise. Different from most prior efforts centered on the last-mile problem where the drone flies from the source to the destination house but overlooks the landing part [1, 2]. In our full paper [3], we make the first attempt on the last-hundred-feet problem, which aims to land at a convenient and safe point (say D-point, in front of the door) in a fully autonomous manner. In this demo, we specifically focus on the key technical component Structural Semantic Segmentation (SSS), a novel semantic segmentation-based approach to identify D-point. Unlike the classical SS, it capitalizes the common single-family house (SFH) structures to refine the misclassified region from SS results. Our results show that the detection precision of boundary between the house and pavement (indicating the door location) reaches 84%, compared to 16% achieved by SS method, and only adds 430 ms to the processing time. We leverage SSS to progressively update the D-point guiding it closer to the door (Figure 1a). Video can be found at https://youtu.be/G6I9XzCyHFQ", "keywords": [ "Drone", "Leverage (statistics)", "Computer science", "Segmentation", "Point (geometry)", "Focus (optics)", "Semantics (computer science)", "Key (lock)", "Artificial intelligence", "Computer graphics (images)", "Computer security", "Programming language" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3638550.3643631" }, { "id": "https://openalex.org/W4409835077", "doi": "10.1002/net.22280", "title": "A New Formulation for the Traveling Salesman Problem With Drone and Lockers", "year": 2025, "publication_date": "2025-04-26", "type": "article", "language": "en", "authors": [ "Danilo Amitrano", "Maurizio Boccia", "Adriano Masone", "Claudio Sterle" ], "first_author": "Danilo Amitrano", "venue": "Networks", "publisher": "Wiley", "abstract": "ABSTRACT Nowadays, driven by factors such as the rapid growth of online sales, different delivery methods are being explored to improve last‐mile logistics processes. Among these, the combined use of trucks and drones and the option of utilizing parcel lockers as an alternative to home delivery have led to the definition of new optimization problems. In this work, we study the Traveling Salesman Problem with Drone and Lockers (TSP‐DL), the first optimization problem to integrate both of these innovative delivery methods. The goal is to determine the optimal route for a tandem consisting of a truck and a drone to serve a set of customers. Each customer can either be served at home or retrieve their package from a parcel locker where the tandem has delivered it. The objective is to minimize total delivery costs, which depend on the total delivery time and compensation provided by the delivery company to customers served via parcel lockers. We propose a new formulation for the TSP‐DL, strengthened by additional valid inequalities and solved using a Branch‐and‐Cut algorithm. Unlike other formulations in the literature, our approach does not use time variables. Computational experiments on benchmark instances demonstrate that the proposed approach outperforms the current state of the art for the TSP‐DL, providing either optimal solutions or improved bounds for several previously unsolved instances. Additionally, we introduce a new set of benchmark instances to evaluate the scalability of the proposed approach. Finally, we provide managerial insights derived from an analysis of different coordination policies between the truck and the drone.", "keywords": [ "Travelling salesman problem", "Computer science", "Benchmark (surveying)", "Scalability", "Truck", "Drone", "Vehicle routing problem", "Set (abstract data type)", "Operations research", "Mathematical optimization", "Routing (electronic design automation)", "Algorithm" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1002/net.22280" }, { "id": "https://openalex.org/W4367181663", "doi": "10.3390/app13095441", "title": "Multi-Location Assortment Optimization with Drone and Human Courier Joint Delivery", "year": 2023, "publication_date": "2023-04-27", "type": "article", "language": "en", "authors": [ "Mengting Wu", "Zhi Pei" ], "first_author": "Mengting Wu", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This paper studies the constrained multi-location assortment optimization problem under joint delivery by drone and human courier. To maximize the revenue of the retailer, product assortment and last-mile delivery methods are configured simultaneously. To handle the joint delivery problem with product assortment, a mixed multinomial logit model is established to depict the customer purchase behavior. We also take into account the load capacity of drones and the delivery distance limit of drones and human couriers. To the best of our knowledge, the present paper is the first to study the assortment optimization problem considering joint delivery by drone and human courier. The problem is first formulated as a mixed-integer linear programming (MILP) model, which is then transformed into a conic quadratic mixed-integer model with McCormick inequalities (Conic + MC). In the numerical study, it is demonstrated that Conic + MC outperforms MILP as Conic + MC finds the optimal solution within 300 s, while MILP needs more than 600 s. Furthermore, several management insights are revealed for retailers regarding assortment planning when facing different kinds of product revenue structures, and advice is provided for online ordering platforms in terms of setting delivery limits.", "keywords": [ "Mathematical optimization", "Computer science", "Drone", "Integer programming", "Operations research", "Product (mathematics)", "Mathematics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/13/9/5441/pdf?version=1682586328" }, { "id": "https://openalex.org/W4297682703", "doi": "10.1002/9781119870869.ch6", "title": "Integration of UAVs and Public Transportation Vehicles for Parcel Delivery", "year": 2022, "publication_date": "2022-09-09", "type": "other", "language": "en", "authors": [ "Hailong Huang PhD", "Andrey V. Savkin", "Chao Huang