[ { "id": "https://openalex.org/W2128911772", "doi": "10.1109/ccca.2012.6417853", "title": "A multi-criteria support system for dynamic aerial vehicle routing problems", "year": 2012, "publication_date": "2012-12-01", "type": "conference-paper", "language": "en", "authors": [ "Jana Ries", "Alessio Ishizaka" ], "first_author": "Jana Ries", "venue": null, "publisher": null, "abstract": "The aim of this paper is to implement a decision support system for routing Unmanned Aerial Vehicles (UAV) in the context of maritime surveillance. The sea environment is highly uncertain and dynamic which requires the routing calculation to be highly responsive to changes and to operate in real-time. The approach adopted for this problem combines the multicriteria decision methods AHP, PROMETHEE and mathematical programming to establish a real-time adaptive routing system.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Context (archaeology)", "Adaptive routing", "Decision support system", "Dynamic programming", "Analytic hierarchy process", "Operations research", "Real-time computing", "Static routing", "Artificial intelligence" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccca.2012.6417853" }, { "id": "https://openalex.org/W2008564508", "doi": "10.1016/s1570-6672(11)60186-4", "title": "Unmanned Aerial Vehicle Route Planning for Traffic Information Collection", "year": 2012, "publication_date": "2012-02-01", "type": "article", "language": "en", "authors": [ "Xiaofeng Liu", "Zhong‐Ren Peng", "Liye Zhang", "Li Li" ], "first_author": "Xiaofeng Liu", "venue": "Journal of Transportation Systems Engineering and Information Technology", "publisher": "Science Press", "abstract": null, "keywords": [ "Cruise", "Genetic algorithm", "Sorting", "Computer science", "Route planning", "Data collection", "Transport engineering", "Real-time computing", "Operations research", "Engineering", "Algorithm", "Aerospace engineering" ], "cited_by_count": 22, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/s1570-6672(11)60186-4" }, { "id": "https://openalex.org/W4413608010", "doi": "10.1080/03081079.2025.2548599", "title": "Green drone multi-package delivery routing problem and its efficient algorithms", "year": 2025, "publication_date": "2025-08-25", "type": "article", "language": "en", "authors": [ "Fuqiang Lu", "Shiguang Tian", "Wenjing Feng", "Hualing Bi" ], "first_author": "Fuqiang Lu", "venue": "International Journal of General Systems", "publisher": "Taylor & Francis", "abstract": "Drones have delivery advantages in some special areas. Green means minimizing the energy consumption of drone delivery. However, minimizing the delivery time or minimizing the delivery distance cannot reflect the delivery cost of drones. Therefore, this paper aims to minimize the total energy consumption. The energy consumption function is established, and the path planning is made for the multi-package delivery of drones. A hybrid algorithm combining heuristic and exact solvers, Advanced Clarke and Wright Saving - Solving Constraint Integer Programs (ACWS-SCIP), is developed to solve the drone delivery problem. In order to improve the speed of solving large-scale problems, another hybrid heuristic algorithm is designed: Advanced Clarke and Wright Saving - Improved crow search algorithm (ACWS-ICSA). In experiments, a case in a remote rural area is studied, and results confirm that the shortest delivery path does not necessarily consume the lowest energy.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Mathematical optimization", "Algorithm", "Mathematics", "Embedded system" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/03081079.2025.2548599" }, { "id": "https://openalex.org/W4402335312", "doi": "10.1016/j.cja.2024.09.005", "title": "C-SPPO: A deep reinforcement learning framework for large-scale dynamic logistics UAV routing problem", "year": 2024, "publication_date": "2024-09-07", "type": "article", "language": "en", "authors": [ "Fei WANG", "Honghai ZHANG", "Sen Du", "Mingzhuang Hua", "Gang Zhong" ], "first_author": "Fei WANG", "venue": "Chinese Journal of Aeronautics", "publisher": "Elsevier BV", "abstract": "Unmanned Aerial Vehicle (UAV) stands as a burgeoning electric transportation carrier, holding substantial promise for the logistics sector. A reinforcement learning framework Centralized - S Proximal Policy Optimization (C-SPPO) based on centralized decision process and considering policy entropy (S) is proposed. The proposed framework aims to plan the best scheduling scheme with the objective of minimizing both the timeout of order requests and the flight impact of UAVs that may lead to conflicts. In this framework, the intents of matching act are generated through the observations of UAV agents, and the ultimate conflict-free matching results are output under the guidance of a centralized decision maker. Concurrently, a pre-activation operation is introduced to further enhance the cooperation among UAV agents. Simulation experiments based on real-world data from New York City are conducted. The results indicate that the proposed C-SPPO outperforms the baseline algorithms in the Average Delay Time (ADT), the Maximum Delay Time (MDT), the Order Delay Rate (ODR), the Average Flight Distance (AFD), and the Flight Impact Ratio (FIR). Furthermore, the framework demonstrates scalability to scenarios of different sizes without requiring additional training.", "keywords": [ "Reinforcement learning", "Adaptive routing", "Scale (ratio)", "Routing (electronic design automation)", "Reinforcement", "Computer science", "Mathematical optimization", "Distributed computing", "Industrial engineering", "Artificial intelligence", "Mathematics", "Engineering" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cja.2024.09.005" }, { "id": "https://openalex.org/W4385782973", "doi": "10.1016/j.ast.2023.108572", "title": "A bi-level planning approach of logistics unmanned aerial vehicle route network", "year": 2023, "publication_date": "2023-08-11", "type": "article", "language": "en", "authors": [ "Shan Li", "Honghai Zhang", "Yi Jia", "Hao Liu" ], "first_author": "Shan Li", "venue": "Aerospace Science and Technology", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Simulated annealing", "Computer science", "Transport engineering", "Network planning and design", "Flow network", "Simulation", "Real-time computing", "Operations research", "Computer network", "Engineering", "Mathematical optimization" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ast.2023.108572" }, { "id": "https://openalex.org/W3081196107", "doi": "10.3390/s20174769", "title": "A Novel Simulated Annealing Based Strategy for Balanced UAV Task Assignment and Path Planning", "year": 2020, "publication_date": "2020-08-24", "type": "article", "language": "en", "authors": [ "Lisu Huo", "Jianghan Zhu", "Guohua Wu", "Zhimeng Li" ], "first_author": "Lisu Huo", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The unmanned aerial vehicle (UAV) has drawn increasing attention in recent years, especially in executing tasks such as natural disaster rescue and detection, and battlefield cooperative operations. Task assignment and path planning for multiple UAVs in the above scenarios are essential for successful mission execution. But, effectively balancing tasks to better excavate the potential of UAVs remains a challenge, as well as efficiently generating feasible solutions from the current one in constrained explosive solution spaces with the increase in the scale of optimization problems. This paper proposes an efficient approach for task assignment and path planning with the objective of balancing the tasks among UAVs and achieving satisfactory temporal resolutions. To be specific, we add virtual nodes according to the number of UAVs to the original model of the vehicle routing problem (VRP), thus make it easier to form a solution suitable for heuristic algorithms. Besides, the concept of the universal distance matrix is proposed to transform the temporal constraints to spatial constraints and simplify the programming model. Then, a Swap-and-Judge Simulated Annealing (SJSA) algorithm is therefore proposed to improve the efficiency of generating feasible neighboring solutions. Extensive experimental and comparative studies on different scenarios demonstrate the efficiency of the proposed algorithm compared with the exact algorithm and meta-heuristic algorithms. The results also inspire us about the characteristics of a population-based algorithm in solving combinatorial discrete optimization problems.", "keywords": [ "Computer science", "Motion planning", "Simulated annealing", "Mathematical optimization", "Heuristics", "Vehicle routing problem", "Heuristic", "Task (project management)", "Routing (electronic design automation)", "Distributed computing", "Algorithm", "Robot" ], "cited_by_count": 75, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/20/17/4769/pdf?version=1598370216" }, { "id": "https://openalex.org/W3198636272", "doi": "10.48550/arxiv.2109.01447", "title": "Coordinating drones with mothership vehicles: The mothership and multiple drones routing problem with Graphs", "year": 2021, "publication_date": "2021-09-03", "type": "preprint", "language": "en", "authors": [ "Lavinia Amorosi", "Justo Puerto", "Carlos Valverde" ], "first_author": "Lavinia Amorosi", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This paper considers the optimization problems that arise to coordinate a tandem between a mothership vehicle and a fleet of drones. %more than one drone. Each drone can be launched from the mothership to perform an operation. After completing the operations the drones return to the mothership to recharge batteries and to be ready for a new operation. Operations consist on (partially) visiting graphs with given lengths to deliver some service or to perform some surveillance/inspection activity. The goal is to minimize the overall weighted distance traveled by the mothership while satisfying some requirements in terms of percentages of visits to the target graphs. In all cases, we develop exact formulations resorting to mixed integer second order cone programs that are compared on a testbed of instances to assess their performance. We also develop a matheuristic algorithm that provides reasonable solutions. Computational experiments show the usefulness of our methods in different scenarios.", "keywords": [ "Drone", "Computer science", "Testbed", "Real-time computing", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2109.01447" }, { "id": "https://openalex.org/W4399559333", "doi": "10.1016/j.cor.2024.106722", "title": "An improved ALNS for hybrid pickup and drones delivery system in disaster by penalizing deprivation time", "year": 2024, "publication_date": "2024-06-12", "type": "article", "language": "en", "authors": [ "Sanaz Khalaj Rahimi", "Donya Rahmani" ], "first_author": "Sanaz Khalaj Rahimi", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pickup", "Drone", "Computer science", "Operations research", "Operations management", "Simulation", "Mathematics", "Artificial intelligence", "Biology", "Engineering", "Botany" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2024.106722" }, { "id": "https://openalex.org/W4409133943", "doi": "10.1038/s41598-025-95854-6", "title": "Unmanned aerial vehicle routing based on frog-leaping optimization algorithm", "year": 2025, "publication_date": "2025-04-02", "type": "article", "language": "en", "authors": [ "Farhad Maleki", "Mohammad Ali Jabraeil Jamali", "Arash Heidari" ], "first_author": "Farhad Maleki", "venue": "Scientific Reports", "publisher": "Nature Portfolio", "abstract": "Routing in Unmanned Aerial Vehicle (UAV) networks is critical for effective data transfer and overall network performance. However, current UAV routing algorithms exhibit high latency, poor route selection, excessive energy consumption, and limited flexibility in changing network topologies. To overcome these limitations, this paper proposes a new routing strategy that uses the Shuffled Frog Leaping Algorithm (SFLA) to improve UAV network routing. Using a two-phase optimization approach considering Quality of Service (QoS), our system combines global exploration with local exploitation, unlike previous techniques. This hybrid method enables UAVs to dynamically change their trajectories, helping to choose the best path even in fast-changing surroundings. Our approach's self-adaptive population-based search mechanism accelerates convergence and removes a common weakness in traditional metaheuristic algorithms-premature standstill elimination-which determines its effectiveness. By constantly adjusting UAV routing patterns depending on energy economy, latency, and throughput characteristics, SFLA guarantees that UAV networks transmit effectively and consistently. Based on experimental data, our method outperforms benchmark alternatives in terms of energy use by 3.11%, latency by 5.14%, and network lifetime by 2.25%. These developments make our approach ideal for real-time applications including aerial surveillance and disaster response that call for high data transfer speeds and great energy economy.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Optimization algorithm", "Routing algorithm", "Algorithm", "Mathematical optimization", "Routing protocol", "Computer network", "Mathematics" ], "cited_by_count": 23, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.nature.com/articles/s41598-025-95854-6.pdf" }, { "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/W4385880701", "doi": "10.1016/j.cie.2023.109542", "title": "A logic-based Benders decomposition algorithm for a repair crew routing and drone scheduling problem after a natural disaster", "year": 2023, "publication_date": "2023-08-16", "type": "article", "language": "en", "authors": [ "Motahhareh Safdari Shadlou", "Mohammad Reza Ranjbar", "Majid Salari" ], "first_author": "Motahhareh Safdari Shadlou", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Damages", "Crew", "Drone", "Computer science", "Natural disaster", "Scheduling (production processes)", "Integer programming", "Operations research", "Routing (electronic design automation)", "Mathematical optimization", "Engineering", "Computer network" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2023.109542" }, { "id": "https://openalex.org/W4367396121", "doi": "10.1016/j.dib.2023.109192", "title": "Dataset for the van-drone routing problem with multiple delivery drop points", "year": 2023, "publication_date": "2023-04-29", "type": "data-paper", "language": "en", "authors": [ "Eleftherios Athanasiadis", "Vasilis P. Koutras", "Vasileios Zeimpekis" ], "first_author": "Eleftherios Athanasiadis", "venue": "Data in Brief", "publisher": "Elsevier BV", "abstract": "The distribution of parcels is one of the most complex and challenging processes in supply chain execution. Lately, the development of both electronic and quick commerce has driven carriers and courier operators to identify more effective methods for express parcel delivery. To this end, the development of efficient distribution networks that aim in increasing customer experience while maintaining low operating costs is significant importance both for researchers as well as for practitioners. This article presents a dataset for the Van Drone Routing Problem with Multiple Delivery Points and Cooperation (VDRPMDPC). The latter examines a van-drone team from an operational viewpoint, where a van moves along a road network while the drone egress and ingress from a van to a nearby delivery location and then travels back to the van. This problem has been created with the aim of assessing the design of more sustainable and cost-effective delivery routes in urban and semi urban environments via the use of Unmanned Aerial Vehicles (UAVs). For the development of this dataset real geographical positions have been used located at two different areas of Athens, Greece. The entire benchmark is composed of 14 instances comprised by 20, 40, 60 and 100 clients respectively. The dataset is publicly available for its use and modification.", "keywords": [ "Drone", "Computer science", "Benchmark (surveying)", "Routing (electronic design automation)", "Vehicle routing problem", "Delivery Performance", "Operations research", "Transport engineering", "Distribution (mathematics)", "Computer network", "Business", "Process management" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.dib.2023.109192" }, { "id": "https://openalex.org/W7128486010", "doi": "10.1016/j.jairtraman.2026.102978", "title": "Environmental sustainability and risk-aware optimization in hybrid truck-drone logistics: A holistic multi-objective framework", "year": 2026, "publication_date": "2026-02-10", "type": "article", "language": "en", "authors": [ "Armin Mahmoodi", "Seyed Mojtaba Sajadi", "Jeremy F. Laliberté", "Said M. Easa" ], "first_author": "Armin Mahmoodi", "venue": "Journal of Air Transport Management", "publisher": "Elsevier BV", "abstract": "Compared to conventional truck-only systems, hybrid truck–drone delivery systems offer transformative potential for last-mile logistics by addressing operational inefficiencies, minimizing environmental impact, and enhancing safety through risk-aware optimization. This study develops a stochastic multi-objective optimization framework grounded in the Specific Operations Risk Assessment (SORA) methodology. By extending the vehicle routing problem with pickup and delivery (VRPPD) and the flying sidekick traveling salesman problem (FSTSP), the model incorporates battery optimization, CO 2 emissions reduction, and energy-efficient routing strategies. Delivery cost, time, energy consumption, operational risk, and battery performance are all optimized using a mixed-integer linear programming (MILP) and non-dominated sorting genetic algorithm II (NSGA-II) technique. Sensitivity analysis show that increasing drone fleet size and efficiency results in significant cost, time, and energy savings while improving safety. The model's flexibility in both urban and remote delivery contexts is confirmed by numerical trials. In line with life cycle analysis (LCA), this study offers practical advice for environmentally responsible and risk-aware logistics, assisting decision-makers and industry participants in the development of scalable and sustainable solutions. • Optimizes cost, time, risk, and energy in hybrid truck–drone logistics. • Uses MILP and NSGA-II for multi-objective routing and energy savings. • Incorporates SORA for risk-aware UAV operations in urban logistics. • Sensitivity shows drone efficiency cuts cost, risk, and CO 2 emissions. • Achieves energy-efficient delivery via battery-aware UAV coordination.", "keywords": [ "Drone", "Flexibility (engineering)", "Sustainability", "Vehicle routing problem", "Travelling salesman problem", "Scalability", "Sensitivity (control systems)", "Sorting", "Routing (electronic design automation)" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.jairtraman.2026.102978" }, { "id": "https://openalex.org/W4411348074", "doi": "10.1016/j.trd.2025.104843", "title": "Multi-modal electric logistics system: Electric trucks as floating chargers for electric drones", "year": 2025, "publication_date": "2025-06-16", "type": "article", "language": "en", "authors": [ "Y. I. Park", "Junyeong Jeong", "Chao Wang", "Soomin Woo" ], "first_author": "Y. I. Park", "venue": "Transportation Research Part D Transport and Environment", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Automotive engineering", "Modal", "Drone", "Electric cars", "Engineering", "Electric vehicle", "Electrical engineering", "Materials science", "Physics" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trd.2025.104843" }, { "id": "https://openalex.org/W4319878675", "doi": "10.1109/access.2023.3244067", "title": "Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review", "year": 2023, "publication_date": "2023-01-01", "type": "review", "language": "en", "authors": [ "T. Rehannara Beegum", "Mohd Yamani Idna Idris", "Mohamad Nizam Bin Ayub", "Hisham A. Shehadeh" ], "first_author": "T. Rehannara Beegum", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Flying Ad hoc Network (FANET) is a self-organizing wireless network that constitutes swarms of flying nodes, namely Unmanned Aerial Vehicles (UAV), and communicates in close proximity. It has various distinguishing characteristics that set it apart from other ad hoc networks, posing some issues, particularly in routing. UAVs are highly dynamic and have frequent topology changes. Hence, the network urges an efficient routing technique to coordinate the node swarms and enhance the evaluation metrics of the network. The biological behavior of various living organisms, such as animals, insects, microbes, and humans, inspires researchers to solve various routing problems in ad hoc networks. Decentralized self-organized swarms of UAVs closely resemble the biological system. Therefore, the Bio-Inspired Algorithms (BIA) resolve a wide range of routing challenges in FANET. A Systematic Literature Review (SLR) is adopted to survey FANET routing methods based on non-hybrid and hybrid BIAs to properly comprehend the existing bio-inspired strategies used in FANET routing. The review will be beneficial for the researchers in the specified area. To our knowledge, no SLR has been conducted about the FANET routing protocol that employs BIA. This paper examines (1) the characteristics and features of existing routing algorithms, (2) the need of both non-hybrid and hybrid BIA for effective and optimal routing, (3) an analysis of the method’s simulation tools, evaluation metrics, and mobility models, (4) the current issues and scope of the study related to the specified method.", "keywords": [ "Computer science", "Wireless ad hoc network", "Computer network", "Routing protocol", "Distributed computing", "Routing (electronic design automation)", "Destination-Sequenced Distance Vector routing", "Link-state routing protocol", "Geographic routing", "Wireless Routing Protocol", "Node (physics)", "Static routing" ], "cited_by_count": 82, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/6287639/6514899/10041916.pdf" }, { "id": "https://openalex.org/W4387089098", "doi": "10.1109/dcoss-iot58021.2023.00046", "title": "Holistic Path Planning for Multi-Drone Data Collection", "year": 2023, "publication_date": "2023-06-01", "type": "conference-paper", "language": "en", "authors": [ "Jonathan Diller", "Peter M. Hall", "Qi Han" ], "first_author": "Jonathan Diller", "venue": null, "publisher": null, "abstract": "This paper presents how to plan for and deploy a team of Unmanned Aerial Vehicles (UAVs) to collect data from wireless sensors while minimizing collection latency. We propose a holistic solution for this problem where we first form an offline plan while considering communication and energy constraints then derive an online strategy for the UAVs to handle sensor failures and the stochastic nature of wireless communication. For offline planning, we propose a heuristic-based algorithm that turns the problem into a variation of the Vehicle Routing Problem. For our online strategy, we propose a coupled offline-online algorithm that adapts UAV behavior as needed. We evaluate our algorithms through extensive simulations as well as a proof-of-concept implementation on a physical UAV and wireless sensor testbed.", "keywords": [ "Computer science", "Drone", "Testbed", "Wireless sensor network", "Motion planning", "Wireless", "Heuristic", "Real-time computing", "Latency (audio)", "Data collection", "Proof of concept", "Distributed computing" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dcoss-iot58021.2023.00046" }, { "id": "https://openalex.org/W4319993356", "doi": "10.1007/978-3-031-15412-6_11", "title": "Two-Echelon Vehicle Routing Problem for Agricultural Area Mapping Operation with Drone", "year": 2023, "publication_date": "2023-01-01", "type": "book-chapter", "language": "en", "authors": [ "A. A. N. Pewira Redi", "Muhammad Reza Chandra Kusuma", "Bertha Maya Sopha", "Anna Maria Sri Asih", "Rahmad Inca Liperda" ], "first_author": "A. A. N. Pewira Redi", "venue": "Ecoproduction", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Agriculture", "Computer science", "Operations research", "Aeronautics", "Engineering", "Transport engineering", "Geography", "Computer network", "Archaeology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-15412-6_11" }, { "id": "https://openalex.org/W1771445186", "doi": "10.3390/s150613874", "title": "A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications", "year": 2015, "publication_date": "2015-06-12", "type": "article", "language": "en", "authors": [ "Jisheng Zhang", "Limin Jia", "Shuyun Niu", "Fan Zhang", "Lu Carol Tong", "Xuesong Simon Zhou" ], "first_author": "Jisheng Zhang", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs' route planning for small and medium-scale networks.", "keywords": [ "Computer science", "Traffic congestion", "Real-time computing", "Flow network", "Routing (electronic design automation)", "Wireless sensor network", "Integer programming", "Lagrangian relaxation", "Drone", "Distributed computing", "Computer network", "Engineering" ], "cited_by_count": 37, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/15/6/13874/pdf?version=1434096105" }, { "id": "https://openalex.org/W7160855060", "doi": "10.3390/wevj17050253", "title": "Electric Vehicle Routing Problem with Drones Considering Weather Conditions and Time Windows", "year": 2026, "publication_date": "2026-05-08", "type": "article", "language": "en", "authors": [ "Meiling He", "Xi Yang", "Xun Han", "Jin Zhang", "Xiaohui Wu", "Xiaolai Ma" ], "first_author": "Meiling He", "venue": "World Electric Vehicle Journal", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Inspired by the practical need for reliable drone-assisted logistics under varying weather conditions, this study investigates the vehicle–drone collaborative routing problem with weather constraints and time windows. The objective is to minimize the total delivery cost, including vehicle fixed costs, vehicle travel costs, drone flight costs, and time window penalty costs. To capture the impact of weather conditions on drone operations, a wind-speed-dependent dynamic flight speed function is introduced. A mathematical model is formulated, and an adaptive large neighborhood search algorithm integrated with genetic operations is proposed to enhance both local search efficiency and global exploration capability. Computational experiments on benchmark instances demonstrate that the proposed algorithm obtains high-quality solutions across different problem scales. Compared with the adaptive large neighborhood search algorithm and the improved genetic algorithm, the proposed approach reduces the optimal total delivery cost by an average of 4% and 2%, respectively. Sensitivity analysis further shows that increasing wind speed levels and the proportion of no-fly periods reduces the number of drone service tasks and increases total system cost, highlighting the significant impact of weather conditions on vehicle–drone collaborative delivery systems.", "keywords": [ "Drone", "Vehicle routing problem", "Benchmark (surveying)", "Genetic algorithm", "Routing (electronic design automation)", "Sensitivity (control systems)", "Local search (optimization)", "Wind speed" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2032-6653/17/5/253/pdf?version=1778235114" }, { "id": "https://openalex.org/W4413974956", "doi": "10.1016/j.swevo.2025.102148", "title": "A bi-objective routing problem for cooperated trucks and drones in river water quality monitoring", "year": 2025, "publication_date": "2025-09-04", "type": "article", "language": "en", "authors": [ "Shuo Dang", "Zhihao Luo", "Zhong Liu", "Yuzhen Zhou", "Jianmai Shi" ], "first_author": "Shuo Dang", "venue": "Swarm and Evolutionary Computation", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Drone", "Truck", "Routing (electronic design automation)", "Quality (philosophy)", "Water quality", "Automotive engineering", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.swevo.2025.102148" }, { "id": "https://openalex.org/W7137217880", "doi": "10.3390/drones10030206", "title": "A Literature Review of Vehicle and Drone Delivery Routing Problems in Different Synchronization Level Scenarios", "year": 2026, "publication_date": "2026-03-15", "type": "article", "language": "en", "authors": [ "Jili Kong", "WEI Litong", "Xuefeng Jiang" ], "first_author": "Jili Kong", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The increasing demand for efficient last-mile delivery has spurred interest in optimizing vehicle and drone routing. This review presents a novel classification of synchronization levels: (i) non-synchronized scenarios, where vehicles and drones operate independently; (ii) low synchronization level scenarios, where one party is passive in the delivery process; (iii) high synchronization level scenarios, where both parties cooperate using diverse strategies. The primary objective is to identify and classify functional preferences of vehicles and drones across these synchronization scenarios. We offer a unique perspective by analyzing the functional setups of vehicles and drones along with synchronization aspects like drone flight synchronization and vehicle synchronization. To the best of our knowledge, these detailed setups based on the operational functionalities of vehicles and drones in last-mile delivery has not been previously explored in the literature. Through a systematic review of the literature, we identify key challenges and emerging trends in vehicle and drone route planning within these scenarios which enable researchers to systematically understand and design vehicle–drone delivery systems. This paper integrates existing models and solution methods and provides new insights into the interactions between vehicle and drone functionalities in last-mile delivery. By analyzing solutions across different synchronization scenarios, it guides researchers in choosing appropriate methodologies and identifying future research directions. Our work presents a novel classification framework, enabling a comprehensive understanding of how the functional setups of vehicles and drones under different synchronization levels influence route planning, thus offering both theoretical and practical insights for advancing last-mile delivery optimization.", "keywords": [ "Drone", "Synchronization (alternating current)", "Key (lock)", "Work (physics)", "Data synchronization" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/3/206/pdf" }, { "id": "https://openalex.org/W2772154329", "doi": "10.1109/iros.2017.8202264", "title": "Quadrotor-UAV optimal coverage path planning in cluttered environment with a limited onboard energy", "year": 2017, "publication_date": "2017-09-01", "type": "conference-paper", "language": "en", "authors": [ "Yasser Bouzid", "Yasmina Bestaoui", "Houria Siguerdidjane" ], "first_author": "Yasser Bouzid", "venue": null, "publisher": null, "abstract": "In this paper, a quadrotor optimal coverage planning approach in damaged area is considered. The quadrotor is assumed to visit a set of reachable points following the shortest path while avoiding the no-fly zones. The problem is solved by using a two-scale proposed algorithm. In the first scale, an efficient tool for cluttered environments based on optimal Rapidly-exploring Random Trees (RRT) approach, called multi-RRT* Fixed Node (RRT*FN), is developed to define the shortest paths from each point to their neighbors. Using the pair-wise costs between points provided by the first-scale algorithm, in the second scale, the overall shortest path is obtained by solving the Traveling Salesman Problem (TSP) using Genetic Algorithms (GA). Taking into consideration the limited onboard energy, a second alternative based on the well-known Vehicle Routing Problem (VRP) is used. This latter is solved using the savings heuristic approach. The effectiveness of this proposed two-scale algorithm is demonstrated through numerical simulations and promising results are obtained.", "keywords": [ "Travelling salesman problem", "Motion planning", "Mathematical optimization", "Shortest path problem", "Computer science", "Heuristic", "Scale (ratio)", "Genetic algorithm", "Path (computing)", "Routing (electronic design automation)", "Energy (signal processing)", "Node (physics)" ], "cited_by_count": 66, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-01716915" }, { "id": "https://openalex.org/W4409199927", "doi": "10.1016/j.cie.2025.111087", "title": "An efficient matheuristic integration with benders decomposition for unmanned aerial vehicle routing problem in forest fire surveillance", "year": 2025, "publication_date": "2025-04-06", "type": "article", "language": "en", "authors": [ "Mir Ehsan Hesam Sadati" ], "first_author": "Mir Ehsan Hesam Sadati", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Benders' decomposition", "Decomposition", "Routing (electronic design automation)", "Environmental science", "Computer science", "Mathematical optimization", "Mathematics", "Biology", "Ecology", "Computer network" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111087" }, { "id": "https://openalex.org/W4408321196", "doi": "10.3390/drones9030200", "title": "E²VRP-CPP: An Energy-Efficient Approach for Multi-UAV Multi-Region Coverage Path Planning Optimization in the Enhanced Vehicle Routing Problem", "year": 2025, "publication_date": "2025-03-11", "type": "article", "language": "en", "authors": [ "Yuechao Zang", "Xueqin Huang", "Min Lu", "Qianzhen Zhang", "Xianqiang Zhu" ], "first_author": "Yuechao Zang", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned Aerial Vehicles (UAVs) are widely used in applications such as land assessment, surveillance, and rescue operations, where they are often required to cover multiple disjoint regions. Coverage Path Planning (CPP) aims to determine optimal paths for UAVs to cover these areas. While CPP for single regions has been extensively studied, multi-region CPP with multiple UAVs remains underexplored. Existing methods typically focus on minimizing path length, but often neglect the nonlinear variations in energy consumption during flight, limiting their practical applicability. This paper addresses the multi-UAV, multi-region CPP as a variant of the Vehicle Routing Problem (VRP) with energy estimation. We propose an approach that optimizes UAV flight speeds to minimize energy consumption, supported by an accurate energy estimation algorithm. In addition, a heuristic algorithm is developed to balance the distribution of tasks among UAVs, considering both the scanning and transit times. Experiments using real-world data from the Changsha urban area demonstrate that our approach outperforms state-of-the-art methods in computational efficiency and energy savings, highlighting its potential for practical UAV deployment.", "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Path (computing)", "Route planning", "Mathematical optimization", "Engineering", "Transport engineering", "Computer network", "Mathematics" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9030200" }, { "id": "https://openalex.org/W4404812283", "doi": "10.1016/j.tre.2024.103878", "title": "Optimizing same-day delivery with vehicles and drones: A hierarchical deep reinforcement learning approach", "year": 2024, "publication_date": "2024-11-28", "type": "article", "language": "en", "authors": [ "Meng Li", "Kaiquan Cai", "Peng Zhao" ], "first_author": "Meng Li", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Reinforcement learning", "Reinforcement", "Computer science", "Transport engineering", "Engineering", "Aeronautics", "Artificial intelligence", "Biology", "Structural engineering" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2024.103878" }, { "id": "https://openalex.org/W4367018342", "doi": "10.2139/ssrn.4429473", "title": "Multi-Depot Vehicle Routing Problem with Drones: Mathematical Formulation, Solution Algorithm and Experiments", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Petr Stodola", "Libor Kutěj" ], "first_author": "Petr Stodola", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Vehicle routing problem", "Drone", "Mathematical optimization", "Computer science", "Routing (electronic design automation)", "Depot", "Algorithm", "Mathematics", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4429473" }, { "id": "https://openalex.org/W2810069740", "doi": "10.1016/j.asoc.2018.06.006", "title": "Autonomous task allocation for multi-UAV systems based on the locust elastic behavior", "year": 2018, "publication_date": "2018-06-25", "type": "article", "language": "en", "authors": [ "Heba A. Kurdi", "Ebtesam Aloboud", "Maram Hassan AlAlwan", "Sarah Alhassan", "Ebtehal Turki Saho Alotaibi", "Guillermo Bautista", "Jonathan P. How" ], "first_author": "Heba A. Kurdi", "venue": "Applied Soft Computing", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Scalability", "Task (project management)", "Benchmark (surveying)", "Throughput", "Distributed computing", "Artificial intelligence", "Ant colony optimization algorithms", "Drone", "Real-time computing", "Systems engineering", "Telecommunications" ], "cited_by_count": 71, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.asoc.2018.06.006" }, { "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/W4398770422", "doi": "10.1016/j.eswa.2024.124172", "title": "Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products", "year": 2024, "publication_date": "2024-05-24", "type": "article", "language": "en", "authors": [ "Tanveer Hossain Bhuiyan", "Victor Walker", "Mohammad Roni", "Imtiaz Ahmed" ], "first_author": "Tanveer Hossain Bhuiyan", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Software deployment", "Drone", "Computer science", "Battery (electricity)", "Automotive engineering", "Engineering", "Operating system" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2024.124172" }, { "id": "https://openalex.org/W3190520612", "doi": "10.1109/icuas51884.2021.9476674", "title": "Deployment Optimization of a Fleet of Drones for Routine Inspection of Networks of Linear Infrastructures", "year": 2021, "publication_date": "2021-06-15", "type": "conference-paper", "language": "en", "authors": [ "Etienne Petitprez", "Frédéric Georges", "Nicolas Raballand", "Sylvain Bertrand" ], "first_author": "Etienne Petitprez", "venue": null, "publisher": null, "abstract": "In this paper, the problem of optimizing the deployment of a fleet of Unmanned Aerial Vehicles (UAVs) for the inspection of networks of linear infrastructures is addressed. In this optimization problem, two levels need to be considered: the UAVs need to follow a trajectory allowing them to fly over linear infrastructures, with a limited endurance, while ground vehicles need to be deployed to act as platforms that can launch and retrieve the UAVs from fixed parking positions. Both parking positions of ground vehicles and the UAVs fleet routes along the linear infrastructures have to be optimized to improve the inspection efficiency, which is related to multiple contradictory objectives such as minimizing operational cost and maximizing inspection performance. The algorithm proposed in this paper was developed by expanding upon several algorithms from literature. More precisely, we propose to solve a Two-Echelon Vehicle Routing Problem (2E-VRP) with a custom multi-objective Hybrid Genetic Algorithm (HGA) based on a Capacitated Arc Routing Problem (CARP). Simulation results are provided on a benchmark of networks of linear infrastructures along with a sensitivity analysis on the parameters of the algorithm, showing promising results.", "keywords": [ "Software deployment", "Drone", "Benchmark (surveying)", "Computer science", "Linear programming", "Routing (electronic design automation)", "Genetic algorithm", "Trajectory", "Real-time computing", "Computer network", "Algorithm" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-03306569" }, { "id": "https://openalex.org/W3004562173", "doi": null, "title": "Using drones in the last-mile logistics processes of medical product delivery: A feasibility case study in Rotterdam", "year": 2020, "publication_date": "2020-01-12", "type": "conference-paper", "language": "en", "authors": [ "Irene Zubin", "Bart van Arem", "Bart Wiegmans", "Ron van Duin" ], "first_author": "Irene Zubin", "venue": "Research Repository (Delft University of Technology)", "publisher": "Delft University of Technology", "abstract": "The term last-mile delivery refers to the final leg of a business-to-customer service, in which products are shipped from a depot to a destination point bymeans of land transportation, such as vans and small trucks. Although these vehicles provide a common and easy way to consign products, companies are striving for new transport technologies to reduce congestion, infrastructure limitations and air pollution. An alternative to road-bounded vehicles that has recently gained attention is the adoption of drones in parcel delivery. Drone applications range from military training, surveillance, path recognition and shipment of perishable products in emergency situations. Research on drones as delivery vehicles is still in its early stages, with some practical trials carried out by leader companies such as Google and Amazon. However, the application of drones in the pharmaceutical sector for home deliveries of medical products, has not been investigated yet. To gain new insights into the feasibility of introducing drones in the delivery fleet, drone applications were studied for the delivery operations of the pharmacy BENU ’t Slag, in Rotterdam. Two scenario alternatives were tested using the Vehicle Routing Problem formulation. A Large-scale Neighborhood Search algorithm was implemented to solve the problem and derive the performance indicators associated with each scenario. Performances were then analyzed through a comparative analysis. When drones were introduced in the delivery fleet, indicators showed improvements in environmental aspects, service time and delivery costs, with a reduction of 9% in CO2 emissions, 12% in service time and 5.6% in cost per item.", "keywords": [ "Drone", "Service (business)", "Last mile (transportation)", "Truck", "Product (mathematics)", "Transport engineering", "Business", "Computer science", "Operations research", "Marketing", "Mile", "Engineering" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "http://resolver.tudelft.nl/uuid:9ccd09cd-608a-489e-b50d-5dbe3483a80e" }, { "id": "https://openalex.org/W4379467993", "doi": "10.1016/j.dib.2023.109296", "title": "Dataset for the cumulative unmanned aerial vehicle routing problem", "year": 2023, "publication_date": "2023-06-01", "type": "data-paper", "language": "en", "authors": [ "Nikolaos A. Kyriakakis", "Themistoklis Stamadianos", "Magdalene Marinaki", "Yannis Marinakis" ], "first_author": "Nikolaos A. Kyriakakis", "venue": "Data in Brief", "publisher": "Elsevier BV", "abstract": "The Cumulative Unmanned Aerial Vehicle Routing Problem applies to area covering operations using UAVs. It is defined on a graph, the nodes of which, ensure the complete coverage of the underlying area of interest. The data generation process takes into account the characteristics of such operations, in particular, the viewing window of the UAVs' sensor, their maximum range, the size of the UAV fleet and the unknown locations of the targets within the area of interest. Instances are created simulating different scenarios, using different values for those UAV characteristics, as well as the different locations where the search targets might be positioned in the area of interest.", "keywords": [ "Computer science", "Range (aeronautics)", "Routing (electronic design automation)", "Point of interest", "Graph", "Process (computing)", "Vehicle routing problem", "Window (computing)", "Real-time computing", "Artificial intelligence", "Computer network", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.dib.2023.109296" }, { "id": "https://openalex.org/W3082135344", "doi": "10.1155/2020/8887057", "title": "Model and Hybrid Algorithm of Collaborative Distribution System with Multiple Drones and a Truck", "year": 2020, "publication_date": "2020-09-01", "type": "article", "language": "en", "authors": [ "Min Lin", "Junyan Lyu", "Jiajing Gao", "Ling-Yu Li" ], "first_author": "Min Lin", "venue": "Scientific Programming", "publisher": "Hindawi Publishing Corporation", "abstract": "This paper studies a coordinated system for multidrone single-truck distribution, where a truck delivers goods to a group of customers along a closed ground path with the help of a number of drones. For each delivery, the truck departs from the distribution centre with drones and all goods needed and returns back to the centre after fulfilling the delivery tasks. That is, the truck assigns these delivery tasks to several of its drones, each of which is responsible for sending goods to a different subgroup of customers in the empty air space. This study provides a new mixed-integer programming model of the routing problem with this distribution system based on urban road network. Meanwhile, a hybrid genetic algorithm and a hybrid particle swarm algorithm are designed. Experimental results show that the performance of the hybrid algorithms is better than that of the corresponding basic algorithms.", "keywords": [ "Truck", "Drone", "Computer science", "Particle swarm optimization", "Hybrid algorithm (constraint satisfaction)", "Genetic algorithm", "Routing (electronic design automation)", "Integer programming", "Vehicle routing problem", "Path (computing)", "Distribution (mathematics)", "Mathematical optimization" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/sp/2020/8887057.pdf" }, { "id": "https://openalex.org/W4392912036", "doi": "10.1007/978-3-031-52760-9_8", "title": "Enhancing Ground Vehicle Route Planning with Multi-Drone Integration", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Murat Bakırcı", "Muhammed Mirac Özer" ], "first_author": "Murat Bakırcı", "venue": "EAI/Springer Innovations in Communication and Computing", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Genetic algorithm", "Heuristic", "Computer science", "Reduction (mathematics)", "Software deployment", "Adaptability", "Mathematical optimization", "Operations research", "Engineering", "Artificial intelligence", "Machine learning" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-52760-9_8" }, { "id": "https://openalex.org/W3215557498", "doi": "10.3390/su132212841", "title": "Collaborative Hybrid Aerial and Ground Vehicle Routing for Post-Disaster Assessment", "year": 2021, "publication_date": "2021-11-19", "type": "article", "language": "en", "authors": [ "Anak Agung Ngurah Perwira Redi", "Bertha Maya Sopha", "Anna Maria Sri Asih", "Rahmad Inca Liperda" ], "first_author": "Anak Agung Ngurah Perwira Redi", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Hybrid aerial and ground vehicles are seen as a promising option for deployment in a post-disaster assessment due to the risk of infrastructure damage that may hinder the assessment operation. The efficient operation of the hybrid aerial and ground vehicle, particularly routings, remains a challenge. The present study proposed a collaborative hybrid aerial and ground vehicle to support the operation of post-disaster assessment. The study developed two models, i.e., the Two-Echelon Vehicle Routing Problem combined with Assignment (2EVRPA) and the Two-Echelon Collaborative Vehicle Routing Problem (2ECoVRP) to evaluate optimal routings for both aerial and ground vehicles. The difference lies in the second echelon in which the 2EVRPA uses a single point-to-point assignment, whereas the 2ECoVRP considers the collaborative routings between the ground vehicle and the aerial vehicle. To demonstrate its applicability, the developed models were applied to solve the post-disaster assessment for the Mount Merapi eruption in Yogyakarta, Indonesia. Sets of numerical experiments based on the empirical case were conducted. The findings indicate that the 2ECoVRP performs better than 2EVRPA in terms of the total operation time. The tabu search algorithm was found to be a promising method to solve the models due to its good quality solution and computational efficiency. The deployment of eight drones appears to be optimum for the given network configuration of the studied case. Flight altitude and battery capacity were found to be influential to the operation time, hence requiring further exploration. Other potential avenues for future research are also discussed.", "keywords": [ "Software deployment", "Drone", "Routing (electronic design automation)", "Tabu search", "Vehicle routing problem", "Point (geometry)", "Computer science", "Transport engineering", "Operations research", "Engineering", "Artificial intelligence", "Computer network" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/13/22/12841/pdf?version=1637825078" }, { "id": "https://openalex.org/W4220765008", "doi": "10.1287/ijoc.2022.1159", "title": "VeRoViz: A Vehicle Routing Visualization Toolkit", "year": 2022, "publication_date": "2022-03-17", "type": "article", "language": "en", "authors": [ "Lan Peng", "Chase C. Murray" ], "first_author": "Lan Peng", "venue": "INFORMS journal on computing", "publisher": "Institute for Operations Research and the Management Sciences", "abstract": "VeRoViz is an open-source vehicle routing visualization package consisting of both Python and web-based components. It was developed to streamline the workflow for vehicle routing researchers by simplifying and automating many of the tedious tasks associated with generating realistic test problems. VeRoViz also provides new functionality to produce customizable visualizations of complex vehicle routing problems. These visualization tools—including Gantt charts, static maps, and dynamic 3-D videos—assist researchers in validating models and communicating results. Additionally, a comprehensive collection of utility functions within VeRoViz provides researchers with useful tools to assess features of their problems. This paper provides an overview of VeRoViz and highlights the flexibility and ease-of-use of the toolkit. Summary of Contribution: This paper describes an open-source software package designed to assist vehicle routing researchers. The software package, named VeRoViz (vehicle routing visualization), streamlines the process of collecting and visualizing data relevant to vehicle routing research. This includes capturing road network travel times (and distances) between locations, displaying turn-by-turn vehicle routes, generating Gantt charts of vehicle assignments, and producing 3-D “movies” of vehicle routing solutions. The VeRoViz suite consists of three components. First, a web-based interface allows researchers (and instructors) to quickly “sketch” elements of a vehicle routing problem, providing visual representations of nodes and arcs on a map. Second, the VeRoViz Python package provides an extensive collection of functions that assist operations researchers in collecting data and displaying solutions. Finally, an HTML/JavaScript plugin is available for displaying time-dynamic 3-D visualization of vehicle routes. VeRoViz is not a vehicle routing solver; it is a software suite that complements and supports researchers who are developing solution approaches to increasingly realistic vehicle routing problems (e.g., problems related to drone delivery, electric vehicles, or dynamic delivery requests).", "keywords": [ "Computer science", "Gantt chart", "Visualization", "Plug-in", "Python (programming language)", "Software", "Suite", "Data visualization", "Vehicle routing problem", "Workflow", "JavaScript", "Routing (electronic design automation)" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1287/ijoc.2022.1159" }, { "id": "https://openalex.org/W4285263096", "doi": "10.2139/ssrn.4096498", "title": "On a Pickup to Delivery Drone Routing Problem: Models and Algorithms", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Alfredo Candia-Véjar", "Javier E. Gómez-Lagos", "Benjamín Rojas-Espinoza" ], "first_author": "Alfredo Candia-Véjar", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Computer science", "Pickup", "Vehicle routing problem", "Mathematical optimization", "Job shop scheduling", "Scheduling (production processes)", "Solver", "GRASP", "Routing (electronic design automation)", "Distributed computing" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4096498" }, { "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/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/W2906774680", "doi": "10.1109/icapr.2017.8593145", "title": "A heuristics based approach for optimizing delivery schedule of an Unmanned Aerial Vehicle (Drone) based delivery system", "year": 2017, "publication_date": "2017-12-01", "type": "conference-paper", "language": "en", "authors": [ "Vivek H Yadav", "Anand M. Narasimhamurthy" ], "first_author": "Vivek H Yadav", "venue": null, "publisher": null, "abstract": "In this work we address the problem of computing a delivery schedule for drones in an Unmanned Aerial Vehicle delivery system. The problem specification consists of a set of orders (each order contains a list of items with quantity) to be delivered to customers whose locations are known, also specified are locations of warehouses and drones. The aim is to minimize the time for completing a batch of order deliveries. The problem has some of the features of the Vehicle Routing Problem (as well as the Travelling Salesman Problem), however the increased number of constraints and dynamic nature of the problem are the distinguishing features in this setting. As most versions of the problem are NP-hard we employ a heuristics based approach since such an approach would be more suitable to solve large scale instances of the problem that might be encountered by a commercial delivery service. The proposed heuristics are compared with regard to the quality of solution and computation overhead.", "keywords": [ "Drone", "Heuristics", "Computer science", "Travelling salesman problem", "Schedule", "Routing (electronic design automation)", "Overhead (engineering)", "Vehicle routing problem", "Set (abstract data type)", "Mathematical optimization", "Distributed computing", "Real-time computing" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icapr.2017.8593145" }, { "id": "https://openalex.org/W2957052833", "doi": "10.1016/j.asoc.2019.105643", "title": "Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour", "year": 2019, "publication_date": "2019-07-18", "type": "article", "language": "en", "authors": [ "Heba A. Kurdi", "Munirah F. Aldaood", "Shiroq Al-Megren", "Ebtesam Aloboud", "Abdulrahman S. Aldawood", "Kamal Youcef‐Toumi" ], "first_author": "Heba A. Kurdi", "venue": "Applied Soft Computing", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Scalability", "Heuristic", "Task (project management)", "Flexibility (engineering)", "Throughput", "Distributed computing", "Artificial intelligence", "Mathematical optimization", "Systems engineering", "Engineering" ], "cited_by_count": 66, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1721.1/136384" }, { "id": "https://openalex.org/W4401011187", "doi": "10.1007/s10479-024-06164-5", "title": "Optimizing last-mile delivery services: a robust truck-drone cooperation model and hybrid metaheuristic algorithm", "year": 2024, "publication_date": "2024-07-26", "type": "article", "language": "en", "authors": [ "Seyed Mohammad Javad Mirzapour Al-e-Hashem", "Taha‐Hossein Hejazi", "Ghazal Haghverdizadeh", "Mohsen Shidpour" ], "first_author": "Seyed Mohammad Javad Mirzapour Al-e-Hashem", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Computer science", "Metaheuristic", "Genetic algorithm", "Last mile (transportation)", "Theory of computation", "Hybrid algorithm (constraint satisfaction)", "Service (business)", "Vehicle routing problem", "Operations research", "Mathematical optimization" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10479-024-06164-5" }, { "id": "https://openalex.org/W4313229347", "doi": "10.1109/tvt.2022.3232607", "title": "Deep Reinforcement Learning for UAV Routing in the Presence of Multiple Charging Stations", "year": 2022, "publication_date": "2022-12-28", "type": "article", "language": "en", "authors": [ "Mingfeng Fan", "Yaoxin Wu", "Tianjun Liao", "Zhiguang Cao", "Hongliang Guo", "Guillaume Sartoretti", "Guohua Wu" ], "first_author": "Mingfeng Fan", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Deploying Unmanned Aerial Vehicles (UAVs) for traffic monitoring has been a hotspot given their flexibility and broader view. However, a UAV is usually constrained by battery capacity due to limited payload. On the other hand, the development of wireless charging technology has allowed UAVs to replenish energy from charging stations.In this paper, we study a UAV routing problem in the presence of multiple charging stations (URPMCS) with the objective of minimizing the total distance traveled by the UAV during traffic monitoring. We present a deep reinforcement learning based method, where a multi-head heterogeneous attention mechanism is designed to facilitate learning a policy that automatically and sequentially constructs the route, while taking the energy consumption into account. In our method, two types of attentions are leveraged to learn the relations between monitoring targets and charging station nodes, adopting an encoder-decoder-like policy network. Moreover, we also employ a curriculum learning strategy to enhance generalization to different numbers of charging stations. Computational results show that our method outperforms conventional algorithms with higher solution quality (except for exact methods such as Gurobi) and shorter runtime in general, and also exhibits strong generalized performance on problem instances with different distributions and sizes.", "keywords": [ "Reinforcement learning", "Computer science", "Energy consumption", "Payload (computing)", "Battery capacity", "Flexibility (engineering)", "Wireless", "Drone", "Real-time computing", "Base station", "Routing (electronic design automation)", "Airfield traffic pattern" ], "cited_by_count": 64, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ink.library.smu.edu.sg/sis_research/8205" }, { "id": "https://openalex.org/W4413143864", "doi": "10.1016/j.tre.2025.104351", "title": "The multi-visit parallel drone scheduling pickup and delivery problem considering multiple trips and time windows", "year": 2025, "publication_date": "2025-08-08", "type": "article", "language": "en", "authors": [ "Shanshan Meng" ], "first_author": "Shanshan Meng", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pickup", "TRIPS architecture", "Drone", "Scheduling (production processes)", "Computer science", "Vehicle routing problem", "Job shop scheduling", "Operations research", "Engineering", "Simulation", "Operations management", "Parallel computing" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104351" }, { "id": "https://openalex.org/W4378908917", "doi": "10.1007/s43069-023-00228-1", "title": "Routing Problems with Electric and Autonomous Vehicles: Review and Potential for Future Research", "year": 2023, "publication_date": "2023-05-31", "type": "article", "language": "en", "authors": [ "Themistoklis Stamadianos", "Nikolaos A. Kyriakakis", "Magdalene Marinaki", "Yannis Marinakis" ], "first_author": "Themistoklis Stamadianos", "venue": "Operations Research Forum", "publisher": "Springer Nature", "abstract": "Abstract The transportation sector has undergone a major transformation in the past few years with the shift to electric mobility and the introduction of new, promising types of vehicles. Sustainability is the driving force of this revolution, but, these changes are expected to greatly impact the space of logistics operations. Electric vans have been in the market for a few years already, and they are comparable to gas-powered vehicles in certain applications; however, they are not the only ones with great potential. Drones and ground robots are two new types of vehicles, the characteristics of which offer remarkable opportunities in supply chains. Nonetheless, theoretical research on logistics operations with the abovementioned vehicles has been distant from reality. This research aims to help researchers explore the untapped potential of electric vehicles. To achieve this, a thorough look into their technical aspects is provided, to determine the key elements that distinguish them, make a comparison to the existing literature, and identify the research gap. Due to the increased complexity and the sensitivity of these vehicles to externalities and uncertainties in general, research should address and explore four major elements of these novel supply chains, energy consumption, new vehicle types, dynamic environment, and communication between vehicles.", "keywords": [ "Externality", "Sustainability", "Computer science", "Drone", "Key (lock)", "Supply chain", "Space (punctuation)", "Transport engineering", "Energy consumption", "Electric vehicle", "Risk analysis (engineering)", "Business" ], "cited_by_count": 22, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s43069-023-00228-1.pdf" }, { "id": "https://openalex.org/W3133866224", "doi": "10.1007/978-3-030-64973-9_1", "title": "Drone Routing for Post-disaster Damage Assessment", "year": 2021, "publication_date": "2021-01-01", "type": "book-chapter", "language": "en", "authors": [ "Birce Adsanver", "Elvin Çoban", "Burcu Balcik" ], "first_author": "Birce Adsanver", "venue": "Springer optimization and its applications", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Solver", "Heuristic", "Computer science", "Routing (electronic design automation)", "Integer programming", "Mathematical optimization", "Constructive", "Linear programming", "Set (abstract data type)", "Operations research", "Artificial intelligence" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-64973-9_1" }, { "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/W4285209996", "doi": "10.1109/tvt.2022.3182335", "title": "Packet Routing in Dynamic Multi-Hop UAV Relay Network: A Multi-Agent Learning Approach", "year": 2022, "publication_date": "2022-06-14", "type": "article", "language": "en", "authors": [ "Ruijin Ding", "Jiawei Chen", "Wen Rong Wu", "Jun Liu", "Feifei Gao", "Xuemin Shen" ], "first_author": "Ruijin Ding", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The multi-hop unmanned aerial vehicle (UAV) network can serve as data relays where ground users (GUs) do not have reliable direct connections to the base station (BS). Existing works mainly focus on simple dual-hop system. In this paper, we investigate the packet routing problem in a multi-hop UAV relay network to minimize the data transmission time and enhance the network throughput. However, the dynamic network topology due to UAV mobility makes the packet routing challenging since the limited communication range of each UAV leads to volatile wireless connection. Moreover, the line-of-sight communication links may cause strong interference among UAVs. Towards this end, we propose a novel multi-agent deep reinforcement learning based algorithm, named as multi-agent QMIX (MAQMIX) to: 1) design proper UAVs’ trajectories to serve the moving GUs while maintaining the network connection; 2) allocate frequency resource properly among UAVs to alleviate the impact of interference; and 3) choose a proper next hop UAV for each data packet to reduce the transmission time and probability of network congestion. The proposed MAQMIX has two novel training mechanisms, i.e., intra-UAV and inter-UAV training mechanisms, which can tackle the large action space issue and coordinate the training among UAVs in the multi-hop UAV relay network. Simulation results demonstrate that the MAQMIX outperforms baseline schemes in terms of the network congestion avoidance, throughput, and transmission time.", "keywords": [ "Computer network", "Computer science", "Relay", "Hop (telecommunications)", "Network packet", "Adaptive routing", "Routing (electronic design automation)", "Packet switching", "Dynamic Source Routing", "Routing protocol", "Distributed computing" ], "cited_by_count": 84, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2022.3182335" }, { "id": "https://openalex.org/W3153448713", "doi": "10.1109/tvt.2021.3072065", "title": "UAV-Aided Low Latency Multi-Access Edge Computing", "year": 2021, "publication_date": "2021-04-09", "type": "article", "language": "en", "authors": [ "Ye Yu", "Xiangyuan Bu", "Kai Ming Yang", "Hongyuan Yang", "Xiaozheng Gao", "Zhu Han" ], "first_author": "Ye Yu", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "As an emerging technique, unmanned aerial vehicle (UAV) aided multi-access edge computing (MEC) network has been improving the performance of the communication network. The novel architecture is beneficial for coverage, flexibility, and reliability. However, reducing network latency is a critical issue. In this paper, we design a UAV-aided network with a millimeter wave (mmWave) backhaul to achieve the multi-access edge computing. The routing problem is formulated and solved first to obtain the optimal routes through the ad hoc link for all users. Then, we formulate the joint trajectory design and resource allocation problem, which is a mixed-integer nonconvex programming, to minimize the network latency. Furthermore, we design a novel iterative algorithm framework to handle this challenging problem. In the outer loop, the proposed problem is separated into the primal problems and master problems by adopting generalized benders decomposition (GBD). In the inner loop, we design the algorithm to solve the continuous nonconvex primal problem by combining the alternating direction method of multipliers (ADMM), Dinkelbach algorithm, and successive convex approximation (SCA) algorithm. The simulation results demonstrate that our proposed algorithm framework is effective and feasible.", "keywords": [ "Computer science", "Mathematical optimization", "Edge computing", "Backhaul (telecommunications)", "Integer programming", "Approximation algorithm", "Latency (audio)", "Optimization problem", "Robustness (evolution)", "Distributed computing", "Iterative method", "Enhanced Data Rates for GSM Evolution" ], "cited_by_count": 55, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2021.3072065" }, { "id": "https://openalex.org/W4414406539", "doi": "10.3390/drones9090664", "title": "Advanced Drone Routing and Scheduling for Emergency Medical Supply Chains in Essex", "year": 2025, "publication_date": "2025-09-22", "type": "article", "language": "en", "authors": [ "Shabnam Sadeghi Esfahlani", "Sarinova Simandjuntak", "Alireza Sanaei", "Alex Fraess-Ehrfeld" ], "first_author": "Shabnam Sadeghi Esfahlani", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at v=60kmh−1. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks.", "keywords": [ "Scheduling (production processes)", "Simulated annealing", "Drone", "Vehicle routing problem", "Particle swarm optimization", "Swarm behaviour", "Embedding" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones9090664" }, { "id": "https://openalex.org/W4414165856", "doi": "10.23919/jsee.2025.000091", "title": "Two-Phase Heuristic for Vehicle Routing Problem with Drones in Multi-Trip and Multi-Drop Mode", "year": 2025, "publication_date": "2025-08-01", "type": "article", "language": "en", "authors": [ "Huawei Ma", "Xiaoxuan Hu", "Waiming Zhu" ], "first_author": "Huawei Ma", "venue": "Journal of Systems Engineering and Electronics", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "As commercial drone delivery becomes increasingly popular, the extension of the vehicle routing problem with drones (VRPD) is emerging as an optimization problem of interests. This paper studies a variant of VRPD in multi-trip and multidrop (VRP-mmD). The problem aims at making schedules for the trucks and drones such that the total travel time is minimized. This paper formulate the problem with a mixed integer programming model and propose a two-phase algorithm, i.e., a parallel route construction heuristic (PRCH) for the first phase and an adaptive neighbor searching heuristic (ANSH) for the second phase. The PRCH generates an initial solution by concurrently assigning as many nodes as possible to the truck-drone pair to progressively reduce the waiting time at the rendezvous node in the first phase. Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase. Numerical tests on some benchmark data are conducted to verify the performance of the algorithm. The results show that the proposed algorithm can found better solutions than some state-of-the-art methods for all instances. Moreover, an extensive analysis highlights the stability of the proposed algorithm.", "keywords": [ "Benchmark (surveying)", "Drone", "Heuristic", "Routing (electronic design automation)", "Integer programming", "Vehicle routing problem", "Stability (learning theory)", "Node (physics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/jsee.2025.000091" }, { "id": "https://openalex.org/W7165837744", "doi": "10.3390/electronics15132781", "title": "Time Dependent Truck–Drone Green Vehicle Routing Problem with Pickup and Delivery in Large Cities", "year": 2026, "publication_date": "2026-06-24", "type": "article", "language": "en", "authors": [ "Xiancheng Zhou", "Qingling Tang", "Shuyi Zhang", "Kun Yang" ], "first_author": "Xiancheng Zhou", "venue": "Electronics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Recognizing the limitations of traditional vehicle routing models in urban environments, this work presents the Time-Dependent Truck-Drone Green Vehicle Routing Problem with Pickup and Delivery (TDTDGVRPPD) to simultaneously optimize environmental impact and operational efficiency. We first develop a truck fuel consumption and carbon emission model that accounts for the effects of time-varying speeds and real-time loads during delivery. A nonlinear energy consumption model is then proposed for drones, considering payload weight. Based on these models, a mathematical formulation is developed to minimize the total operational cost, including truck and drone usage costs, truck fuel and carbon emission costs, drone energy consumption costs, truck–drone coordination time costs, and time-window violation penalties. The model also incorporates truck no-entry zones, time-varying speeds, and customers’ simultaneous pickup and delivery demands. An Improved Whale Optimization Algorithm (IWOA) hybridized with Variable Neighborhood Search (VNS) is developed to solve the problem. Simulation results show that the proposed model and algorithm effectively optimize truck departure times to avoid traffic congestion, reduce truck–drone coordination time, and lower total logistics costs and energy consumption, thereby contributing to energy conservation and emission reduction in logistics operations.", "keywords": [ "Truck", "Payload (computing)", "Energy consumption", "Fuel efficiency", "Pickup", "Routing (electronic design automation)", "Drone", "Vehicle routing problem", "Nonlinear programming" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-9292/15/13/2781/pdf?version=1782304206" }, { "id": "https://openalex.org/W4407880468", "doi": "10.2139/ssrn.5077473", "title": "An Exact Algorithm to Solve Vehicle Routing Problem with Drones for Delivery and Surveillance Tasks after Disasters", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Wenbo Sun", "Lingxiao Wu", "Fangni Zhang" ], "first_author": "Wenbo Sun", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Routing algorithm", "Computer security", "Operations research", "Engineering", "Computer network", "Routing protocol" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5077473" }, { "id": "https://openalex.org/W4287903504", "doi": "10.48550/arxiv.2001.06456", "title": "Computational Approaches for Solving Two-Echelon Vehicle and UAV Routing Problems for Post-Disaster Humanitarian Operations", "year": 2020, "publication_date": "2020-01-17", "type": "preprint", "language": "en", "authors": [ "Tasnim Ibn Faiz", "Chrysafis Vogiatzis", "Md. Noor‐E‐Alam" ], "first_author": "Tasnim Ibn Faiz", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Humanitarian logistics service providers have two major responsibilities immediately after a disaster: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and telecommunications networks, which are often rendered unusable by the disaster at hand. In this work, we propose a two-echelon vehicle routing framework for performing these operations using aerial uncrewed autonomous vehicles (UAVs or drones) to address the issues associated with these failures. In our proposed framework, we assume that ground vehicles cannot reach the trapped population directly, but they can only transport drones from a depot to some intermediate locations. The drones launched from these locations serve to both identify demands for medical and other aids (e.g., epi-pens, medical supplies, dry food, water) and make deliveries to satisfy them. Specifically, we present a decision framework, in which the resulting optimization problem is formulated as a two-echelon vehicle routing problem with trucks as the first echelon vehicles and for the second echelon vehicles, we consider two types of drones. Hotspot drones have the capability of providing a cell phone and internet reception and hence are used to capture demands. Delivery drones are subsequently employed to satisfy the observed demand. To handle demand uncertainty, we decompose the decision problem into two stages: providing telecommunications capabilities in the first stage thereby capturing demand precisely, and satisfying the resulting demands in the second stage. To solve the resulting models, we propose efficient computational approaches by designing a decomposition algorithm with column generation (CG)-based heuristics to identify optimal drone routes.", "keywords": [ "Drone", "Vehicle routing problem", "Truck", "Column generation", "Computer science", "Operations research", "Routing (electronic design automation)", "Population", "Humanitarian Logistics", "Computer security", "Engineering", "Computer network" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2001.06456" }, { "id": "https://openalex.org/W4386124207", "doi": "10.1109/tem.2023.3299693", "title": "Dynamic Truck–UAV Collaboration and Integrated Route Planning for Resilient Urban Emergency Response", "year": 