[ { "id": "https://openalex.org/W4323782864", "doi": "10.1007/978-3-031-25891-6_17", "title": "A Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Nikolaos A. Kyriakakis", "Themistoklis Stamadianos", "Magdalene Marinaki", "Nikolaos F. Matsatsinis", "Yannis Marinakis" ], "first_author": "Nikolaos A. Kyriakakis", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Swarm intelligence", "Artificial bee colony algorithm", "Metaheuristic", "Routing (electronic design automation)", "Variable neighborhood search", "Mathematical optimization", "Swarm behaviour", "Electric vehicle", "Vehicle routing problem", "Optimization problem" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-25891-6_17" }, { "id": "https://openalex.org/W4353095477", "doi": "10.1016/j.cie.2023.109179", "title": "Optimal delivery route planning for a fleet of heterogeneous drones: A rescheduling-based genetic algorithm approach", "year": 2023, "publication_date": "2023-03-22", "type": "article", "language": "en", "authors": [ "Xudong Wang", "Zeyu Liu", "Xueping Li" ], "first_author": "Xudong Wang", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Scheduling (production processes)", "Vehicle routing problem", "Genetic algorithm", "Knapsack problem", "Mathematical optimization", "Shortest path problem", "Job shop scheduling", "Integer programming", "Operations research", "Travelling salesman problem" ], "cited_by_count": 58, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2023.109179" }, { "id": "https://openalex.org/W4313445735", "doi": "10.1007/978-3-031-18716-2_1", "title": "The Evolution of the Vehicle Routing Problem—A Survey of VRP Research and Practice from 2005 to 2022", "year": 2023, "publication_date": "2023-01-01", "type": "book-chapter", "language": "en", "authors": [ "Bruce Golden", "Xingyin Wang", "Edward Wasil" ], "first_author": "Bruce Golden", "venue": "Synthesis lectures on operations research and applications", "publisher": "Morgan & Claypool Publishers", "abstract": null, "keywords": [ "Vehicle routing problem", "Heuristic", "Routing (electronic design automation)", "Key (lock)", "Computer science", "Operations research", "Engineering", "Artificial intelligence", "Computer network" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-18716-2_1" }, { "id": "https://openalex.org/W4403516281", "doi": "10.3390/s24206698", "title": "Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem", "year": 2024, "publication_date": "2024-10-18", "type": "article", "language": "en", "authors": [ "Xiu Shu", "Anping Lin", "Xupeng Wen" ], "first_author": "Xiu Shu", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL's superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.", "keywords": [ "Reinforcement learning", "Drone", "Computer science", "Routing (electronic design automation)", "Energy (signal processing)", "Reinforcement", "Artificial intelligence", "Mathematical optimization", "Distributed computing", "Engineering", "Computer network", "Mathematics" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/24/20/6698/pdf?version=1729239110" }, { "id": "https://openalex.org/W3001261420", "doi": "10.1016/j.robot.2020.103435", "title": "Solving the area coverage problem with UAVs: A vehicle routing with time windows variation", "year": 2020, "publication_date": "2020-01-23", "type": "article", "language": "en", "authors": [ "Fatih Semiz", "Faruk Polat" ], "first_author": "Fatih Semiz", "venue": "Robotics and Autonomous Systems", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Vehicle routing problem", "Cluster analysis", "Cover (algebra)", "Set (abstract data type)", "Variation (astronomy)", "Routing (electronic design automation)", "Mathematical optimization", "Real-time computing", "Computer network", "Artificial intelligence" ], "cited_by_count": 36, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2003.07124" }, { "id": "https://openalex.org/W4309676774", "doi": "10.1109/smc53654.2022.9945266", "title": "Solving Vehicle Routing Problem with Drones Based on a Bi-level Heuristic Approach", "year": 2022, "publication_date": "2022-10-09", "type": "conference-paper", "language": "en", "authors": [ "Jian Yang", "Haobin Yang", "Zonghan He", "Qi Gang Zhao", "Yuhui Shi" ], "first_author": "Jian Yang", "venue": "2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)", "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs), or drones, have the potential to be applied to delivery services, which are expected to bring economic benefits. One of the key issues is planning routes for vehicles and drones with specific constraints and objectives, known as Vehicle Routing Problem with Drones (VRPD). This paper considers a scenario involving multiple trucks, multiple UAV stations, and UAVs within each station to serve the customers. A bi-level approach that combines the Brain Storm optimization algorithm and Adaptive Large Neighborhood Search is proposed by designing the solution representation, new solution generation mechanism, and other operations. The experimental results show that the proposed method has the ability to solve the problem and deserves further development.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Heuristic", "Key (lock)", "Truck", "Representation (politics)", "Operations research", "Mathematical optimization", "Distributed computing", "Computer network" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/smc53654.2022.9945266" }, { "id": "https://openalex.org/W7125419041", "doi": "10.1287/trsc.2024.0987", "title": "An Exact Algorithm to Solve Vehicle Routing Problem with Drones for Delivery and Surveillance Tasks After Disasters", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "W. D. Sun", "Lingxiao Wu", "Fangni Zhang" ], "first_author": "W. D. Sun", "venue": "Transportation Science", "publisher": "Institute for Operations Research and the Management Sciences", "abstract": "Disasters cause severe economic and human losses, and delays in rescue efforts can lead to more deaths due to damaged infrastructure. To enhance post-disaster response efficiency, this study proposes a collaborative truck-and-drone system that leverages the high capacity of trucks and the speed and flexibility of drones to overcome road accessibility challenges. Although truck-drone collaborations have been studied in commercial logistics, their application in disaster relief remains understudied, particularly for simultaneous delivery and surveillance tasks, which are critical for saving lives and assessing damage. Unlike cost- or time-focused commercial logistics, humanitarian operations require prioritizing urgency, where tasks with higher mortality risks should be prioritized. This study addresses the truck-and-drone routing problem with flexible collaboration strategies for delivery and surveillance tasks to minimize the priority and penalty costs in the disaster response. We model the problem as a mixed integer linear programming model and develop an exact method based on the branch-and-bound algorithm and the Benders decomposition approach to solve the problem. Several valid inequalities are derived based on problem properties to improve the computational efficiency. Numerical experiments demonstrate that the proposed algorithm outperforms the state-of-the-art solver Gurobi in terms of objective function value, optimality gap, and computational time. The proposed flexible truck-and-drone collaboration can significantly enhance rescue efficiency in disaster response. Funding: The work described in this paper was partially supported by the National Natural Science Foundation of China [Grants 72371216 and 72301230] and the Research Grants Council of Hong Kong, China [Grants 17205124, 25223223, and T32-707-22]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0987 .", "keywords": [ "Solver", "Flexibility (engineering)", "Drone", "Routing (electronic design automation)", "Integer programming", "Truck", "Linear programming", "Vehicle routing problem", "Natural disaster" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1287/trsc.2024.0987" }, { "id": "https://openalex.org/W4399842762", "doi": "10.1016/j.trc.2024.104691", "title": "Exact and heuristic approaches for the Truck–Drone Team Logistics Problem", "year": 2024, "publication_date": "2024-06-20", "type": "article", "language": "en", "authors": [ "Maurizio Boccia", "Andrea Mancuso", "Adriano Masone", "Claudio Sterle" ], "first_author": "Maurizio Boccia", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": "Truck-and-drone routing problems are a topic of increasing interest due to advancements in drone technology. Initially, drones were thought to serve only one customer per flight; however, recent improvements in drone technology have led to the definition of multi-visit truck-and-drone routing problems, where a drone can serve multiple customers in each flight. These routing problems, which require a careful synchronization between the truck and the drone, are challenging to solve, even with a small number of customers. In this work, we address a multi-visit, single-truck, single-drone routing problem also known in the literature as the Truck–Drone Team Logistics Problem ( TDTLP ). In particular, we study two versions of the TDTLP arising from the possibility for the drone to land or hover at the recollection points. Our study proposes a novel mixed-integer linear programming formulation for the TDTLP , solved by a Branch-and-Cut ( B&C ) algorithm. Then, we introduce a matheuristic approach that incorporates our B&C algorithm to solve medium- and large-size instances. Computational results on benchmark instances demonstrate the robustness and efficacy of the proposed methods. • We study the Truck–Drone Team Logistics Problem (TDTLP). • The impact of the drone hovering on the TDTLP solution is evaluated. • A new MILP formulation for the TDTLP is proposed. • Exact and matheuristic approaches have been developed to tackle the TDTLP. • Computational results demonstrate the effectiveness of our approaches.", "keywords": [ "Drone", "Heuristic", "Truck", "Computer science", "Operations research", "Aeronautics", "Engineering", "Artificial intelligence", "Aerospace engineering" ], "cited_by_count": 41, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2024.104691" }, { "id": "https://openalex.org/W4409517350", "doi": "10.1080/17509653.2025.2490246", "title": "Two-echelon vehicle routing problem with integrated drones and unmanned ground vehicles", "year": 2025, "publication_date": "2025-04-15", "type": "article", "language": "en", "authors": [ "John F. Castaneda", "Ramón Alfonso Gallego Rendón", "Eliana Mirledy Toro Ocampo" ], "first_author": "John F. Castaneda", "venue": "International Journal of Management Science and Engineering Management", "publisher": "Taylor & Francis", "abstract": "The