PhD" ], "first_author": "Hailong Huang PhD", "venue": null, "publisher": null, "abstract": "In this chapter, we discuss applications of unmanned aerial vehicles ( UAVs ) in the last-mile parcel delivery. UAVs have been considered as a promising tool for the future logistics industry by many companies thanks to the reduced cost and increased mobility. However, one barrier is the limited flight time due to the limitation of the onboard battery, which has not been explicitly considered yet in the previous chapters. This chapter presents the recent research work on exploiting public transportation vehicles to assist UAV delivery. Particular attention is paid to the path planning problem when a UAV can travel with public transportation vehicles, and several algorithms are presented to deal with the considered problem in different situations.", "keywords": [ "Public transport", "Transport engineering", "Computer science", "Work (physics)", "Path (computing)", "Transportation planning", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1002/9781119870869.ch6" }, { "id": "https://openalex.org/W4406904407", "doi": "10.3390/drones9020098", "title": "Efficient Coverage Path Planning for a Drone in an Urban Environment", "year": 2025, "publication_date": "2025-01-27", "type": "article", "language": "en", "authors": [ "Joanne Sabag", "Barak Pinkovich", "Ehud Rivlin", "Héctor P. Rotstein" ], "first_author": "Joanne Sabag", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera’s field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms’ performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously.", "keywords": [ "Drone", "Motion planning", "Path (computing)", "Computer science", "Urban planning", "Environmental planning", "Transport engineering", "Aeronautics", "Business", "Geography", "Artificial intelligence", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9020098" }, { "id": "https://openalex.org/W4401568576", "doi": "10.1109/itc-egypt61547.2024.10620589", "title": "Towards Efficient Collaborative Delivery Solutions Using Drones and Ground Transportation: A Review", "year": 2024, "publication_date": "2024-07-22", "type": "conference-paper", "language": "en", "authors": [ "Jahanara Thasnim", "Eiman A. Elghanam", "Mohamed S. Hassan", "Ahmed H. Osman" ], "first_author": "Jahanara Thasnim", "venue": null, "publisher": null, "abstract": "The increasing popularity of unmanned aerial vehi-cles (UAVs), also known as drones, in the logistics industry is attributed to their advantages such as low cost, energy efficiency, and independence from road conditions. However, drones face limitations including short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model integrating drones with existing transportation modes holds both theoretical research significance and practical application value. This literature review aims to bridge the gap between theoretical discussions on routing algorithms and practical implementation strategies for efficient delivery using drones in collaboration with trucks, public transportation, and electric vehicles (EVs), While existing literature predominantly focuses optimal UAV routing algorithms, little research addresses the potential challenges faced during the practical implementation of integrated drone-ground transportation systems. Addressing these gaps is crucial for maximizing the potential of delivery drones within a sustainable logistics ecosystem. This review explores key areas including current UAV battery charging techniques, types of delivery drones, and the integration between drones and trucks, public transportation systems, and EVs for last-mile delivery (LMD) tasks. By addressing these critical aspects, we aim to provide a comprehensive understanding of the challenges and opportunities associated with integrating drones into the existing transportation infrastructure, ultimately enhancing delivery efficiency.", "keywords": [ "Drone", "Ground transportation", "Computer science", "Transport engineering", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itc-egypt61547.2024.10620589" }, { "id": "https://openalex.org/W4401754666", "doi": "10.1109/iciteics61368.2024.10625394", "title": "Unmanned Aerial Vehicles by Implementing Mobile Edge Computing for Resource Allocation", "year": 2024, "publication_date": "2024-06-28", "type": "conference-paper", "language": "en", "authors": [ "Vraj Nilay Shah", "Raj Singh", "Sapthak Mohajon Turjya", "Parv Ahuja", "Anjan Bandyopadhyay", "Sujata Swain" ], "first_author": "Vraj Nilay Shah", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) have become indispensable across various sectors, spanning from environmental surveillance and agricultural monitoring to last-mile package delivery services. The efficiency and effectiveness of UAV operations heavily rely on robust resource allocation strategies. Recognizing this need, Mobile Edge Computing (MEC) has been a absolute solution. MEC offers computational and storage resources, bringing them closer to where UAVs operate. This proximity minimizes latency and enhances data processing capabilities, thus significantly improving UAV performance. In this research, we delve into the potential of MEC in optimizing resource allocation for UAVs. Our proposed framework capitalizes on the Preferential Cyclic Reassignment Mechanism (PCRAM), a sophisticated mechanism within an MEC-enabled architecture.By integrating PCRAM with MEC infrastructure, we aim to streamline resource allocation processes for UAVs. This approach ensures that computational tasks and data storage are allocated efficiently, maximizing UAV performance while minimizing resource wastage and latency. Through this investigation, we seek to demonstrate the viability and efficacy of leveraging MEC for resource allocation in UAV operations. Ultimately, our research aims to contribute to the advancement of UAV technology by enhancing their capabilities through intelligent resource management mechanisms.", "keywords": [ "Computer science", "Mobile edge computing", "Resource allocation", "Mobile