2023, "publication_date": "2023-08-24", "type": "article", "language": "en", "authors": [ "Yuying Long", "Gangyan Xu", "Jinqiu Zhao", "Binglei Xie", "Meng Fang" ], "first_author": "Yuying Long", "venue": "IEEE Transactions on Engineering Management", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Efficient urban emergency response is vital for saving lives and property after disasters. However, urban emergency response is challenging because it involves many demands, with a very tight time for making decisions, and is frequently threatened by the disruption of road networks and infrastructures. Taking these challenges into consideration, this article proposes a dynamic truck-UAV (DTU) collaboration strategy to realize efficient and resilient urban emergency response. Specifically, a DTU collaboration strategy is first proposed based on the characteristics of urban emergency management. Then, an integrated truck-UAV collaborative scheduling model is developed and indicated to be an NP-hard problem. Then, a tabu search-based integrated (TSI) scheduling algorithm is developed, with several tailored local search operators and a recursion-based evaluation (RE) algorithm. Finally, comprehensive experiments are conducted to demonstrate the superiority of our proposed strategy over existing truck-UAV collaboration strategies and verify the performance of our proposed solution algorithm.Managerial Relevance Statement—This article proposes a dynamic truck-UAV (DTU) collaboration strategy and a corresponding route planning method for urban emergency response. The findings of this work demonstrate that emergency management agencies can utilize the DTU collaboration strategy for efficient emergency response in urban areas with high-density road networks. Meanwhile, the emergency response under this strategy is resilient to cope with different degrees of road network disruptions. In addition, the DTU collaboration strategy shows a distinct improvement over the parallel and truck-UAV flying sidekick strategies in large-scale emergency response scenarios, and it will perform better when demand density increases. Furthermore, the proposed integrated scheduling algorithm is demonstrated to be suitable and efficient for solving the routing problems associated with the DTU collaboration strategy.", "keywords": [ "Truck", "Computer science", "Emergency response", "Scheduling (production processes)", "Emergency management", "Operations research", "Recursion (computer science)", "Transport engineering", "Engineering", "Operations management", "Algorithm" ], "cited_by_count": 75, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tem.2023.3299693" }, { "id": "https://openalex.org/W4395064706", "doi": "10.48550/arxiv.2404.13068", "title": "SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle Routing Problem with Drones Using Reinforcement Learning", "year": 2024, "publication_date": "2024-04-13", "type": "preprint", "language": "en", "authors": [ "Navid Mohammad Imran", "Myounggyu Won" ], "first_author": "Navid Mohammad Imran", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The Vehicle Routing Problem with Drones (VRPD) seeks to optimize the routing paths for both trucks and drones, where the trucks are responsible for delivering parcels to customer locations, and the drones are dispatched from these trucks for parcel delivery, subsequently being retrieved by the trucks. Given the NP-Hard complexity of VRPD, numerous heuristic approaches have been introduced. However, improving solution quality and reducing computation time remain significant challenges. In this paper, we conduct a comprehensive examination of heuristic methods designed for solving VRPD, distilling and standardizing them into core elements. We then develop a novel reinforcement learning (RL) framework that is seamlessly integrated with the heuristic solution components, establishing a set of universal principles for incorporating the RL framework with heuristic strategies in an aim to improve both the solution quality and computation speed. This integration has been applied to a state-of-the-art heuristic solution for VRPD, showcasing the substantial benefits of incorporating the RL framework. Our evaluation results demonstrated that the heuristic solution incorporated with our RL framework not only elevated the quality of solutions but also achieved rapid computation speeds, especially when dealing with extensive customer locations.", "keywords": [ "Drone", "Reinforcement learning", "Heuristic", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Mathematical optimization", "Reinforcement", "Artificial intelligence", "Mathematics", "Engineering", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2404.13068" }, { "id": "https://openalex.org/W4394859404", "doi": "10.1016/j.eswa.2024.123766", "title": "Management of a post-disaster emergency scenario through unmanned aerial vehicles: Multi-Depot Multi-Trip Vehicle Routing with Total Completion Time Minimization", "year": 2024, "publication_date": "2024-04-16", "type": "article", "language": "en", "authors": [ "Tiziana Calamoneri", "Federico Corò", "Simona Mancini" ], "first_author": "Tiziana Calamoneri", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": "One of the most valuable and promising applications for Unmanned aerial vehicles (UAVs) is in natural disaster management, where these aircraft can operate autonomously without any need for human intervention during their flights. In this paper, we foster the interface of Operational Research with computer science in general and sensor networking in particular by focusing on managing a post-disaster emergency scenario where the use of a fleet of UAVs helps rescue teams identify people needing help inside an affected area. We model this situation as an original graph theoretical problem called Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Time minimization (MDMT-VRP-TCT). The main novelty of the MDMT-VRP-TCT is the combination of the following three features: multi-depot, multi-trip, and completion time minimization. We propose a mixed-integer linear programming (MILP) formulation, develop a matheuristic framework to address large instances, and present an extended set of experiments to test the performance of the proposed matheuristic: first, we compare the matheuristic with the MILP formulation on a set of small instances (up to 30 nodes); then, we compare our matheuristic with two heuristics from networking literature, showing that it outperforms the existing algorithms.", "keywords": [ "Computer science", "Vehicle routing problem", "Heuristics", "Minification", "Integer programming", "Novelty", "Operations research", "Set (abstract data type)", "Routing (electronic design automation)", "Linear programming", "Real-time computing", "Mathematical optimization" ], "cited_by_count": 36, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2024.123766" }, { "id": "https://openalex.org/W4322707283", "doi": "10.1109/access.2023.3247501", "title": "Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption", "year": 2023, "publication_date": "2023-01-01", "type": "article", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti", "Guido Perboli" ], "first_author": "Maria Elena Bruni", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper studies the drone-aided last-mile delivery problem with shared depot resources. Our research motivation comes from E-commerce logistics, where big companies such as Amazon, are already filing up patents for the development of drone-friendly fulfillment centers towers that could serve as both charging hubs and convenient pit stops for delivery drones to pick up and drop off packages efficiently. We mainly focus on the tactical decisions about the selection of shared fulfillment centers used as the drone launch and retrieve stations and the fleet size plans. The operational drone route decisions are also incorporated into a unified framework to account for the mutual impact between tactical and operational plans. Moreover, we consider explicitly the non-linear and load-dependent nature of the energy consumption function for drone batteries. The problem is formulated as a mixed integer program with linear constraints, developed in the realm of layered networks, where the non-linear nature of energy consumption and its load dependency are incorporated and efficiently handled without the need of approximating non-linear terms. The proposed model is tested on an extensive set of instances with up to 75 customers, showing its computational efficiency. Insights about the route costs and spatial configuration of depots are also discussed.", "keywords": [ "Drone", "Computer science", "Energy consumption", "Last mile (transportation)", "Linear programming", "Operations research", "Efficient energy use", "Mile", "Engineering" ], "cited_by_count": 45, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/6287639/10005208/10049551.pdf" }, { "id": "https://openalex.org/W4226104768", "doi": "10.48550/arxiv.2112.12545", "title": "A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone", "year": 2021, "publication_date": "2021-12-22", "type": "preprint", "language": "en", "authors": [ "Aigerim Bogyrbayeva", "Yoon, Taehyun", "Hanbum Ko", "Sungbin Lim", "Hyokun Yun", "Changhyun Kwon" ], "first_author": "Aigerim Bogyrbayeva", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. In particular, the attention-based encoder-decoder models show high effectiveness on various routing problems, including the Traveling Salesman Problem (TSP). Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring routing a heterogeneous fleet of vehicles in coordination -- a truck and a drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at a node for the other vehicle to join. State-less attention-based decoder fails to make such coordination between vehicles. We propose a hybrid model that uses an attention encoder and a Long Short-Term Memory (LSTM) network decoder, in which the decoder's hidden state can represent the sequence of actions made. We empirically demonstrate that such a hybrid model improves upon a purely attention-based model for both solution quality and computational efficiency. Our experiments on the min-max Capacitated Vehicle Routing Problem (mmCVRP) also confirm that the hybrid model is more suitable for the coordinated routing of multiple vehicles than the attention-based model. The proposed model demonstrates comparable results as the operations research baseline methods.", "keywords": [ "Travelling salesman problem", "Reinforcement learning", "Computer science", "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Node (physics)", "2-opt", "Mathematical optimization", "Artificial intelligence", "Computer network", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2112.12545" }, { "id": "https://openalex.org/W3198672146", "doi": "10.1007/978-3-030-85906-0_1", "title": "Sustainable Facility Location-Routing Problem for Blood Package Delivery by Drones with a Charging Station", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Shirin Ghasemi", "Reza R. Tavakkoli-Moghaddam", "Mahdi Hamid", "Meysam Hosseinzadeh" ], "first_author": "Shirin Ghasemi", "venue": "IFIP advances in information and communication technology", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "TOPSIS", "Battery (electricity)", "Battery capacity", "Transport engineering", "Sustainability", "Computer science", "Engineering", "Operations research", "Embedded system" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://inria.hal.science/hal-04022139" }, { "id": "https://openalex.org/W4386760375", "doi": "10.1016/j.engappai.2023.107113", "title": "A double-adaptive general variable neighborhood search for an unmanned electric vehicle routing and scheduling problem in green manufacturing systems", "year": 2023, "publication_date": "2023-09-15", "type": "article", "language": "en", "authors": [ "Wenheng Liu", "Mahjoub Dridi", "Jintong Ren", "Amir Hajjam El Hassani", "Shuying Li" ], "first_author": "Wenheng Liu", "venue": "Engineering Applications of Artificial Intelligence", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Variable neighborhood search", "Vehicle routing problem", "Scheduling (production processes)", "Mathematical optimization", "Job shop scheduling", "Routing (electronic design automation)", "Operations research", "Metaheuristic", "Computer network", "Algorithm" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.engappai.2023.107113" }, { "id": "https://openalex.org/W4378903692", "doi": "10.3390/drones7060362", "title": "Drones Routing with Stochastic Demand", "year": 2023, "publication_date": "2023-05-30", "type": "article", "language": "en", "authors": [ "Nan Hua Yu", "Bin Dong", "Yuben Qu", "Mingwei Zhang", "Yanyan Wang", "Haipeng Dai", "Changhua Yao" ], "first_author": "Nan Hua Yu", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Motivated by the increasing number of drones used for package delivery, we first study the problem of Multiple drOne collaborative Routing dEsign (MORE) in this article. That is, given a fixed number of drones and customers, determining the delivery trip for drones under capacity constraint with stochastic demand for customers such that the overall expected traveling cost is minimized. To address the MORE problem, we first prove that MORE falls into the realm of the classical vehicle routing problem with stochastic demand and then propose an effective algorithm for MORE. Next, we have a scheme of resplitting customers into different individual delivery trips while the stochastic demands are determined. Moreover, we consider a variety of MORE, MORE-TW, and design an effective algorithm to address it. We conduct simulation experiments for MORE to verify our theoretical findings. The results show that our algorithm outperforms other comparison algorithms by at least 79.60%.", "keywords": [ "Drone", "TRIPS architecture", "Routing (electronic design automation)", "Vehicle routing problem", "Computer science", "Mathematical optimization", "Constraint (computer-aided design)", "Operations research", "Mathematics", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/7/6/362/pdf?version=1685699926" }, { "id": "https://openalex.org/W4288047850", "doi": "10.1109/icuas54217.2022.9836044", "title": "Heterogenous vehicle routing: comparing parameter tuning using genetic algorithm and bayesian optimization", "year": 2022, "publication_date": "2022-06-21", "type": "conference-paper", "language": "en", "authors": [ "Subramanian Ramasamy", "Md Safwan Mondal", "Jean-Paul Reddinger", "James M. Dotterweich", "James D. Humann", "Marshal Childers", "Pranav A. Bhounsule" ], "first_author": "Subramanian Ramasamy", "venue": "2022 International Conference on Unmanned Aircraft Systems (ICUAS)", "publisher": null, "abstract": "Heterogeneous vehicles (e.g., unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV)) are best suited for surveillance application over large areas. UAVs are fast, but fuel limited, while, UGVs have a larger fuel capacity, but are relatively slow. When UAVs are combined with UGVs they can provide larger coverage at a relatively fast speed. The UAV may also be recharged on the UGV as needed. The resulting route optimization problem is computationally complex, but may be solved relatively fast using heuristics. In this paper, we solve for a mission route using a two-level optimization; (1) the UGV route is assigned using heuristics with free parameters, (2) the UAV route is solved using a vehicle routing problem formulation with capacity constraints, time windows, and dropped visits. However, this open-loop two-level optimization may yield non-optimal solutions or fail completely because of poor choice of UGV parameters. Our primary objective is to explore closed loop optimization where the free parameters of the UGV routes are optimized using Bayesian optimization and Genetic algorithms. Our results show that both methods produce good quality solutions, but bayesian optimization is computationally more efficient than genetic algorithm.", "keywords": [ "Bayesian optimization", "Unmanned ground vehicle", "Heuristics", "Computer science", "Genetic algorithm", "Vehicle routing problem", "Mathematical optimization", "Routing (electronic design automation)", "Optimization problem", "Algorithm", "Real-time computing", "Artificial intelligence" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas54217.2022.9836044" }, { "id": "https://openalex.org/W3151580055", "doi": "10.5505/pajes.2020.75725", "title": "The optimization of UAV routing problem with a genetic algorithm to observe the damages of possible Istanbul earthquake", "year": 2021, "publication_date": "2021-01-01", "type": "article", "language": "en", "authors": [ "Muhammed Halat", "Ömer Özkan" ], "first_author": "Muhammed Halat", "venue": "Pamukkale University Journal of Engineering Sciences", "publisher": "Pamukkale University", "abstract": "ÖzIn this study, the problem is to find a route for a UAV that takes off from Istanbul to observe the damages that may occur after the possible Istanbul earthquake within the first 24 hours.In the problem, 230 candidate grid points that UAV can visit on Istanbul are determined and the weight values combining the risk values based on earthquake degree zones and the population densities of the grid points are calculated for each candidate point.It is aimed to find a route for the UAV to maximize the total weights of the visited grid points under the UAV range constraint.The described problem is adapted to the Orienteering Problem in the literature.Since the Orienteering Problem is an NP-hard problem, a problem-specific genetic algorithm and a simulated annealing algorithm are developed to solve the problem.The parameters of the algorithms are tuned by experiments.15 different scenarios including the daily number of visits (of taken images) and the airports that the UAV takes and lands off after the earthquake are created and tried to be solved exactly via ILOG and approximately via developed metaheuristics.While the optimal solutions are found for 2 of 15 scenarios via ILOG, the designed genetic algorithm has better solutions and can solve the problem within acceptable CPU times for the rest of the scenarios.Bu çalışmada, olası İstanbul depremi sonrası oluşabilecek hasarları havadan gözlemlemek için ilk 24 saat içerisinde İstanbul'dan kaldırılan bir İHA'nın hangi rotada uçması gerektiği problemi ele alınmıştır.Problemde, İstanbul üzerinde İHA'nın ziyaret edebileceği 230 aday grid nokta belirlenmiş ve her aday nokta için noktanın deprem riski ağırlığı ile nüfus yoğunluğunu birleştiren ağırlık değerleri belirlenmiştir.Problemde en fazla sayıda aday noktanın ağırlığını toplayacak şekilde İHA'nın menzil kısıtı altında rotalanması amaçlanmıştır.Tanımlanan bu problem, literatürdeki Oryantring problemine uyarlanmıştır.Oryantring problemi NP-zor bir problem olduğundan dolayı, problemin çözümü için probleme özgü bir genetik algoritma ve bir tavlama benzetimi algoritması geliştirilmiştir.Algoritmaların parametreleri deneyler ile ayarlanmıştır.Gerçek hayata uygun olarak deprem sonrası İHA'nın kalktığı havalimanı ile günlük ziyaret (veya görüntü sayısı) durumlarını kapsayan 15 farklı senaryo oluşturulmuş ve senaryolar ILOG ile kesin ve geliştirilen metasezgisel algoritmalar ile yaklaşık olarak çözülmeye çalışılmıştır.15 senaryonun 2'sinde optimal çözüm bulunmuş olup diğer senaryolar için genetik algoritma daha iyi sonuçlar elde etmiş ve kabul edilebilir CPU süreleri içinde problemi çözebilmiştir.", "keywords": [ "Orienteering", "Damages", "Genetic algorithm", "Computer science", "Grid", "Mathematical optimization", "Simulated annealing", "Range (aeronautics)", "Metaheuristic", "Constraint (computer-aided design)", "Algorithm", "Vehicle routing problem" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://jag.journalagent.com/z4/download_fulltext.asp?pdir=pajes&plng=eng&un=PAJES-75725" }, { "id": "https://openalex.org/W2982221244", "doi": "10.3390/su11215932", "title": "Mathematical Investigation on the Sustainability of UAV Logistics", "year": 2019, "publication_date": "2019-10-25", "type": "article", "language": "en", "authors": [ "Joonyup Eun", "Byung Duk Song", "Sangbok Lee", "Dae-Eun Lim" ], "first_author": "Joonyup Eun", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicles (UAVs) are expected to make groundbreaking changes in the logistics industry. Leading logistics companies have been developing and testing their usage of UAVs recently as an environmentally friendly and cost-effective option. In this paper, we investigate how much the UAV delivery service is environmentally friendly compared to the traditional ground vehicle (GV) delivery service. Since there are fuel (battery) and loadable weight restrictions in the UAV delivery, multi-hopping of UAV is necessary, which may cause a large consumption of electrical energy. We present a two-phase approach. In Phase I, a new vehicle routing model to obtain optimal delivery schedules for both UAV-alone and GV-alone delivery systems is proposed, which considers each system’s restrictions, such as the max loadable weight and fuel replenishment. In Phase II, CO2 emissions are computed as a sustainability measure based on the travelling distance of the optimal route obtained from Phase I, along with various GV travel-speeds. A case study finds that the UAV-alone delivery system is much more CO2 efficient in all ranges of the GV speeds investigated.", "keywords": [ "Environmentally friendly", "Sustainability", "Phase (matter)", "User Friendly", "Computer science", "Vehicle routing problem", "Automotive engineering", "Service (business)", "Delivery system", "Routing (electronic design automation)", "Energy consumption", "Fuel efficiency" ], "cited_by_count": 35, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/11/21/5932/pdf?version=1571981720" }, { "id": "https://openalex.org/W7107956111", "doi": "10.1016/j.tre.2025.104543", "title": "Dynamic truck-drone cooperative delivery of emergency supplies considering secondary disasters", "year": 2025, "publication_date": "2025-11-30", "type": "article", "language": "en", "authors": [ "Yong Shi", "Keyi Zhang", "Jian Zhang", "Miao Chen", "Junhao Yang", "Haixiang Guo" ], "first_author": "Yong Shi", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Genetic algorithm", "Sorting", "Natural disaster", "Dynamic programming", "Duration (music)", "Emergency rescue", "Optimization problem" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104543" }, { "id": "https://openalex.org/W7171288372", "doi": "10.1016/j.tre.2026.105107", "title": "Electric vehicle routing problem with multi-visit drones considering time windows and battery swapping stations", "year": 2026, "publication_date": "2026-07-26", "type": "article", "language": "en", "authors": [ "Shanshan Meng", "Hongwei Ma" ], "first_author": "Shanshan Meng", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Electric vehicle", "Vehicle routing problem", "Benchmark (surveying)", "Drone", "Battery (electricity)", "Metaheuristic", "Charging station", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105107" }, { "id": "https://openalex.org/W4407090406", "doi": "10.1287/ijoc.2023.0484.cd", "title": "Code and Data Repository for A Branch-and-Price Algorithm for Robust Drone-Vehicle Routing Problem with Time Windows", "year": 2025, "publication_date": "2025-02-03", "type": "article", "language": "en", "authors": [ "Jaegwan Joo", "Chungmok Lee" ], "first_author": "Jaegwan Joo", "venue": "INFORMS journal on computing", "publisher": "Institute for Operations Research and the Management Sciences", "abstract": "The goal of this repository is to share data and results related to the Robust Drone-Vehicle Routing Problem with Time Windows (RDVRRPTW), solved using our solution approaches.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Code (set theory)", "Parallel computing", "Routing (electronic design automation)", "Algorithm", "Programming language", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1287/ijoc.2023.0484.cd" }, { "id": "https://openalex.org/W3145560686", "doi": "10.1109/wsc.2008.4736199", "title": "Multi-objective UAV mission planning using evolutionary computation", "year": 2008, "publication_date": "2008-12-01", "type": "conference-paper", "language": "en", "authors": [ "Adam J. Pohl", "Gary B. Lamont" ], "first_author": "Adam J. Pohl", "venue": null, "publisher": null, "abstract": "This investigation develops an innovative algorithm for multiple autonomous unmanned aerial vehicle (UAV) mission routing. The concept of a UAV swarm routing problem (SRP) as a new combinatorics problem, is developed as a variant of the vehicle routing problem with time windows (VRPTW). Solutions of SRP problem model result in route assignments per vehicle that successfully track to all targets, on time, within distance constraints. A complexity analysis and multi-objective formulation of the VRPTW indicates the necessity of a stochastic solution approach leading to a multi-objective evolutionary algorithm. A full problem definition of the SRP as well as a multi-objective formulation parallels that of the VRPTW method. Benchmark problems for the VRPTW are modified in order to create SRP benchmarks. The solutions show the SRP solutions are comparable or better than the same VRPTW solutions, while also representing a more realistic UAV swarm routing solution.", "keywords": [ "Vehicle routing problem", "Benchmark (surveying)", "Computer science", "Routing (electronic design automation)", "Mathematical optimization", "Evolutionary algorithm", "Computation", "Evolutionary computation", "Swarm behaviour", "Mathematics", "Artificial intelligence", "Algorithm" ], "cited_by_count": 36, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wsc.2008.4736199" }, { "id": "https://openalex.org/W7128732494", "doi": "10.1109/tits.2026.3658745", "title": "A Constraint Relaxation Co-Evolutionary Algorithm for the Two-Echelon Vehicle Routing Problem With Load-Dependent Drones", "year": 2026, "publication_date": "2026-02-12", "type": "article", "language": null, "authors": [ "Fan Yu", "Jinlong Zhou", "Qun Chen", "Yange Li", "Chao Li", "Sheng Qi", "Weixiong Huang" ], "first_author": "Fan Yu", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The growing adoption of drones in last-mile logistics has raised urgent demands for controlling energy consumption costs while ensuring delivery efficiency. To address this, we formulate the Two-Echelon Vehicle Routing Problem with Load-dependent Drones (2E-VRPLD) within a depot-satellite-customer framework, which reflects realistic logistics conditions where drones experience continuously changing payloads as they pick up and deliver parcels. These load fluctuations cause discrete shifts in power consumption, making their actual flight endurance highly uncertain under a fixed energy budget and thereby significantly increasing the complexity of planning feasible routes and minimizing overall transportation costs. To solve this complex VRP variant, we develop a novel Constraint Relaxation Co-evolutionary Algorithm (CRCEA) that integrates a customer-satellite matching mechanism to minimize drone endurance redundancy and a minimal impact assessment strategy for route segment merging. Experiments show that CRCEA attains nearly negligible optimality gaps relative to Gurobi on small-scale instances, significantly outperforms seven representative heuristics on large-scale instances, and consistently delivers superior solutions in real-world delivery scenarios from leading express carriers JD and SF Express in Changsha, China. Furthermore, ablation studies identify key factors affecting algorithmic performance, and sensitivity analyses illustrate how drone performance parameters influence the efficiency of solving the 2E-VRPLD. This work provides a robust theoretical and practical approach for solving the two-echelon pickup-and-delivery routing problem with load-dependent drones, offering important insights for advancing smart logistics and improving enterprise operational efficiency.", "keywords": [ "Drone", "Heuristics", "Routing (electronic design automation)", "Relaxation (psychology)", "Redundancy (engineering)", "Key (lock)", "Lagrangian relaxation", "Constraint (computer-aided design)", "Vehicle routing problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2026.3658745" }, { "id": "https://openalex.org/W2736635616", "doi": "10.1063/1.4992399", "title": "Parallel optimization algorithm for drone inspection in the building industry", "year": 2017, "publication_date": "2017-01-01", "type": "conference-paper", "language": "en", "authors": [ "Maciej Walczyński", "Wojciech Bożejko", "Dariusz Skorupka" ], "first_author": "Maciej Walczyński", "venue": "AIP conference proceedings", "publisher": "American Institute of Physics", "abstract": "In this paper we present an approach for Vehicle Routing Problem with Drones (VRPD) in case of building inspection from the air. In autonomic inspection process there is a need to determine of the optimal route for inspection drone. This is especially important issue because of the very limited flight time of modern multicopters. The method of determining solutions for Traveling Salesman Problem(TSP), described in this paper bases on Parallel Evolutionary Algorithm (ParEA)with cooperative and independent approach for communication between threads. This method described first by Bożejko and Wodecki [1] bases on the observation that if exists some number of elements on certain positions in a number of permutations which are local minima, then those elements will be in the same position in the optimal solution for TSP problem. Numerical experiments were made on BEM computational cluster with using MPI library.", "keywords": [ "Drone", "Computer science", "Optimization algorithm", "Algorithm", "Mathematical optimization", "Mathematics" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1063/1.4992399" }, { "id": "https://openalex.org/W4410237067", "doi": "10.1007/s10479-025-06581-0", "title": "A hybrid ant colony optimization algorithm for drone-based cooperative EV routing problem with simultaneously delivery and pickup", "year": 2025, "publication_date": "2025-05-09", "type": "article", "language": "en", "authors": [ "Yongjian Yang", "Tianjiao Feng", "Chenyu Luo", "Chenglong Li", "Jun Wang", "Yuning Xiong" ], "first_author": "Yongjian Yang", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Pickup", "Ant colony optimization algorithms", "Theory of computation", "Drone", "Computer science", "Routing (electronic design automation)", "Mathematical optimization", "Ant colony", "Vehicle routing problem", "Metaheuristic", "Distributed computing", "Algorithm" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10479-025-06581-0" }, { "id": "https://openalex.org/W4410942069", "doi": "10.1016/j.trc.2025.105165", "title": "Path pool based transformer model in reinforcement framework for dynamic urban drone delivery problem", "year": 2025, "publication_date": "2025-06-02", "type": "article", "language": "en", "authors": [ "Chuankai Xiang", "Yanfang Mo", "Wei Liu", "Zhibin Wu", "Lishuai Li" ], "first_author": "Chuankai Xiang", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": "Unmanned Aerial Vehicles (UAVs), particularly drones used for delivery, represent a rapidly expanding segment of the commercial services industry. A critical challenge for scaling operations is the real-time scheduling of a large fleet of drones. This paper addresses the dynamic urban drone delivery problem, focusing on the assignment of orders and the routing of drones. The complexity of such dynamic optimization problems increases exponentially with the drone fleet size and the intricacy of their routing networks, making traditional exact and heuristic algorithms insufficient for effective resolution. To tackle this computational complexity, we introduce a novel Path Pool-based Transformer model combined with Reinforcement Learning (PPTRL). Unlike existing transformer-based models that predict the next node using only the embedding of the previously visited node, our model employs a dependency decay pooling strategy (DDPS) that incorporates the entire path context of the drone into the decision-making process. By leveraging the full path context, our approach captures long-term dependencies in routing decisions, leading to more globally efficient paths. The experimental results confirm the efficacy of the path pool approach. Experimentally, our model achieves near-optimal performance on small-scale problems with significantly reduced runtime compared to Gurobi. For larger-scale problems, our approach surpasses both heuristics and advanced learning-based algorithms in performance. Furthermore, our method demonstrates excellent scalability and robustness against variations in fleet size and the proportion of dynamically arriving tasks and shows good capabilities in the real-world scenario. © 2025 The Authors.", "keywords": [ "Drone", "Reinforcement learning", "Transformer", "Reinforcement", "Engineering", "Motion planning", "Computer science", "Simulation", "Transport engineering", "Operations research", "Artificial intelligence", "Electrical engineering" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2025.105165" }, { "id": "https://openalex.org/W4410943283", "doi": "10.1080/00207543.2025.2512445", "title": "On-demand heterogeneous drone delivery problem", "year": 2025, "publication_date": "2025-06-02", "type": "article", "language": "en", "authors": [ "Xupeng Wen", "Zhiguang Cao", "Shu Xu", "Dapeng Ren", "Guohua Wu", "Yaoxin Wu" ], "first_author": "Xupeng Wen", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "In the on-demand problem domain, actual demand frequently deviates from the expected demand. This paper intricately delves into the exploration of on-demand heterogeneous multi-drone routing problem (ODHDRP), in which a transport drone carries multiple terminal drones to subregions in the first echelon, and the terminal drones deliver parcels during a flight trip to customers with demands in subregions to maintain economies of scale in the second echelon. We formulate the customer demands using a normal distribution, and exploit a reliability model of customer demands with chance constraints. To solve the ODHDRP efficiently, we propose a hybrid iterative optimisation heuristic (HIOH) approach. Firstly, a clustering algorithm considering the drone's payload is designed to divide the customer-region into several subregions. Subsequently, the dynamic programming algorithm is introduced to generate the initial route to each subregion. Secondly, an iterative optimisation algorithm with heuristic operators and reliability-based strategies is designed to handle the chance constraints and optimise the routes. The experiments substantiate the superior performance of the proposed method in contrast to baseline algorithms. Moreover, the sensitivity of key factors in the proposed model is analysed and several managerial insights are derived.", "keywords": [ "Drone", "Computer science", "On demand", "Operations research", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2025.2512445" }, { "id": "https://openalex.org/W3172922310", "doi": "10.1016/j.cor.2022.105702", "title": "Routing Problem for Unmanned Aerial Vehicle Patrolling Missions - A Progressive Hedging Algorithm", "year": 2022, "publication_date": "2022-02-05", "type": "article", "language": "en", "authors": [ "Sudarshan Rajan", "Kaarthik Sundar", "Natarajan Gautam" ], "first_author": "Sudarshan Rajan", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Patrolling", "Context (archaeology)", "Set (abstract data type)", "Computer science", "Fidelity", "Routing (electronic design automation)", "Sequence (biology)", "Real-time computing", "Algorithm", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2022.105702" }, { "id": "https://openalex.org/W4388551615", "doi": "10.1007/978-981-99-7025-4_40", "title": "MARL$$_{4}DRP$$: Benchmarking Cooperative Multi-agent Reinforcement Learning Algorithms for Drone Routing Problems", "year": 2023, "publication_date": "2023-11-10", "type": "conference-paper", "language": "en", "authors": [ "Shiyao Ding", "Hideki Aoyama", "Donghui Lin" ], "first_author": "Shiyao Ding", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Reinforcement learning", "Computer science", "Benchmark (surveying)", "Benchmarking", "Routing (electronic design automation)", "Distributed computing", "Modular design", "Artificial intelligence", "Embedded system", "Programming language" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-99-7025-4_40" }, { "id": "https://openalex.org/W2531267769", "doi": "10.3390/ijgi5100184", "title": "Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform", "year": 2016, "publication_date": "2016-10-10", "type": "article", "language": "en", "authors": [ "Ming Zhang", "Chen Su", "Yuan Liu", "Mingyuan Hu", "Yuesheng Zhu" ], "first_author": "Ming Zhang", "venue": "ISPRS International Journal of Geo-Information", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a threat environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common threat model is constructed for threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.", "keywords": [ "Planner", "Route planning", "Terrain", "Computer