Vehicle Routing Problem (VRP) faces numerous challenges, including high-traffic areas, restricted access for cargo vehicles, high pollutant emissions, pedestrian-only zones, and limited parking. The VRP with Unmanned Vehicles seeks to address these issues by incorporating vehicles attached to the primary delivery vehicle. These attached vehicles encounter fewer access restrictions in delivery areas but have limited load capacity and range. This paper proposes formulating a two-echelon VRP using Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) to compare their performance against the classical Capacitated Vehicle Routing Problem (CVRP). The objective is to identify the benefits—such as reduced pollutant emissions—of using these vehicles, and to highlight the advantages and disadvantages of each type of attached vehicle for the second echelon. The development is carried out in three phases. First, the problem is solved using the classical vehicle routing problem for the first echelon. Then, clusters are generated to reconfigure the routes from the initial solution, assign customers to the second echelon, and verify feasibility. Finally, a modified Iterated Local Search (ILS) is used to obtain solutions for the two-echelon problem. Performance and quality control are conducted using benchmark sets for the classical VRP to validate the proposed methodology.", "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Computer network" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.tandfonline.com/doi/pdf/10.1080/17509653.2025.2490246" }, { "id": "https://openalex.org/W2947008563", "doi": "10.1109/taes.2019.2917578", "title": "Cooperative Aerial–Ground Vehicle Route Planning With Fuel Constraints for Coverage Applications", "year": 2019, "publication_date": "2019-05-27", "type": "article", "language": "en", "authors": [ "Parikshit Maini", "Kaarthik Sundar", "Mandeep Singh", "Sivakumar Rathinam", "P. B. Sujit" ], "first_author": "Parikshit Maini", "venue": "IEEE Transactions on Aerospace and Electronic Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Low-cost unmanned aerial vehicles (UAVs) need multiple refuels to accomplish large area coverage. We propose the use of a mobile ground vehicle (GV), constrained to travel on a given road network, as a refueling station for the UAV. Determining optimal routes for a UAV and GV, and selecting rendezvous locations for refueling to minimize coverage time is NP-hard. We develop a two-stage strategy for coupled route planning for UAV and GV to perform a coverage mission. The first-stage computes refueling sites that ensure reachability of all points of interest by the UAV and feasible routes for both the UAV and GV. In the second stage, mixed-integer linear programming (MILP) based exact methods are developed to plan optimal routes for the UAV and GV. As the problem is NP-Hard, we also develop computationally efficient heuristics that can find good feasible solutions within a given time limit. Extensive simulations are conducted to corroborate the effectiveness of the developed approaches. Field experiments are also performed to verify the performance of the UAV-GV solution.", "keywords": [ "Reachability", "Rendezvous", "Heuristics", "Computer science", "Integer programming", "Benchmark (surveying)", "Linear programming", "Plan (archaeology)", "Limit (mathematics)", "Trajectory", "Mathematical optimization", "Real-time computing" ], "cited_by_count": 86, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/taes.2019.2917578" }, { "id": "https://openalex.org/W4287308100", "doi": "10.1177/03611981221108378", "title": "Multiple-Drones-Multiple-Trucks Routing Problem for Disruption Assessment", "year": 2022, "publication_date": "2022-07-25", "type": "article", "language": "en", "authors": [ "Alireza Ermagun", "Nazanin Tajik" ], "first_author": "Alireza Ermagun", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "This study proposes a multiple-drones-multiple-trucks (MDMT) routing problem to assess infrastructure in the areas of disruption epicenters using multiple drones that operate in synchronicity with multiple trucks. Incorporating the trucks as moving depots enables drones to move across disrupted areas further from their flight range. The MDMT routing problem is formulated as mixed integer linear programming (MILP). This is an NP-Hard (Non-deterministic Polynomial-time Hardness) problem, which is solved by introducing a greedy heuristic algorithm to cluster the disrupted locations and plan each drone’s schedule within each disrupted cluster and between clusters. Both MILP and the greedy heuristic algorithm are tested for small to large test problems extracted from the Minneapolis–St. Paul freeway system with a grid-like network topology. Results show that disrupted locations’ spatial distribution affects the efficient number of active drones and trucks in the system. When direct travel between two nodes takes longer than other alternative paths, increasing the number of trucks accelerates the assessment procedure. Findings also indicate that the spatial distribution of disruptions clustered by the greedy heuristic algorithm is correlated with the routing time and affects drone scheduling. If clusters are scattered, each drone is typically assigned to one cluster for damage assessments. With a dense pocket of clusters in the network, however, a drone moves back and forth between multiple clusters. The proposed framework for disruption assessment provides insights on the optimal deployment of resources to collect information following a network disruption. Practitioners will also benefit from the findings to augment resource management.", "keywords": [ "Drone", "Truck", "Greedy algorithm", "Computer science", "Routing (electronic design automation)", "Integer programming", "Scheduling (production processes)", "Heuristic", "Cluster analysis", "Schedule", "Linear programming", "Mathematical optimization" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981221108378" }, { "id": "https://openalex.org/W3096364569", "doi": "10.1016/j.ejor.2020.10.035", "title": "Solving the length constrained K -drones rural postman problem", "year": 2020, "publication_date": "2020-11-01", "type": "article", "language": "en", "authors": [ "James F. Campbell", "Ángel Corberán", "Isaac Plana", "José María Sanchis", "Paula Segura" ], "first_author": "James F. Campbell", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Mathematics", "Scalable Vector Graphics", "Computer science", "World Wide Web" ], "cited_by_count": 41, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S0377221720309115" }, { "id": "https://openalex.org/W4405842334", "doi": "10.1016/j.cor.2024.106959", "title": "An adaptive large neighborhood search method for the drone–truck arc routing problem", "year": 2024, "publication_date": "2024-12-27", "type": "article", "language": "en", "authors": [ "Xufei Liu", "Sung Hoon Chung", "Changhyun Kwon" ], "first_author": "Xufei Liu", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Arc routing", "Drone", "Truck", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Arc (geometry)", "Mathematical optimization", "Mathematics", "Computer network", "Aerospace engineering", "Engineering" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2024.106959" }, { "id": "https://openalex.org/W4383894985", "doi": "10.59973/emjsr.11", "title": "Last mile delivery Optimisation model for drone-enabled Vehicle Routing Problem", "year": 2023, "publication_date": "2023-07-11", "type": "article", "language": "en", "authors": [ "Ismail Tausif" ], "first_author": "Ismail Tausif", "venue": "Emerging Minds Journal for Student Research", "publisher": null, "abstract": "In light of e-commerce's exponential growth trajectory over recent years, it has never been more critical to evaluate the last-mile delivery segment for its efficiency and cost-effectiveness. Autonomous vehicles (drones) offer considerable promise as potential solutions with ongoing investigations into emerging technologies in this context. To address problems related to last mile-delivery in logistics operations, the practicality of adopting a hybrid truck-drone delivery system is examined through this study. The researchers utilized Mixed-integer linear programming (MILP)and Gurobi optimization solvers both for optimizing performance as well as facilitating execution. While testing a drone dataset of a well-known logistics company as part of their research using an optimization model, the findings suggested that were remarkable competitive advantages including significant gains in reduction of timing. Nevertheless, there are several constraints like maintenance, recharging, difficult weather conditions & traffic congestion-necessitating focused innovative AI-based approaches. In spite of these impediments, a hybrid truck-drone’s potential applicability can remarkably boost the efficiency of last-mile delivery operations.", "keywords": [ "Drone", "Truck", "Last mile (transportation)", "Computer science", "Context (archaeology)", "Integer programming", "Mile", "Vehicle routing problem", "Operations research", "Transport engineering", "Routing (electronic design automation)", "Linear programming" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ipipublishing.org/index.php/emjsr/article/download/11/14" }, { "id": "https://openalex.org/W4410348960", "doi": "10.1016/j.cor.2025.107141", "title": "Time-dependent electric vehicle-drone routing problem for scheduled deliveries and on-demand pickups considering variable drone speeds and no-fly zones", "year": 2025, "publication_date": "2025-05-14", "type": "article", "language": "en", "authors": [ "Xiaoxue Ren", "Houming Fan", "Hao Fan", "Mengzhi Ma" ], "first_author": "Xiaoxue Ren", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Variable (mathematics)", "Pickup", "Routing (electronic design automation)", "Electric vehicle", "Mathematical optimization", "Operations research", "Real-time computing", "Computer network", "Mathematics" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2025.107141" }, { "id": "https://openalex.org/W4403260292", "doi": "10.1016/j.tre.2024.103802", "title": "Dynamic collaborative truck-drone delivery with en-route synchronization and random requests", "year": 2024, "publication_date": "2024-10-09", "type": "article", "language": "en", "authors": [ "Haipeng Cui", "Keyu Li", "Shuai Dong Jia", "Qiang Meng" ], "first_author": "Haipeng Cui", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Drone", "Synchronization (alternating current)", "Computer science", "Transport engineering", "Aeronautics", "Operations research", "Simulation", "Engineering", "Automotive engineering", "Computer network" ], "cited_by_count": 43, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2024.103802" }, { "id": "https://openalex.org/W4391554721", "doi": "10.1016/j.ejor.2024.01.045", "title": "Fleet resupply by drones for last-mile delivery", "year": 2024, "publication_date": "2024-02-02", "type": "article", "language": "en", "authors": [ "Juan C. Pina-Pardo", "Daniel F. Silva", "Alice E. Smith", "Ricardo A. Gatica" ], "first_author": "Juan C. Pina-Pardo", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Drone", "Computer science", "Vehicle routing problem", "Integer programming", "Operations research", "Routing (electronic design automation)", "Transport engineering", "Engineering", "Automotive engineering", "Computer network" ], "cited_by_count": 29, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2024.01.045" }, { "id": "https://openalex.org/W4385062483", "doi": "10.2139/ssrn.4516190", "title": "The Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Roberto Montemanni", "Mauro Dell'Amico", "Andrea Corsini" ], "first_author": "Roberto Montemanni", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Travelling salesman problem", "Vehicle routing problem", "Computer science", "Scheduling (production processes)", "Integer programming", "Constraint programming", "Routing (electronic design automation)", "Operations research", "Mathematical optimization", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4516190" }, { "id": "https://openalex.org/W3088787567", "doi": "10.1007/s00500-020-05316-6", "title": "An optimization drone routing model for inspecting wind farms", "year": 2020, "publication_date": "2020-09-22", "type": "article", "language": "en", "authors": [ "Hyeoncheol Baik", "Jorge F. Valenzuela" ], "first_author": "Hyeoncheol Baik", "venue": "Soft Computing", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Travelling salesman problem", "Routing (electronic design automation)", "Cluster analysis", "Wind power", "Integer programming", "Computer science", "Linear programming", "Mathematical optimization", "Process (computing)", "Real-time computing", "Simulation" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s00500-020-05316-6" }, { "id": "https://openalex.org/W4379805013", "doi": "10.1109/jiot.2023.3284056", "title": "IoMT-Assisted Medical Vehicle Routing Based on UAV-Borne Human Crowd Sensing and Deep Learning in Smart Cities", "year": 2023, "publication_date": "2023-06-08", "type": "article", "language": "en", "authors": [ "Khosro Rezaee", "Mohammad R. Khosravi", "Hani H. Attar", "Varun G. Menon", "Mohammad Ayoub Khan", "Haitham Issa", "Lianyong Qi" ], "first_author": "Khosro Rezaee", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "An emergency medical vehicle can save the patient’s life if it arrives at his location as quickly as possible. Unmanned aerial vehicles (UAVs) offer wide visibility and mobility, making them a viable choice for smart cities and intelligent transportation systems (ITSs) as edge devices for the Internet of Things (IoT). Based on population behavior and overcrowding, video surveillance through the Internet of multimedia things (IoMT) and public safety in smart cities can help determine the most efficient routes for emergency medical vehicles. This study investigates UAV overcrowding and abnormal population activity patterns, which affect the flow of emergency medical vehicles and traffic flow. Moreover, the purpose of this article is to analyze received video frames from UAVs in order to identify the most efficient route for emergency medical vehicles in smart cities to transfer patients in the event of abnormalities or overcrowding. In order to detect overcrowding on the streets, a hybrid Cascade-ResNet is utilized, which detects congestion based on many data points. Based on our proposed approach, we achieve a 2.5% improvement over similar methods because it is effective, flexible, and accurate. UAV video frames can be used to communicate with emergency response vehicles, to monitor traffic congestion, and to monitor other aspects of smart city life.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Deep learning", "Computer network", "Artificial intelligence", "Real-time computing", "Human–computer interaction" ], "cited_by_count": 55, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2023.3284056" }, { "id": "https://openalex.org/W4392124631", "doi": "10.1016/j.cie.2024.110000", "title": "Optimization of trucks and drones in tandem delivery network with drone trajectory planning", "year": 2024, "publication_date": "2024-02-23", "type": "article", "language": "en", "authors": [ "Chih‐Yuan Chu", "C.S. Shui", "Keng-Hsuan Lin" ], "first_author": "Chih‐Yuan Chu", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Tandem", "Trajectory", "Computer science", "Aeronautics", "Engineering", "Aerospace engineering", "Biology", "Physics" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2024.110000" }, { "id": "https://openalex.org/W3125535900", "doi": "10.1109/access.2019.2914352", "title": "Cooperative Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance, and Reconnaissance Missions", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "Yao Liu", "Zhihao Luo", "Zhong Liu", "Jianmai Shi", "Guangquan Cheng" ], "first_author": "Yao Liu", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In this paper, we investigate a novel two-echelon ground vehicle and its mounted unmanned aerial vehicle cooperative routing problem (2E-GUCRP) for intelligence, surveillance, and reconnaissance (ISR) missions. In a typical cooperative system with ground vehicle (GV) and unmanned aerial vehicle (UAV), the UAV is launched from the GV, automatically flies to the designated target and return to the GV before powering off. Meanwhile, acting as a mobile platform, the GV can carry the UAV moving in a larger region and charge or exchange the UAV's battery. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A mixed integer programming (MIP) model, which could be solved by commercial software (e.g. CPLEX), is developed to formulate the problem. In order to quickly solve the medium-scale or above problems, two heuristics are designed. The random instances and practical case based on the road network in Changsha, China, are used for experiments. The computational results indicate that, compared to the mode of two-echelon vehicle routing problem (2E-VRP), the cooperative GV and UAV can complete the ISR missions in a much shorter time through utilizing the proposed 2E-GUCRP mode. Furthermore, discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.", "keywords": [ "Computer science", "Unmanned ground vehicle", "Aeronautics", "Routing (electronic design automation)", "Remotely operated underwater vehicle", "Remote sensing", "Real-time computing", "Mobile robot", "Artificial intelligence", "Computer network", "Engineering", "Robot" ], "cited_by_count": 105, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/6287639/8600701/08704298.pdf" }, { "id": "https://openalex.org/W2997832306", "doi": "10.1016/j.ifacol.2019.12.088", "title": "Supporting Humans in Solving Multi-UAV Dynamic Vehicle Routing Problems", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "N.W. Klein Koerkamp", "Clark Borst", "Max Mulder", "Marinus M. van Paassen" ], "first_author": "N.W. Klein Koerkamp", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "Real-time optimization of Vehicle Routing Problems (VRP) during mission operations raises concerns regarding obtaining a solution within a reasonable timeframe, especially in domains where operations cannot easily be paused and the number of control parameters is high. Humans, however, are heuristic problem solvers and could potentially complement VRP algorithms in providing quickly a workable and safe solution from which the algorithms can further find the optimum. In this study, a visual interface was developed and evaluated aiming to support humans in manually solving a dynamic VRP in which they needed to solve various simulated payload delivery missions, featuring multiple Unmanned Aerial Vehicles, under failure conditions. Experiment results (n = 16) indicate that the interface enabled the majority of participants to quickly solve the perturbed scenarios, although not always in the most efficient way. Interestingly, participants experienced most difficulty in solving the seemingly easier scenarios, featuring less customers and a relatively low number of vehicles compared to the more complex scenarios.", "keywords": [ "Payload (computing)", "Vehicle routing problem", "Heuristic", "Computer science", "Interface (matter)", "Complement (music)", "Routing (electronic design automation)", "Mathematical optimization", "Distributed computing", "Operations research", "Embedded system", "Artificial intelligence" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2019.12.088" }, { "id": "https://openalex.org/W3120407099", "doi": "10.1109/ieem45057.2020.9309923", "title": "Simulated Annealing Algorithm Performance on Two-Echelon Vehicle Routing Problem-Mapping Operation with Drones", "year": 2020, "publication_date": "2020-12-14", "type": "conference-paper", "language": "en", "authors": [ "Rahmad Inca Liperda", "A. A. N. Pewira Redi", "Nandini N. Sekaringtyas", "Handina B. Astiana", "Bertha Maya Sopha", "Anna Maria Sri Asih" ], "first_author": "Rahmad Inca Liperda", "venue": null, "publisher": null, "abstract": "Flood is one of the most threatening hazards in Indonesia. The disaster may affect road disruptions. Disaster mapping with the combination of ground vehicles and drones would be a possible way to respond to flood disasters. The study aimed to determine parameter values that generate the best solution by metaheuristics algorithm and compare each method's solution values. The modeled problem was solved by the exact method using AMPL and the Simulated Annealing algorithm using the C# programming language, and the parameters tuning process was performed before the implementation of the metaheuristic algorithm. The results show that there are 4.28% differences between the optimal solution generated by AMPL and Simulated Annealing.", "keywords": [ "Simulated annealing", "Drone", "Metaheuristic", "Computer science", "Flood myth", "Algorithm", "Mathematical optimization", "Vehicle routing problem", "Routing (electronic design automation)", "Mathematics" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ieem45057.2020.9309923" }, { "id": "https://openalex.org/W2128940723", "doi": "10.1109/cdc.2006.377732", "title": "Lower and Upper Bounds for a Multiple Depot UAV Routing Problem", "year": 2006, "publication_date": "2006-01-01", "type": "conference-paper", "language": "en", "authors": [ "Sivakumar Rathinam", "Raja Sengupta" ], "first_author": "Sivakumar Rathinam", "venue": null, "publisher": null, "abstract": "This paper extends the well known Held-Karp's lower bound available