computing", "Enhanced Data Rates for GSM Evolution", "Resource management (computing)", "Edge computing", "Resource (disambiguation)", "Mobile telephony", "Computer network", "Distributed computing", "Mobile radio" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iciteics61368.2024.10625394" }, { "id": "https://openalex.org/W7116770540", "doi": "10.1016/j.eswa.2025.130816", "title": "Research on collaborative path planning of UAV swarms for urban logistics distribution in dense building environments", "year": 2025, "publication_date": "2025-12-22", "type": "article", "language": "en", "authors": [ "Yuanda Lai", "Husheng Wu", "Yuanqing Xia", "Xiaowei Fu", "Dongli Duan", "Aiai Wang", "Meimei Shi" ], "first_author": "Yuanda Lai", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Motion planning", "Swarm behaviour", "Collision avoidance", "Initialization", "Resilience (materials science)", "Population", "Convergence (economics)", "Path (computing)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2025.130816" }, { "id": "https://openalex.org/W4382366469", "doi": "10.1007/978-3-031-37111-0_21", "title": "A Bi-objective Routing Problem with Trucks and Drones: Minimizing Mission Time and Energy Consumption", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Mahdi Moeini", "Oliver Wendt", "Marius Schummer" ], "first_author": "Mahdi Moeini", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Computer science", "Energy consumption", "Job shop scheduling", "Integer programming", "Benchmark (surveying)", "Simulated annealing", "Vehicle routing problem", "Routing (electronic design automation)", "Mathematical optimization", "Minification" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-37111-0_21" }, { "id": "https://openalex.org/W4367355755", "doi": "10.1007/978-3-031-30855-0_14", "title": "Vehicle Routing Problem for an Integrated Electric Vehicles and Drones System", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Setyo Tri Windras Mara", "Saber Mohammed Elsayed", "Daryl Essam", "Ruhul Amin Sarker" ], "first_author": "Setyo Tri Windras Mara", "venue": "Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering", "publisher": null, "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Set (abstract data type)", "Computer science", "Electric vehicle", "Operations research", "Mathematical optimization", "Engineering", "Computer network", "Mathematics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-30855-0_14" }, { "id": "https://openalex.org/W4389666952", "doi": "10.1109/apeie59731.2023.10347781", "title": "Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring", "year": 2023, "publication_date": "2023-11-10", "type": "conference-paper", "language": "en", "authors": [ "Aleksey S. Rodionov", "Tulkin Matkurbanov", "Madina R. Yagibayeva" ], "first_author": "Aleksey S. Rodionov", "venue": null, "publisher": null, "abstract": "Military, construction, picture and video mapping, medical, search and rescue, parcel delivery, covert area survey, oil rig and power line monitoring are just a few of the applications for unmanned aerial vehicles (UAVs). UAVs are gaining popularity as a model for agricultural, wireless communications and aerial surveillance, as well as service and delivery. Unmanned aerial vehicle (UAV) research has attracted a lot of interest in recent decades due to the rapid growth of computer technology, autonomous control technology, and communication technology. In various industries, including aviation, unmanned systems are replacing manned systems. Due to their high success rates in military and civilian missions, unmanned aerial vehicles (UAVs), one of the most popular and successful unmanned systems, are gradually becoming an important part of the industry. air. The biggest problem with drones is finding the best trajectory in difficult conditions. It seeks to tour all control locations as efficiently as possible in order to receive information from sensors set in wide areas. In this paper, we employed a Genetic Algorithm (GA) to discover the best flight path for unmanned aerial vehicles (UAVs) in a three-dimensional environment, i.e. space. One of the important elements in the suggested strategy is solving the travelling salesman problem (TSP) to find the optimal path. In the future, we can enhance the complexity of our problem through the addition of new constraints imposed by the dynamic environment in which we operate. The experimental results suggest that GA can be used to optimize UAV path planning.", "keywords": [ "Drone", "Motion planning", "Computer science", "Search and rescue", "Flight plan", "Genetic algorithm", "Travelling salesman problem", "Real-time computing", "Aeronautics", "Robot", "Engineering", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/apeie59731.2023.10347781" }, { "id": "https://openalex.org/W4402054745", "doi": "10.3390/su16177516", "title": "Toward Greener Supply Chains by Decarbonizing City Logistics: A Systematic Literature Review and Research Pathways", "year": 2024, "publication_date": "2024-08-30", "type": "review", "language": "en", "authors": [ "Doğukan Toktaş", "M. Ali Ülkü", "Muhammad Ahsanul Habib" ], "first_author": "Doğukan Toktaş", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The impacts of climate change (CC) are intensifying and becoming more widespread. Greenhouse gas emissions (GHGs) significantly contribute to CC and are primarily generated by transportation—a dominant segment of supply chains. City logistics is responsible for a significant portion of GHGs, as conventional vehicles are the primary mode of transportation in logistical operations. Nonetheless, city logistics is vital for urban areas’ economy and quality of life. Therefore, decarbonizing city logistics (DCL) is crucial to promote green cities and sustainable urban living and mitigate the impacts of CC. However, sustainability encompasses the environment, economy, society, and culture, collectively called the quadruple bottom line (QBL) pillars of sustainability. This research uses the QBL approach to review