science", "Globe", "Simulation", "Real-time computing", "Motion planning", "Masking (illustration)", "Engineering", "Robot", "Artificial intelligence" ], "cited_by_count": 23, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2220-9964/5/10/184/pdf?version=1476071000" }, { "id": "https://openalex.org/W2009525320", "doi": "10.1142/s2301385014500058", "title": "Cooperative Mission Planning with Multiple UAVs in Realistic Environments", "year": 2014, "publication_date": "2014-01-01", "type": "article", "language": "en", "authors": [ "Lin Geng", "Yunfeng Zhang", "Jingjing Wang", "Jerry Ying Hsi Fuh", "S. H. Teo" ], "first_author": "Lin Geng", "venue": "Unmanned Systems", "publisher": "World Scientific", "abstract": "In this paper, a mission planning system is developed for managing multiple unmanned aerial vehicles (UAVs) of various capabilities to execute a series of missions over multiple targets. A target may need up to three tasks (i.e., classification, attack, and verification) to be carried out in sequence. The problem is addressed in two decision-making stages. First, the shortest feasible flying path for a UAV to fly between any pair of task locations is obtained using a customized A* algorithm. During the search, constraints such as collision avoidance from terrain, circumvention of flight prohibition zones, and the flying capabilities of UAVs are considered. In the second stage, with the obtained UAV flying paths between task locations, mission planning is modeled as a vehicle routing problem (VRP) with time window and precedence requirements. A genetic algorithm with a built-in mixed integer programming (MIP) solver is developed to solve the problem. The outputs of this proposed planning system include (1) task assignment among the UAVs, (2) mission execution schedule for each UAV, and (3) flying paths of the UAVs.", "keywords": [ "Computer science", "Task (project management)", "Schedule", "Motion planning", "Solver", "Integer programming", "Collision avoidance", "Real-time computing", "Genetic algorithm", "Terrain", "Plan (archaeology)", "Route planning" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s2301385014500058" }, { "id": "https://openalex.org/W4407081556", "doi": "10.1111/itor.13621", "title": "Drone‐enabled material handling in smart manufacturing", "year": 2025, "publication_date": "2025-02-03", "type": "article", "language": "en", "authors": [ "Julio Jiménez‐Sarda", "Daniel F. Silva", "Alice E. Smith" ], "first_author": "Julio Jiménez‐Sarda", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Abstract Amidst labor shortages, increasing land prices, and other aspects of the post‐pandemic world, our logistics chain must consider new technological alternatives. Automated material handling equipment often requires valuable floor space and specialized layouts. Uncrewed aerial vehicles, popularly called drones, offer a flexible and cost‐effective alternative. To explore the potential benefits and challenges of such systems, we used a version of the multi‐trip vehicle routing problem with time windows to model two realistic manufacturing environments as use cases. We provide the mixed integer linear programming formulation of this problem and compare results with those of a human‐only based system in a detailed simulated environment. Furthermore, as a physical proof‐of‐concept, we outfitted a commercially available drone with pick‐up and carry capabilities. We found that, even with limited drone carrying capacity, there are economic benefits realized from time savings, compared to ground‐based material handling systems. Besides these time savings, drones are sustainable as they operate on battery power and do not impair the air quality or the traffic experienced on the ground.", "keywords": [ "Drone", "Computer science", "Supply chain", "Material handling", "Economic shortage", "Operations research", "Manufacturing engineering", "Engineering", "Business" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1111/itor.13621" }, { "id": "https://openalex.org/W2103026506", "doi": "10.5555/1516744.1516965", "title": "Multi-objective UAV mission planning using evolutionary computation", "year": 2008, "publication_date": "2008-12-07", "type": "article", "language": "en", "authors": [ "Adam J. Pohl", "Gary B. Lamont" ], "first_author": "Adam J. Pohl", "venue": "Defense Technical Information Center (DTIC)", "publisher": null, "abstract": "This investigation purports to develop a new model for multiple autonomous aircraft mission routing. Previous research both related and unrelated to this endeavor have used classic combinatoric problems as models for \\\\ac{UAV} routing and mission planning. This document presents the concept of the Swarm Routing Problem (SRP) as a new combinatorics problem for use in modeling UAV swarm routing, developed as a variant of the Vehicle Routing Problem with Time Windows (VRPTW). The SRP removes the single vehicle per target restraint and changes the customer satisfaction requirement to one of vehicle on location volume. The impact of these alterations changes the vehicle definitions within the problem model from discrete units to cooperative members within a swarm. This represents a more realistic model for multi-agent routing as a real world mission plan would require the use of all airborne assets across multiple targets, without constraining a single vehicle to a single target. Solutions to the SRP problem model result in route assignments per vehicle that successfully track to all targets, on time, within distance constraints. A complexity analysis and multi-objective formulation of the VRPTW indicates the necessity of a stochastic solution approach leading to the development of a multi-objective evolutionary algorithm. This algorithm design is implemented using C++ and an evolutionary algorithm library called Open Beagle. Benchmark problems applied to the VRPTW show the usefulness of this solution approach. A full problem definition of the SRP as well as a multi-objective formulation parallels that of the VRPTW method. Benchmark problems for the VRPTW are modified in order to create SRP benchmarks. These solutions show the SRP solution is comparable or better than the same VRPTW solutions, while also representing a more realistic UAV swarm routing solution.", "keywords": [ "Vehicle routing problem", "Benchmark (surveying)", "Computer science", "Evolutionary algorithm", "Routing (electronic design automation)", "Mathematical optimization", "Evolutionary computation", "Computation", "Swarm behaviour", "Particle swarm optimization", "Mathematics", "Algorithm" ], "cited_by_count": 38, "is_retracted": false, "is_paratext": false, "open_access_url": "http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA486956" }, { "id": "https://openalex.org/W7125513106", "doi": "10.1007/s10479-026-07051-x", "title": "Multi-echelon location routing problem with vehicle-drone delivery and transshipment for cold chain transportation", "year": 2026, "publication_date": "2026-01-23", "type": "article", "language": "en", "authors": [ "Yanfeng Li", "Jie Zhang" ], "first_author": "Yanfeng Li", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Transshipment (information security)", "Facility location problem", "Vehicle routing problem", "Pareto principle", "Cluster analysis", "Key (lock)", "Metaheuristic", "Supply chain", "Routing (electronic design automation)" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10479-026-07051-x" }, { "id": "https://openalex.org/W4390001595", "doi": "10.1287/trsc.2021.0170", "title": "Dual Bounds from Decision Diagram-Based Route Relaxations: An Application to Truck-Drone Routing", "year": 2023, "publication_date": "2023-12-20", "type": "article", "language": "en", "authors": [ "Ziye Tang", "Willem‐Jan van Hoeve" ], "first_author": "Ziye Tang", "venue": "Transportation Science", "publisher": "Institute for Operations Research and the Management Sciences", "abstract": "For vehicle routing problems, strong dual bounds on the optimal value are needed to develop scalable exact algorithms as well as to evaluate the performance of heuristics. In this work, we propose an iterative algorithm to compute dual bounds motivated by connections between decision diagrams and dynamic programming models used for pricing in branch-and-cut-and-price algorithms. We apply techniques from the decision diagram literature to generate and strengthen novel route relaxations for obtaining dual bounds without using column generation. Our approach is generic and can be applied to various vehicle routing problems in which corresponding dynamic programming models are available. We apply our framework to the traveling salesman with drone problem and show that it produces dual bounds competitive to those from the state of the art. Applied to larger problem instances in which the state-of-the-art approach does not scale, our method outperforms other bounding techniques from the literature. Funding: This work was supported by the National Science Foundation [Grant 1918102] and the Office of Naval Research [Grants N00014-18-1-2129 and N00014-21-1-2240]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0170 .", "keywords": [ "Column generation", "Heuristics", "Bounding overwatch", "Travelling salesman problem", "Arc routing", "Computer science", "Dual (grammatical number)", "Routing (electronic design automation)", "Drone", "Mathematical optimization", "Branch and price", "Scalability" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1287/trsc.2021.0170" }, { "id": "https://openalex.org/W1978978926", "doi": "10.1109/control.2014.6915216", "title": "Ant Colony Optimization for routing and tasking problems for teams of UAVs", "year": 2014, "publication_date": "2014-07-01", "type": "conference-paper", "language": "en", "authors": [ "Theopisti Zaza", "Arthur Richards" ], "first_author": "Theopisti Zaza", "venue": null, "publisher": null, "abstract": "Thi s paper presents an enhanced version of Ant Colony Optimization (ACO) for solving a variant of the Vehicle Routing Problem (VRP), which is utilized for Unmanned Aerial Vehicle (UAV) task allocation and route planning. The extended VRP incorporates multiple UAVs, collision avoidance between intersecting routes, and the possibility of loitering between tasks. The ACO adopts a multi-colony approach to incorporate variable loitering times. Numerical experiments and comparison to previous work are illustrated to demonstrate the efficiency of the proposed algorithm.", "keywords": [ "Ant colony optimization algorithms", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Task (project management)", "Collision avoidance", "Route planning", "Ant colony", "Mathematical optimization", "Variable (mathematics)", "Collision", "Computer network" ], "cited_by_count": 33, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/control.2014.6915216" }, { "id": "https://openalex.org/W4388473693", "doi": "10.1016/j.trc.2023.104401", "title": "Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty", "year": 2023, "publication_date": "2023-11-07", "type": "article", "language": "en", "authors": [ "Robert van Steenbergen", "Martijn Mes", "Wouter van Heeswijk" ], "first_author": "Robert van Steenbergen", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": "Effective humanitarian relief operations are challenging in the aftermath of disasters, as trucks are often faced with considerable travel time uncertainties due to damaged transportation networks. Efficient deployment of Unmanned Aerial Vehicles (UAVs) potentially mitigates this problem, supplementing truck fleets in an impactful manner. To plan last-mile relief distribution in this setting, we introduce a multi-trip, split-delivery vehicle routing problem with trucks and UAVs, soft time windows, and stochastic travel times for last-mile relief distribution, formulated as a stochastic dynamic program. Within a finite time horizon, we aim to maximize a weighted objective function comprising the number of goods delivered, the number of different locations visited, and late arrival penalties. Our study offers insights into dealing with travel time uncertainty in humanitarian logistics by (i) deploying Unmanned Aerial Vehicles (UAVs) as partial substitutes for trucks, (ii) evaluating dynamic solutions generated by two deep reinforcement learning (RL) approaches – specifically value function approximation (VFA) and policy function approximation (PFA) – and (iii) comparing the RL solutions with solutions stemming from mathematical programming and dynamic heuristics. Experiments are performed on both Solomon-based instances and two real-world cases. The real-world cases – the 2015 Nepal earthquake and the 2018 Indonesia tsunami – are based on locally collected field data and real-world UAV specifications, and aim to provide practical insights. The experimental results show that dynamic decision-making improves both performance and robustness of humanitarian operations, achieving reductions in lateness penalties of around 85% compared to static solutions based on expected travel times. Furthermore, the results show that replacing half of the trucks with UAVs improves the weighted objective value by 11% to 56%, benefitting both reliability and location coverage. The results indicate that both the deployment of UAVs and the use of dynamic methods successfully mitigate travel time uncertainties in humanitarian operations.", "keywords": [ "Truck", "Heuristics", "Reinforcement learning", "Computer science", "Operations research", "Time horizon", "Dynamic programming", "Vehicle routing problem", "Robustness (evolution)", "Mathematical optimization", "Transport engineering", "Routing (electronic design automation)" ], "cited_by_count": 54, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2023.104401" }, { "id": "https://openalex.org/W3193471358", "doi": "10.1109/bdai52447.2021.9515289", "title": "A Hybrid Large Neighborhood Search for Solving the Unmanned Aerial Vehicle Routing Problem with Multi-depot", "year": 2021, "publication_date": "2021-07-02", "type": "conference-paper", "language": "en", "authors": [ "Xiaohui Li", "Lu Zhang", "Peifan Li", "Yi Zhao" ], "first_author": "Xiaohui Li", "venue": null, "publisher": null, "abstract": "The Unmanned aerial vehicles (UAVs) are widely used for inspection tasks both in the military and civilian areas in which the route planning problem could be critical. The Unmanned Aerial Vehicle Routing Problem with Multi-depot (MDUAVRP) is a combination and extension of the Unmanned Aerial Vehicle Route Planning (UAVRP) and the Multi-depot Vehicle Routing Problem (MDVRP), which determines a set of the route visited by a fleet of UAV to perform a set of tasks. The objective of the MDUAVRP is to minimize the total flight cost. A novel hybrid heuristic approach is proposed aiming at this problem. Our proposal combines Adaptive Large Neighborhood Search (ALNS) and Variable Neighborhood Descent (VND) algorithm. The experimental results verify the effectiveness of the proposed algorithm.", "keywords": [ "Vehicle routing problem", "Computer science", "Heuristic", "Variable neighborhood search", "Routing (electronic design automation)", "Set (abstract data type)", "Variable (mathematics)", "Depot", "Route planning", "Extension (predicate logic)", "Real-time computing", "Mathematical optimization" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/bdai52447.2021.9515289" }, { "id": "https://openalex.org/W4404957831", "doi": "10.3390/drones8120724", "title": "Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems", "year": 2024, "publication_date": "2024-11-30", "type": "article", "language": "en", "authors": [ "Diyar Altinses", "David Orlando Salazar Torres", "Asrat Mekonnen Gobachew", "Stefan Lier", "Andreas Schwung" ], "first_author": "Diyar Altinses", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Street delivery faces significant challenges due to outdated road infrastructure, which was not designed to handle current vehicle volumes, leading to congestion and inefficiencies, especially in last-mile delivery. Integrating drones into the delivery system offers a promising solution by bypassing congested roads, thereby enhancing delivery speed and reducing infrastructure strain. However, optimizing this multimodal delivery system is complex and data-driven, with real-world data often being costly and restricted. To address this, we propose a synthetic dataset generator that creates diverse and realistic delivery scenarios, incorporating environmental variables, customer profiles, and vehicle characteristics. The key contribution of our work is the development of a dynamic generator for multiple optimization problems with diverse complexities or even combinations of optimization problems. This generator allows for the incorporation of real-world factors such as traffic congestion and synthetically generated factors such as wind conditions and communication constraints, as well as others. The primary objective is to establish a foundation for creating benchmark scenarios that enable the comparison of existing and new approaches. We evaluate the generated dataset by applying it to three optimization problems, including facility location, vehicle routing, and path planning, using different techniques to demonstrate the dataset’s effectiveness and operational viability.", "keywords": [ "Computer science", "Drone", "Key (lock)", "Benchmark (surveying)", "Last mile (transportation)", "Generator (circuit theory)", "Delivery Performance", "Multimodal transport", "Routing (electronic design automation)", "Transport engineering", "Engineering", "Industrial engineering" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones8120724" }, { "id": "https://openalex.org/W4376641432", "doi": "10.1007/s11590-023-02013-9", "title": "A two-phase multi-objective metaheuristic for a green UAV grid routing problem", "year": 2023, "publication_date": "2023-05-16", "type": "article", "language": "en", "authors": [ "Elias L. Marques", "Vitor N. Coelho", "Igor Machado Coelho", "Luiz Satoru Ochi", "Nelson Maculan", "Nenad Mladenović", "Bruno N. Coelho" ], "first_author": "Elias L. Marques", "venue": "Optimization Letters", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Grid", "Grid reference", "Mathematical optimization", "Preprocessor", "Routing (electronic design automation)", "Metaheuristic", "Interface (matter)", "Point (geometry)", "Heuristic", "Algorithm", "Artificial intelligence" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s11590-023-02013-9" }, { "id": "https://openalex.org/W2615159178", "doi": "10.1145/3059336.3059337", "title": "Method of Task Assignment for UAV Based on Particle Swarm Optimization in logistics", "year": 2017, "publication_date": "2017-03-25", "type": "conference-paper", "language": "en", "authors": [ "Xiaowei Jiang", "Qiang Zhou", "Ying Ye" ], "first_author": "Xiaowei Jiang", "venue": null, "publisher": null, "abstract": "In recent years, there are many research achievements in the fields of logistics and Unmanned Aerial Vehicle (UAV). But the research achievement of the combination of the two fields is few. Researching on the combination of the UAV filed and the logistics filed has theoretical significance. Effective logistics system and task assignment strategy play an important role in reducing the operation cost of logistics enterprise as well as improving transport efficiency. In this paper, according to the Vehicle Routing Problems with Time Windows (VRPTW), we establish the model of task assignment for UAV in logistics. This model takes multi-constraints (such as weight coefficients, time-windows constraints, the constraints of the UAV and so on ) into account. And then the task assignment problem with multiple constraints is solved by improved Particle Swarm Optimization (PSO) algorithm which is suitable for solving complex combinatorial optimization problems. Meanwhile, we make some modification for the PSO to suit for the acquirement of mutually exclusive. The basic principle and simulation steps of the improved PSO algorithm is described in detail. And a simulation example is given. Furthermore, we compare PSO with Genetic Algorithm. The simulation results show that this algorithm is efficient to solve the problem of task assignment for UAV.", "keywords": [ "Particle swarm optimization", "Task (project management)", "Vehicle routing problem", "Computer science", "Mathematical optimization", "Swarm behaviour", "Genetic algorithm", "Assignment problem", "Routing (electronic design automation)", "Artificial intelligence", "Algorithm", "Engineering" ], "cited_by_count": 41, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3059336.3059337" }, { "id": "https://openalex.org/W4403161465", "doi": "10.34229/2707-451x.24.3.4", "title": "About the Problem of Drone Routing", "year": 2024, "publication_date": "2024-09-24", "type": "article", "language": "en", "authors": [ "Leonid Hulianytskyi", "Natalia Byshovets", "Олена Григорівна Жданова" ], "first_author": "Leonid Hulianytskyi", "venue": "Cybernetics and Computer Technologies", "publisher": null, "abstract": "Introduction. Unmanned aerial vehicles (UAVs) are attracting considerable interest in a variety of areas, such as logistics, defence, search and rescue, agriculture, manufacturing and environmental monitoring. The effective use of these flexible resources requires the development of models and methods that ensure the creation of safe and efficient flight routes for UAVs. The purpose of the paper is to study the current state of UAV routing problems, including an analysis of existing research in this field and systematization of scientific approaches and algorithms as well as the formalization of some practically important problems. Results. A classification of UAV routing problems has been proposed based on various criteria, including the number of UAVs, the number of base locations, UAV recharging constraints, additional conditions, evaluation criteria, and the constancy of conditions. An analysis and classification of UAV routing problems are presented, highlighting the significance of these problems in various fields such as logistics, defense, agriculture, and others. Meaningful formulations of several interrelated practical UAV routing problems have been proposed, and their formalization has been carried out in the form of specific mathematical models. Conclusions. The development of unmanned aerial vehicles (UAVs) is a dynamic and relevant area with a wide range of applications. The paper systemises scientific approaches and algorithms for optimising UAV routes, analyses and classifies routing problems according to various criteria. The proposed classification allows for a better understanding of the structure of the problems and the selection of appropriate methods for solving them. The main result is the formalisation of a number of practical UAV routing problems in the form of mathematical models, which allows developing effective algorithms for solving these problems, increasing the efficiency of UAVs in various fields, such as logistics, defence and agriculture. Keywords: route optimization, unmanned aerial vehicle, drone, mathematical model, flight resource, combinatorial optimization, monitoring, logistics.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Computer network", "Biology" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.34229/2707-451x.24.3.4" }, { "id": "https://openalex.org/W4415377267", "doi": "10.1016/j.cor.2025.107306", "title": "Improved Benders decomposition for home healthcare vehicle and drone routing problem in two-echelon supply chains", "year": 2025, "publication_date": "2025-10-21", "type": "article", "language": "en", "authors": [ "Behrouz Mohammadi-Kordkheili", "Rashed Sahraeian", "Yasel Costa" ], "first_author": "Behrouz Mohammadi-Kordkheili", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Supply chain", "Routing (electronic design automation)", "Particle swarm optimization", "Vehicle routing problem", "Payload (computing)", "Genetic algorithm", "Wind power", "Decomposition" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2025.107306" }, { "id": "https://openalex.org/W4291824990", "doi": "10.3390/s22166077", "title": "Asymmetric Arc Routing by Coordinating a Truck and Multiple Drones", "year": 2022, "publication_date": "2022-08-14", "type": "article", "language": "en", "authors": [ "Shuangxi Tian", "Honghui Chen", "Guohua Wu", "Jiaqi Cheng" ], "first_author": "Shuangxi Tian", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned Aerial Vehicles, commonly known as drones, have been widely used in transmission line inspection and traffic patrolling due to their flexibility and environmental adaptability. To take advantage of drones and overcome their limited endurance, the patrolling tasks are parallelized by concurrently dispatching the drones from a truck which travels on the road network to the nearby task arc. The road network considered in previous research is undirected; however, in reality, the road network usually contains unidirectional arcs, i.e., the road network is asymmetric. Hence, we propose an asymmetric coordinated vehicle-drones arc routing mode for traffic patrolling. In this mode, a truck travelling on an asymmetric road network with multiple drones needs to patrol multiple task arcs, and the drones can be launched and recovered at certain nodes on the truck route, making it possible for drones and the truck to patrol the task in parallel. The total patrol time is the objective function that needs to be minimized given the time limit constraints of drones. The whole problem can be considered as an asymmetric arc routing problem of coordinating a truck and multiple drones. To solve this problem, a large-scale neighborhood search with simulated annealing algorithm (LNS-SA) is proposed. Finally, extensive computation experiments and a real case are carried out. The experimental results show the efficiency of the proposed algorithm. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.", "keywords": [ "Drone", "Patrolling", "Computer science", "Truck", "Routing (electronic design automation)", "Task (project management)", "Flexibility (engineering)", "Real-time computing", "Computer network", "Engineering", "Aerospace engineering", "Geography" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/22/16/6077/pdf?version=1660478365" }, { "id": "https://openalex.org/W4212962419", "doi": "10.5220/0010983100003117", "title": "Addressing the Challenges of Last-mile: The Drone Routing Problem with Shared Fulfillment Centers", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti" ], "first_author": "Maria Elena Bruni", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Mile", "Routing (electronic design automation)", "Last mile (transportation)", "Computer science", "Computer network", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5220/0010983100003117" }, { "id": "https://openalex.org/W4403522559", "doi": "10.1016/j.tre.2024.103820", "title": "A variable neighborhood search algorithm for locker-based drone delivery makespan minimization problem", "year": 2024, "publication_date": "2024-10-18", "type": "article", "language": "en", "authors": [ "Waiming Zhu", "Haiquan Sun", "Xiaoxuan Hu", "Yingying Ma" ], "first_author": "Waiming Zhu", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Minification", "Job shop scheduling", "Variable neighborhood search", "Drone", "Mathematical optimization", "Variable (mathematics)", "Computer science", "Algorithm", "Mathematics", "Metaheuristic", "Computer network", "Biology" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2024.103820" }, { "id": "https://openalex.org/W3212738876", "doi": "10.1109/lwc.2021.3127205", "title": "Fast Trajectory Planning for UAV-Enabled Maritime IoT Systems: A Fermat-Point Based Approach", "year": 2021, "publication_date": "2021-11-11", "type": "article", "language": "en", "authors": [ "Ling Lyu", "Zhenhang Chu", "Bin Lin", "Yanpeng Dai", "Nan Sheng Cheng" ], "first_author": "Ling Lyu", "venue": "IEEE Wireless Communications Letters", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This letter proposes a fast Unmanned Aerial Vehicle (UAV) trajectory planning algorithm based on the Fermat-point theory in maritime Internet of Things systems. Specifically, we first construct Delaunay triangles based on the deployment of unmanned surface vehicles. Then, the Fermat point of each Delaunay triangle is calculated and as a hovering point of UAVs to improve the channel condition. Finally, the trajectory planning problem can be transformed into a vehicle routing problem with pickup, which is solved by the proposed algorithm based on C-W saving method. Simulation results show the proposed algorithm can efficiently increase collected data volume of UAVs.", "keywords": [ "Delaunay triangulation", "Trajectory", "Computer science", "Point (geometry)", "Software deployment", "Motion planning", "Routing (electronic design automation)", "Real-time computing", "Mathematical optimization", "Algorithm", "Mathematics", "Artificial intelligence" ], "cited_by_count": 44, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/lwc.2021.3127205" }, { "id": "https://openalex.org/W4393535466", "doi": "10.5281/zenodo.2600809", "title": "Instances of the Truck-Drone-ATV Routing Problem", "year": 2019, "publication_date": "2019-03-25", "type": "dataset", "language": "en", "authors": [ "Hagen Salewski", "Mahdi Moeini" ], "first_author": "Hagen Salewski", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This dataset contains the test instances of the Tuck-Drone-ATV Routing Problem as described in our Article \"A Genetic Algorithm for Solving the Truck-Drone-ATV Routing Problem\" written for the 6th World Congress on Global Optimization (WCGO 2019).", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Aeronautics", "Engineering", "Automotive engineering", "Computer network", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/Instances_of_the_Truck-Drone-ATV_Routing_Problem/11573556" }, { "id": "https://openalex.org/W3034405176", "doi": null, "title": "Hybrid Vehicle-drone Routing Problem For Pick-up And Delivery Services Mathematical Formulation And Solution Methodology", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Aline Karak" ], "first_author": "Aline Karak", "venue": "SMU Scholar (Southern Methodist University)", "publisher": "Southern Methodist University", "abstract": "The fast growth of online retail and associated increasing demand for same-day delivery have pushed online retail and delivery companies to develop new paradigms to provide faster, cheaper, and greener delivery services. Considering drones’ recent technological advancements over the past decade, they are increasingly ready to replace conventional truck-based delivery services, especially for the last mile of the trip. Drones have significantly improved in terms of their travel ranges, load-carrying capacity, positioning accuracy, durability, and battery charging rates. Substituting delivery vehicles with drones could result in $50M of annual cost savings for major U.S. service providers. The first objective of this research is to develop a mathematical formulation and efficient solution methodology for the hybrid vehicle-drone routing problem (HVDRP) for pick-up and delivery services. The problem is formulated as a mixed-integer program, which minimizes the vehicle and drone routing cost to serve all customers. The formulation captures the vehicle-drone routing interactions during the drone dispatching and collection processes and accounts for drone operation constraints related to flight range and load carrying capacity limitations. A novel solution methodology is developed which extends the classic Clarke and Wright algorithm to solve the HVDRP. The performance of the developed heuristic is benchmarked against two other heuristics, namely, the vehicle-driven routing heuristic and the drone-driven routing heuristic. Anticipating the potential risk of using drones for delivery services, aviation authorities in the U.S. and abroad have mandated necessary regulatory rules to ensure safe operations. The U.S. Federal Aviation Administration (FAA) is examining the feasibility of drone flights in restricted airspace for product delivery, requiring drones to fly at or below 400-feet and to stay within the pilot’s line of sight (LS). Therefore, a second objective of this research is considered to develop a modeling framework for the integrated vehicle-drone routing problem for pick-up and delivery services considering the regulatory rule requiring all drone flights to stay within the pilot’s line of sight (LS). A mixed integer program (MIP) and an efficient solution methodology were developed for the problem. The solution determines the optimal vehicle and drone routes to serve all customers without violating the LS rule such that the total routing cost of the integrated system is minimized. Two different heuristics are developed to solve the problem, which extends the Clarke and Wright Algorithm to cover the multimodality aspects of the problem and to satisfy the LS rule. The first heuristic implements a comprehensive multimodal cost saving search to construct the most efficient integrated vehicle-drone routes. The second heuristic is a light version of the first heuristic as it adopts a vehicle-driven cost saving search. Several experiments are conducted to examine the performance of the developed methodologies using hypothetical grid networks of different sizes. The capability of the developed model in answering a wide variety of questions related to the planning of the vehicle-drone delivery system is illustrated. In addition, a case study is presented in which the developed methodology is applied to provide pick-up and delivery services in the downtown area of the City of Dallas. The results show that mandating the LS rule could double the overall system operation cost especially in dense urban areas with LS obstructions.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Engineering", "Operations research", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://scholar.smu.edu/cgi/viewcontent.cgi?article=1008&context=engineering_civil_etds" }, { "id": "https://openalex.org/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/W2953791940", "doi": null, "title": "Comparative Evaluation of Drone Delivery Systems in Last-Mile Delivery", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "Oriol Garcia", "Antonius Santoso" ], "first_author": "Oriol Garcia", "venue": "DSpace@MIT (Massachusetts Institute of Technology)", "publisher": "Massachusetts Institute of Technology", "abstract": "With the latest technological advancement, the use of drones has emerged as an innovative and viable business solution for last-mile distribution. An efficient drone delivery system has to address the classic vehicle routing problem (VRP): \"What is the optimal set of routes for a fleet of drones to serve a given set of customers?.\" The goal of this research project is to evaluate the optimal design and operational performance of four different drone delivery systems, using real-life last-mile truck delivery data. The authors quantitatively model four different drone delivery systems, from a pure drone delivery system to an unsynchronized drone-truck system and compare their relative benefits and shortcomings under various scenarios. A Memetic Algorithm, an extension of a Genetic Algorithm, is developed and used to optimize delivery routes of truck and drones for all the four delivery models. Our research shows that Memetic Algorithm is quite robust handling VRP with 50 customers, yielding only 3.7% gap from the optimal solution. Among the four considered delivery models in this research, the Delivery System model 4 - where truck and drone share same area of service - performs superior than other three models, providing 100% coverage to all customers and reducing minimum tour time as high as 80%. The outcome of this research will help shape the quantitative and qualitative comparison of drone delivery systems and set the foundation for modelling and analysis of more advanced systems (e.g. synchronized truck-drone delivery system). It also helps industry to understand the possible use cases for drones in last-mile delivery and the most crucial levers of these models to maximize the performance of such drone delivery systems.