for a single travelling salesman problem to the following multiple depot UAV routing problem (MDURP): Given a collection of UAVs that start at different depots, a set of terminals and destinations, the problem is to choose paths for each of the UAVs so that (1) each UAV starts at its respective depot, visits atleast one destination and reaches any one of the terminals not visited by other UAVs; (2) each destination is visited by exactly one UAV; and (3) the cost of the paths is a minimum among all possible paths for the UAVs. The criteria for the cost of paths considered is the total cost of the edges travelled by the entire collection. This MDURP is formulated as a minimum cost constrained forest problem subject to side constraints. By dualizing the side constraints, one can obtain an infinite family of lower bounds for MDURP. Each lower bound can be computed in a tractable way using a matroid intersection algorithm. Also, when the costs of traveling between any two locations satisfy triangle inequality, it is shown that there exists a 2-approximation algorithm for solving the MDURP", "keywords": [ "Travelling salesman problem", "Intersection (aeronautics)", "Upper and lower bounds", "Mathematical optimization", "Vehicle routing problem", "Routing (electronic design automation)", "Depot", "Matroid", "Set (abstract data type)", "Computer science", "Triangle inequality", "Mathematics" ], "cited_by_count": 31, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cdc.2006.377732" }, { "id": "https://openalex.org/W4377224967", "doi": "10.1007/s10479-023-05381-8", "title": "RETRACTED ARTICLE: A new truck-drone routing problem for parcel delivery by considering energy consumption and altitude", "year": 2023, "publication_date": "2023-05-22", "type": "article", "language": "en", "authors": [ "Maryam Momeni", "Seyed Mohammad Javad Mirzapour Al-e-Hashem", "Ali Akbar Heidari" ], "first_author": "Maryam Momeni", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Heuristic", "Routing (electronic design automation)", "Energy consumption", "Solver", "Operations research", "Truck", "Theory of computation", "Mathematical optimization", "Metropolitan area", "Simulation" ], "cited_by_count": 18, "is_retracted": true, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10479-023-05381-8.pdf" }, { "id": "https://openalex.org/W4367355755", "doi": "10.1007/978-3-031-30855-0_14", "title": "Vehicle Routing Problem for an Integrated Electric Vehicles and Drones System", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Setyo Tri Windras Mara", "Saber Mohammed Elsayed", "Daryl Essam", "Ruhul Amin Sarker" ], "first_author": "Setyo Tri Windras Mara", "venue": "Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering", "publisher": null, "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Set (abstract data type)", "Computer science", "Electric vehicle", "Operations research", "Mathematical optimization", "Engineering", "Computer network", "Mathematics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-30855-0_14" }, { "id": "https://openalex.org/W4286582025", "doi": "10.1109/tits.2022.3190698", "title": "Heuristics Based Optimization for Multidepot Drone Location and Routing Problem to Detect Post-Earthquake Damages", "year": 2022, "publication_date": "2022-07-22", "type": "article", "language": "en", "authors": [ "Nezir Aydın", "Oktay Yilmaz", "Muhammet Deveci", "Zhihan Lv" ], "first_author": "Nezir Aydın", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The aim of this research is to detect the post-disaster damage by drones as soon as possible so that decision makers can assign search and rescue teams effectively and efficiently. The main differences of this research from the others, which use drones in literature, are as: First, the regions are divided into grids and different importance values are assigned according to the number of buildings that are likely to be damaged and are vital for the response stage, such as hospitals, schools, and fire stations. Second, these importance levels are updated based on the day and time, which helps ordering the grids in a more realistic manner. Third, the depots are selected among the pre-determined candidate locations in accordance with the purpose of objective function. Fourth, detection times at grids are considered as uncertain. Fifth, two versions of Ant Colony Optimization (ACO) are developed as alternatives to exact solution tools. Last, sensitivity analyzes are performed by reducing the number of sorties, reducing the number of drones, and comparing day and night importance values for each instance. According to the results, only for very small-scale instances, exact solution tool was able to reach the optimal while both versions of ACO reached to similar results within a very less CPU times. Additionally, these ACO algorithms also found good results for the larger scaled problems. Then the performance of these ACO algorithms and the exact solution method are compared based on the CPU time and solution quality.", "keywords": [ "Drone", "Heuristics", "Ant colony optimization algorithms", "Computer science", "Mathematical optimization", "Sensitivity (control systems)", "Routing (electronic design automation)", "Damages", "Benchmark (surveying)", "Operations research", "Engineering", "Algorithm" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2022.3190698" }, { "id": "https://openalex.org/W4200587457", "doi": "10.1007/978-981-16-5559-3_19", "title": "A Parallel Genetic Algorithm for Solving the Vehicle Routing Problem with Drone Medication Delivery", "year": 2021, "publication_date": "2021-12-09", "type": "book-chapter", "language": "en", "authors": [ "Mustapha Ouiss", "Abdelaziz Ettaoufik", "Abdelaziz Marzak", "Abderrahim Tragha" ], "first_author": "Mustapha Ouiss", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Domain (mathematical analysis)", "Vehicle routing problem", "Genetic algorithm", "Routing algorithm", "Computer network", "Machine learning", "Routing protocol", "Mathematics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-16-5559-3_19" }, { "id": "https://openalex.org/W4393128414", "doi": "10.1016/j.ejtl.2024.100127", "title": "Modeling optimal drone fleet size considering stochastic demand", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Yuval Hadas", "Miguel Figliozzi" ], "first_author": "Yuval Hadas", "venue": "EURO Journal on Transportation and Logistics", "publisher": "Springer Science+Business Media", "abstract": "The last mile delivery is particularly challenging for stochastic deliveries with narrow time windows. This topic is timely due to the rise of e-commerce and courier type services and the impacts of fleet size and vehicle type on delivery costs. A novel contribution of this research is to provide an optimization approach, extending the newsvendor model, to provide an optimal drone fleet sizing solution with stochastic demand in terms of number of deliveries and deliveries weight or payload from one central depot. The solutions obtained are robust, as shown in a comprehensive sensitivity analysis.", "keywords": [ "Drone", "Computer science", "Mathematical optimization", "Operations research", "Mathematics", "Biology" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejtl.2024.100127" }, { "id": "https://openalex.org/W4407561019", "doi": "10.3390/app15042007", "title": "Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing", "year": 2025, "publication_date": "2025-02-14", "type": "article", "language": "en", "authors": [ "Aigerim Bogyrbayeva", "Bissenbay Dauletbayev", "Meraryslan Meraliyev" ], "first_author": "Aigerim Bogyrbayeva", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Many exact algorithms, heuristics, and metaheuristics have been proposed to solve the Vehicle Routing Problem with Drones, which involves using a fleet of trucks and drones to fulfil customer orders in last-mile delivery. In this study, the problem is formulated using the Markov Decision Process, and a Reinforcement Learning (RL) based solution is proposed. The proposed RL model is based on an attention-encoder and a recurrent neural network-decoder architecture. This approach enhances coordination by determining which vehicles should visit specific customers and where vehicles can rendezvous, effectively leveraging drones and reducing the overall completion time. The RL model has demonstrated competitive performance compared to benchmark algorithms through extensive experiments.", "keywords": [ "Reinforcement learning", "Computer science", "Drone", "Artificial intelligence", "Biology" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/15/4/2007/pdf?version=1739532726" }, { "id": "https://openalex.org/W4409710710", "doi": "10.3390/drones9040280", "title": "Integrating Autonomous Vehicles and Drones for Last-Mile Delivery: A Routing Problem with Two Types of Drones and Multiple Visits", "year": 2025, "publication_date": "2025-04-07", "type": "article", "language": "en", "authors": [ "Jili Kong", "Minhui Xie", "Hao Wang" ], "first_author": "Jili Kong", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the growing demand for delivery services and the escalating labor costs, much effort has been made to achieve faster and cost-efficient delivery. A promising emerging strategy involves the integration of autonomous delivery vehicles or drones into the last-mile delivery. This study presents a fully automated last-mile delivery system that synergistically integrates autonomous vehicles and drones. We also introduce a novel variant of the vehicle routing problem with drones, referred to as the hybrid autonomous vehicle-drone routing problem (HAVDRP). In HAVDRP, we employ three delivery tools: autonomous vehicles, vehicle-carried drones, and independent drones. The aim is to fully leverage the advantages of autonomous vehicles and drones to provide customers with more efficient last-mile delivery services. An improved adaptive large neighborhood search algorithm is developed to address this problem. The algorithm incorporates a tabu list and an adaptive mechanism specifically designed for the HAVDRP, thereby augmenting the search efficiency. Computational experiments are conducted to evaluate the efficiency of the designed algorithm. Additionally, sensitivity analyses are conducted to explore the influences of some key parameters on the total time, which includes the cumulative working time of autonomous vehicles and drones. Based on the results of sensitivity analyses, we propose some management recommendations for the fully automated last-mile delivery system utilizing autonomous vehicles and drones.", "keywords": [ "Drone", "Mile", "Routing (electronic design automation)", "Last mile (transportation)", "Computer science", "Aeronautics", "Engineering", "Computer