the extant literature on DCL. We searched for articles on SCOPUS, focusing on analytical scholarly studies published in the past two decades. By analyzing publication years, journals, countries, and keyword occurrences, we present an overview of the current state of DCL research. Additionally, we examine the methods and proposals outlined in the reviewed articles, along with the QBL aspects they address. Finally, we discuss the evolution of DCL research and provide directions for future research. The results indicate that optimization is the predominant solution approach among the analytical papers in the DCL literature. Our analysis reveals a lack of consideration for the cultural aspect of QBL, which is essential for the applicability of any proposed solution. We also note the integration of innovative solutions, such as crowdsourcing, electric and hydrogen vehicles, and drones in city logistics, indicating a promising research area that can contribute to developing sustainable cities and mitigating CC.", "keywords": [ "Supply chain", "Business", "Systematic review", "Marketing", "MEDLINE", "Political science" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/16/17/7516/pdf?version=1725006860" }, { "id": "https://openalex.org/W4413755027", "doi": "10.1109/access.2025.3603185", "title": "Aircraft-Cargo Separation Rate-Driven Ground Risk–Cost Optimization for Urban Logistics UAVs", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Tao Yang", "Baoli Zheng", "Mingli Mo", "Jie Shang", "Huang Dan" ], "first_author": "Tao Yang", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The surge in demand for urban logistics has positioned unmanned aerial vehicles (UAVs) as a vital solution for improving last mile delivery efficiency in complex urban environments. To overcome the ground safety bottleneck in the large-scale application of logistics UAVs, this paper establishes a full-chain framework of “risk characteristics–quantitative model–risk cost” that proposes an innovative dual-state risk–cost optimization model based on aircraft-cargo separation probability. The study quantifies the separation probability using the Weibull distribution, corrects the risk value assessment deviation under the traditional single fall assumption, and establishes a risk probability model for the separation state by combining the dynamic characteristic differences between the UAV body and cargo, enabling the generation of rasterized risk maps by integrating spatial parameters, including population density and shelter coefficients, to implement risk–cost conversion and support airspace management. Case verification reveals that in areas with different land uses, there are significant differences in the ground risk values and risk costs of logistics UAVs. Additionally, introducing the aircraft-cargo separation rate improves risk assessment accuracy by 57%, and the ground risk in the aircraft-cargo integrated state is 4.31 times higher than in the separated state. This study provides core theoretical support for the safe operation and route decision-making involving logistics UAVs in complex urban scenarios.", "keywords": [ "Separation (statistics)", "Computer science", "Environmental science", "Aerospace engineering", "Aeronautics", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2025.3603185" }, { "id": "https://openalex.org/W4393338740", "doi": "10.2139/ssrn.4775458", "title": "Cost-effectiveness of aerial logistics for immunization: a model-based evaluation of centralized storage and drone delivery of vaccines in Ghana using empirical data", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Maria Jose Ospina-Fadul", "Pedro Kremer", "Scott E. Stevens", "Florence Haruna", "Marion Okoh-Owusu", "Godfred Sarpong", "Kingsley Osei-Kwakye", "Joshua Billy", "Osey Sakyi" ], "first_author": "Maria Jose Ospina-Fadul", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Immunization", "Business", "Operations research", "Medicine", "Engineering", "Immunology", "Immune system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4775458" }, { "id": "https://openalex.org/W7171555348", "doi": "10.1016/j.tre.2026.105127", "title": "Beyond efficiency: societal benefit perception as a driver of public acceptance for UAM-based last-mile delivery in Japan", "year": 2026, "publication_date": "2026-07-29", "type": "article", "language": "en", "authors": [ "Dohyung Kim", "Sunbin Yoo", "Shunsuke Managi", "Zheng Chang" ], "first_author": "Dohyung Kim", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Perception", "Risk perception", "Social benefits", "Social acceptance", "Social impact", "Public opinion", "Poison control" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105127" }, { "id": "https://openalex.org/W3123821647", "doi": "10.2139/ssrn.3549622", "title": "The Multiple Flying Sidekicks Traveling Salesman Problem with Variable Drone Speeds", "year": 2020, "publication_date": "2020-01-01", "type": "preprint", "language": "en", "authors": [ "Ritwik Raj", "Chase C. Murray" ], "first_author": "Ritwik Raj", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Variable (mathematics)", "Computer science", "Mathematics", "Algorithm", "Biology" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.3549622" }, { "id": "https://openalex.org/W4409872486", "doi": "10.1080/00207543.2025.2496965", "title": "A bi-objective optimisation model for the drone scheduling problem in island delivery", "year": 2025, "publication_date": "2025-04-28", "type": "article", "language": "en", "authors": [ "Ying Yang", "Jiaxin Liu", "Shuaian Wang" ], "first_author": "Ying Yang", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "Drone-assisted parcel delivery to remote islands is increasingly replacing traditional methods, offering improved efficiency and enhanced service reliability. This paper addresses the drone scheduling problem in island delivery (DSP-ID) by optimising drone delivery routes. In particular, we first introduce a bi-objective mixed-integer linear programming model that concurrently optimises delivery