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Delivery system", "Computer science", "Engineering", "Geography", "Biology" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1721.1/121319" }, { "id": "https://openalex.org/W7128471767", "doi": "10.1080/00207543.2026.2626537", "title": "Collaborative truck multi-drone pollution routing problem with pickup and delivery under variable truck speeds: a cost minimisation approach", "year": 2026, "publication_date": "2026-02-10", "type": "article", "language": "en", "authors": [ "Arash Alizadeh", "Sharan Srinivas", "James S. Noble", "Alena Otto" ], "first_author": "Arash Alizadeh", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "Collaborative truck-drone systems offer significant potential for reducing cost and emissions in last-mile logistics. However, prior studies often assume constant truck speeds, oversimplify fuel and drone energy use, and overlook differences between labor- and energy-dominant cost structures. This study introduces the Truck Multi-Drone Pollution Routing Problem with Pickup and Delivery under Variable Truck Speeds (TMD-PRP-PDVS), a generalisation of the PRP that integrates multi-drone coordination, segment-specific truck speed control, and realistic cost modelling. It is the first to unify variable truck speeds and coordinated truck-drone operations within a PRP framework for comparative analysis across distinct cost environments. A mixed-integer programming model is developed to minimise total cost, comprising truck fuel consumption, drone energy usage, and driver labor cost. For larger instances, an Adaptive Large Neighborhood Search heuristic is proposed with an embedded local search procedure that refines truck speeds at the route-segment level in polynomial time. Computational results show that drone deployment reduces total cost by about 40%, while speed optimisation yields an additional 7-11% on average and up to 18% in some cases. Case studies on real-world networks demonstrate applicability across contrasting cost regimes. The findings highlight the joint influence of truck speed regulation, drone deployment, and cost structures.", "keywords": [ "Truck", "Minimisation (clinical trials)", "Vehicle routing problem", "Pickup", "Variable (mathematics)", "Routing (electronic design automation)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2026.2626537" }, { "id": "https://openalex.org/W4409170764", "doi": "10.1016/j.cor.2025.107057", "title": "Integrated electric ground vehicle and drone with blockchain-driven approach for routing delivery", "year": 2025, "publication_date": "2025-04-04", "type": "article", "language": "en", "authors": [ "Ramdhan Nugraha", "Adriansyah Dwi Rendragraha", "Soo Young Shin" ], "first_author": "Ramdhan Nugraha", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Blockchain", "Drone", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Electric vehicle", "Computer security", "Embedded system", "Physics" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2025.107057" }, { "id": "https://openalex.org/W4319065767", "doi": "10.1007/978-3-031-25844-2_5", "title": "Proactive-Reactive Approach to Disruption-Driven UAV Routing Problem", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Grzegorz Radzki", "Grzegorz Bocewicz", "Zbigniew Antoni Banaszak" ], "first_author": "Grzegorz Radzki", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Computer science", "Work (physics)", "Route planning", "Order (exchange)", "Operations research", "Engineering", "Transport engineering", "Business" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-25844-2_5" }, { "id": "https://openalex.org/W3202829182", "doi": "10.1016/j.cie.2021.107714", "title": "Multiobjective routing optimization of mobile charging vehicles for UAV power supply guarantees", "year": 2021, "publication_date": "2021-09-29", "type": "article", "language": "en", "authors": [ "Weijian Qin", "Zhichao Shi", "Wenhua Li", "Kaiwen Li", "Tao Zhang", "Rui Wang" ], "first_author": "Weijian Qin", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Scheduling (production processes)", "Vehicle routing problem", "Routing (electronic design automation)", "Real-time computing", "Mathematical optimization", "Embedded system" ], "cited_by_count": 48, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2021.107714" }, { "id": "https://openalex.org/W4393973226", "doi": "10.1109/fists60717.2024.10485588", "title": "Using Mobile Charging Drones to Mitigate Battery Disruptions of Electric Vehicles on Highways", "year": 2024, "publication_date": "2024-02-26", "type": "conference-paper", "language": "en", "authors": [ "Xi Cheng", "Hui Shen", "Yantao Huang", "Yi-Ling Cheng", "Jane Lin" ], "first_author": "Xi Cheng", "venue": null, "publisher": null, "abstract": "Our research explores innovative solutions to address the challenge of battery disruptions in electric vehicles (EVs) on highways. We propose a centralized fleet ownership model where a company manages a fleet of Mobile Charging Drones (MCDs) guided by a k-VRP (Vehicle Routing Problem) framework. This model is designed to tackle a multi-objective optimization issue with three primary goals: reduction of the overall operating costs, decrease in the cumulative waiting time, and minimization of the combined operating costs and waiting times. This approach extends beyond the usual VRP constraints, encompassing specific limitations for both MCDs and disrupted EVs (DEVs). Additionally, our study delves into the concept of decentralized fleet ownership through the lens of crowdsourcing. Preliminary numerical analyses indicate that the capital cost of MCDs is a significant factor on the charging service, and the system performance is sensitive to DEV owner's value of time (VOT) when VOTs are relatively low.", "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Battery (electricity)", "Electric vehicle", "Crowdsourcing", "Service (business)", "Battery capacity", "Automotive engineering", "Minification", "Routing (electronic design automation)", "Computer network" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.osti.gov/servlets/purl/2448323" }, { "id": "https://openalex.org/W4410316585", "doi": "10.3390/drones9050368", "title": "Bridging ACO-Based Drone Logistics and Computing Continuum for Enhanced Smart City Applications", "year": 2025, "publication_date": "2025-05-13", "type": "article", "language": "en", "authors": [ "Salvatore Rosario Bassolillo", "Egidio D’Amato", "Immacolata Notaro", "Luca D’Agati", "Giovanni Merlino", "Giuseppe Tricomi" ], "first_author": "Salvatore Rosario Bassolillo", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In the context of evolving Smart Cities, the integration of drone technology and distributed computing paradigms presents significant potential for enhancing urban infrastructure and services. This paper proposes a comprehensive approach to optimizing urban delivery logistics through a cloud-based model that employs Ant Colony Optimization (ACO) for planning and Model Predictive Control (MPC) for trajectory tracking within a broader Computing Continuum framework. The proposed system addresses the Capacitated Vehicle Routing Problem (CVRP) by considering both drone capacity constraints and autonomy, using the ACO-based algorithm to efficiently assign delivery destinations while minimizing travel distances. Collision-free paths are computed by using a Visibility Graph (VG) based approach, and MPC controllers enable drones to adapt to dynamic obstacles in real time. Additionally, this work explores how clusters of drones can be deployed as edge devices within the Computing Continuum, seamlessly integrating with IoT sensors and fog computing infrastructure to support various urban applications, such as traffic management, crowd monitoring, and infrastructure inspections. This dual-architecture approach, combining the optimization capabilities of ACO with the flexible, distributed nature of the Computing Continuum, allows for scalable and efficient urban drone deployment. Simulation results validate the effectiveness of the proposed model in enhancing delivery efficiency and collision avoidance while demonstrating the potential of integrating drone technology into Smart City environments for improved data collection and real-time response.", "keywords": [ "Drone", "Bridging (networking)", "Computer science", "Computer security" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/5/368/pdf?version=1747193597" }, { "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/W3202651099", "doi": "10.22070/jqepo.2021.14935.1201", "title": "A Hybrid Truck-Drone Routing Problem Considering Deprivation Cost in the Post-Disaster Situation", "year": 2021, "publication_date": "2021-09-25", "type": "article", "language": "en", "authors": [ "Sanaz Khalaj Rahimi", "Donya Rahmani" ], "first_author": "Sanaz Khalaj Rahimi", "venue": null, "publisher": null, "abstract": "Natural disasters and their destructive effects on life and property are some of the most important issues these days. Implementation time of the relief operation and limitations associated with the ground infrastructure in disaster situations is the main priority of humanitarian logistics. So, the necessity of using unmanned aerial vehicles (UAVs) to reduce the service time is evident. On the Other hand, considering the deprivation cost function as an appropriate objective function can improve existing problems in disaster situations. This paper proposes a hybrid vehicle-drone routing model in the post-disaster phase. Multi trucks and multi drones with complementary capabilities are applied. Trucks are restricted to travel to the nodes with undamaged road networks, known as LD nodes. Drones can fly from the trucks to the nodes with damaged roads, known as DN nodes. They are used multiple times from each truck at LD nodes. The objective is to minimize the deprivation cost as a function of the deprivation time of both types of nodes. So, by minimizing the deprivation cost, the vehicles’ arrival times are minimized, and better routes are selected for vehicles. The effect of the population rate of the affected area on the route determination also is considered by the deprivation cost function. Finally, the validation of the proposed model is tested by solving it in GAMS software. Some examples are solved to show how applying the deprivation cost can improve the selecting routes by this model.", "keywords": [ "Truck", "Drone", "Routing (electronic design automation)", "Function (biology)", "Computer science", "Vehicle routing problem", "Population", "Transport engineering", "Natural disaster", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://jqepo.shahed.ac.ir/article_3406.html" }, { "id": "https://openalex.org/W3209250175", "doi": "10.1007/978-981-15-3514-7_91", "title": "A Dynamic Programming Algorithm for Energy-Aware Routing of Delivery Drones", "year": 2020, "publication_date": "2020-08-13", "type": "book-chapter", "language": "en", "authors": [ "Yusuke Funabashi", "Atsuya Shibata", "Shunsuke Negoro", "Ittetsu Taniguchi", "Hiroyuki Tomiyama" ], "first_author": "Yusuke Funabashi", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Energy consumption", "Dynamic programming", "Minification", "Energy (signal processing)", "Mathematical optimization", "Routing algorithm", "Distributed computing", "Algorithm", "Computer network" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-15-3514-7_91" }, { "id": "https://openalex.org/W3012033521", "doi": "10.1109/ojvt.2020.2979559", "title": "A Generic Spatiotemporal Scheduling for Autonomous UAVs: A Reinforcement Learning-Based Approach", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Omar Bouhamed", "Hakim Ghazzai", "Hichem Besbes", "Yehia Massoud" ], "first_author": "Omar Bouhamed", "venue": "IEEE Open Journal of Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Considerable attention has been given to leverage a variety of smart city applications using unmanned aerial vehicles (UAVs). The rapid advances in artificial intelligence can empower UAVs with autonomous capabilities allowing them to learn from their surrounding environment and act accordingly without human intervention. In this paper, we propose a spatiotemporal scheduling framework for autonomous UAVs using reinforcement learning. The framework enables UAVs to autonomously determine their schedules to cover the maximum of pre-scheduled events spatially and temporally distributed in a given geographical area and over a pre-determined time horizon. The designed framework has the ability to update the planned schedules in case of unexpected emergency events. The UAVs are trained using the Q-learning (QL) algorithm to find effective scheduling plan. A customized reward function is developed to consider several constraints especially the limited battery capacity of the flying units, the time windows of events, and the delays caused by the UAV navigation between events. Numerical simulations show the behavior of the autonomous UAVs for various scenarios and corroborate the ability of QL to handle complex vehicle routing problems with several constraints. A comparison with an optimal deterministic solution is also provided to validate the performance of the learning-based solution.", "keywords": [ "Reinforcement learning", "Computer science", "Scheduling (production processes)", "Leverage (statistics)", "Distributed computing", "Time horizon", "Real-time computing", "Artificial intelligence", "Engineering", "Mathematical optimization" ], "cited_by_count": 36, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/8782711/8815895/09028197.pdf" }, { "id": "https://openalex.org/W2209947542", "doi": null, "title": "Uav data mule vehicle routing problems in sparse sensor networks", "year": 2012, "publication_date": "2012-01-01", "type": "article", "language": "en", "authors": [ "João P. Hespanha", "Jason T. Isaacs" ], "first_author": "João P. Hespanha", "venue": null, "publisher": null, "abstract": "Recent advances in technology have enabled to the use of wireless sensor networks for environmental monitoring and surveillance. Wireless sensor networks are particularly beneficial for monitoring environments that are unsuitable for human presence, such as those arising in the monitoring of permafrost, volcanos, forest fires, and battlefields. Mobile agents called data mules can be used to enhance sensor networks by visiting individual sensors to collect measurements. In this thesis, the mobility of the data collector is exploited to mitigate energy depletion of the stationary nodes and allow for sparse deployments at the cost of additional data latency. This motivates us to seek efficient strategies for the mobile vehicle in order to alleviate this data latency. \r\nThe policies developed here apply directly to an acoustic source localization problem in which the objective is to localize the source of a transient acoustic event using measurements from a group of unattended ground sensors. First we study the optimal sensor placement of acoustic time of arrival sensors for localization using techniques from information theory and optimization. A sparse sensor configuration is shown to be optimal when maximizing the expected determinant of the Fisher information matrix for truncated, radially-symmetric source distributions. Next, we use ideas from optimal sensor placement and optimal sensor selection to adaptively adjust the route of the data mule to minimize the time to localize events of interest. When the data mule is a small fixed wing unmanned aerial vehicle (UAV), this algorithm can be improved by including the kinematic constraints of the UAV and exploring the ability of the UAV to communicate with faraway sensors within its line of sight. Finally, we provide data mule routing policies for acoustic source localization over a large area involving many sources and sensors and analyze their performance.", "keywords": [ "Wireless sensor network", "Real-time computing", "Computer science", "Latency (audio)", "Kinematics", "Computer network", "Telecommunications" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dl.acm.org/citation.cfm?id=2519198" }, { "id": "https://openalex.org/W2888590584", "doi": "10.1007/978-3-319-99707-0_22", "title": "Node and Edge Surveillance Using Drones Considering Required Camera Altitude and Battery Charging", "year": 2018, "publication_date": "2018-01-01", "type": "conference-paper", "language": "en", "authors": [ "Ivan Kristianto Singgih", "Jong-Hwa Lee", "Byung‐In Kim" ], "first_author": "Ivan Kristianto Singgih", "venue": "IFIP advances in information and communication technology", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Battery (electricity)", "Real-time computing", "Enhanced Data Rates for GSM Evolution", "Low altitude", "Node (physics)", "Process (computing)", "Computer network", "Altitude (triangle)", "Engineering" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://inria.hal.science/hal-02177871" }, { "id": "https://openalex.org/W4389924585", "doi": "10.3390/rs15245758", "title": "A Simulation Framework of Unmanned Aerial Vehicles Route Planning Design and Validation for Landslide Monitoring", "year": 2023, "publication_date": "2023-12-16", "type": "article", "language": "en", "authors": [ "Dongmei Xie", "Ruifeng Hu", "Chisheng Wang", "Chuanhua Zhu", "Hui Xu", "Qipei Li" ], "first_author": "Dongmei Xie", "venue": "Remote Sensing", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm.", "keywords": [ "Correctness", "Computer science", "Landslide", "Motion planning", "Path (computing)", "Range (aeronautics)", "Flight test", "Flight safety", "Real-time computing", "Simulation", "Flight planning", "Algorithm" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/rs15245758" }, { "id": "https://openalex.org/W4411600323", "doi": "10.1109/cec65147.2025.11042978", "title": "Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms", "year": 2025, "publication_date": "2025-06-08", "type": "conference-paper", "language": "en", "authors": [ "Eneko Osaba", "Pablo Miranda-Rodriguez", "Andreas Oikonomakis", "Matic Petrič", "Alejandra Ruiz", "Sebastian Böck", "Michail‐Alexandros Kourtis" ], "first_author": "Eneko Osaba", "venue": null, "publisher": null, "abstract": "This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System’s devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.", "keywords": [ "Quantum annealing", "Drone", "Computer science", "Quantum computer", "Simulated annealing", "Quantum", "Routing (electronic design automation)", "Annealing (glass)", "Quantum gate", "Distributed computing", "Theoretical computer science", "Computer network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cec65147.2025.11042978" }, { "id": "https://openalex.org/W2978613931", "doi": "10.1016/j.cor.2019.104811", "title": "A flexible reference point-based multi-objective evolutionary algorithm: An application to the UAV route planning problem", "year": 2019, "publication_date": "2019-10-03", "type": "article", "language": "en", "authors": [ "Erdi Daşdemir", "Murat Köksalan", "Diclehan Tezcaner Öztürk" ], "first_author": "Erdi Daşdemir", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pareto principle", "Computer science", "Mathematical optimization", "Terrain", "Evolutionary algorithm", "Point (geometry)", "Genetic algorithm", "Multi-objective optimization", "Population", "Algorithm", "Mathematics" ], "cited_by_count": 63, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2019.104811" }, { "id": "https://openalex.org/W2996670208", "doi": null, "title": "Noise Abatement Trajectories for a UAV Delivery Fleet", "year": 2019, "publication_date": "2019-12-02", "type": "conference-paper", "language": "en", "authors": [ "Geoffrey Scozzaro", "Daniel Delahaye", "Adan Vela" ], "first_author": "Geoffrey Scozzaro", "venue": null, "publisher": null, "abstract": "International audience", "keywords": [ "Computer science", "Noise (video)", "Environmental science", "Aeronautics", "Marine engineering", "Engineering", "Artificial intelligence" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal-enac.archives-ouvertes.fr/hal-02388280" }, { "id": "https://openalex.org/W4411358574", "doi": "10.1109/tase.2025.3580473", "title": "A Two-Phase Planner for Messenger Routing Problem in UAV-UGV Coordination Systems", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Zhao Zhang", "Chen Chen", "Lingda Wang", "Yulong Ding", "Fang Yi Deng" ], "first_author": "Zhao Zhang", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In this paper, a new Messenger Routing Problem (MRP) is studied, which is motivated by Unmanned Aerial Vehicles (UAVs) accessing Unmanned Ground Vehicles (UGVs) to deliver information in UAV-UGV coordination systems. The objective is to minimize the longest path among multiple messengers, ensuring fast and reliable information transmission. Two key challenges arise in tackling this problem. First, the targets are moving, incurring the travel cost between targets to vary with the travel process. Second, the messengers accessing the neighborhood of targets needs to satisfy the communication time constraint. Based on the idea of decoupling, a two-phase planner is proposed to sequentially determine global access sequence and optimize local access locations. In the first phase, a motion prediction module is introduced in the Adaptive Large Neighborhood Search (ALNS) framework to deal with the dynamic characteristics of MRP. In the second phase, an efficient bisection sampling method based on the prediction points is proposed to obtain a shorter access path while satisfying the communication time constraint. Finally, the effectiveness and efficiency of the proposed method are demonstrated by performance evaluation and comparison with the state-of-the-art algorithms.", "keywords": [ "Planner", "Routing (electronic design automation)", "Computer science", "Remotely operated underwater vehicle", "Phase (matter)", "Robot kinematics", "Telerobotics", "Mobile robot", "Engineering", "Distributed computing", "Robot", "Real-time computing" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tase.2025.3580473" }, { "id": "https://openalex.org/W4399246380", "doi": "10.1002/net.22233", "title": "A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations", "year": 2024, "publication_date": "2024-05-30", "type": "article", "language": "en", "authors": [ "Tasnim Ibn Faiz", "Chrysafis Vogiatzis", "Jiongbai Liu", "Md. Noor‐E‐Alam" ], "first_author": "Tasnim Ibn Faiz", "venue": "Networks", "publisher": "Wiley", "abstract": "Abstract Providing first aid and other supplies (e.g., epi‐pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emerging technologies like uncrewed autonomous vehicles can help humanitarian logistics providers reach otherwise stranded populations after transportation network failures. However, due to the failures in telecommunication infrastructure, demand for emergency aid can become uncertain. To address the challenges of delivering emergency aid to trapped populations with failing infrastructure networks, we propose a novel robust computational framework for a two‐echelon vehicle routing problem that uses uncrewed autonomous vehicles (UAVs), or drones, for the deliveries. We formulate the problem as a two‐stage robust optimization model to handle demand uncertainty. Then, we propose a column‐and‐constraint generation approach for worst‐case demand scenario generation for a given set of truck and UAV routes. Moreover, we develop a decomposition scheme inspired by the column generation approach to generate UAV routes for a set of demand scenarios heuristically. Finally, we combine the decomposition scheme within the column‐and‐constraint generation approach to determine robust routes for both trucks (first echelon vehicles) and UAVs (second echelon vehicles), the time that affected communities are served, and the quantities of aid materials delivered. To validate our proposed algorithms, we use a simulated dataset that aims to recreate emergency aid requests in different areas of Puerto Rico after Hurricane Maria in 2017.", "keywords": [ "Vehicle routing problem", "Humanitarian Logistics", "Robust optimization", "Computer science", "Routing (electronic design automation)", "Operations research", "Aeronautics", "Business", "Operations management", "Engineering", "Process management", "Mathematical optimization" ], "cited_by_count": 37, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1002/net.22233" }, { "id": "https://openalex.org/W2923421005", "doi": "10.1109/tase.2019.2902360", "title": "Multiagent UAV Routing: A Game Theory Analysis With Tight Price of Anarchy Bounds", "year": 2019, "publication_date": "2019-03-20", "type": "article", "language": "en", "authors": [ "Omkar Thakoor", "Jugal Garg", "Rakesh Nagi" ], "first_author": "Omkar Thakoor", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "We study the multiagent unmanned aerial vehicle (UAV) routing problem where a set of UAVs needs to collect information via surveillance of an area of operation. Each UAV is autonomous and does not rely on a reliable communication medium to coordinate with other UAVs. We formulate the problem as a game where UAVs are players and their strategies are the different routes they can take. Our model also incorporates the useful concept of information fusion. This results in a new variant of weighted congestion-type games. We show that the price of anarchy (PoA) of the game is at most 2, irrespective of the number of UAVs and their sensor capabilities. This also validates the empirical results of earlier works. Furthermore, we identify classes of games for the existence of a pure Nash equilibrium. To the best of our knowledge, these are the first such theoretical results in the related literature. Finally, we conduct experimental studies using randomly generated instances with several multiagent UAV routing policies. Our insights are that PoA increases with the congestion level when the same number of UAVs search a smaller area or more UAVs search the same area, and on an average, our proposed policies are less than 10% worse than the centralized optimal for the problem scenarios attempted. Note to Practitioners-UAVs are becoming increasingly popular for information collection tasks in defense and civilian applications alike. When the collection area is large, it is not unusual that a fleet of UAVs is deployed. Routing of a fleet can be performed in a centralized or decentralized manner. Decentralized routing might be the only possibility when centralized situational awareness is not possible due to bandwidth limitations and centralized optimal routes for each UAV in the fleet are too complex to compute. Autonomous solutions have several other advantages, let alone simplicity. For managers of UAV systems, our work provides the first theoretical characterization of how bad could decentralized routing be. Under various scenarios of information fusion, specifically weak and strong, and the attribution of information collected to each UAV of a team, we prove that the fleet will collect at least 50% of the best-centralized solution. Empirically, we show that, in fact, the performance of the fleet is much better and generally not worse than 10% of the best-centralized solution. Hopefully, our routing strategies provide valuable guidance to the practicing engineer or manager of a UAV fleet.", "keywords": [ "Price of anarchy", "Routing (electronic design automation)", "Computer science", "Nash equilibrium", "Game theory", "Vehicle routing problem", "Set (abstract data type)", "Distributed computing", "Operations research", "Computer network", "Mathematical optimization", "Engineering" ], "cited_by_count": 51, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/8856/8956010/08671711.pdf" }, { "id": "https://openalex.org/W4225749429", "doi": "10.1007/978-981-16-9492-9_149", "title": "Hybridization of Variable Neighborhood Search and Simulated Annealing for the Collaborative Truck-Drone Routing Problem", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Qizhang Luo", "Guohua Wu", "Ni Mao", "Jianmai Shi" ], "first_author": "Qizhang Luo", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Simulated annealing", "Drone", "Metaheuristic", "Vehicle routing problem", "Variable neighborhood search", "Computer science", "Benchmark (surveying)", "Truck", "Variable (mathematics)", "Mathematical optimization", "Routing (electronic design automation)", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-16-9492-9_149" }, { "id": "https://openalex.org/W4220996526", "doi": "10.2139/ssrn.4066378", "title": "Multi-Depot Electric Vehicle and Drones Synchronized Delivery Routing Problem Under Time-Dependent Networks", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Hao Fan", "Yueguang Zhang", "Houming Fan" ], "first_author": "Hao Fan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Depot", "Vehicle routing problem", "Routing (electronic design automation)", "Electric vehicle", "Computer science", "Engineering", "Computer network", "Biology", "Physics", "Geography", "Power (physics)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4066378" }, { "id": "https://openalex.org/W2903413433", "doi": "10.3390/s18124188", "title": "Multi-UAV Path Planning for Autonomous Missions in Mixed GNSS Coverage Scenarios", "year": 2018, "publication_date": "2018-11-29", "type": "article", "language": "en", "authors": [ "Flavia Causa", "Giancarmine Fasano", "Michele Grassi" ], "first_author": "Flavia Causa", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This paper presents an algorithm for multi-UAV path planning in scenarios with heterogeneous Global Navigation Satellite Systems (GNSS) coverage. In these environments, cooperative strategies can be effectively exploited when flying in GNSS-challenging conditions, e.g., natural/urban canyons, while the different UAVs can fly as independent systems in the absence of navigation issues (i.e., open sky conditions). These different flight environments are taken into account at path planning level, obtaining a distributed multi-UAV system that autonomously reconfigures itself based on mission needs. Path planning, formulated as a vehicle routing problem, aims at defining smooth and flyable polynomial trajectories, whose time of flight is estimated to guarantee coexistence of different UAVs at the same challenging area. The algorithm is tested in a simulation environment directly derived from a real-world 3D scenario, for variable number of UAVs and waypoints. Its solution and computational cost are compared with optimal planning methods. Results show that the computational burden is almost unaffected by the number of UAVs, and it is compatible with near real time implementation even for a relatively large number of waypoints. The provided solution takes full advantage from the available flight resources, reducing mission time for a given set of waypoints and for increasing UAV number.", "keywords": [ "GNSS applications", "Motion planning", "Computer science", "Real-time computing", "Satellite system", "Set (abstract data type)", "Global Positioning System", "Robot", "Artificial intelligence" ], "cited_by_count": 38, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/s18124188" }, { "id": "https://openalex.org/W2968697433", "doi": null, "title": "Green location and routing problems with conventional vehicles and drones", "year": 2019, "publication_date": "2019-05-01", "type": "dissertation", "language": "en", "authors": [ "Okan Dükkancı" ], "first_author": "Okan Dükkancı", "venue": "Bilkent University Institutional Repository (Bilkent University)", "publisher": "Bilkent University", "abstract": "Cataloged from PDF version of article.", "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Transport engineering", "Engineering", "Computer network", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://repository.bilkent.edu.tr/bitstreams/b50993c7-1bc2-419f-92fd-0c14b2f95970/download" }, { "id": "https://openalex.org/W4401994197", "doi": "10.1016/j.swevo.2024.101719", "title": "Deep reinforcement learning assisted memetic scheduling of drones for railway catenary deicing", "year": 2024, "publication_date": "2024-08-29", "type": "article", "language": "en", "authors": [ "Yu‐Jun Zheng", "Xicheng Xie", "Zhi-Yuan Zhang", "Jin-Tang Shi" ], "first_author": "Yu‐Jun Zheng", "venue": "Swarm and Evolutionary Computation", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Drone", "Reinforcement learning", "Memetic algorithm", "Scheduling (production processes)", "Catenary", "Artificial intelligence", "Mathematical optimization", "Local search (optimization)" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.swevo.2024.101719" }, { "id": "https://openalex.org/W4389666029", "doi": "10.1109/iros55552.2023.10342311", "title": "Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem", "year": 2023, "publication_date": "2023-10-01", "type": "conference-paper", "language": "en", "authors": [ "Guangyao Shi", "Pratap Tokekar" ], "first_author": "Guangyao Shi", "venue": null, "publisher": null, "abstract": "We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output). Our motivating case study is a specific type of vehicle routing problem, in which a team of Unmanned Ground Vehicles (UGVs) can serve as mobile charging stations to recharge a team of Unmanned Ground Vehicles (UAVs) that execute persistent monitoring tasks. We want to learn the mapping from observations of UAV task routes and wind field to the parameters of a submodular objective function, which describes the distribution of landing positions of the UAVs. Traditionally, such a learning problem is solved independently as a prediction phase without considering the downstream task optimization phase. However, the loss function used in prediction may be misaligned with our final goal, i.e., a good routing decision. Good performance in the isolated prediction phase does not necessarily lead to good decisions in the downstream routing task. In this paper, we propose a framework that incorporates task optimization as a differentiable layer in the prediction phase. Our framework allows end-to-end training of the prediction model without using engineered intermediate loss that is targeted only at the prediction performance. In the proposed framework, task optimization (submodular maximization) is made differentiable by introducing stochastic perturbations into deterministic algorithms (i.e., stochastic smoothing). We demonstrate the efficacy of the proposed framework using synthetic data. Experimental results of the mobile charging station routing problem show that the proposed framework can result in better routing decisions, e.g. the average number of UAVs recharged increases, compared to the prediction-optimization separate approach.", "keywords": [ "Submodular set function", "Computer science", "Routing (electronic design automation)", "Smoothing", "Mathematical optimization", "Vehicle routing problem", "Task (project management)", "Maximization", "Context (archaeology)", "Optimization problem", "Function (biology)", "Artificial intelligence" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/iros55552.2023.10342311" }, { "id": "https://openalex.org/W4235848402", "doi": "10.5194/nhess-2017-192", "title": "Brief communication: Vehicle routing problem and UAV application in the post-earthquake scenario", "year": 2017, "publication_date": "2017-06-13", "type": "preprint", "language": "en", "authors": [ "Marco Cannioto", "Antonino D’Alessandro", "Giosuè Lo Bosco", "Salvatore Scudero", "Giovanni Vitale" ], "first_author": "Marco Cannioto", "venue": null, "publisher": null, "abstract": "Abstract. In this paper we simulate a Unmanned Aerial Vehicle's (UAV) recognition after a possible case of diffuse damage after a seismic event in the town of Acireale (Sicily, Italy). Given a set of sites (84 relevant buildings) and the range of the UAV, we are able to find the number of vehicles to employ and the shortest survey path. The problem of finding the shortest survey path is an operational research problem called Vehicle Routing Problem (VRP) whose solution is known to be computationally time-consuming. We used the Simulated Annealing (SA) heuristic that is able to provide stable solutions in relatively short computing time. We also examined the distribution of the cost of the solutions varying the depot on a regular grid in order to assess the best area where to execute the survey.", "keywords": [ "Vehicle routing problem", "Shortest path problem", "Heuristic", "Computer science", "Simulated annealing", "Grid", "Routing (electronic design automation)", "Range (aeronautics)", "Set (abstract data type)", "Event (particle physics)", "Path (computing)", "Real-time computing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5194/nhess-2017-192" }, { "id": "https://openalex.org/W3084685113", "doi": "10.1109/globecom42002.2020.9322626", "title": "Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines", "year": 2020, "publication_date": "2020-12-01", "type": "conference-paper", "language": "en", "authors": [ "Oussama Ghdiri", "Wael Jaafar", "Safwan Alfattani", "Jihene Ben Abderrazak", "Halim Yanıkömeroğlu" ], "first_author": "Oussama Ghdiri", "venue": null, "publisher": null, "abstract": "In this paper, we focus on energy-efficient UAV-based IoT data collection in sensor networks in which the sensed data have different time deadlines. In the investigated setting, the sensors are clustered and managed by cluster heads (CHs), and multiple UAVs are used to collect data from the CHs. The formulated problem is solved through a two-step approach. In the first step, an efficient method is proposed to determine the minimal number of CHs and their best locations. Subsequently, the minimal number of UAVs and their trajectories are obtained by solving the associated capacitated vehicle routing problem. Results show the efficiency of our proposed CHs placement method compared to baseline approaches, where bringing the CHs closer to the dockstation allows significant energy savings. Moreover, among different UAV trajectory planning algorithms, Tabu search achieves the best energy consumption. Finally, the impact of the battery capacity and time deadline are investigated in terms of consumed energy, number of visited CHs, and number of deployed UAVs.", "keywords": [ "Computer science", "Energy consumption", "Real-time computing", "Tabu search", "Data collection", "Efficient energy use", "Routing (electronic design automation)", "Energy (signal processing)", "Wireless sensor network", "Baseline (sea)", "Distributed computing", "Mathematical optimization" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2009.06838" }, { "id": "https://openalex.org/W4410346172", "doi": "10.3390/s25103087", "title": "Research on Collaborative Delivery Path Planning for Trucks and Drones in Parcel Delivery", "year": 2025, "publication_date": "2025-05-13", "type": "article", "language": "en", "authors": [ "Ting Ting Fu", "Sheng Li", "Zhiguo Li" ], "first_author": "Ting Ting Fu", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid development of e-commerce, the logistics industry faces multiple challenges, including high delivery costs, long delivery times, and a shortage of delivery personnel. Truck-drone collaborative delivery combines the high load capacity of trucks with the flexibility and speed of drones, offering an innovative and practical solution. This paper proposes the Truck-Drone Collaborative Delivery Routing Problem (TDCRPTW) and develops a multi-objective optimization model that minimizes delivery costs and maximizes time reliability under capacity and time window constraints in multi-truck, multi-drone scenarios. To solve the model, an innovative two-stage solution strategy that combines the adaptive k-means++ clustering algorithm with temperature-controlled memory simulated annealing (TCMSA) is proposed. The experimental results demonstrate that the proposed model reduces delivery costs by 10% to 50% and reduces delivery time by 15% to 40%, showcasing the superiority of the truck-drone collaborative delivery model. Moreover, the proposed algorithm demonstrates outstanding performance and reliability across multiple dimensions. Therefore, the proposed approach provides an efficient solution to the truck-drone collaborative delivery problem and offers valuable insights for enhancing the efficiency and reliability of e-commerce logistics systems.", "keywords": [ "Drone", "Truck", "Path (computing)", "Delivery system", "Transport engineering", "Computer science", "Engineering", "Simulation", "Aerospace engineering", "Computer network", "Biomedical engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/25/10/3087/pdf?version=1747146885" }, { "id": "https://openalex.org/W4391768525", "doi": "10.1109/itsc57777.2023.10422533", "title": "Drone Delivery Routing with Stochastic Urban Wind", "year": 2023, "publication_date": "2023-09-24", "type": "conference-paper", "language": "en", "authors": [ "Minghao Chen", "Andrew W. Smyth", "Marco G. Giometto", "Max Z. Li" ], "first_author": "Minghao Chen", "venue": null, "publisher": null, "abstract": "Urban areas present unique challenges for drone-based deliveries due to the spatially and temporally variable nature of the wind field at low altitudes. In this paper, we propose a novel approach to address these challenges and ensure safe and reliable drone delivery routings in windy urban environments. Our approach incorporates the stochastic and spatially heterogeneous urban wind field using scenario-based stochastic programming and probabilistic modeling techniques. We develop stochastic mixed integer linear programs (SMILPs) that integrate realistic wind scenarios generated via large-eddy simulations (LESs) with the stochastic drone routing problem (SDRP). We employ the simulated annealing (SA) algorithm, which effectively explores the solution space and handles hundreds of customers' requests in a short time. We compare the performance of SA implementation with SMILPs implementation on small cases and show its superiority in terms of solution quality. Through simulations and performance evaluations on large-scale instances, we demonstrate the effectiveness of our approach under different wind conditions and number of customers. We also conduct a sensitivity analysis to investigate the influence of operational altitude (i.e., the height of the wind field) on the performance of our method. Our results provide valuable insights for optimizing drone routing decisions in windy urban environments.