network", "Geography", "Biology" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/4/280/pdf?version=1744183678" }, { "id": "https://openalex.org/W4323309378", "doi": "10.1080/01605682.2023.2185548", "title": "Truck and rotary-wing drone routing problem considering flight-level selection", "year": 2023, "publication_date": "2023-03-06", "type": "article", "language": "en", "authors": [ "Hongqi Li", "Feilong Wang", "Zhuopeng Zhan" ], "first_author": "Hongqi Li", "venue": "Journal of the Operational Research Society", "publisher": "Palgrave Macmillan", "abstract": "Last-mile logistics is one of the most promising areas for rotary-wing drone (RWD) applications. In the literature, routing variants that use drones in collaboration with trucks assume that both drones and trucks move along arcs in two-dimensional networks. There is a need for methodological innovations to cater to more practical and general scenarios involving flight-level decisions of RWDs in the three-dimensional network. In this study, flight level is defined as the specified height at which an RWD is allowed to fly. We introduce the truck and RWD routing problem considering flight-level selection and time windows (TDRP-FT). The TDRP-FT is characterized by RWD flight-level selection, time windows, direct delivery, multiple trucks, and multiple RWDs carried by each truck. We develop a mixed-integer linear programming model and propose a hybrid algorithm based on a greedy randomized adaptive search procedure and an adaptive large neighborhood search. We experimentally evaluate the effectiveness of the TDRP-FT model and the applicability of the hybrid algorithm to small-, medium- and large-scale instances. The computational experiment results indicate that the TDRP-FT model can be used to determine how flight levels of RWDs should be selected.", "keywords": [ "Drone", "Truck", "Aeronautics", "Selection (genetic algorithm)", "Computer science", "Scheduling (production processes)", "Stall (fluid mechanics)", "Operations research", "Aerospace engineering", "Automotive engineering", "Engineering", "Operations management" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/01605682.2023.2185548" }, { "id": "https://openalex.org/W4402532571", "doi": "10.20944/preprints202409.0886.v1", "title": "A Survey of the Routing Problem for Cooperated Trucks and Drones", "year": 2024, "publication_date": "2024-09-12", "type": "preprint", "language": "en", "authors": [ "Shuo Dang", "Yao Liu", "Zhihao Luo", "Zhong Liu", "Jianmai Shi" ], "first_author": "Shuo Dang", "venue": "Preprints.org", "publisher": null, "abstract": "The emerging working mode of coordinated trucks and drones has demonstrated significant practical potential in various fields, including logistics and delivery, intelligence surveillance reconnaissance, area monitoring, and patrol. The seamless collaboration between trucks and drones is garnering widespread attention in academia and has emerged as a key technology for achieving efficient and secure transportation. This paper provides a comprehensive and in-depth review of the research status on the routing problem for coordinated trucks and drones, covering aspects such as application background, cooperative modes, configurations, issues have been taken into consideration, and solution methodologies.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Key (lock)", "Computer science", "Transport engineering", "Mode (computer interface)", "Operations research", "Computer security", "Engineering", "Computer network", "Aerospace engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.20944/preprints202409.0886.v1" }, { "id": "https://openalex.org/W4412452768", "doi": "10.1016/j.cie.2025.111388", "title": "Drone on-demand delivery routing problem considering order splitting and battery swapping", "year": 2025, "publication_date": "2025-07-15", "type": "article", "language": "en", "authors": [ "Shuxuan Li", "Tianjun Liao", "Guohua Wu", "Yalin Wang", "Ponnuthurai Nagaratnam Suganthan" ], "first_author": "Shuxuan Li", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Order (exchange)", "Battery (electricity)", "Routing (electronic design automation)", "Computer science", "Computer network", "Engineering", "Business", "Physics", "Biology", "Finance" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111388" }, { "id": "https://openalex.org/W4401327244", "doi": "10.3390/drones8080373", "title": "Drone Arc Routing Problems and Metaheuristic Solution Approach", "year": 2024, "publication_date": "2024-08-03", "type": "article", "language": "en", "authors": [ "İslam Altın", "Aydın Sipahioğlu" ], "first_author": "İslam Altın", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The drone arc routing problem (DARP) is one of the arc routing problems (ARPs) that has been studied by researchers recently. Unlike traditional ARPs, drones can travel directly between any two points in the graph. Due to the flexibility of drones, it is possible to use edges not defined in the graphs when deadheading the edges. This advantage of drones makes this problem more challenging than any other ARP. With this study, the energy capacities of drones are considered in a DARP. Thus, a novel DARP called the drone arc routing problem with deadheading demand (DARP-DD) is addressed in this study. Drone capacities are used both when servicing the edges and when deadheading the edges in the DARP-DD. A special case of the DARP-DD, called the multiple service drone arc routing problem with deadheading demand (MS-DARP-DD), is also discussed, where some critical required edges may need to be served more than once. To solve these challenging problems, a simulated annealing algorithm is used, and the components of the algorithm are designed. Additionally, novel neighbor search operators are developed in this study. The computational results show that the proposed algorithm and its components are effective and useful in solving the DARP-DD and MS-DARP-DD.", "keywords": [ "Arc routing", "Drone", "Computer science", "Routing (electronic design automation)", "Simulated annealing", "Vehicle routing problem", "Mathematical optimization", "Mathematics", "Algorithm", "Computer network" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones8080373" }, { "id": "https://openalex.org/W3048391685", "doi": "10.4018/978-1-7998-3805-0.ch008", "title": "Mixed Delivery and Pickup Vehicle Routing Problem With Limited Flow and Assignment of Drones in an Urban Network", "year": 2020, "publication_date": "2020-08-12", "type": "book-chapter", "language": "en", "authors": [ "Hamdi Radhoui", "Atour Taghipour", "Béatrice Canel-Depitre" ], "first_author": "Hamdi Radhoui", "venue": "Advances in logistics, operations, and management science book series", "publisher": "Routledge", "abstract": "A new variant of the delivery and pickup transportation problem called mixed delivery and pickup routing problem with unmanned aerial vehicles in case of limited flow is introduced. The objective is to minimize operational costs including total transportation costs and service time at each point. This variant is a solution for the urban congestion, and consequently, it is an improvement of the general transport system. First, the problem is formulated mathematically. It is considered as NP-hard; therefore, the authors proposed an iterated local search algorithm to solve the problem of mixed pickup and delivery without drone. Then, a vehicle first-drone second algorithm is used to solve the mixed delivery and pickup problem with drone. The performance of the method is compared through numerical experiments based on instance derived from the literature as well as on a set of randomly generated instances. Numerical results have shown that proposed metaheuristic method performs consistently well in terms of both the quality of the solution and the computational time when using drone with vehicle.", "keywords": [ "Pickup", "Drone", "Iterated local search", "Vehicle routing problem", "Mathematical optimization", "Computer science", "Routing (electronic design automation)", "Metaheuristic", "Mathematics", "Computer network", "Artificial intelligence" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/978-1-7998-3805-0.ch008" }, { "id": "https://openalex.org/W2617499418", "doi": "10.1007/978-3-319-60285-1_4", "title": "A Cost-Optimization Model in Multi-agent System Routing for Drone Delivery", "year": 2017, "publication_date": "2017-01-01", "type": "conference-paper", "language": "en", "authors": [ "Miae Kim", "Eric T. Matson" ], "first_author": "Miae Kim", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Context (archaeology)", "Path (computing)", "Distributed computing", "Real-time computing", "Computer network" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-319-60285-1_4" }, { "id": "https://openalex.org/W4399575423", "doi": "10.1504/ijstl.2024.139062", "title": "Vehicle routing problem with drones as an aid for epidemic relief", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Shih Hao Lu", "Marco Fabio Benaglia", "Nguyen Anh Tu", "Edgar Rojas Rivera", "Jen Wei Cheng" ], "first_author": "Shih Hao Lu", "venue": "International Journal of Shipping and Transport Logistics", "publisher": "Inderscience Publishers", "abstract": "In response to epidemics, especially if vaccines and cures are not readily available, governments issue lockdowns and impose social distancing. Under such circumstances, to avoid potentially dangerous person-to-person contacts, drones can replace people in performing functions like delivering supplies, collecting swabs, measuring human body temperatures, etc. This paper introduces a new type of vehicle routing problem with multiple drones for epidemic relief (VRP-mD_ER), defined by a MILP mathematical model solved to near-optimality through the combined application of the 'single-linkage' (S-LINK) algorithm, and a heuristic based on greedy randomised adaptive search procedure (GRASP) and genetic algorithm (GA). The proposed model helps reduce the total service time by 15%-19%, to deliver critical supplies quickly, and make relief operations more efficient and cost-effective by saving up to 59% of variable costs. Additionally, we provide a list of optimal combinations of trucks and drones to minimise delivery time and costs, and a sensitivity analysis.