time and energy consumption. To address the model, both a heuristic non-dominated sorting genetic algorithm II (NSGA-II) and an exact augmented ε-constraint method are developed. The efficacy and robustness of the proposed model and algorithms are evaluated through experiments across various scales. Results indicate that both algorithms yield high-quality solutions for DSP-ID in small-scale scenarios. However, as the problem size expands, the performance of the augmented ε-constraint method wanes under time constraints, whereas the NSGA-II consistently delivers high-quality solutions. Additionally, we provide decision-makers with actionable insights for selecting the most effective drone delivery routes.", "keywords": [ "Drone", "Scheduling (production processes)", "Computer science", "Job shop scheduling", "Operations research", "Mathematical optimization", "Engineering", "Operations management", "Mathematics", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2025.2496965" }, { "id": "https://openalex.org/W4391768801", "doi": "10.1109/itsc57777.2023.10422537", "title": "Tightly Joining Positioning and Control for Trustworthy Unmanned Aerial Vehicles Based on Factor Graph Optimization in Urban Transportation", "year": 2023, "publication_date": "2023-09-24", "type": "conference-paper", "language": "en", "authors": [ "Peiwen Yang", "Weisong Wen" ], "first_author": "Peiwen Yang", "venue": null, "publisher": null, "abstract": "Unmanned aerial vehicles (UAV) showed great potential in improving the efficiency of parcel delivery applications in the coming smart cities era. Unfortunately, the trustworthy positioning and control algorithms of the UAV are significantly challenged in complex urban areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, for example, the system dynamics of the control can largely help with the positioning, this paper proposed a joint positioning and control method (JPCM) based on factor graph optimization (FGO), which combines sensors' measurements and control intention. In particular, the positioning measurements are formulated as the factors in the factor graph model, such as the positioning from the GNSS. The model predictive control (MPC) is also formulated as the additional factors in the factor graph model. By solving the factor graph contributed by both the positioning factor and the MPC-based factors, the complementariness of positioning and control can be fully explored. To guarantee reliable system dynamic parameters, we validate the effectiveness of the proposed method using a simulated quadrotor system which showed significantly improved trajectory following performance. To benefit the research community, we open-source our code and make it available at https://github.com/RoboticsPolyu/IPN_MPC.", "keywords": [ "Trustworthiness", "Computer science", "Computer security" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itsc57777.2023.10422537" }, { "id": "https://openalex.org/W2949262399", "doi": "10.25292/atlr.v1i1.33", "title": "Mechanism For Drones Delivering The Medical First Aid Kits", "year": 2018, "publication_date": "2018-01-01", "type": "article", "language": "en", "authors": [ "Wan Mazlina Wan", "Muhamad Syaiful Azrie", "Adibah Shuib" ], "first_author": "Wan Mazlina Wan", "venue": "Advances in Transportation and Logistics Research", "publisher": null, "abstract": "Transportation plays vital roles in lives. Its connections are associated with almost everything, from postal deliveries to multi-million cargo shipments. Currently, transportation of medical goods during critical need is limited to wheeled motor vehicles and manned aircrafts, which can be costly, slow, and sometimes impossible when emergency site is out of reach. Nowadays, drones revolutionise healthcare by transporting lifesaving medical supplies such as medical aids and blood. Unfortunately, with current technology, drone’s battery lifespan is a limitation that can hinder the delivery process. This paper describes our study that concerns with design of mechanism that prolongs drone’s battery life and offers effective delivery of medical first aids. The mechanism is attached to the drone and carries medical aids safely while decreasing the number of take-off and landing by deploying the kits from air to land (using parachute method). The main objective of this study is to design a mechanism that can carry and release the medical kit with care at the targeted area. Based on the results, the mechanism was proven to be able to carry and release medical first aids securely during flight and landing.", "keywords": [ "Drone", "Mechanism (biology)", "Medical care", "Process (computing)", "Aeronautics", "Health care", "Medical emergency", "Business", "Computer security", "Computer science", "Engineering", "Medicine" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://proceedings.itltrisakti.ac.id/index.php/ATLR/article/download/33/44" }, { "id": "https://openalex.org/W4404036906", "doi": "10.1109/itecasia-pacific63159.2024.10738631", "title": "Optimization of collaborative delivery routes for electric trucks and drones with integrated recharging strategies", "year": 2024, "publication_date": "2024-10-10", "type": "conference-paper", "language": "en", "authors": [ "Shu Wang", "Yixuan Cheng", "Tian Min Guan", "Liming Ma", "Yi Han" ], "first_author": "Shu Wang", "venue": null, "publisher": null, "abstract": "With the rapid expansion of global e-commerce, the logistics industry faces increasing demand for last-mile delivery solutions, highlighting the need for sustainable solutions such as the integration of Electric Trucks (ETs) and drones to improve the delivery operational efficiency and reduce the environmental impact. This paper presents a novel routing model to optimize the delivery routes by considering the collaboration of ETs and drones. In response to the problem of limited driving range due to battery capacity constraints in large-scale delivery scenarios, our approach considers the battery limitations of both ETs and drones, allowing ETs to