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/itsc57777.2023.10422533" }, { "id": "https://openalex.org/W4386487790", "doi": "10.1007/978-3-031-43612-3_17", "title": "The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Robert van Steenbergen", "Eduardo Lalla‐Ruiz", "Wouter van Heeswijk", "Martijn Mes" ], "first_author": "Robert van Steenbergen", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Truck", "Vehicle routing problem", "Computer science", "Heuristic", "Routing (electronic design automation)", "Operations research", "Transportation theory", "Transport engineering", "Risk analysis (engineering)", "Business", "Mathematical optimization", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ris.utwente.nl/ws/files/320201482/978-3-031-43612-3_17.pdf" }, { "id": "https://openalex.org/W4411338823", "doi": "10.1117/12.3059314", "title": "Split-delivery-based collaborative delivery routing problem of trucks and drones in emergency rescue scenarios", "year": 2025, "publication_date": "2025-06-16", "type": "conference-paper", "language": "en", "authors": [ "Xiangyi Chen", "Chuang Wang", "Huanyu Chen" ], "first_author": "Xiangyi Chen", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Truck", "Drone", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Emergency rescue", "Computer network", "Engineering", "Medical emergency", "Automotive engineering", "Medicine" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3059314" }, { "id": "https://openalex.org/W2157250389", "doi": "10.1142/s2301385014500010", "title": "A Formulation and Heuristic Approach to Task Allocation and Routing of UAVs under Limited Communication", "year": 2013, "publication_date": "2013-12-12", "type": "article", "language": "en", "authors": [ "Chelsea M. Sabo", "Derek B. Kingston", "Kelly De Oliveira Cohen" ], "first_author": "Chelsea M. Sabo", "venue": "Unmanned Systems", "publisher": "World Scientific", "abstract": "Unmanned Air Vehicle (UAV) teams are anticipated to provide surveillance support through algorithms, software, and automation. It is desirable to have algorithms that compute effective and efficient routes for multiple UAVs across a variety of missions. These algorithms must be realizable, practical, and account for uncertainties. In surveillance missions, UAVs act as mobile wireless communication nodes in a larger, underlying network consisting of targets where information is to be collected and base stations where information is to be delivered. The role of UAVs in these networks has primarily been to maintain or improve connectivity while undervaluing routing efficiency. Moreover, many current routing strategies for UAVs ignore communication constraints even though neglecting communication can lead to suboptimal tour designs. Generating algorithms for autonomous vehicles that work effectively despite these communication restrictions is key for the future of UAV surveillance missions. A solution is offered here based on a variation of the traditional vehicle routing problem and a simple communication model. In this work, the new routing formulation is defined, analyzed, and a heuristic approach is motivated and described. Simulation results show that the heuristic algorithm gives near-optimal results in real time, allowing it to be used for large problem sizes and extended to dynamic scenarios.", "keywords": [ "Computer science", "Heuristic", "Routing (electronic design automation)", "Task (project management)", "Distributed computing", "Wireless", "Base station", "Variety (cybernetics)", "Static routing", "Vehicle routing problem", "Key (lock)", "Automation" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s2301385014500010" }, { "id": "https://openalex.org/W2621483924", "doi": "10.14529/ctcr170201", "title": "Applying Bio-Inspired Algorithms to Routing Problem Solution in FANET", "year": 2017, "publication_date": "2017-01-01", "type": "article", "language": "en", "authors": [ "A.V. Leonov" ], "first_author": "A.V. Leonov", "venue": "Bulletin of the South Ural State University Ser Computer Technologies Automatic Control & Radioelectronics", "publisher": null, "abstract": "The advances in Unmanned Aerial Vehicles (UAVs) development provide new opportunities for their civil application. UAVs are an integral part of the scientific research nowadays. UAVs implementation requires that a group of interacting UAVs takes part in the task completion. Organizing a multi-UAV network calls for special routing algorithms developed with due concern of their features. The article gives a brief review of the existing routing algorithms for ad hoc networks based on swarm intelligence. The test analysis has been carried out proving that bioinspired algorithms can be effectively applied to solve the routing problem in FANET networks. This has been proved on the example of BeeAdHoc and AntHocNet, modeling the natural behavior of bees and ants.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Algorithm", "Routing algorithm", "Computer network", "Routing protocol" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://vestnik.susu.ru/ctcr/article/download/5842/5338" }, { "id": "https://openalex.org/W2976997795", "doi": "10.3390/app9193972", "title": "A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions", "year": 2019, "publication_date": "2019-09-22", "type": "article", "language": "en", "authors": [ "Amila Thibbotuwawa", "Grzegorz Bocewicz", "Zbigniew Antoni Banaszak", "Peter Nielsen" ], "first_author": "Amila Thibbotuwawa", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g., size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs’ fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction under the given conditions. The mission plans are created in such a manner that they are suitable to be sent to Air Traffic Control for flight approval.", "keywords": [ "Payload (computing)", "Software deployment", "Computer science", "Bridge (graph theory)", "Operations research", "Energy consumption", "Systems engineering", "Aeronautics", "Engineering", "Computer security" ], "cited_by_count": 38, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/9/19/3972/pdf?version=1569293595" }, { "id": "https://openalex.org/W4404445861", "doi": "10.1007/978-3-031-77367-9_25", "title": "Safe Multi-agent Reinforcement Learning for Drone Routing Problems", "year": 2024, "publication_date": "2024-11-15", "type": "conference-paper", "language": "en", "authors": [ "Masahiro Kaji", "Donghui Lin", "Fumito Uwano" ], "first_author": "Masahiro Kaji", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Reinforcement learning", "Drone", "Routing (electronic design automation)", "Artificial intelligence", "Distributed computing", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-77367-9_25" }, { "id": "https://openalex.org/W4400650222", "doi": "10.1109/iv55156.2024.10588709", "title": "A Lagrangian Relaxation Algorithm for the Drone Routing Problem with Backhauls and Wind", "year": 2024, "publication_date": "2024-06-02", "type": "conference-paper", "language": "en", "authors": [ "Ting Jiang", "Riju Lavanya", "Yihuai Liang", "HanByul Ryu", "Daisik Nam" ], "first_author": "Ting Jiang", "venue": null, "publisher": null, "abstract": "Most urban logistics providers are able to deliver goods efficiently using conventional vehicles, except in areas that have low quality road infrastructure and steep road slopes, which pose a challenge for delivery. This lowers the quality of delivery services, creating pockets of underserved populations in urban areas, which is undesirable from an economic and social perspective. The flexibility of drones has made them an attractive option for last-mile delivery in this context. Optimizing the utilization of remaining energy and payload capacity during drone return trips is essential for maximizing overall utility. However, prior studies have overlooked the consideration of backhaul in the context of drone-only systems, as well as the assessment of energy consumption under wind conditions. This paper proposes a new model for the Drone Delivery Routing Problem with Backhaul (DDRPB), which incorporates both wind conditions and backhauling requirements, and is applicable for areas that have high delivery demand and return requests, but suffer from low delivery service quality. We introduce a linearized drone-wind-energy consumption model and design a Lagrangian relaxation heuristic algorithm to solve the resulting NP-hard optimization problem efficiently. A case study of an urban area with delivery challenges in Seoul, South Korea is presented. The results show that the proposed model effectively decreases overall costs.", "keywords": [ "Drone", "Lagrangian relaxation", "Computer science", "Relaxation (psychology)", "Lagrangian", "Routing (electronic design automation)", "Vehicle routing problem", "Mathematical optimization", "Algorithm", "Mathematics", "Computer network", "Applied mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/iv55156.2024.10588709" }, { "id": "https://openalex.org/W4388820328", "doi": "10.1109/icnsc58704.2023.10319036", "title": "A new formulation for the multi-period vessel-drone routing problem", "year": 2023, "publication_date": "2023-10-25", "type": "conference-paper", "language": "en", "authors": [ "Lianhua Tang", "Yantong Li" ], "first_author": "Lianhua Tang", "venue": null, "publisher": null, "abstract": "To address the limitations of conventional offshore oil-and-gas field inspections, such as high cost, low frequency, and high risk, a new method called vessel-drone collaborative inspection is proposed. This approach involves a drone-carrying vessel departing from the port and sailing to a designated distance from the oil-and-gas field. The drones are then released to conduct field inspections. Once the drones complete their tasks, the vessel retrieves them at a specific location. This paper focuses on addressing the multi-period \"vessel-drone\" collaborative inspection route planning problem. The objective is to minimize the fixed cost of the vessel over the planning horizon while considering the overall path cost of both the vessel and the drone. The planning model takes into account various factors, including the coordination of space and time between the vessel and drone, drone endurance limitations, and the inspection cycle limitations for each period. To solve the problem, a mixed-integer second-order cone optimization formulation is proposed, along with several valid inequalities. Through empirical research and sensitivity analyses, the effectiveness of the proposed method is demonstrated. It is shown that the method can provide a cost-effective plan for oilfield inspections, offering potential value in guiding offshore oil-and-gas field inspection operations.", "keywords": [ "Drone", "Time horizon", "Field (mathematics)", "Computer science", "Offshore oil and gas", "Routing (electronic design automation)", "Port (circuit theory)", "Operations research", "Motion planning", "Submarine pipeline", "Marine engineering", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icnsc58704.2023.10319036" }, { "id": "https://openalex.org/W7117165054", "doi": "10.1016/j.cie.2025.111786", "title": "Multi-objective optimization of a truck–drone delivery system for fair and efficient humanitarian logistics under disruption and disinformation", "year": 2025, "publication_date": "2025-12-24", "type": "article", "language": "en", "authors": [ "Ramin Talebi Khameneh", "Nafiseh Ghorbani-Renani", "José Emmanuel Ramírez-Márquez" ], "first_author": "Ramin Talebi Khameneh", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Humanitarian aid", "Multi-objective optimization", "Stackelberg competition", "Humanitarian Logistics", "Pareto principle", "Sorting", "Drone", "Resilience (materials science)", "Genetic algorithm" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111786" }, { "id": "https://openalex.org/W4223421509", "doi": "10.1109/iceic54506.2022.9748803", "title": "An ILP-based Approach to Delivery Drone Routing under Load-dependent Flight Speed", "year": 2022, "publication_date": "2022-02-06", "type": "conference-paper", "language": "en", "authors": [ "Mao Nishira", "Satoshi Ito", "Hiroki Nishikawa", "Xiangbo Kong", "Hiroyuki Tomiyama" ], "first_author": "Mao Nishira", "venue": null, "publisher": null, "abstract": "This paper proposes an Integer Linear Programming (ILP) -based approach to delivery drone routing under load-dependent flight speed. Drone routing problems are attracting attention due to the increasing importance of drone delivery as a courier service. In general, delivery time is one of the most crucial concerns for both customers and couriers. Experiments demonstrate that the ILP-based approach can efficiently find a flight route in such a way that the overall delivery time is minimized, compared to the state-of-the-art methods.", "keywords": [ "Drone", "Integer programming", "Routing (electronic design automation)", "Computer science", "Linear programming", "Distributed computing", "Computer network", "Algorithm" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iceic54506.2022.9748803" }, { "id": "https://openalex.org/W4213436558", "doi": "10.1109/wsc52266.2021.9715292", "title": "A Genetic Algorithm Simheuristic for the Open UAV Task Assignment and Routing Problem with Stochastic Traveling and Servicing Times", "year": 2021, "publication_date": "2021-12-12", "type": "conference-paper", "language": "en", "authors": [ "Angel Alejandro Juan", "Alfons Freixes", "Pedro Copado", "Javier Panadero", "Juan F. Gomez", "Carles Serrat" ], "first_author": "Angel Alejandro Juan", "venue": null, "publisher": null, "abstract": "Due to their flexibility, unmanned aerial vehicles (UAVs) are gaining importance in transportation and surveillance activities. The usage of UAV swarms raises the need for coordination and optimization of task assignments. Some of these operations can be modeled as team orienteering problems (TOP). This paper analyzes an open TOP in which a given fleet of homogeneous UAVs, initially located at a single depot, need to be coordinated in order to maximize the collection of rewards from visiting nodes without exceeding a maximum operation time. As in most real-life applications, both traveling times and servicing times at each node are modeled as random variables. To solve this NP-hard and stochastic optimization problem, a simheuristic based on the combination of a genetic algorithm with Monte Carlo simulation is proposed.", "keywords": [ "Task (project management)", "Computer science", "Flexibility (engineering)", "Genetic algorithm", "Vehicle routing problem", "Node (physics)", "Orienteering", "Routing (electronic design automation)", "Mathematical optimization", "Monte Carlo method", "Travelling salesman problem", "Stochastic optimization" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wsc52266.2021.9715292" }, { "id": "https://openalex.org/W4312334862", "doi": "10.1109/tvt.2022.3221538", "title": "$Q$-Learning Aided Intelligent Routing With Maximum Utility in Cognitive UAV Swarm for Emergency Communications", "year": 2022, "publication_date": "2022-11-11", "type": "article", "language": "en", "authors": [ "Long Zhang", "Xiaozheng Ma", "Zirui Zhuang", "Haitao Xu", "Vishal Sharma", "Zhu Han" ], "first_author": "Long Zhang", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This article studies the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which employs the cognitive radio into a swarm of UAVs within a three-layer hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a$Q$-learning framework to achieve the intelligent routing to maximize the utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric jointly captures the achievable rate and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for UAV to optimize the candidate UAV selection. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update$Q$-values during the training process for achieving a fast convergence speed. Simulation results are provided to assess the potential of the$Q$-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm converges in a few number of iterations. Furthermore, the proposed algorithm can increase the accumulated rewards, and achieve significant performance gains, as compared to the benchmark schemes.", "keywords": [ "Swarm behaviour", "Routing (electronic design automation)", "Metric (unit)", "Computer science", "Swarm intelligence", "Algorithm", "Artificial intelligence", "Engineering", "Particle swarm optimization", "Embedded system" ], "cited_by_count": 50, "is_retracted": false, "is_paratext": false, "open_access_url": "https://pureadmin.qub.ac.uk/ws/files/385368968/q_2.pdf" }, { "id": "https://openalex.org/W4285196401", "doi": "10.2139/ssrn.4111273", "title": "The Vehicle Routing Problem with Heterogeneous Autonomous Assistants", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Cheng Chen", "Emrah Demir", "Xisheng Hu", "Shuaijv Du" ], "first_author": "Cheng Chen", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Computer network", "Business" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4111273" }, { "id": "https://openalex.org/W2999089938", "doi": null, "title": "Robust Two Echelon Vehicle and Drone Routing for Post Disaster Humanitarian Operations", "year": 2020, "publication_date": "2020-01-17", "type": "article", "language": "en", "authors": [ "Tasnim Ibn Faiz", "Chrysafis Vogiatzis", "Noor-E-Alam" ], "first_author": "Tasnim Ibn Faiz", "venue": null, "publisher": null, "abstract": "Following a disaster, humanitarian logistics providers have two major responsibilities: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and communications networks, often rendered unusable by the disaster. In this work, we propose a two-echelon vehicle routing problem framework for performing these operations using aerial uncrewed autonomous vehicles (UAVs or drones). Our setup employs ground vehicles carrying drones that serve to both identify demands for medical and other aid (e.g., epi-pens, medical supplies, dry food, water) and satisfy them. We present a deterministic and a stochastic approach for handling the emergency aid delivery problem faced by such a humanitarian logistics provider. In the deterministic approach, we consider two types of drones: hotspot drones for capturing demand and delivery drones for satisfying demand. We present a framework to identify optimal routes for both types of drones following a column-generation scheme. In the stochastic case, we avoid the extra cost of using hotspot drones to capture demands deterministically and develop a two-stage robust optimization framework to handle the resulting demand uncertainty. To solve the resulting two-stage robust model for the two-echelon vehicle and drone routing problem, we combine an outer level column-and constraint-generation (CCG) method with an inner level column generation (CG) scheme. We present a case study based on real-life data from Hurricane Maria in Puerto Rico and showcase the applicability of our framework. The data generated for this research is available online at this http URL.", "keywords": [ "Drone", "Vehicle routing problem", "Column generation", "Computer science", "Humanitarian Logistics", "Operations research", "Routing (electronic design automation)", "Robust optimization", "Humanitarian aid", "Mathematical optimization", "Engineering", "Computer network" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://experts.illinois.edu/en/publications/robust-two-echelon-vehicle-and-drone-routing-for-post-disaster-hu" }, { "id": "https://openalex.org/W4414270551", "doi": "10.1016/j.ejor.2026.04.046", "title": "Drone station location and routing optimization for infrastructure inspection", "year": 2026, "publication_date": "2026-04-30", "type": "article", "language": "en", "authors": [ "Peisong Liu", "Xiangpei Hu", "Chun Cheng" ], "first_author": "Peisong Liu", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Linear programming", "Homogeneous", "Task (project management)", "Key (lock)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5452574" }, { "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/W3045724203", "doi": "10.23919/acc45564.2020.9147214", "title": "Cooperative Air-Ground Vehicle Routing using Chance-Constrained Optimization", "year": 2020, "publication_date": "2020-07-01", "type": "conference-paper", "language": "en", "authors": [ "Bin Du", "Dengfeng Sun", "Satyanarayana G. Manyam", "David W. Casbeer" ], "first_author": "Bin Du", "venue": null, "publisher": null, "abstract": "This paper studies a cooperative air-ground routing problem, in which an unmanned aerial vehicle (UAV) and a ground vehicle coordinate with one another to visit a set of targets. The UAV, subject to limits on storage capacity and maximum flight distance, must rendezvous with the ground vehicle periodically, to upload the collected data and recharge. To solve such a cooperative routing problem, we formulate a chance-constrained mixed-integer linear program (CC-MILP), where the chance constraint captures uncertainties in the model, such as uncertain speed or travel time of the two vehicles. Consequently, the obtained solution is less conservative compared to those found using robust methods, since only the worst case realization of uncertainties is considered in the later methods. Also novel to our approach, is that air ground rendezvous locations are not restricted to occur only at targets. Instead, the two vehicles are free to meet at any point on the UAV's path, which significantly increases the feasible solution space and leads to better solutions. We corroborate the effectiveness of the proposed approach through a set of numerical simulations on randomly generated instances.", "keywords": [ "Rendezvous", "Routing (electronic design automation)", "Computer science", "Mathematical optimization", "Vehicle routing problem", "Upload", "Constraint (computer-aided design)", "Set (abstract data type)", "Linear programming", "Point (geometry)", "Optimization problem", "Mathematics" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/acc45564.2020.9147214" }, { "id": "https://openalex.org/W1971174066", "doi": "10.2514/1.35658", "title": "Finding Good Dubins Tours for UAVs Using Particle Swarm Optimization", "year": 2008, "publication_date": "2008-02-01", "type": "article", "language": "en", "authors": [ "Richard J. Kenefic" ], "first_author": "Richard J. Kenefic", "venue": "Journal of Aerospace Computing Information and Communication", "publisher": "American Institute of Aeronautics and Astronautics", "abstract": "Unmanned aerial vehicles (UAVs) perform important surveillance functions on the battlefield. When a set of fixed targets for surveillance is known a priori it is desirable to find a minimum length tour subject to the kinematic constraints of the UAV and the terrain and threat constraints of the environment. The UAV path planning problem can be reduced to the traveling salesman problem (TSP) or the vehicle routing problem (VRP), both of which are known to be NP-hard. The problem of finding an optimal tour subject to kinematic constraints, the Dubins vehicle problem (DVP), is also NP-hard and several heuristics have recently been proposed. In this paper several instances of the DVP are posed and solved with a heuristic and the particle swarm optimization method. The particle swarm optimization (PSO) results are compared to another standard optimization method, and the best configurations found for these instances are reported.", "keywords": [ "Particle swarm optimization", "Swarm behaviour", "Computer science", "Mathematical optimization", "Multi-swarm optimization", "Particle (ecology)", "Metaheuristic", "Aerospace engineering", "Mathematics", "Engineering", "Biology", "Ecology" ], "cited_by_count": 27, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/1.35658" }, { "id": "https://openalex.org/W2807776825", "doi": "10.1007/978-3-319-93815-8_19", "title": "Solving Vehicle Routing Problem Through a Tabu Bee Colony-Based Genetic Algorithm", "year": 2018, "publication_date": "2018-01-01", "type": "conference-paper", "language": "en", "authors": [ "Lingyan Lv", "Yuxin Liu", "Chao Gao", "Jianjun Chen", "Zili Zhang" ], "first_author": "Lingyan Lv", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Tabu search", "Vehicle routing problem", "Computer science", "Genetic algorithm", "Mathematical optimization", "Algorithm", "Routing (electronic design automation)", "Mathematics", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/conference_contribution/Solving_vehicle_routing_problem_through_a_tabu_bee_colony-based_genetic_algorithm/20799511" }, { "id": "https://openalex.org/W3016098794", "doi": "10.1007/978-3-030-44932-2_11", "title": "A Multi-objective Metaheuristic for a Green UAV Grid Routing Problem", "year": 2020, "publication_date": "2020-01-01", "type": "conference-paper", "language": "en", "authors": [ "Elias L. Marques", "Vitor N. Coelho", "Igor Machado Coelho", "Bruno N. Coelho", "Luiz Satoru Ochi" ], "first_author": "Elias L. Marques", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Grid", "Battery capacity", "Battery (electricity)", "Set (abstract data type)", "Range (aeronautics)", "Routing (electronic design automation)", "Point (geometry)", "Drone", "Sequence (biology)", "Real-time computing", "Distributed computing" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-44932-2_11" }, { "id": "https://openalex.org/W4283072798", "doi": "10.3390/app12126140", "title": "A Decision System for Routing Problems and Rescheduling Issues Using Unmanned Aerial Vehicles", "year": 2022, "publication_date": "2022-06-16", "type": "article", "language": "en", "authors": [ "I-Ching Lin", "Tsan-Hwan Lin", "Sheng-Hung Chang" ], "first_author": "I-Ching Lin", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In recent years, consumers have come to expect faster and better delivery services. Logistics companies, therefore, must implement innovative technologies or services in their logistics processes. It is critical to adopt unmanned aerial vehicles (UAV) in last mile delivery and urban logistics. The service provider applies the characteristics of UAVs to complete more requests, benefiting more revenue. However, it may not be a satisfactory solution, because the customers will be dissatisfied if the actual delivery time does not align with their expectations. This study constructs a revenue maximization model subject to time windows and customer satisfaction. Instead of addressing the traveling salesmen problem, this model takes new customer requests during the delivery process into account. We solved the problem using a genetic algorithm. The results show: (1) the model found an approximate and effective solution in the real-time delivery environment; (2) customer satisfaction is inversely proportional to the total delivery distance; (3) regarding the result of the sensitivity analysis of this study, investment in UAV has no influence on total profit and customer satisfaction. Moreover, the customer is a key factor in the logistics decision-making platform, not the provider’s investment in UAVs.", "keywords": [ "Customer satisfaction", "Revenue", "Maximization", "Profit (economics)", "Vehicle routing problem", "Business", "Operations research", "Computer science", "Process management", "Marketing", "Routing (electronic design automation)", "Engineering" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/12/12/6140/pdf?version=1655433261" }, { "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/W2029204796", "doi": "10.1109/siu.2013.6531559", "title": "Autonomous unmanned aerial vehicle route planning", "year": 2013, "publication_date": "2013-04-01", "type": "conference-paper", "language": "en", "authors": [ "Nuri Özalp", "Özgür Koray Şahingöz", "Uğur Ayan" ], "first_author": "Nuri Özalp", "venue": null, "publisher": null, "abstract": "In this study, a methodology detecting the radar make caught in real time during the flight or pre-flight route planning has been developed for an autonomous unmanned aerial vehicles (UAVs). The proposed method and genetic algorithms are implemented in parallel and duration is reduced to a large extent. The developed methodology can provide fast and safe routes for autonomous single or multiple UAV or operator-assisted flight.", "keywords": [ "Route planning", "Computer science", "Radar", "Motion planning", "Flight planning", "Remotely operated underwater vehicle", "Real-time computing", "Operator (biology)", "Genetic algorithm", "Artificial intelligence", "Aeronautics", "Simulation" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/siu.2013.6531559" }, { "id": "https://openalex.org/W268842794", "doi": null, "title": "A Hybrid Jump Search and Tabu Search Metaheuristic for the Unmanned Aerial Vehicle (UAV) Routing Problem", "year": 2000, "publication_date": "2000-03-01", "type": "article", "language": "en", "authors": [ "Gary W. Kinney" ], "first_author": "Gary W. Kinney", "venue": "Defense Technical Information Center (DTIC)", "publisher": null, "abstract": "In this research, we provide a new meta-heuristic, a jump search I tabu search hybrid, for addressing the vehicle routing problem with real-life constraints. A tour construction heuristic creates candidate solutions or jump points for the problem. A tabu search algorithm uses these jump points as starting points for a guided local search. We provide statistical analysis on the performance of our algorithm and compare it to other published algorithms. Our algorithm provides solutions within 10% of the best known solutions to benchmark problems and does so in a fraction of the time required by competing algorithms. The timeliness of the solution is vitally import to the unmanned aerial vehicle (UAV) routing problem. UAVs provide the lion's share of reconnaissance support for the US military. This reconnaissance mission requires the UAVs to visit hundreds of target areas in a rapidly changing combat environment. Air vehicle operators (AVOs) must prepare a viable mission plan for the UAVs while contending with such real-life constraints as time windows, target priorities, multiple depots, heterogeneous vehicle fleet, and pop-up threats. Our algorithm provides the AVOs with the tools to perform their mission quickly and efficiently.", "keywords": [ "Tabu search", "Benchmark (surveying)", "Computer science", "Vehicle routing problem", "Metaheuristic", "Jump", "Heuristic", "Local search (optimization)", "Guided Local Search", "Mathematical optimization", "Routing (electronic design automation)", "Artificial intelligence" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA378103" }, { "id": "https://openalex.org/W3120918496", "doi": "10.1016/j.ast.2021.106507", "title": "Multiple UAVs trajectory generation and waypoint assignment in urban environment based on DOP maps", "year": 2021, "publication_date": "2021-01-18", "type": "article", "language": "en", "authors": [ "Flavia Causa", "Giancarmine Fasano" ], "first_author": "Flavia Causa", "venue": "Aerospace Science and Technology", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Waypoint", "GNSS applications", "Computer science", "Dilution of precision", "Real-time computing", "Global Positioning System", "Trajectory", "Covariance", "Metric (unit)", "Motion planning", "Artificial intelligence", "Engineering" ], "cited_by_count": 44, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ast.2021.106507" }, { "id": "https://openalex.org/W2998645645", "doi": null, "title": "GENERALIZATION OF A UAV LOCATION AND ROUTING PROBLEM BY TIME WINDOWS", "year": 2016, "publication_date": "2016-11-14", "type": "article", "language": "en", "authors": [ "Ertan Yakıcı" ], "first_author": "Ertan Yakıcı", "venue": "DergiPark (Istanbul University)", "publisher": "Istanbul University", "abstract": "In this study we extend\\nand generalize a locating and routing problem for UAVs, with an objective of\\nmaximization of the total score collected from interest points visited. By solving the problem we determine\\nsimultaneously take-off and landing stations and visit order of interest points\\nfor each UAV. The problem is defined by an integer linear programming (ILP) formulation.\\nAn ant colony optimization approach is altered for the introduced problem.