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Operations research", "Business", "Computer network", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijstl.2024.139062" }, { "id": "https://openalex.org/W2931293404", "doi": "10.1109/mnet.2019.1800222", "title": "When UAV Swarm Meets Edge-Cloud Computing: The QoS Perspective", "year": 2019, "publication_date": "2019-03-01", "type": "article", "language": "en", "authors": [ "Wuhui Chen", "Baichuan Liu", "Huawei Huang", "Song Guo", "Zibin Zheng" ], "first_author": "Wuhui Chen", "venue": "IEEE Network", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In this article, we propose a hybrid computing model, UAV-Edge-Cloud, bringing edge/cloud computing and UAV swarm together to achieve high quality of service (QoS) guarantees. First, we design this novel hybrid computing framework to provide powerful resources to support resource-intensive applications and real-time tasks at edge networks. Next, we discuss some potential applications for smart cities and raise open research issues of the proposed hybrid framework. We then study a joint task placement and routing problem for latency-critical applications as a case study. Finally, the simulation results show that our approach can improve the QoS of UAV swarms effectively.", "keywords": [ "Computer science", "Cloud computing", "Quality of service", "Distributed computing", "Edge computing", "Enhanced Data Rates for GSM Evolution", "Latency (audio)", "Swarm behaviour", "Computer network", "Artificial intelligence", "Operating system", "Telecommunications" ], "cited_by_count": 186, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mnet.2019.1800222" }, { "id": "https://openalex.org/W3148439643", "doi": "10.1016/j.phycom.2021.101337", "title": "UAVs path planning architecture for effective medical emergency response in future networks", "year": 2021, "publication_date": "2021-04-06", "type": "article", "language": "en", "authors": [ "Sara Imran Khan", "Zakria Qadir", "Hafiz Suliman Munawar", "Soumya Ranjan Nayak", "Anil Kumar Budati", "Kritika Verma", "Deo Prakash" ], "first_author": "Sara Imran Khan", "venue": "Physical Communication", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Ant colony optimization algorithms", "Particle swarm optimization", "Emergency vehicle", "Vehicle routing problem", "Path (computing)", "Genetic algorithm", "Routing (electronic design automation)", "Drone", "Motion planning", "Real-time computing", "Domain (mathematical analysis)" ], "cited_by_count": 105, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.phycom.2021.101337" }, { "id": "https://openalex.org/W4327781373", "doi": "10.1109/ickecs56523.2022.10060807", "title": "Solving Vehicle Routing Problem for Intelligent Systems using Delaunay Triangulation", "year": 2022, "publication_date": "2022-12-28", "type": "conference-paper", "language": "en", "authors": [ "M. Sakthivel", "Shashi Kant Gupta", "D.A. Karras", "Alex Khang", "Chandra Kumar Dixit", "Bhadrappa Haralayya" ], "first_author": "M. Sakthivel", "venue": null, "publisher": null, "abstract": "Intelligent Systems are of many types. This work focuses mainly about the autonomous drones and UAVs. Only for military objectives were Unmanned Aerial Vehicles (UAV) invented and developed. They have lately increased in use, nevertheless, in the civil applications this study shall examine. This technology has a wide range of applications, including in agriculture, environmental protection, public safety, and traffic flow management. The use of unmanned aerial vehicles (UAVs) in smart cities is another promising new use for this technology. By 2050, the world's population is expected to have doubled. As a result, towns and communities face new difficulties and possibilities as a result of these expectations. For any smart city design, efficiency and cost-effectiveness are the primary objectives. After the global economic downturn, people's curiosity for smart cities has grown exponentially. Unmanned aerial vehicles' (UAVs') to discover the optimal route between a source and a destination, vehicle routing is a crucial field for study. There are a number of crucial considerations to be made in order to address these challenges in vehicle routing for UAVs. UAVs must be able to see where they are in relation to the map or graph in order to make these judgments. Planned routes for unmanned aerial vehicles are not only designed to minimize the risk of collisions, but they are also designed to be as efficient as possible. To get at the end destination in the least amount of time feasible, solution for vehicle routing problems is essential. In this work, we provide a solution to the vehicle routing issue in the form of an algorithm. The use of Delaunay triangulation is done to achieve the shortest path for the intelligent systems from source to destination. The experimental findings demonstrate that the Lin-Kernighan technique performs better than alternative algorithms for addressing the vehicle routing issue.", "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Resilience (materials science)", "Population", "Transport engineering", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ickecs56523.2022.10060807" }, { "id": "https://openalex.org/W2810024636", "doi": "10.1109/iciea.2018.8397948", "title": "Unmanned aerial vehicle route planning method based on a star algorithm", "year": 2018, "publication_date": "2018-05-01", "type": "conference-paper", "language": "en", "authors": [ "Tianyou Chen", "Guofeng Zhang", "Xiaoguang Hu", "Jin Xiao" ], "first_author": "Tianyou Chen", "venue": null, "publisher": null, "abstract": "On the basis of studying and analyzing the basic principles of A star algorithm, this paper compares its advantages and disadvantages with those of genetic algorithm and ant colony algorithm. In the meantime, the paper builds the model of unmanned aerial vehicle(UAV) battlefield environment for route planning and applies the A star algorithm to UAV route planning under the constraints of bearing the least danger and consuming the least fuel. Finally, the paper uses python programming language to simulate the UAV route planning, whose result shows the validity of the model and the feasibility of the algorithm.", "keywords": [ "Computer science", "A* search algorithm", "Python (programming language)", "Battlefield", "Route planning", "Star (game theory)", "Algorithm", "Genetic algorithm", "Ant colony optimization algorithms", "Drone", "Artificial intelligence", "Mathematical optimization" ], "cited_by_count": 61, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iciea.2018.8397948" }, { "id": "https://openalex.org/W4388596637", "doi": "10.1007/s10732-023-09520-z", "title": "A hybrid variable neighborhood search heuristic for the sustainable time-dependent truck-drone routing problem with rendezvous locations", "year": 2023, "publication_date": "2023-11-11", "type": "article", "language": "en", "authors": [ "Ebrahim Teimoury", "Reza Rashid" ], "first_author": "Ebrahim Teimoury", "venue": "Journal of Heuristics", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Travelling salesman problem", "Rendezvous", "Computer science", "Routing (electronic design automation)", "Heuristic", "Vehicle routing problem", "Variable (mathematics)", "Variable neighborhood search", "Operations research", "Transport engineering" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10732-023-09520-z" }, { "id": "https://openalex.org/W4285186437", "doi": "10.1109/tvt.2022.3179732", "title": "Congestion-Aware Multi-Drone Delivery Routing Framework", "year": 2022, "publication_date": "2022-06-02", "type": "article", "language": "en", "authors": [ "Seonhoon Lee", "Dooyoung Hong", "Jaemin Kim", "Donkyu Baek", "Naehyuck Chang" ], "first_author": "Seonhoon Lee", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Drones have been attracting the attention of diverse industries thanks to their superior maneuverability. Logistics companies especially keep trying to utilize drones for fast delivery following the growing market size of e-commerce. Accordingly, methods for safely operating multi-drone have been researched, and many researchers have proposed various optimal or near-optimal routing methods. However, such methods have some problems that cause routing failures or huge routing computation time in a drone-dense space due to many collisions. In this paper, we propose a centralized framework that deals with enormous collisions and obtains collision-free paths rapidly. We first build a drone energy consumption model with a data-driven method using flight experiment data of a commercial drone to estimate the drone battery state-of-charge (SoC). Then, we develop a novel routing method that generates collision-free paths by considering both the congestion of the space and the SoC of each drone. The proposed method is inspired by the VLSI circuit routing method that connects all signal nets among thousands of logic components. Through numerous delivery routing simulations, we confirm that the proposed method achieves a maximum of 6 times higher routing success rate with a 10x faster runtime compared with the state-of-the-art optimal method. In addition, we validate that the proposed method is applicable to delivery routing problems with various drone battery capacities.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Static routing", "Routing table", "Multipath routing", "Computer network", "Routing protocol" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2022.3179732" }, { "id": "https://openalex.org/W4391180110", "doi": "10.1016/j.cor.2024.106559", "title": "Exact solution method for vehicle-and-drone cooperative delivery routing of blood products", "year": 2024, "publication_date": "2024-01-24", "type": "article", "language": "en", "authors": [ "Yunqiang Yin", "Ling Qing", "Dujuan Wang", "T.C.E. Cheng", "Joshua Ignatius" ], "first_author": "Yunqiang Yin", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Product (mathematics)", "Operations research", "Integer programming", "Set (abstract data type)", "Mathematical optimization", "Computer network", "Mathematics", "Algorithm" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2024.106559" }, { "id": "https://openalex.org/W4413439129", "doi": "10.1016/j.eswa.2025.129196", "title": "Multi-objective hyper heuristic memetic algorithm for time-dependent vehicle routing problem with drone considering simultaneous pickup and delivery", "year": 2025, "publication_date": "2025-08-22", "type": "article", "language": "en", "authors": [ "Haohao Duan", "Xiaoling Li", "Yanxiang Feng", "Guanghui Zhang", "Qing-Chang Lu", "Xu Meng" ], "first_author": "Haohao Duan", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pickup", "Memetic algorithm", "Drone", "Vehicle routing problem", "Computer science", "Heuristic", "Mathematical optimization", "Routing (electronic design automation)", "Local search (optimization)", "Algorithm", "Artificial intelligence", "Mathematics" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2025.129196" }, { "id": "https://openalex.org/W4391944067", "doi": "10.1016/j.eswa.2024.123472", "title": "Ensemble multi-objective optimization approach for heterogeneous drone delivery problem", "year": 