make multiple trips and recharge at designated charging stations, while drones can be recharged by returning to corresponding ETs during the deliveries. Finally, a practical case study based on real-word logistics scenario in Las Vegas is conducted to evaluate the performance of the proposed model, demonstrating significant cost savings and increased delivery efficiency when ETs and drones are allowed to recharge during the delivery process, providing a robust solution for sustainable and efficient large-scale logistics distribution", "keywords": [ "Truck", "Drone", "Computer science", "Transport engineering", "Aeronautics", "Automotive engineering", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itecasia-pacific63159.2024.10738631" }, { "id": "https://openalex.org/W4293233951", "doi": "10.1109/edge55608.2022.00027", "title": "Continuous Authentication for UAV Delivery Systems Under Zero-Trust Security Framework", "year": 2022, "publication_date": "2022-07-01", "type": "conference-paper", "language": "en", "authors": [ "Chengzu Dong", "Frank Jiang", "Shiping Chen", "Xiao Liu" ], "first_author": "Chengzu Dong", "venue": null, "publisher": null, "abstract": "In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used in smart logistics to address the last-mile delivery challenge. To complete the delivery tasks, UAVs usually need to store or have access to some sensitive data such as the private information about the parcel and its receiver during their flight, and only the authenticated people or devices are authorised to access the UAV's data. However, there are many potential risks such as injection attacks and spoofing attacks that can cause data leakage. Therefore, how to preserve users' sensitive data and authenticate the identities of people and devices in the UAV delivery system has become an essential requirement under the emerging zero-trust security framework. In this paper, to address such an issue, we propose a continuous authentication scheme for UAV delivery systems that is featured with continuous authentication, multi-factor authentication (MFA), and digital authentication using the blockchain-based Self-Sovereign Identity (SSI) technique. Specifically, continuous authentication is applied to both users and UAVs in the system. The evaluation results demonstrate that our proposed continuous authentication scheme can effectively prevent various attacks and provide security analysis for UAV delivery systems.", "keywords": [ "Spoofing attack", "Computer science", "Computer security", "Authentication (law)", "Scheme (mathematics)", "Authentication protocol", "Computer network" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/conference_contribution/Continuous_Authentication_for_UAV_Delivery_Systems_Under_Zero-Trust_Security_Framework/21132268" }, { "id": "https://openalex.org/W4323313076", "doi": "10.3390/futuretransp3010020", "title": "Congestion-Aware Bi-Modal Delivery Systems Utilizing Drones", "year": 2023, "publication_date": "2023-03-03", "type": "article", "language": "en", "authors": [ "Mark Beliaev", "Negar Mehr", "Ramtin Pedarsani" ], "first_author": "Mark Beliaev", "venue": "Future Transportation", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With e-commerce demand rising, logistic operators are investing in alternative delivery methods such as drones. Because of their aerial reach, drones can provide much needed utility in the last mile by taking the load off of vehicles delivering parcels to customers on the road. Our goal is to assess the potential drones have in mitigating traffic congestion. To do so, we develop a mathematical model for a bi-modal delivery system composed of parcels carrying trucks and drones, combining it with an optimization problem that can be solved for the socially optimal routing and allocation policy efficiently. Within this formulation, we include multiple stakeholder perspectives by modeling the objective function in terms of both traffic congestion and parcel latency. This allows our model to quantify the impact of drones on reducing traffic congestion, and simultaneously finds the path routing that minimizes the given objective. To account for the effects of stopping trucks on road latency, we simulate roads shared between trucks and cars by utilizing SUMO. We then use quadratic optimization techniques to test our proposed framework on a variety of real-world transportation networks. Our findings highlight the trade-off between reducing traffic congestion and increasing parcel latency—while routing trucks along less time-efficient paths may alleviate traffic congestion, this disproportionately increases the parcel latency. This suggests the need for a balanced approach that considers both factors when solving for the routing policy.", "keywords": [ "Drone", "Traffic congestion", "Computer science", "Truck", "Routing (electronic design automation)", "Transport engineering", "Latency (audio)", "Computer network", "Operations research", "Engineering", "Telecommunications" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2673-7590/3/1/20/pdf?version=1677836143" }, { "id": "https://openalex.org/W4396862347", "doi": "10.1111/itor.13476", "title": "Drone scheduling in a hexagon‐based delivery network with multiple fulfillment centers", "year": 2024, "publication_date": "2024-05-13", "type": "article", "language": "en", "authors": [ "Chuang Liu", "Huaping Chen", "Xueping Li", "Shengchao Zhou", "Zhaohong Jia" ], "first_author": "Chuang Liu", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Abstract As an emerging technology, drones have great potential in small parcel delivery, reducing operational costs, and improving efficiency. However, battery technologies restrict the endurance of drones. Inspired by the cellular networks used in mobile communications, a hexagon‐based delivery network (HBDN) is proposed to overcome such restrictions. Battery‐swapping stations are incorporated into the HBDN to expand the delivery service range of drones. Based on the proposed HBDN, the multidrone scheduling