\\nComputational experiments are performed to compare CPLEX solver and the\\nheuristic. We observe that the heuristic performed well on the experienced instances.", "keywords": [ "Integer programming", "Solver", "Generalization", "Heuristic", "Mathematical optimization", "Maximization", "Computer science", "Routing (electronic design automation)", "Ant colony optimization algorithms", "Integer (computer science)", "Vehicle routing problem", "Linear programming" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dergipark.gov.tr/jnse/issue/26764/281651" }, { "id": "https://openalex.org/W2768498353", "doi": null, "title": "Two-stage stochastic programming model for routing multiple drones with fuel constraints", "year": 2017, "publication_date": "2017-11-14", "type": "preprint", "language": "en", "authors": [ "Saravanan Venkatachalam", "Kaarthik Sundar", "Sivakumar Rathinam" ], "first_author": "Saravanan Venkatachalam", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Uses of drones and unmanned vehicles (UAVs) in ground or aerial are increasing in both civil and military applications. This paper develops a two-stage stochastic optimization model with a recourse for a multiple drone-routing problem with fuel constraints under uncertainty for the travel between any pair of targets/refueling-sites/depot. We are given a set of $n$ heterogeneous drones or UAVs, a set of targets, a set of refueling stations where any drone can refuel, and the objective is to minimize the overall fuel consumed or distance travelled by all the drones. To approximate the value function of the two-stage stochastic programming model, we use the sample average approximation method (SAA). Though the solution of SAA asymptotically converges to the optimal solution for a two-stage model, finding a lower bound for the original problem is computationally intensive. Therefore, we develop a heuristic to handle large scale instances. In the computational experiments, we have evaluated the robustness of solutions from a deterministic approach and the bounds obtained using the proposed methods.", "keywords": [ "Drone", "Robustness (evolution)", "Mathematical optimization", "Stochastic programming", "Computer science", "Routing (electronic design automation)", "Heuristic", "Robust optimization", "Set (abstract data type)", "Vehicle routing problem", "Mathematics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/abs/1711.04936v1" }, { "id": "https://openalex.org/W4404648719", "doi": "10.48550/arxiv.2411.13151", "title": "Enhancements of Fragment Based Algorithms for Vehicle Routing Problems", "year": 2024, "publication_date": "2024-11-20", "type": "preprint", "language": "en", "authors": [ "Lucas Sippel", "Forbes, Michael" ], "first_author": "Lucas Sippel", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The method of fragments was recently proposed, and its effectiveness has been empirically shown for three specialised pickup and delivery problems. We propose an enhanced fragment algorithm that for the first time, effectively solves the Pickup and Delivery Problem with Time Windows. Additionally, we describe the approach in general terms to exemplify its theoretical applicability to vehicle routing problems without pickup and delivery requirements. We then apply it to the Truck-Based Drone Delivery Routing Problem Problem with Time Windows. The algorithm uses a fragment formulation rather than a route one. The definition of a fragment is problem specific, but generally, they can be thought of as enumerable segments of routes with a particular structure. A resource expanded network is constructed from the fragments and is iteratively updated via dynamic discretization discovery. Additionally, we introduce two new concepts called formulation leveraging and column enumeration for row elimination that are crucial for solving difficult problems. These use the strong linear relaxation of the route formulation to strengthen the fragment formulation. We test our algorithm on instances of the Pickup and Delivery Problem with Time Windows and the Truck-Based Drone Delivery Routing Problem with Time Windows. Our approach is competitive with, or outperforms the state-of-the-art algorithm for both.", "keywords": [ "Fragment (logic)", "Vehicle routing problem", "Computer science", "Routing algorithm", "Algorithm", "Routing (electronic design automation)", "Computer network", "Routing protocol" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2411.13151" }, { "id": "https://openalex.org/W2313405190", "doi": "10.2514/6.2010-3318", "title": "A Column Generation Approach to the Vehicle Routing Problem", "year": 2010, "publication_date": "2010-04-20", "type": "conference-paper", "language": "en", "authors": [ "David W. Casbeer", "Raymond Holsapple" ], "first_author": "David W. Casbeer", "venue": "AIAA Infotech@Aerospace 2010", "publisher": null, "abstract": "We apply column generation with branch-and-price optimization to the multi-target, multi-task assignment problem, with precedence constraints. The cost of any feasible UAV trajectory is calculated using Dubin’s ∞yable paths. We present results for the multitarget, single-task assignment problem. Along with the multi-target, multi-task assignment problem with precedence constraints. olumn generation is a means of solving linear programs. 1 In this paper, we apply column generation to the multi-target, multi-task assignment problem, with precedence constraints. Currently task assignment algorithms are calculated at a central (base) location, with assignments being fed to each agent in the team. The motivation for this work to to investigate the possibility of using column generation as a distributed means of calculating optimal assignments. Column generation is based on the Dantzig-Wolfe decomposition 2 and permits distribution of certain linear programs depending on the constraints. Rather than optimizing a large linear program, column generation divides and conquers by repeatedly iterating between a so called master problem with coupling constraints and a series of sub-problems, each with its own respective local constraints. 1,3,4 Some interesting properties of column generation are that by relaxing the integrality constraints of the master problem, the resulting bound is tighter than if the original problem were relaxed. 5 Furthermore, by applying the Dantzig-Wolfe decomposition, an optimization problem with non-linear costs and constraints becomes a linear program if separability holds. 6,7 This last property permits us to solve the task assignment problem with non-linear costs and precedence constraints determined by Dubin’s ∞yable paths as a linear program. Similar works, and the references therein, include Ref. 8 which is a single-assignment problem without precedence constraints, Ref. 9 which solves the routing with time windows using column generation and a branch-and-price scheme, and Ref. 10 which uses column generation to solve large tra‐c scheduling problems. We begin this paper by expressing the vehicle routing problem with precedence constraints in Section II. Then we explain column generation using a simple ∞ow problem in Section III. This toy problem explains column generation in an intuitive fashion, since it is easy to visualize. Then, in Section IV, we apply the Dantzig-Wolfe decomposition to the vehicle routing problem with precedence constraints and set up the column generation problem. We conclude with some illustrative examples and remarks in Sections V and VI.", "keywords": [ "Column generation", "Vehicle routing problem", "Computer science", "Column (typography)", "Routing (electronic design automation)", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2010-3318" }, { "id": "https://openalex.org/W4404206262", "doi": "10.1016/j.cor.2024.106894", "title": "The min max multi-trip drone location arc routing problem", "year": 2024, "publication_date": "2024-11-09", "type": "article", "language": "en", "authors": [ "Teresa Corberán", "Isaac Plana", "José María Sanchis" ], "first_author": "Teresa Corberán", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": "This paper studies the Min Max Multi-Trip drone Location Arc Routing Problem (MM-MT-dLARP), an arc routing problem that combines trucks and drones. We have a set of lines (usually curved) that have to be flown over by drones to perform a service (inspection, for example). There is a depot from which the trucks leave, each one carrying a drone, and a set of potential launching points where the truck can launch and pick up the drone. Drones have limited autonomy, but they can make several flights. We consider a min-max objective, in which the makespan, or time necessary to complete the service, must be minimized. Using aerial drones instead of ground vehicles allows to travel off the network: drones can enter a line through any of its points, service only a portion of that line and then exit through another of its points, without following the lines of the network. This allows for finding better solutions but also increases the difficulty of the problem. This issue can be addressed by digitizing the MM-MT-dLARP instances, approximating each line by a polygonal chain with a finite number of intermediate points, and requiring that drones can only enter and exit a line through those intermediate points. Thus, an instance of a discrete Min Max Multi-Trip Location Arc Routing Problem (MM-MT-LARP) is obtained. Here, an integer formulation for the MM-MT-LARP is proposed, some families of valid inequalities are proved to be facet-inducing of a relaxed polyhedron, and a branch-and-cut algorithm based on the strengthened formulation is developed. This algorithm has only been applied to small instances without intermediate points on the lines. In addition, we have developed a matheuristic algorithm for the MM-MT-dLARP that combines a construction phase, four local search procedures integrated into a Variable Neighborhood Descent (VND) algorithm, and a set of rules for selecting intermediate points to improve the solutions. We present the results obtained on a set of randomly generated instances involving up to 6 launching points and 88 original lines.", "keywords": [ "Arc routing", "Drone", "Computer science", "Arc (geometry)", "Routing (electronic design automation)", "Mathematical optimization", "Operations research", "Computer network", "Mathematics", "Geometry" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2024.106894" }, { "id": "https://openalex.org/W4388491442", "doi": "10.1080/19427867.2023.2278855", "title": "The paired pickup and delivery problem with profit in a two-echelon delivery system with multiple trucks and drones", "year": 2023, "publication_date": "2023-11-08", "type": "article", "language": "en", "authors": [ "Ebrahim Teimoury", "Reza Rashid" ], "first_author": "Ebrahim Teimoury", "venue": "Transportation Letters", "publisher": "Taylor & Francis", "abstract": "Recently researchers proposed truck and drone coordination to increase delivery efficiency and suggested various truck-drone routing problems. In this paper, we also focused on truck and drone coordination and introduced the paired pickup and delivery problem with profit in a two-echelon delivery system. To solve the problem, we propose a hybrid variable neighborhood search algorithm. For this algorithm, we adapted existing neighborhood search operators from the literature and considering the structure of the proposed problem, developed new neighborhood search operators. Also, we have carried out numerous computational experiments to evaluate the proposed solution methods’ performance, where the results show the efficiency of the proposed algorithms. The results highlight that in the paired pickup and delivery problem, for small values of drone operational costs, employing the two-echelon truck and drone routing system increases the profit by up to 5.6 percent in comparison to the vehicle routing system with drones.", "keywords": [ "Drone", "Truck", "Pickup", "Vehicle routing problem", "Computer science", "Profit (economics)", "Routing (electronic design automation)", "Mathematical optimization", "Operations research", "Engineering", "Computer network", "Mathematics" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/19427867.2023.2278855" }, { "id": "https://openalex.org/W4225761091", "doi": "10.1007/978-981-16-9492-9_152", "title": "A Variable Neighborhood Descent Method for Cooperative Delivery Routing Problem of Trucks and Drones", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Ni Mao", "Guohua Wu", "Qizhang Luo", "Jianmai Shi" ], "first_author": "Ni Mao", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Descent (aeronautics)", "Payload (computing)", "Routing (electronic design automation)", "Travelling salesman problem", "Truck", "Computer science", "Vehicle routing problem", "Cluster analysis", "Tabu search", "Variable (mathematics)", "Mathematical optimization" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-16-9492-9_152" }, { "id": "https://openalex.org/W3011408233", "doi": null, "title": "Optimization of drone routing for humanitarian applications.", "year": 2019, "publication_date": "2019-09-04", "type": "dissertation", "language": "en", "authors": [ "Thomas J. Lambert" ], "first_author": "Thomas J. Lambert", "venue": "Open Repository and Bibliography (University of Liège)", "publisher": "University of Liège", "abstract": "The present thesis aims to determine if drones could effectively replace in-situ inspection for the collect of information in humanitarian crisis situations. This study focuses on the elaboration of optimization models and their application to route efficiently an Unmanned Aerial Vehicle for a given humanitarian mission. The four models developed were all implemented in a mixed-integer linear programming utility so the solutions for UAV routing could be compared with a land vehicle completing the same mission. This report is divided in four main chapters. The first one introduces the drone technology environment and the humanitarian applications with these drones. A macroenvironmental study is performed using a \"PESTEL\" analysis to better understand the reasons why drones would be useful in the humanitarian context. In the second chapter, a hypothetical mission based on a simplistic version of the transportation network of Haiti will be presented. This scenario will be used in the following chapters as a baseline case study. Chapter three concerns the elaboration of four different optimization models. The first three are a subset of node routing problems (Traveling Salesman Problem and Distance-constrained Vehicle Routing Problems), while the last one is closer to the arc routing category (Capacitated General Routing Problem). The results obtained for of all these models show that a UAV is always faster than a single land vehicle operating in normal conditions for the test network. However, due to the very large network used as a basic example, the endurance limitations of existing UAVs appear to be a major issue for the real-world applications. Some existing UAV systems could fulfill the mission but they are likely still too expensive for humanitarian organizations. Fortunately, the models elaborated here can be applied to any network, and therefore the advantages of drones with a smaller autonomy can be verified, especially in jungle or mountain environments.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Humanitarian aid", "Political science", "Computer network", "Law", "Biology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://orbi.uliege.be/handle/2268/240101" }, { "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/W4312398142", "doi": "10.2139/ssrn.4283395", "title": "A Grasp/Vnd Algorithm for the Energy Minimizing Drone Routing Problem with Pickups and Deliveries", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Nikolaos A. Kyriakakis", "Stylianos Aronis", "Magdalene Marinaki", "Ioannis Marinakis" ], "first_author": "Nikolaos A. Kyriakakis", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "GRASP", "Computer science", "Drone", "Algorithm", "Routing (electronic design automation)", "Energy (signal processing)", "Mathematical optimization", "Mathematics", "Computer network", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4283395" }, { "id": "https://openalex.org/W2115715940", "doi": "10.1109/icma.2010.5589035", "title": "UAV route planning based on the genetic simulated annealing algorithm", "year": 2010, "publication_date": "2010-08-01", "type": "conference-paper", "language": "en", "authors": [ "Hao Meng", "Guizhou Xin" ], "first_author": "Hao Meng", "venue": null, "publisher": null, "abstract": "For the local minimum problem of genetic algorithm in unmanned aerial vehicle route planning, the Metropolis acceptance criteria of simulated annealing algorithm is incorporated into the genetic algorithm in this paper. In the algorithm, the original DEM (Digital Elevation Map) is processed into the smallest threat surface. In order to obtain a more smooth surface of flight, the original digital elevation map are processed in four directions, and then the genetic simulated annealing algorithm is used for three-dimensional route planning in this minimal threat surface. In addition, the distance between the track segment and threats are converted into elevation values and the value is added to the fitness function, a smaller code space was proposed at the same time. The simulation results show that the Genetic Simulated Annealing Algorithm proposed is good.", "keywords": [ "Simulated annealing", "Adaptive simulated annealing", "Genetic algorithm", "Fitness function", "Route planning", "Algorithm", "Computer science", "Digital elevation model", "Mathematical optimization", "Elevation (ballistics)", "Mathematics", "Machine learning" ], "cited_by_count": 27, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icma.2010.5589035" }, { "id": "https://openalex.org/W2947187142", "doi": "10.2197/ipsjtsldm.13.65", "title": "A Dynamic Programming Algorithm for Energy-aware Routing of Delivery Drones", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Yusuke Funabashi", "Atsuya Shibata", "Shunsuke Negoro", "Ittetsu Taniguchi", "Hiroyuki Tomiyama" ], "first_author": "Yusuke Funabashi", "venue": "IPSJ Transactions on System and LSI Design Methodology", "publisher": null, "abstract": "Energy consumption is one of the most critical concerns in delivery services using drones. This paper studies a routing problem for energy minimization of delivery drones. This paper formally defines Energy Minimizing Vehicle Routing Problem (EMVRP) and proposes a dynamic programming algorithm to efficiently solve the problem. Experiments show the effectiveness of the proposed algorithm in terms of both quality of results and algorithm runtime.", "keywords": [ "Computer science", "Drone", "Routing (electronic design automation)", "Energy consumption", "Dynamic programming", "Minification", "Energy (signal processing)", "Routing algorithm", "Mathematical optimization", "Algorithm", "Energy minimization", "Static routing" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.jstage.jst.go.jp/article/ipsjtsldm/13/0/13_65/_pdf" }, { "id": "https://openalex.org/W4393372681", "doi": "10.1007/978-3-031-47686-0_13", "title": "Optimal Drone Routing for Seal Pup Counts", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Lavinia Amorosi", "Dáire Carroll", "Paula Carroll", "Annunziata Esposito Amideo" ], "first_author": "Lavinia Amorosi", "venue": "AIRO Springer series/AIRO Springer Series", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Seal (emblem)", "Routing (electronic design automation)", "Computer science", "Computer network", "Aeronautics", "Marine engineering", "Engineering", "Geography", "Biology", "Archaeology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10197/28627" }, { "id": "https://openalex.org/W4318819980", "doi": "10.2514/1.i011171", "title": "Edge-Enhanced Attentions for Drone Delivery in Presence of Winds and Recharging Stations", "year": 2023, "publication_date": "2023-02-01", "type": "article", "language": "en", "authors": [ "Ruifan Liu", "Hyo‐Sang Shin", "Antonios Tsourdos" ], "first_author": "Ruifan Liu", "venue": "Journal of Aerospace Information Systems", "publisher": "American Institute of Aeronautics and Astronautics", "abstract": "Existing variants of vehicle routing problems have limited capabilities in describing real-world drone delivery scenarios in terms of drone physical restrictions, mission constraints, and stochastic operating environments. To that end, this paper proposes a specific drone delivery problem with recharging (DDP-R) characterized by directional edges and stochastic edge costs subject to wind conditions. To address it, the DDP-R is cast into a Markov decision process over a graph, with the next node chosen according to a stochastic policy based on the evolving observation. An edge-enhanced attention model (AM-E) is then suggested to map the optimal policy via the deep reinforcement learning (DRL) approach. The AM-E comprises a succession of edge-enhanced dot-product attention layers and is designed with the aim of capturing the heterogeneous node relationship for DDP-Rs by incorporating adjacent edge information. Simulations show that edge enhancement facilitates the training process, achieving superior performance with less trainable parameters and simpler architecture in comparison with other deep learning models. Furthermore, a stochastic drone energy cost model in consideration of winds is incorporated into validation simulations, which provides a practical insight into drone delivery problems. In terms of both nonwind and windy cases, extensive simulations demonstrate that the proposed DRL method outperforms state-of-the-art heuristics for solving DDP-Rs, especially at large sizes.", "keywords": [ "Drone", "Heuristics", "Markov decision process", "Computer science", "Reinforcement learning", "Enhanced Data Rates for GSM Evolution", "Markov process", "Edge computing", "Node (physics)", "Mathematical optimization", "Simulation", "Real-time computing" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arc.aiaa.org/doi/pdf/10.2514/1.I011171?download=true" }, { "id": "https://openalex.org/W4315472112", "doi": "10.1109/cdc51059.2022.9993358", "title": "Risk-aware UAV-UGV Rendezvous with Chance-Constrained Markov Decision Process", "year": 2022, "publication_date": "2022-12-06", "type": "conference-paper", "language": "en", "authors": [ "Guangyao Shi", "Nare Karapetyan", "Ahmad Bilal Asghar", "Jean-Paul Reddinger", "James M. Dotterweich", "James D. Humann", "Pratap Tokekar" ], "first_author": "Guangyao Shi", "venue": "2022 IEEE 61st Conference on Decision and Control (CDC)", "publisher": null, "abstract": "We study a chance-constrained variant of the cooperative aerial-ground vehicle routing problem, in which an Unmanned Aerial Vehicle (UAV) with limited battery capacity and an Unmanned Ground Vehicle (UGV) that can also act as a mobile recharging station need to jointly accomplish a mission such as monitoring a set of points. Due to the limited battery capacity of the UAV, two vehicles sometimes have to deviate from their task to rendezvous and recharge the UAV. Unlike prior work that has focused on the deterministic case, we address the challenge of stochastic energy consumption of the UAV. We are interested in finding the optimal policy that decides when and where to rendezvous such that the expected travel time of the UAV is minimized and the probability of running out of charge is less than a user-defined tolerance. We formulate this problem as a Chance Constrained Markov Decision Process (CCMDP), which can be equivalently transformed into a Constrained Markov Decision Process (CMDP). To the best knowledge of the authors, this is the first CMDP-based formulation for the UAVUGV routing problems under power consumption uncertainty. We adopt a Linear Programming (LP) based approach to solve the problem optimally. We demonstrate the effectiveness of our formulation in the context of an Intelligence Surveillance and Reconnaissance (ISR) mission.", "keywords": [ "Markov decision process", "Rendezvous", "Computer science", "Partially observable Markov decision process", "Markov process", "Routing (electronic design automation)", "Context (archaeology)", "Vehicle dynamics", "Reinforcement learning", "Mathematical optimization", "Process (computing)", "Task (project management)" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cdc51059.2022.9993358" }, { "id": "https://openalex.org/W2964247317", "doi": "10.1109/iros.2018.8593365", "title": "Heterogeneous Vehicles Routing for Water Canal Damage Assessment", "year": 2018, "publication_date": "2018-10-01", "type": "conference-paper", "language": "en", "authors": [ "Di Deng", "Prasanth Palli", "Fang Shu", "Kenji Shimada", "Tao Pang" ], "first_author": "Di Deng", "venue": null, "publisher": null, "abstract": "In Japan, inspection of irrigation water canals has been mostly conducted manually. However, the huge demand for more regular inspections as infrastructure ages, coupled with the limited time window available for inspection, has rendered manual inspection increasingly insufficient. With shortened inspection time and reduced labor cost, automated inspection using a combination of unmanned aerial vehicles (UAVs) and ground vehicles (cars) has emerged as an attractive alternative to manual inspection. In this paper, we propose a path planning framework that generates optimal plans for UAVs and cars to inspect water canals in a large agricultural area (tens of square kilometers). In addition to optimality, the paths need to satisfy several constraints, in order to guarantee UAV navigation safety and to abide by local traffic regulations. In the proposed framework, the canal and road networks are first modeled as two graphs, which are then partitioned into smaller subgraphs that can be covered by a given fleet of UAVs within one battery charge. The problem of finding optimal paths for both UAVs and cars on the graphs, subject to the constraints, is formulated as a integer quadratic program (IQP). The proposed framework can also quickly generate new plans when a current plan is interrupted. The effectiveness of the proposed framework is validated by simulation results showing the successful generation of plans covering all given canal segments, and the ability to quickly revise the plan when conditions change.", "keywords": [ "Routing (electronic design automation)", "Computer science", "Environmental science", "Computer network" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros.2018.8593365" }, { "id": "https://openalex.org/W4399055649", "doi": "10.3390/jmse12060891", "title": "Routing a Fleet of Drones from a Base Station for Emission Detection of Moving Ships by Genetic Algorithm", "year": 2024, "publication_date": "2024-05-27", "type": "article", "language": "en", "authors": [ "Xiaoqiong Bao", "Zhi‐Hua Hu", "Yanling Huang" ], "first_author": "Xiaoqiong Bao", "venue": "Journal of Marine Science and Engineering", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "A fleet of drones is considered in the routing problems with an offshore drone base station, considering the simultaneous movements of drones and ships. A model, entitled meeting model, between a drone and a moving ship is devised, and an extended model is developed based on the vehicle routing problem model. A genetic algorithm based on a sequential insert heuristic (SIH) is designed to solve the model as a holistic framework with two strategies to determine the sequential assignments of ships to drones, namely, the DroneByDrone, and ShipByShip strategies. The proposed models and solution algorithms are demonstrated and verified by experiments. Numerical studies show that the DroneByDrone strategy can overperform the ShipByShip strategy regarding traveling distances. In addition, when considering the simultaneous movement of the ship and drone, improving the drone flying speeds can reduce the flying time of drones rather than optimizing the ship’s moving speed. The managerial implications and possible extensions are discussed based on modeling and experimental studies.", "keywords": [ "Drone", "Base (topology)", "Routing algorithm", "Routing (electronic design automation)", "Genetic algorithm", "Computer science", "Aeronautics", "Environmental science", "Marine engineering", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2077-1312/12/6/891/pdf?version=1716811990" }, { "id": "https://openalex.org/W4295860426", "doi": "10.2139/ssrn.4211063", "title": "A Collaborative Truck Multi-Drone Routing Problem with Flexible Launch and Recovery Operations", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Liu Changshi", "Wu Zhang", "Gang Kou", "Feng Xiao", "Xiancheng Zhou", "Lijun Fan" ], "first_author": "Liu Changshi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Aeronautics", "Routing (electronic design automation)", "Vehicle routing problem", "Computer science", "Operations research", "Engineering", "Business", "Aerospace engineering", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4211063" }, { "id": "https://openalex.org/W4416074900", "doi": "10.3390/drones9110782", "title": "Resilient Last-Mile Logistics in Smart Cities Through Multi-Visit and Time-Dependent Drone–Truck Collaboration", "year": 2025, "publication_date": "2025-11-10", "type": "article", "language": "en", "authors": [ "Qinxin Xiao", "Jiaojiao Gao" ], "first_author": "Qinxin Xiao", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Urban logistics in smart cities are increasingly challenged by congestion, sustainability pressures, and the growing demand for resilient delivery systems. To address these challenges, this study introduces the Multi-Visit Time-Dependent Truck–Drone Routing Problem with simultaneous Pickup and Delivery (MTTRP-PD), a novel framework that integrates three realistic features: (i) drones serving multiple customers per sortie, (ii) time-dependent truck speeds reflecting dynamic traffic conditions, and (iii) synchronized pickup and delivery between trucks and drones. By incorporating these elements, the proposed model provides a more realistic and comprehensive representation of urban air-ground collaborative logistics in the last mile. An optimization framework and an efficient solution approach are developed and validated through computational experiments. The results demonstrate that enabling multi-visit sortie and simultaneous pickup–delivery operations can significantly reduce logistics costs compared with conventional single-visit or delivery-only strategies. Sensitivity analyses further reveal the critical influence of dynamic traffic conditions on fleet configuration and operational decision making. The findings offer actionable insights for logistics operators and policymakers, illustrating how coordinated UAV–truck collaboration can enhance efficiency, resilience, and environmental sustainability in next-generation urban logistics systems.", "keywords": [ "Truck", "Sustainability", "Drone", "City logistics", "Supply chain", "Vehicle routing problem", "Routing (electronic design automation)", "Humanitarian Logistics" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/11/782/pdf?version=1762786573" }, { "id": "https://openalex.org/W2896855302", "doi": "10.1109/ivs.2018.8500391", "title": "The Mathematical Modeling of the Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem", "year": 2018, "publication_date": "2018-06-01", "type": "conference-paper", "language": "en", "authors": [ "Zhihao Luo", "Zhong Liu", "Jianmai Shi", "Qi Wang", "Tianren Zhou", "Yao Liu" ], "first_author": "Zhihao Luo", "venue": null, "publisher": null, "abstract": "In this paper, we presents a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP), which consists of optimizing the route of both ground vehicle (GV) and its mounted Unmanned Aerial Vehicle(UAV) in the context of Intelligence, Surveillance and Reconnaissance(ISR) mission. The UAV is launched from the ground vehicle and automatically flies to the designated target to accomplish the ISR mission. Meanwhile, the ground vehicle is synchronized to charge or change the UAV's battery on the designated landing points based on the UAV's battery life. The objective is to design efficient ground vehicle and UAV routes to minimize the total mission time while meeting the operational constraints. The experimental results show that the model proposed in this paper is correct, but the existing commercial software cannot solve the large-scale problem with an acceptable time.", "keywords": [ "Context (archaeology)", "Unmanned ground vehicle", "Routing (electronic design automation)", "Computer science", "Battery (electricity)", "Vehicle routing problem", "Real-time computing", "Embedded system", "Artificial intelligence", "Power (physics)" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ivs.2018.8500391" }, { "id": "https://openalex.org/W2575590951", "doi": "10.1109/ispacs.2016.7824758", "title": "A self-sustaining unmanned aerial vehicle routing protocol for smart farming", "year": 2016, "publication_date": "2016-10-01", "type": "conference-paper", "language": "en", "authors": [ "Prusayon Nintanavongsa", "Weerachai Yaemvachi", "Itarun Pitimon" ], "first_author": "Prusayon Nintanavongsa", "venue": null, "publisher": null, "abstract": "Increasing agricultural productivity has been a long quest for farmers and only a few can achieve it. One major factor that hinders them to achieve such goal is the lack of proper agricultural monitoring technique. Recent advancement in technology has enabled the integration of sensor networks and traditional farming, resulting in effective monitoring through smart farming. However, there exists a hefty investment in equipment and infrastructure installation throughout the coverage area. We design two routing approaches, called Location-agnostic (LA) and Location-specific (LS) protocols, to facilitate the self-sustaining agricultural monitoring platform, requiring no infrastructure installation, comprises of Unmanned Aerial Vehicle (UAV) with solar energy harvesting and wireless power transfer capability. The LA protocol does not require location information of monitoring stations to be visited prior to the flight, and is useful for dynamic environment. The LS protocol relies on the complete view of the topology prior to the flight and is suitable for static environment. These protocols determine the optimal UAV routing path from a set of monitoring stations under various conditions. Through a combination of simulation and experimentation studies, we demonstrate significant energy efficiency and coverage area improvement over the classical routing protocol.", "keywords": [ "Routing protocol", "Computer science", "Protocol (science)", "Zone Routing Protocol", "Computer network", "Routing (electronic design automation)", "Wireless sensor network", "Wireless Routing Protocol" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ispacs.2016.7824758" }, { "id": "https://openalex.org/W4416902085", "doi": "10.3390/su172310775", "title": "Hybrid Drone and Truck Delivery Optimization in Remote Areas Using Geospatial Analytics", "year": 2025, "publication_date": "2025-12-01", "type": "article", "language": "en", "authors": [ "Md Abdul Quddus", "Md Fashiar Rahman", "Mahathir Mohammad Bappy" ], "first_author": "Md Abdul Quddus", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This study introduces a novel strategy for optimizing hybrid drone-and-truck delivery systems in remote areas by leveraging geospatial analytics. Geospatial methods are employed to identify optimal depot and drone nest locations, which serve as critical nodes for efficient delivery operations. After determining these locations, a customized Vehicle Routing Problem (VRP) model is applied to solve the routing problem. We use Network Analyst (NA) from ArcGIS Pro to solve the VRP problem and improve the solution by customizing the algorithm so that all delivery orders for a vehicle are geographically clustered within the service area. Comparative analysis between truck-only and hybrid truck-and-drone scenarios reveals significant efficiency gains, including reductions in delivery routes, on-road minutes, and total miles traveled. A case study conducted in parts of Wyoming, Idaho, Nevada, Utah, and Colorado validates these findings. The results demonstrate a 10.5% reduction in delivery routes, a 15% reduction in on-road minutes, and a 28% decrease in total miles. Further improvements were achieved through spatial clustering, optimizing delivery routes by grouping orders geographically. These findings emphasize the potential of hybrid delivery systems to improve logistics in remote areas, providing actionable insights for supply chain decision-makers, highlighting the robustness of the proposed method.", "keywords": [ "Geospatial analysis", "Drone", "Truck", "Vehicle routing problem", "Robustness (evolution)", "Analytics", "Routing (electronic design automation)", "Geographic information system" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/su172310775" }, { "id": "https://openalex.org/W4224244708", "doi": "10.1088/1742-6596/2245/1/012013", "title": "An Improved Particle Swarm Optimization Algorithm for Unmanned Aerial Vehicle Route Planning", "year": 2022, "publication_date": "2022-04-01", "type": "conference-paper", "language": "en", "authors": [ "Xiaolu Wang", "Chen Yuan Huang", "Fuhao Chen" ], "first_author": "Xiaolu Wang", "venue": "Journal of Physics Conference Series", "publisher": "IOP Publishing", "abstract": "Abstract A global path planning method based on improved particle swarm optimization (PSO) algorithm was proposed to find a high quality flight trajectory in three-dimensional complex environment under multiple threats for UAV. The improved path planning algorithm combines the standard PSO with A* method to compensate for the slow convergence rate of PSO. The objective function with multiple constraints of A* method is used to