2024, "publication_date": "2024-02-19", "type": "article", "language": "en", "authors": [ "Xupeng Wen", "Guohua Wu", "Shuanglin Li", "Ling Wang" ], "first_author": "Xupeng Wen", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Crossover", "Pareto principle", "Heuristic", "Cluster analysis", "Genetic algorithm", "Multi-objective optimization", "Payload (computing)", "Benchmark (surveying)", "Mathematical optimization", "Machine learning" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2024.123472" }, { "id": "https://openalex.org/W4377090877", "doi": "10.3390/systems11050259", "title": "A Hybrid Metaheuristic Solution Method to Traveling Salesman Problem with Drone", "year": 2023, "publication_date": "2023-05-19", "type": "article", "language": "en", "authors": [ "Noyan Sebla Günay", "Emre Çakmak", "Serol Bulkan" ], "first_author": "Noyan Sebla Günay", "venue": "Systems", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The challenging idea of using drones in last-mile delivery systems of logistics addresses a new routing problem referred to as the traveling salesman problem with drone (TSP-D). TSP-D aims to construct a route to deliver parcels to a set of customers by either a truck or a drone, thereby minimizing operational costs. Since TSP-D is considered NP-hard, using metaheuristics is one of the most promising solutions. This paper presents a hybrid metaheuristic solution method of TSP-D based on two state-of-the-art algorithms: the genetic algorithm and ant colony optimization algorithm. Heuristics in TSP-D literature are based on two consequent decisions: truck routing and drone assignment. Unlike those in the existing literature, the proposed metaheuristic constructs both truck and drone routes simultaneously. Additionally, to the best of our knowledge, we introduce for the first time a solution method on the basis of an ant colony optimization approach to TSP-D. Additionally, we propose a binary pheromone framework for both drone and truck, diverging from the traditional pheromone structure. Computational experiments indicate that the proposed hybrid metaheuristic algorithm is able to generate optimal routes for provided instances of TSP-D benchmarking. In addition, the algorithm improves the best-known solutions of some instances found by rival heuristics.", "keywords": [ "Metaheuristic", "Drone", "Travelling salesman problem", "Ant colony optimization algorithms", "Computer science", "Heuristics", "Mathematical optimization", "Vehicle routing problem", "Genetic algorithm", "Routing (electronic design automation)", "Ant colony", "Parallel metaheuristic" ], "cited_by_count": 31, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-8954/11/5/259/pdf?version=1684492686" }, { "id": "https://openalex.org/W4403827832", "doi": "10.1016/j.cie.2024.110659", "title": "Delivery optimization for collaborative truck–drone routing problem considering vehicle obstacle avoidance", "year": 2024, "publication_date": "2024-10-28", "type": "article", "language": "en", "authors": [ "Fanhui Kong", "Bin Jiang" ], "first_author": "Fanhui Kong", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Obstacle avoidance", "Vehicle routing problem", "Obstacle", "Routing (electronic design automation)", "Computer science", "Aeronautics", "Engineering", "Transport engineering", "Automotive engineering", "Computer network" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2024.110659" }, { "id": "https://openalex.org/W4415395950", "doi": "10.1016/j.trb.2025.103335", "title": "A dynamic drone routing problem with uncertain demand and energy consumption", "year": 2025, "publication_date": "2025-10-21", "type": "article", "language": "en", "authors": [ "Guilherme Oliveira Chagas", "Leandro Callegari Coelho", "Demetrio Laganà", "Patrizia Beraldi" ], "first_author": "Guilherme Oliveira Chagas", "venue": "Transportation Research Part B Methodological", "publisher": "Elsevier BV", "abstract": "This work addresses a drone routing problem with an identical fleet performing same-day deliveries in a dynamic and uncertain environment. We model the problem as a Markov Decision Process to capture the stochastic nature of customer demand and the uncertainty in energy consumption due to varying payloads and weather conditions. To tackle this problem, we propose an approximate dynamic algorithm that integrates routing planning, drone usage, and battery management. Uncertainty in energy consumption is dealt with the chance constraints ensuring that drone trips are completed safely, preventing premature returns to the depot. The proposed approach features a cost function approximation policy that accounts for a restricted number of trips to be assigned to drones. This ensures that the drones are ready at the depot to fulfill new requests that may arise during the day. Extensive computational experiments on 300 instances validate the effectiveness of our method, demonstrating its superiority over a myopic strategy, a policy function approximation approach, and an oracle method, thus highlighting its potential for practical applications.", "keywords": [ "Drone", "Energy consumption", "Routing (electronic design automation)", "Consumption (sociology)", "Energy (signal processing)", "Dynamic programming", "Vehicle routing problem" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trb.2025.103335" }, { "id": "https://openalex.org/W4393119409", "doi": "10.1111/itor.13457", "title": "The multidepot drone general routing problem with duration and capacity constraints", "year": 2024, "publication_date": "2024-03-23", "type": "article", "language": "en", "authors": [ "Teresa Corberán", "Isaac Plana", "José María Sanchis", "Paula Segura" ], "first_author": "Teresa Corberán", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Abstract This paper studies the multidepot drone general routing problem with duration and capacity constraints (MDdGRP), an extension of the classical general routing problem with several depots. A fleet of drones with limited flight time and payload, each one located in a different depot, must jointly perform the service. In this continuous optimization problem, a set of lines of a network has to be serviced and a set of nodes has to be visited to deliver a given commodity. The aim is to find a set of drone routes performing the complete service with the shortest total duration. Aerial drones can enter a line at any point, service a portion of that line, and exit through another point, without following the lines of the network. While this flexibility allows for potentially better solutions, it also adds complexity to the problem. To deal with this, we digitize the MDdGRP instances by approximating each line with a polygonal chain containing a finite number of points that can be used to enter and exit each line. Thus, an instance of a discrete multidepot general routing problem (MDGRP) is obtained. This paper proposes an integer formulation for the MDGRP, some families of valid inequalities, and a branch‐and‐cut algorithm based on the strengthened formulation. Two matheuristic algorithms are presented, focused on sequentially selecting (and adding) some promising intermediate points to enter and exit a line. Instances involving up to six depots, 22 delivery points, and 88 original lines have been generated to test the performance of the methods proposed.", "keywords": [ "Drone", "Duration (music)", "Computer science", "Vehicle routing problem", "Operations research", "Routing (electronic design automation)", "Mathematical optimization", "Mathematics", "Computer network" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13457" }, { "id": "https://openalex.org/W4387331274", "doi": "10.1002/net.22190", "title": "Last‐mile delivery with drone and lockers", "year": 2023, "publication_date": "2023-10-04", "type": "article", "language": "en", "authors": [ "Marco Antonio Boschetti", "Stefano Novellani" ], "first_author": "Marco Antonio Boschetti", "venue": "Networks", "publisher": "Wiley", "abstract": "Abstract In this article, we define a new routing problem that arises in the last‐mile delivery of parcels, in which customers can be served either directly at home by a capacitated truck, or possibly with a drone carried on the truck, or in a self‐service mode using one of the available lockers. We investigate four different formulations, and for one of them, we propose a branch‐and‐cut approach. We also discuss some possible variants of the original problem. In the computational experiments, we analyze and compare the performance of the four formulations for the problem and its variants, and we provide some useful managerial insights.", "keywords": [ "Drone", "Truck", "Computer science", "Last mile (transportation)", "Vehicle routing problem", "Routing (electronic design automation)", "Service (business)", "Operations research", "Mile", "Computer network", "Mathematics", "Business" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://cris.unibo.it/bitstream/11585/945394/4/TSP_with_Drone_and_Lockers_Final_nonetwroks.pdf" }, { "id": "https://openalex.org/W2007016814", "doi": "10.1142/s230138501450006x", "title": "A Hierarchical Market Solution to the Min–Max Multiple Depots Vehicle Routing Problem", "year": 2014, "publication_date": "2014-01-01", "type": "article", "language": "en", "authors": [ "Elad H. Kivelevitch", "Balaji R. Sharma", "Nicholas D. Ernest", "Manish Kumar", "Kelly De Oliveira Cohen" ], "first_author": "Elad H. Kivelevitch", "venue": "Unmanned Systems", "publisher": "World Scientific", "abstract": "The problem of assigning a group of Unmanned Aerial Vehicles (UAVs) to perform spatially distributed tasks often requires that the tasks will be performed as quickly as possible. This problem can be defined as the Min–Max Multiple Depots Vehicle Routing Problem (MMMDVRP), which is a benchmark combinatorial optimization problem. In this problem, UAVs are assigned to service tasks so that each task is serviced once and the goal is to minimize the longest tour performed by any UAV in its motion from its initial location (depot) to the tasks and back to the depot. This problem arises in many time-critical applications, e.g. mobile targets assigned to UAVs in a military context, wildfire fighting, and disaster relief efforts in civilian applications. In this work, we formulate the problem using Mixed Integer Linear Programming (MILP) and Binary Programming and show the scalability limitation of these formulations. To improve scalability, we propose a hierarchical market-based solution (MBS). Simulation results demonstrate the ability of the MBS to solve large scale problems and obtain better costs compared with other known heuristic solution.", "keywords": [ "Scalability", "Computer science", "Vehicle routing problem", "Integer programming", "Task (project