problem in multiple fulfillment centers (FCs) is addressed. The objective is to minimize the makespan. A mixed‐integer programming model is constructed for the problem. A memetic algorithm with hill climbing (MAHC) is developed to address the problem. A strategy is proposed to prevent inbreeding in the crossover procedure. A local search based on hill climbing is combined with the mutation operation. A lower bound of the problem is proposed to measure the performance of MAHC. Furthermore, load balancing and nearest‐priority strategies are proposed to select FCs. Comparative experiments show that MAHC performs competitively in terms of running time and solution quality.", "keywords": [ "Drone", "Computer science", "Scheduling (production processes)", "Distributed computing", "Mathematical optimization", "Mathematics", "Biology" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1111/itor.13476" }, { "id": "https://openalex.org/W4320927624", "doi": "10.48550/arxiv.2109.14442", "title": "A Survey on Security and Privacy Issues of UAVs", "year": 2021, "publication_date": "2021-09-29", "type": "preprint", "language": "en", "authors": [ "Yassine Mekdad", "Ahmet Arış", "Leonardo Babun", "Abdeslam EL Fergougui", "Mauro Conti", "Riccardo Lazzeretti", "Arif Selcuk Uluagac" ], "first_author": "Yassine Mekdad", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands its usage at a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs' security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs.", "keywords": [ "Drone", "Computer security", "Compromise", "Implementation", "Computer science", "Scale (ratio)", "Software", "Internet privacy", "Political science" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2109.14442" }, { "id": "https://openalex.org/W3129470082", "doi": null, "title": "Possibility of Order Delivery from e-Shop Within Last Mile. Practical Example from Slovakia", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Peter Jucha", "Tatiana Čorejová" ], "first_author": "Peter Jucha", "venue": "RePEc: Research Papers in Economics", "publisher": "Federal Reserve Bank of St. Louis", "abstract": "The paper focuses on the possibilities and methods of delivering orders from e-shops to stomers within the last mile. There are currently many ways in which goods can reach e customer directly. In addition to the traditional postal and courier services, dispensers th lockers, drones, autonomous vehicles or various shops and stores have been used. The eoretical part of the paper focuses on an overview of such possibilities of delivery of ods to the customer within the last mile. In the results is then a example of four e-shops lling books in Slovakia and comparing just the possibility of order delivery to the stomer from the perspective of delivery options orders, prices for these delivery options, der delivery time, and order delivery accompanying services. Based on the comparison, was concluded that e-shops offer customers good order delivery services, but some things n still be improved.", "keywords": [ "Last mile (transportation)", "Order (exchange)", "Mile", "Business", "Food delivery", "Delivery system", "Drone", "Delivery Performance", "Computer science", "Marketing", "Operations management", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.toknowpress.net/ISBN/978-961-6914-26-0/147.pdf" }, { "id": "https://openalex.org/W4391096209", "doi": "10.1109/icecet58911.2023.10389555", "title": "Image Registration for Virtual Drone Flight Simulation", "year": 2023, "publication_date": "2023-11-16", "type": "conference-paper", "language": "en", "authors": [ "Konrad Koniarski", "Andrzej Myśliński" ], "first_author": "Konrad Koniarski", "venue": null, "publisher": null, "abstract": "In the past decade, drones have expanded their applications, such as surveying, search and rescue, and last-mile delivery. Drone autonomy is a growing focus in these areas, requiring test environments for both drone operators and autonomous algorithms. This work addresses the creation of a realistic test environment for accurate position perception, motion planning, and drone control. Our approach uses RGB-D sensors and image registration techniques to construct a 3D map of the real environment, enabling drone navigation simulation. We adapt the Iterative Closest Point (ICP) algorithm, incorporating feature point filtering and local optical flow to expedite 3D point processing, particularly in static scenes. A challenge in the standard ICP algorithm is selecting suitable initial points for effective matching between consecutive point clouds. Our modified approach tackles this issue, optimizing the algorithm for 3D map construction. Since our application involves RGB-D image sequences with minimal variation between successive key frames, we use a scale-space pyramid and FAST techniques to select a limited number of key points, reducing the computational load. Moreover, by employing a local optical flow-based point-matching approach, we eliminate resource-intensive operations in each ICP optimization iteration. We demonstrate the practical utility of our method through a virtual 3D drone flight simulator.", "keywords": [ "Drone", "Computer science", "Iterative closest point", "Computer vision", "Point cloud", "Artificial intelligence", "Optical flow", "Real-time computing", "Image (mathematics)" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/icecet58911.2023.10389555" }, { "id": "https://openalex.org/W4401414828", "doi": "10.1109/icra57147.2024.10611698", "title": "Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms", "year": 2024, "publication_date": "2024-05-13", "type": "conference-paper", "language": "en", "authors": [ "Akash Kopparam Sreedhara", "Deepesh Padala", "Shashank Mahesh", "Kai Cui", "Mengguang Li", "Heinz Koeppl" ], "first_author": "Akash Kopparam Sreedhara", "venue": null, "publisher": null, "abstract": "Swarms of aerial drones have recently been considered for last-mile deliveries in urban logistics or automated construction. At the same time, collaborative