evaluate the quality of the waypoint, and the objective function of PSO algorithm is designed to evaluate the quality of the candidate path. To verify the effectiveness of the improved algorithm, the improved PSO algorithm is used to compare with basic PSO algorithms. The experiment in complex environment shows it has stronger search ability, convergence ability due to the improvement of population diversity and convergence speed.", "keywords": [ "Particle swarm optimization", "Waypoint", "Convergence (economics)", "Mathematical optimization", "Computer science", "Algorithm", "Path (computing)", "Motion planning", "Swarm behaviour", "Population", "Mathematics", "Real-time computing" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://iopscience.iop.org/article/10.1088/1742-6596/2245/1/012013/pdf" }, { "id": "https://openalex.org/W4414826128", "doi": "10.1016/j.tre.2025.104427", "title": "Optimizing autonomous multimodal last-mile delivery systems with time windows: Analyzing trade-offs between drones, robots, and trucks", "year": 2025, "publication_date": "2025-10-05", "type": "article", "language": "en", "authors": [ "Giovanni Campuzano", "Eduardo Lalla‐Ruiz", "Martijn Mes" ], "first_author": "Giovanni Campuzano", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "This paper studies multi-modal last-mile delivery systems. More specifically, it introduces the Drone-Assisted Vehicle Routing Problem with Robot Stations and Time Windows (VRPD-RS-TW) to face exigent delivery schedules in last-mile logistics. The VRPD-RS-TW applies to highly populated city centers, where due to infrastructural issues, customers might be hard to reach by conventional trucks. This problem is characterized by utilizing three transportation modes: trucks, drones, and robots collaborating to deliver parcels and meet time window requirements. To solve this problem, we develop a Mixed Integer Linear Programming (MILP) formulation, valid inequalities to strengthen the linear relaxation, and a Three-Phased Granular Multi-Start Iterated Local Search (3P-GMS-ILS) metaheuristic algorithm. A computational study on instances of Amsterdam (the Netherlands) demonstrates that the 3P-GMS-ILS outperforms the MILP formulation and state-of-the-art metaheuristic approaches in terms of both objective function values and computational times. Furthermore, we provide insights into the effectiveness of the VRPD-RS-TW, showing that, under various cost scenarios, the combination of trucks, drones, and robots outperforms scenarios with fewer transportation modes. Across the studied cost scenarios, the VRPD-RS-TW simultaneously coordinates a higher volume of delivery operations, with 48.75 % performed by drones and robots, resulting in better objective function values than the related transportation systems. In scenarios where truck routing costs are lower than those of drones and robots, the VRPD-RS-TW still achieves better objective function values compared to the related transportation systems, although the share of drone and robot deliveries drops to 14.78 %. This shows that adding drones is not necessarily beneficial when trucks carry drones, as trucks should wait for drones, which might result in higher costs related to drone routing and drivers’ wages.", "keywords": [ "Iterated local search", "Metaheuristic", "Vehicle routing problem", "Benchmark (surveying)", "Truck", "Integer programming", "Linear programming", "Routing (electronic design automation)" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104427" }, { "id": "https://openalex.org/W4393339600", "doi": "10.35784/acs-2024-06", "title": "OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS.", "year": 2024, "publication_date": "2024-03-30", "type": "article", "language": "en", "authors": [ "Rumesh Edirimanne", "W. Madushan Fernando", "Peter Nielsen", "H. Niles Perera", "Amila Thibbotuwawa" ], "first_author": "Rumesh Edirimanne", "venue": "Applied Computer Science", "publisher": "Polish Association for Knowledge Promotion", "abstract": "In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.", "keywords": [ "Vehicle routing problem", "Computer science", "Food delivery", "Payload (computing)", "Routing (electronic design automation)", "Embedded system", "Business", "Computer network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ph.pollub.pl/index.php/acs/article/download/5474/4479" }, { "id": "https://openalex.org/W4312829268", "doi": "10.2478/logi-2022-0019", "title": "VRP of Drones Considering Power Consumption Rate and Wind Effects", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Seongheon Kim", "Seongheon Kim", "Suhwan Kim", "Suhwan Kim" ], "first_author": "Seongheon Kim", "venue": "Logi", "publisher": "De Gruyter Open", "abstract": "Abstract The drone industry is one of the most important areas of the Fourth Industrial Revolution. In the drone industry, delivery systems using drones are now facing commercialization as they have undergone many experiments and discussions. The purpose of this study is to find the best route in a delivery system using a drone. In this study, we have developed the existing Vehicle Routing Problem (VRP) into a more realistic mathematical model considering the two differences between drones and vehicles; one is that power consumption varies with the weight of the loaded cargo and the other is that velocity is influenced by wind. This study also presents an Ant Colony System (ACS) algorithm to effectively solve VRP, a well-known NP-hard problem. The methodology of this study is quite successful and is expected to enable more realistic and effective routing decisions.", "keywords": [ "Drone", "Commercialization", "Vehicle routing problem", "Routing (electronic design automation)", "Power consumption", "Computer science", "Operations research", "Mathematical optimization", "Power (physics)", "Engineering", "Business", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://sciendo.com/pdf/10.2478/logi-2022-0019" }, { "id": "https://openalex.org/W4319309566", "doi": "10.1109/compauto55930.2022.00008", "title": "Drones-aided Asset Maintenance in Hospitals", "year": 2022, "publication_date": "2022-08-01", "type": "conference-paper", "language": "en", "authors": [ "Muhammad Asif Khan", "Hamid Menouar", "Ridha Hamila" ], "first_author": "Muhammad Asif Khan", "venue": null, "publisher": null, "abstract": "The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets.", "keywords": [ "Drone", "Service (business)", "Asset (computer security)", "Heuristic", "Computer science", "Pandemic", "Vehicle routing problem", "Routing (electronic design automation)", "Operations research", "Coronavirus disease 2019 (COVID-19)", "Business", "Computer security" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/compauto55930.2022.00008" }, { "id": "https://openalex.org/W4280564686", "doi": "10.3390/en15103730", "title": "Energy Efficient UAV Flight Control Method in an Environment with Obstacles and Gusts of Wind", "year": 2022, "publication_date": "2022-05-19", "type": "article", "language": "en", "authors": [ "Marcin Chodnicki", "B. Siemiątkowska", "Wojciech Stecz", "Slawomir Jan Stepien" ], "first_author": "Marcin Chodnicki", "venue": "Energies", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This article presents an energy-efficient method of controlling unmanned aircraft (fixed-wing UAVs), which consists of three groups of algorithms: aerial vehicle route planning, in-flight control, and algorithms to correct the preplanned flight trajectory. All algorithms shall take into account the existence of obstacles that the UAV must avoid and wind gusts in the UAV’s area of operation. Tests were carried out on the basis of the UAV mathematical model, stabilization and navigation algorithms, and Dryden turbulence model, considering the parameters of the UAV’s propulsion system. The work includes a detailed description of constructing a network of connection that is used to plan a UAV mission. It presents the algorithm for determining the actual distances between the different points in the field of action, which takes into account the existence of obstacles. The algorithm shall be based on methods for determining the flight trajectory on a hexagonal grid. It presents the developed proprietary UAV path planning algorithm based on a model from a group of algorithms of mixed integer linear problem (MILP) optimization. It presents the manner in which the pre-prepared flight path was used by UAV controllers that supervised the flight along the preset path. It details the architecture of contemporary unmanned aerial vehicles, which have embedded capability to realize autonomous missions, which require the integration of UAV systems into the route planning algorithms set out in the article. Particular attention has been paid to the planning and implementation methods of UAV missions under conditions where wind gusts are present, which support the determination of UAV flight routes to minimize the vehicle’s energy consumption. The models developed were tested within a computer architecture based on ARM processors using the hardware-in-the-loop (HIL) technique, which is commonly used to control unmanned vehicles. The presented solution makes use of two computers: FCC (flight control computer) based on a real-time operating system (RTOS) and MC (mission computer) based on Linux and integrated with the Robot Operating System (ROS). A new contribution of this work is the integration of planning and monitoring methods for the implementation of missions aimed at minimizing energy consumption of the vehicle, taking into account wind conditions.", "keywords": [ "Trajectory", "Motion planning", "Flight planning", "Computer science", "Path (computing)", "Field (mathematics)", "Flight plan", "Grid", "Real-time computing", "Control engineering", "Engineering", "Aerospace engineering" ], "cited_by_count": 33, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1996-1073/15/10/3730/pdf?version=1653020570" }, { "id": "https://openalex.org/W2914310415", "doi": "10.1016/j.ijdrr.2019.101080", "title": "Proposal to planning facility location using UAV and geographic information systems in a post-disaster scenario", "year": 2019, "publication_date": "2019-02-10", "type": "article", "language": "en", "authors": [ "Leandro de Oliveira Silva", "Renata Albergaria de Mello Bandeira", "Vânia Barcellos Gouvêa Campos" ], "first_author": "Leandro de Oliveira Silva", "venue": "International Journal of Disaster Risk Reduction", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Geographic information system", "Context (archaeology)", "Structuring", "Process (computing)", "Computer science", "Natural disaster", "Disaster recovery", "Event (particle physics)", "Transport engineering", "Operations research", "Business", "Engineering" ], "cited_by_count": 35, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ijdrr.2019.101080" }, { "id": "https://openalex.org/W1561141423", "doi": null, "title": "AFIT UAV Swarm Mission Planning and Simulation System", "year": 2012, "publication_date": "2012-11-16", "type": "article", "language": "en", "authors": [ "James N. Slear" ], "first_author": "James N. Slear", "venue": "Defense Technical Information Center (DTIC)", "publisher": null, "abstract": "The purpose of this research is to design and implement a comprehensive mission planning system for swarms of autonomous aerial vehicles. The system integrates several problem domains including path planning, vehicle routing, and swarm behavior. The developed system consists of a parallel, multi-objective evolutionary algorithm-based path planner, a genetic algorithm-based vehicle router, and a parallel UAV swarm simulator. Each of the system's three primary components are developed on AFIT's Beowulf parallel computer clusters. Novel aspects of this research include: integrating terrain following technology into a swarm model as a means of detection avoidance, combining practical problems of path planning and routing into a comprehensive mission planning strategy, and the development of a swarm behavior model with path following capabilities.", "keywords": [ "Swarm behaviour", "Motion planning", "Path (computing)", "Planner", "Terrain", "Computer science", "Router", "Genetic algorithm", "Routing (electronic design automation)", "Particle swarm optimization", "Distributed computing", "Real-time computing" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA454271" }, { "id": "https://openalex.org/W4417341468", "doi": "10.34229/2707-451x.25.4.3", "title": "Two-Level Algorithm for the UAV Routing Problem with Moving Targets", "year": 2025, "publication_date": "2025-12-08", "type": "article", "language": "en", "authors": [ "N. M. Skybytskyi" ], "first_author": "N. M. Skybytskyi", "venue": "Cybernetics and Computer Technologies", "publisher": null, "abstract": "Introduction. UAV routing problems gain relevance considering expanding uses in the defense industry. Contrary to the civil aviation with static depots and end users, the problems arising in defense require drone routing with moving targets. The theory of differential games has extensively studied the problems related to pursuit of dynamic targets in the general case, but the resulting solutions are difficult to apply in practical scenarios. Hence combinatorial methods are needed for simplified practical models. In particular, we consider all targets to be moving with a constant velocity vector. This article proposes a two-level algorithm for the problem of routing a group of UAVs with moving targets. We compare different clustering methods, including clustering by position, velocity, and a combined approach. The purpose of the article is to show the superiority of a two-level algorithm over the classical algorithm in solution quality under certain conditions. Additionally, we establish the necessity of the combined clustering methods in the general case and predict a correspondence between optimal clustering methods and practical routing scenarios. Results. We established that a two-level algorithm is better than the original algorithm under clustering conditions. We developed software that can be used to solve instances of the UAVRP with moving targets. Conclusions. A two-level algorithm improves the process of routing a group of UAVs in pursuit of a group of moving targets. A combined clustering strategy proved superior for total route length minimization. Keywords: unmanned aerial vehicle, drone, route optimization, clustering methods.", "keywords": [ "Cluster analysis", "Routing (electronic design automation)", "Canopy clustering algorithm", "Correlation clustering", "Relevance (law)", "CURE data clustering algorithm", "Process (computing)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://cctech.org.ua/images/docs/Articles/2025/paper_25_4_3.pdf" }, { "id": "https://openalex.org/W4410800233", "doi": "10.1016/j.cor.2025.107154", "title": "Energy-Efficient UAV routing problem based on approximate cellular decomposition for geohazards monitoring", "year": 2025, "publication_date": "2025-05-28", "type": "article", "language": "en", "authors": [ "Zonglei Han", "Chao Fang", "Wei Wang", "Jianyu Xu" ], "first_author": "Zonglei Han", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Decomposition", "Computer science", "Routing (electronic design automation)", "Energy (signal processing)", "Mathematical optimization", "Algorithm", "Mathematics", "Statistics", "Computer network", "Chemistry" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2025.107154" }, { "id": "https://openalex.org/W2267688860", "doi": "10.1007/s10846-016-0343-2", "title": "GPS Denied UAV Routing with Communication Constraints", "year": 2016, "publication_date": "2016-02-09", "type": "article", "language": "en", "authors": [ "Satyanarayana G. Manyam", "Sivakumar Rathinam", "Swaroop Darbha", "David W. Casbeer", "Yongcan Cao", "Phil R. Chandler" ], "first_author": "Satyanarayana G. Manyam", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Beacon", "Computer science", "Global Positioning System", "Travelling salesman problem", "Transformation (genetics)", "Set (abstract data type)", "Real-time computing", "Computer network", "Algorithm", "Telecommunications" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10846-016-0343-2" }, { "id": "https://openalex.org/W4392843675", "doi": "10.1142/s0217595924400049", "title": "A Drone-Driven Delivery Network Design for an On-Demand O2O Platform Considering Hazard Risks and Customer Heterogeneity", "year": 2024, "publication_date": "2024-02-14", "type": "article", "language": "en", "authors": [ "Xuting Sun", "Xinhang Li" ], "first_author": "Xuting Sun", "venue": "Asia Pacific Journal of Operational Research", "publisher": "World Scientific", "abstract": "Nowadays, the online-to-offline (O2O) retailers provide on-demand delivery service for online orders by their own fleets and riders. An intelligent delivery network lays an important foundation to support cost-effective delivery service in the long run. Drones have great potential to revolutionize the instant delivery industry regarding cost and timeliness, while the hazard risks to humans and the environment should be seriously considered through sophisticated network design. In this paper, we propose a framework for a drone-driven intelligent delivery network design problem with the consideration of the multi-dimensional risk map, which needs to determine store location, drone fleet size and allocation, customer assignment, customer delivery mode selection, and delivery routing. A bi-objective non-linear programming model is formulated to maximize profit and minimize integrated risks as well. To tackle large instances, a modified NSGA-III algorithm is developed, which is incorporated with problem-specific search operators and Pareto local search to obtain Pareto solutions efficiently. Real-world data-based numerical experiments are conducted to verify the performance of the modified NSGA-III algorithm compared to the modified NSGA-II. A case study based on the geographical information in Shanghai is analyzed to validate the effectiveness of the proposed model. Moreover, sensitivity analysis is presented to evaluate the effects of multiple parameters on the drone delivery service network design. Some managerial insights are obtained for the O2O retailer who offers on-demand delivery service through online platform.", "keywords": [ "Drone", "Hazard", "Business", "Computer science" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s0217595924400049" }, { "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/W7128597070", "doi": "10.1016/j.tre.2026.104732", "title": "Robust drone delivery with partial recharging strategy in urban medical logistics", "year": 2026, "publication_date": "2026-02-11", "type": "article", "language": "en", "authors": [ "Wenhao Peng", "Dujuan Wang", "Hengfei Yang", "T.C.E. Cheng" ], "first_author": "Wenhao Peng", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "City logistics", "Humanitarian Logistics", "Robustness (evolution)", "Work (physics)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104732" }, { "id": "https://openalex.org/W4388053268", "doi": "10.2139/ssrn.4619458", "title": "A Modelling and Solution Approach for Wind-Affected Drone-Truck Routing Problem Under Uncertainty", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Mersedeh Dodge", "S.​A. MirHassani", "Farnaz Hooshmand" ], "first_author": "Mersedeh Dodge", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Marine engineering", "Aeronautics", "Mathematical optimization", "Aerospace engineering", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4619458" }, { "id": "https://openalex.org/W4401588457", "doi": "10.1007/s40747-024-01591-0", "title": "Location-routing optimization of UAV collaborative blood delivery vehicle distribution on complex roads", "year": 2024, "publication_date": "2024-08-14", "type": "article", "language": "en", "authors": [ "Zhiyi Meng", "Ke Yu", "Rui Qiu" ], "first_author": "Zhiyi Meng", "venue": "Complex & Intelligent Systems", "publisher": "Springer Science+Business Media", "abstract": "To address the protracted blood transportation time prevalent in contemporary urban settings, we proposed a location-routing optimization problem tailored to the distribution of blood within intricate road networks. This involved a comprehensive assessment that encompassed the judicious selection of sites for both stations and blood centers, coupled with the meticulous planning of delivery routes for unmanned aerial vehicles (UAVs) that orchestrate the transportation of blood. First, a model was formulated to minimize the overall cost, including transportation expenses, costs associated with the site, and other relevant costs related to blood transportation vehicles coordinated by UAVs. Subsequently, a two-stage hybrid heuristic algorithm was designed based on the distinctive characteristics of the problem at hand. Moreover, an enhanced k-means algorithm was employed to generate clustering schemes, utilizing the centroid method to address the challenge of location selection for delivery sites effectively. A genetic algorithm enhanced with adaptive operators was employed to address the challenging large-scale NP-hard problem associated with route planning in intricate urban road networks. The results indicated that, compared to the traditional blood delivery model using vehicles, the total blood transportation cost decreased by 12.65% and the overall delivery time was reduced by 37.5% with the adoption of drone-assisted delivery; ultimately, case and sensitivity analyses were conducted to investigate the impact of variables including the number of blood transportation vehicles, UAVs, driver wages, and unit costs of blood transportation vehicles on the location-routing problem.", "keywords": [ "Vehicle routing problem", "Computational intelligence", "Routing (electronic design automation)", "Distribution (mathematics)", "Computer science", "Computer network", "Artificial intelligence", "Mathematics" ], "cited_by_count": 23, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s40747-024-01591-0.pdf" }, { "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/W4285118510", "doi": "10.1109/tmc.2022.3172444", "title": "CEDAN: Cost-Effective Data Aggregation for UAV-Enabled IoT Networks", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Abhishek Bera", "Sudip Misra", "Chandranath Chatterjee", "Shiwen Mao" ], "first_author": "Abhishek Bera", "venue": "IEEE Transactions on Mobile Computing", "publisher": "IEEE Computer Society", "abstract": "One of the crucial challenges in networked Unmanned Aerial Vehicles (UAVs) is to configure them to serve as aerial base stations (BSs) for collecting data from distributed Internet of Things (IoT) devices in a region devoid of backbone connectivity. To address this challenge, it is required to compute optimized trajectories of UAVs to collect data while considering the different activation patterns of IoT devices. We propose a scheme to optimize the trade-off between the number of covered IoT devices and travel time of UAVs. The formulated cost minimization problem is known as the capacitated single depot vehicle routing problem (CSDVRP), which is NP-hard. We propose a solution scheme, named Cost-Effective Data Aggregation for UAV-Enabled IoT Networks (CEDAN), which operates in four steps. First, it determines the optimized hovering locations (HLs) for UAVs. Subsequently, CEDAN determines the optimized route adopting the Christofides's approximation algorithm for Travelling Salesman Problem (TSP). Further, a split function produces the optimized trajectories for all UAVs. Finally, a route adjustment algorithm applies the cost function and rearranges the order of visiting each HL. Extensive simulation results depict that the CEDAN outperforms than Clarke-Wright (CW) savings heuristics, CEDAN without route adjustment (CWRA), and Zhan et al., respectively.", "keywords": [ "Computer science", "Heuristics", "Travelling salesman problem", "Internet of Things", "Routing (electronic design automation)", "Base station", "Minification", "Scheme (mathematics)", "Function (biology)", "Distributed computing", "Computer network", "Real-time computing" ], "cited_by_count": 22, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tmc.2022.3172444" }, { "id": "https://openalex.org/W4390738682", "doi": "10.1109/access.2024.3353193", "title": "Energy Optimization and Trajectory Planning for Constrained Multi-UAV Data Collection in WSNs", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Amira A. Amer", "Reem Ahmed", "Irene Samy Fahim", "Tawfik Ismail" ], "first_author": "Amira A. Amer", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Wireless sensor networks (WSNs) deployed in remote areas face a challenge in uploading the collected data to data centers due to limited network coverage. Unmanned aerial vehicles (UAVs) can extend network coverage to remote WSNs by flying and communicating with WSN aggregator nodes to collect data. UAV-assisted data collection systems need to be carefully developed to collect all data efficiently while considering the UAV and WSN constraints. This paper provides an energy-efficient multi-UAV data collection framework for WSNs. We formulate the data collection system as a problem that jointly optimizes the system cost and energy consumption constrained by the communication power, UAV mission time, and memory size. The problem is resolved over two steps: First, the location and number of aggregators needed are determined using a triangulation-based K-means clustering that minimizes the number of aggregators used and the system cost. Second, the dockstation position that minimizes the energy consumption is obtained using the gaining-sharing knowledge (GSK) optimization algorithm. The optimum UAV trajectory for each GSK candidate solution is designed by solving a capacitated vehicle routing problem (CVRP) that combines heuristic and metaheuristic solving techniques. Simulations show that our framework outperforms other recent techniques by minimizing the overall system cost and energy consumption.", "keywords": [ "Computer science", "Trajectory", "Wireless sensor network", "Trajectory optimization", "Data collection", "Real-time computing", "Computer network", "Mathematics" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/6287639/6514899/10388295.pdf" }, { "id": "https://openalex.org/W4392406301", "doi": "10.1063/5.0194742", "title": "A review on medical drone for tribal locations", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Gokul Kumarasamy", "Magudapathi Periyasamy", "Sabitha Balasubramanian" ], "first_author": "Gokul Kumarasamy", "venue": "AIP conference proceedings", "publisher": "American Institute of Physics", "abstract": "It is essential to deliver medical products to designated locations exactly, securely, and on time during the COVID-19 pandemic.By bypassing roadblocks, cutting delivery times, and enabling contactless delivery, the utilization of drones for leading healthcare delivery can reduce the likelihood of running across COVID-19 patients.However, the needs for the delivery of medical supplies in public health circumstances cannot be met by the present optimization technique for drone delivery.As a result, this research offers a macro -and micro mixed integer computing framework for the multi-trip quadcopter location routing problem, allowing for simultaneous grab and delivery while cutting down on the amount of time needed to bring medical supplies to their destination.Drones, as unmanned aerial vehicles, have enormous promise in healthcare and are being proposed as an efficient new means to deliver urgent medicines and medical gadgets, particularly in remote areas.Although the benefits are evident, user viewpoints are critical, particularly during the development phase.Human-drone interaction research could potentially improve usefulness and usability.This study demonstrates that the suggested optimization method with continuous pickup and delivery channels achieves a lesser duration, is safer, and spends more energy when compared to separate delivery and collection techniques.Finally, sensitivity analysis is carried out by changing a few factors and providing a few examples for managing the delivery of medical goods using drones.", "keywords": [ "Drone", "Computer science", "Biology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0194742/19706525/020014_1_5.0194742.pdf" }, { "id": "https://openalex.org/W4283278550", "doi": "10.1007/s10589-022-00383-x", "title": "Polyhedral analysis and a new algorithm for the length constrained K–drones rural postman problem", "year": 2022, "publication_date": "2022-06-21", "type": "article", "language": "en", "authors": [ "James F. Campbell", "Ángel Corberán", "Isaac Plana", "José María Sanchis", "Paula Segura" ], "first_author": "James F. Campbell", "venue": "Computational Optimization and Applications", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Mathematics", "Drone", "Algorithm", "Mathematical optimization", "Combinatorics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10251/200285" }, { "id": "https://openalex.org/W4401929985", "doi": "10.1016/j.tra.2024.104201", "title": "Fixed Automated stations location and UAVs routing problems in urban road Networks: A tailored Branch-&-price algorithm", "year": 2024, "publication_date": "2024-08-26", "type": "article", "language": "en", "authors": [ "Hao Li", "Liujiang Kang", "Huijun Sun", "Jianjun Wu", "Yue Zhao", "Samuel Amihere" ], "first_author": "Hao Li", "venue": "Transportation Research Part A Policy and Practice", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Transport engineering", "Computer science", "Vehicle routing problem", "Operations research", "Computer network", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tra.2024.104201" }, { "id": "https://openalex.org/W3186667887", "doi": "10.1109/iros51168.2021.9636123", "title": "Capacitated Vehicle Routing with Target Geometric Constraints", "year": 2021, "publication_date": "2021-09-27", "type": "conference-paper", "language": "en", "authors": [ "Kaizhou Gao", "Jingjin Yu" ], "first_author": "Kaizhou Gao", "venue": "2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)", "publisher": null, "abstract": "We investigate the capacitated vehicle routing problem (CVRP) under a robotics context, where a vehicle with limited payload must complete delivery (or pickup) tasks to serve a set of geographically distributed customers with varying demands. In classical CVRP, a customer location is modeled as a point. In many robotics applications, however, it is more appropriate to model such \"customer locations\" as 2D regions. For example, in aerial delivery, a drone may drop a package anywhere in a customer’s lot. This yields the problem of CVRG (Capacitated Vehicle Routing with Target Geometric Constraints). Computationally, CVRP is already strongly NPhard; CVRG is therefore more challenging. Nevertheless, we develop fast algorithms for CVRG, capable of computing high quality solutions for hundreds of regions. Our algorithmic solution is guaranteed to be optimal when customer regions are convex. Numerical evaluations show that our proposed methods significantly outperform greedy best-first approaches. Comprehensive simulation studies confirm the effectiveness of our methods.", "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros51168.2021.9636123" }, { "id": "https://openalex.org/W4404371247", "doi": "10.1109/icsintesa62455.2024.10748193", "title": "Optimization of the Unmanned Aerial Vehicle Route Based on Chimpanzee Leader Election Optimization", "year": 2024, "publication_date": "2024-07-12", "type": "conference-paper", "language": "en", "authors": [ "Ferry Wahyu Wibowo", "Wihayati" ], "first_author": "Ferry Wahyu Wibowo", "venue": null, "publisher": null, "abstract": "Pest and disease control often requires large amounts of pesticides, which can damage the environment and human health. In addition, unplanned control can result in inefficient use of chemicals. Unmanned aerial vehicles (UAVs) with spraying systems can highly spray pesticides or nutrients. UAV or drone technology makes it possible to target areas affected by pests or diseases precisely, reducing the amount of pesticides needed and minimizing negative environmental impacts. Farmers can increase productivity, reduce environmental impact, and better manage resources by leveraging drone technology. This paper aims to optimize the drone spray route in rice fields using the Chimpanzee Leader Election Optimization (CLEO) algorithm. The method to improve performance efficiency was to simulate the drone flight route by placing several obstacles the drone should not pass through. The optimization results have used several flight reference points, namely 2,3, and 4. This experiment was carried out 30 times for each reference point number to obtain statistical results as a performance analysis of the modeling. The best results have shown that the placement of two reference points for the drone flight route has a superior value, with a mean of 9.08 and a standard deviation of 0.91.", "keywords": [ "Computer science", "Aeronautics", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icsintesa62455.2024.10748193" }, { "id": "https://openalex.org/W3008827961", "doi": "10.3390/app10041388", "title": "Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment", "year": 2020, "publication_date": "2020-02-19", "type": "article", "language": "en", "authors": [ "Moning Zhu", "Xuehua Zhang", "He Luo", "Guoqiang Wang", "Binbin Zhang" ], "first_author": "Moning Zhu", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In the last decade, with the wide application of UAVs in post-earthquake relief operations, the images and videos of affected areas obtained by UAVs immediately after a seismic event have become an important source of information for post-earthquake rapid assessment, which is crucial for initiating effective emergency response operations. In this study, we first consider the kinematic constraints of UAV and the Dubins curve is introduced to fit the shortest flyable path for each UAV that meets the maximum curvature constraint. Second, based on the actual requirements of post-earthquake rapid assessment, heterogeneous UAVs, multi-depot launching, and targets allowed access to multiple times, the paper proposes a multi-UAV rapid-assessment routing problem (MURARP). The MURARP is modeled as the multi-depot revisit-allowed Dubins TOP with variable profit (MD-RDTOP-VP) which is a variant of the team orienteering problem (TOP). Third, a hybrid genetic simulated annealing (HGSA) algorithm is developed to solve the problem. The result of numerical experiments shows that the HGSA algorithm can quickly plan flyable paths for heterogeneous UAVs to maximize the expected profit. Finally, a case study based on real data of the 2017 Jiuzhaigou earthquake in China shows how the method can be applied in a post-earthquake scenario.", "keywords": [ "Computer science", "Kinematics", "Shortest path problem", "Operations research", "Mathematical optimization", "Engineering", "Mathematics", "Graph" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/10/4/1388/pdf?version=1582210040" }, { "id": "https://openalex.org/W4409890810", "doi": "10.1080/15568318.2025.2493138", "title": "Route optimization of green truck-drone collaborative delivery with multiple drops considering demand distribution", "year": 2025, "publication_date": "2025-04-28", "type": "article", "language": "en", "authors": [ "Lan Zhu", "Ting Wang", "Xiao Ma", "Yang Lu", "Xuming Lou" ], "first_author": "Lan Zhu", "venue": "International Journal of Sustainable Transportation", "publisher": "Taylor & Francis", "abstract": "In recent years, drones have been used to assist delivering small goods as a solution to traffic jams and air pollutions to serve the purpose of effective and sustainable delivery. Thus, a green truck-drone collaborative delivery routing problem with multiple drops is studied in this paper. A multi-objective truck-drone route planning model considering carbon emissions and traveling time is developed. Compared to most of the existing research in which drones can deliver only one node per trip, this study extends the model to a more complicated realistic situation that drones deliver multiple drops per trip. A NSGA-II algorithm is designed to address the above-described problem. Then, cases with clustered, semi-clustered and randomly distributed customers are analyzed to find out the influence of customer distribution characteristics on the environmental performance and time efficiency. As numerical experiment results show, the improved collaborative delivery model significantly reduces carbon emissions and improves delivery efficiency, especially when customers are randomly distributed or semi-clustered. Finally, the optimal traveling speed range is obtained through the speed sensitivity analysis, which provide a sustainable decision-making basis for daily operations.", "keywords": [ "Drone", "Truck", "Transport engineering", "Distribution (mathematics)", "On demand", "Computer science", "Engineering", "Aerospace engineering", "Multimedia", "Mathematics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/15568318.2025.2493138" } ]