transportation of payloads by multiple drones is another important area of recent research. However, efficient coordination algorithms for collaborative transportation of many payloads by many drones remain to be considered. In this work, we formulate the collaborative transportation of payloads by a swarm of drones as a novel, under-capacitated generalization of vehicle routing problems (VRP), which may also be of separate interest. In contrast to standard VRP and capacitated VRP, we must additionally consider waiting times for payloads lifted cooperatively by multiple drones, and the corresponding coordination. Algorithmically, we provide a solution encoding that avoids deadlocks and formulate an appropriate alternating minimization scheme to solve the problem. On the hardware side, we integrate our algorithms with collision avoidance and drone controllers. The approach and the impact of the system integration are successfully verified empirically, both on a swarm of real nano-quadcopters and for large swarms in simulation. Overall, we provide a framework for collaborative transportation with aerial drone swarms, that uses only as many drones as necessary for the transportation of any single payload.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Transport engineering", "Aeronautics", "Computer network", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icra57147.2024.10611698" }, { "id": "https://openalex.org/W4408734715", "doi": "10.1016/j.cie.2025.111061", "title": "Drone-assisted delivery optimization: Balancing time and cost with multiple truck routes for efficient service", "year": 2025, "publication_date": "2025-03-22", "type": "article", "language": "en", "authors": [ "Shalabh Singh" ], "first_author": "Shalabh Singh", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Drone", "Service (business)", "Computer science", "Operations research", "Engineering", "Transport engineering", "Operations management", "Business", "Automotive engineering", "Marketing" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111061" }, { "id": "https://openalex.org/W4413705309", "doi": "10.3233/atde250526", "title": "Crowdsourcing Technology Innovation for Last Mile Delivery Based on Routing Clusters and Flight Ranges Using Simulated Annealing", "year": 2025, "publication_date": "2025-08-26", "type": "book-chapter", "language": "en", "authors": [ "Pipit Sari Puspitorini", "Suparno Suparno", "Niniet Indah Arvitrida" ], "first_author": "Pipit Sari Puspitorini", "venue": "Advances in transdisciplinary engineering", "publisher": "IOS Press", "abstract": "Crowdsourcing clustering is a strategic decision for partitioning delivery locations into small clusters where the rendezvous point is a launching point (centroid cluster). Crowdsourcing is a resource owned by third-party logistics (3PL) that consists of drone trucks and crowd agents. The main objectives of this study are (i) to develop a mathematical model for optimal clustering and (ii) to determine the routing of package delivery using drones and trucks. To optimize the cluster, we used simulated annealing (SA). SA is a part of the metaheuristic algorithms, which are well-known unsupervised machine learning algorithms. For instance, we used 100 datasets of customer location data to conduct experiments, and the silhouette index was used to validate the proposed model. The novelty of this study lies in the closed-form mathematical solutions for optimal crowdsourcing in delivery considering the delivery range and weight of the package. The brief of this study is to adjudicate (i) a mathematical model of the optimal number of clusters using a power function without overlapping clusters and, (ii) partition routing crowdsourcing to assist delivery by considering range and vehicle capacity.", "keywords": [ "Crowdsourcing", "Last mile (transportation)", "Mile", "Simulated annealing", "Annealing (glass)", "Materials science", "Environmental science", "Computer science", "Geography", "World Wide Web", "Metallurgy", "Machine learning" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3233/atde250526" }, { "id": "https://openalex.org/W3193102693", "doi": "10.1109/yef-ece52297.2021.9505161", "title": "Flight control of hybrid drones towards enabling parcel relay manoeuvres", "year": 2021, "publication_date": "2021-07-09", "type": "conference-paper", "language": "en", "authors": [ "Bruno A. F. Neves", "Bruno J. Guerreiro" ], "first_author": "Bruno A. F. Neves", "venue": null, "publisher": null, "abstract": "This work addresses the modeling and controlling process of a hybrid unmanned aerial vehicle (UAV), aimed for parcel delivery with relay maneuvers. Hybrid UAVs bring significant advantages to the drone industry and related applications, such as the capability of flying in two flight modes, rotary and fixed-wing. Nonetheless, this versatility implies the added complexity both in modeling and controlling these vehicles. This work is based on a popular airframe, the Convergence tilt tri-rotor UAV, for which the overall system dynamics for all operation modes are deduced with plausible assumptions. The model is then validated by designing two separate controllers for the main flight modes, rotary and fixed-wing, capable of trajectory tracking in each mode. The control strategy makes use of a custom hybrid control allocation technique that differentiates the control in three parts: vertical, horizontal, and transitional flight modes. Finally, a hybrid controller is proposed, using a finite state machine capable of handling logical events, with the aim to provide control logic to perform autonomous mid flight transitions. The proposed control strategies are validated using Matlab.", "keywords": [ "Airframe", "Drone", "Controller (irrigation)", "Process (computing)", "Control engineering", "Computer science", "Trajectory", "Relay", "Flight dynamics", "Aerodynamics", "Engineering", "Control theory (sociology)" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://run.unl.pt/bitstreams/4df5c129-33c4-4b5d-870e-ff540979dd16/download" } ]