management)", "Mathematical optimization", "Context (archaeology)", "Linear programming", "Benchmark (surveying)", "Heuristic", "Routing (electronic design automation)", "Distributed computing" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s230138501450006x" }, { "id": "https://openalex.org/W4414808908", "doi": "10.1016/j.ejor.2025.09.021", "title": "Vehicle routing problem with drones and variable service times for agricultural virus monitoring", "year": 2025, "publication_date": "2025-10-04", "type": "article", "language": "en", "authors": [ "Yuxin Li", "Yuxin Li", "Yu Zhang", "Yanfeng Li", "Yanfeng Li", "Xingdong Zhu" ], "first_author": "Yuxin Li", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Profit (economics)", "Drone", "Variable (mathematics)", "Truck", "Vehicle routing problem", "Scheduling (production processes)", "Heuristic", "Variable neighborhood search" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2025.09.021" }, { "id": "https://openalex.org/W4399457434", "doi": "10.1016/j.cor.2024.106723", "title": "A mathematical formulation and a tabu search heuristic for the joint vessel-UAV routing problem", "year": 2024, "publication_date": "2024-06-08", "type": "article", "language": "en", "authors": [ "Yantong Li", "Shengjie Wang", "Shengjie Wang", "Shanshan Zhou", "Zheng Wang" ], "first_author": "Yantong Li", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Tabu search", "Mathematical optimization", "Routing (electronic design automation)", "Computer science", "Heuristic", "Joint (building)", "Guided Local Search", "Mathematics", "Engineering", "Computer network", "Structural engineering" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2024.106723" }, { "id": "https://openalex.org/W4398776737", "doi": "10.1007/s10479-024-06019-z", "title": "Vehicle routing with multiple UAVs for the last-mile logistics distribution problem: hybrid distributed optimization", "year": 2024, "publication_date": "2024-05-24", "type": "article", "language": "en", "authors": [ "Abdeljawed Sadok", "Jalel Euchi", "Patrick Siarry" ], "first_author": "Abdeljawed Sadok", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Vehicle routing problem", "Last mile (transportation)", "Mile", "Computer science", "Routing (electronic design automation)", "City logistics", "Distribution (mathematics)", "Transport engineering", "Computer network", "Engineering", "Geography", "Mathematics" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10479-024-06019-z" }, { "id": "https://openalex.org/W4406752813", "doi": "10.1016/j.trd.2025.104610", "title": "Optimizing drone logistics in complex urban industrial infrastructure", "year": 2025, "publication_date": "2025-01-23", "type": "article", "language": "en", "authors": [ "Tomasz Dudek", "Karolina Kaśkosz" ], "first_author": "Tomasz Dudek", "venue": "Transportation Research Part D Transport and Environment", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Business", "City logistics", "Transport engineering", "Critical infrastructure", "Engineering", "Aeronautics", "Computer science", "Computer security" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trd.2025.104610" }, { "id": "https://openalex.org/W4401414828", "doi": "10.1109/icra57147.2024.10611698", "title": "Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms", "year": 2024, "publication_date": "2024-05-13", "type": "conference-paper", "language": "en", "authors": [ "Akash Kopparam Sreedhara", "Deepesh Padala", "Shashank Mahesh", "Kai Cui", "Mengguang Li", "Heinz Koeppl" ], "first_author": "Akash Kopparam Sreedhara", "venue": null, "publisher": null, "abstract": "Swarms of aerial drones have recently been considered for last-mile deliveries in urban logistics or automated construction. At the same time, collaborative transportation of payloads by multiple drones is another important area of recent research. However, efficient coordination algorithms for collaborative transportation of many payloads by many drones remain to be considered. In this work, we formulate the collaborative transportation of payloads by a swarm of drones as a novel, under-capacitated generalization of vehicle routing problems (VRP), which may also be of separate interest. In contrast to standard VRP and capacitated VRP, we must additionally consider waiting times for payloads lifted cooperatively by multiple drones, and the corresponding coordination. Algorithmically, we provide a solution encoding that avoids deadlocks and formulate an appropriate alternating minimization scheme to solve the problem. On the hardware side, we integrate our algorithms with collision avoidance and drone controllers. The approach and the impact of the system integration are successfully verified empirically, both on a swarm of real nano-quadcopters and for large swarms in simulation. Overall, we provide a framework for collaborative transportation with aerial drone swarms, that uses only as many drones as necessary for the transportation of any single payload.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Transport engineering", "Aeronautics", "Computer network", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icra57147.2024.10611698" }, { "id": "https://openalex.org/W4385462098", "doi": "10.1016/j.eswa.2023.120992", "title": "A new hybrid distribution paradigm: Integrating drones in medicines delivery", "year": 2023, "publication_date": "2023-08-01", "type": "article", "language": "en", "authors": [ "Tânia Rodrigues Pereira Ramos", "Daniele Vigo" ], "first_author": "Tânia Rodrigues Pereira Ramos", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": "This paper analyses a new hybrid paradigm resulting from the integration of unmanned aerial vehicles (UAV), commonly referred to as drones, in logistics and distribution processes. This work is motivated by a real application, where the company Connect Robotics, the first drone delivery provider in Portugal, made a partnership with a pharmacy located at a rural region to start implementing the delivery of medicines by drone. The pharmacy receives orders throughout the day and has to deliver in the same day with tight lead-times. The resulting problem is modelled as a Dynamic Parallel Drone Scheduling Vehicle Routing Problem with Lead-Time. A solution method is devised to solve it, thus helping the pharmacist to plan the car and drone delivery routes during the day. The results obtained on real instances revealed that the solution method is effective when compared to the optimal solutions of the static version of the problem, since the dynamic solution only differs, on average, about 7% from the static one. Moreover, some managerial insights about the impact of adding drones to the distribution operation are discussed, namely the economic and environmental impacts with cost savings up to 41% and reduction of monthly CO2 emissions of 310 kg, the use of spare batteries which increase the benefit from 16% to 41%, and same-day versus next-day delivery.", "keywords": [ "Drone", "Spare part", "Computer science", "Scheduling (production processes)", "Robotics", "General partnership", "Operations research", "Distribution (mathematics)", "Robot", "Artificial intelligence", "Operations management", "Business" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2023.120992" }, { "id": "https://openalex.org/W2011272678", "doi": "10.1007/s10846-012-9778-2", "title": "UAV Path Planning for Structure Inspection in Windy Environments", "year": 2012, "publication_date": "2012-09-06", "type": "article", "language": "en", "authors": [ "J. A. Guerrero", "Yasmina Bestaoui" ], "first_author": "J. A. Guerrero", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Travelling salesman problem", "Computer science", "Path (computing)", "Simple (philosophy)", "Algorithm", "Motion planning", "Routing (electronic design automation)", "Mathematical optimization", "Artificial intelligence", "Mathematics", "Robot" ], "cited_by_count": 113, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10846-012-9778-2" }, { "id": "https://openalex.org/W4413913983", "doi": "10.1016/j.ejor.2025.08.057", "title": "The stochastic location-routing problem with parallel truck–drone operations for humanitarian aid delivery", "year": 2025, "publication_date": "2025-09-02", "type": "article", "language": "en", "authors": [ "Hannan Tureci-Isik", "Melih Çelik", "Ece Sancı" ], "first_author": "Hannan Tureci-Isik", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": "Timely response in the aftermath of a disaster is crucial to alleviate loss of life and suffering. Timeliness of relief may be hampered by road network disruptions caused by the disaster, such as damage to road segments or debris covering the roads. The use of drones simultaneously with trucks can potentially help overcome issues around network disruptions and achieve more timely delivery of post-disaster aid. In an effort to shed more light into this potential, we address the problem of network design for parallel truck–drone operations by depot location prior to the disaster and routing of the vehicles in its aftermath. We incorporate the uncertainty on network disruption by modelling this problem as a two-stage stochastic program, which proves computationally challenging to solve to optimality for real-life disaster scenarios. Consequently, we propose a tailored heuristic based on variable neighbourhood search to find high-quality solutions efficiently. Our computational results on randomly generated instances and a case study from the 2011 Van Earthquake in Turkiye demonstrate the effectiveness of the heuristic, the benefits of employing both trucks and drones, and the significance of accounting for uncertainties in pre-disaster planning. • Network design and for a parallel truck–drone routing to deliver humanitarian aid. • Considers uncertain travel times due to network vulnerability and latency objective. • Introduces a flow-based formulation for sparse networks lacking triangle inequality. • Develops a tailored VNS for scenario-specific depot location and routing. • Applies model on a case study to draw managerial insights.", "keywords": [ "Drone", "Vehicle routing problem", "Truck", "Computer science", "Routing (electronic design automation)", "Operations research", "Mathematical optimization", "Computer network", "Engineering", "Mathematics", "Automotive engineering" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2025.08.057" }, { "id": "https://openalex.org/W4410252316", "doi": "10.1016/j.cor.2025.107104", "title": "A branch-price-and-cut algorithm for the time-dependent multiple truck–drone routing problem", "year": 2025, "publication_date": "2025-05-10", "type": "article", "language": "en", "authors": [ "Xiaoning Zang", "Li Jiang", "Changyong Liang", "Xiang Fang" ], "first_author": "Xiaoning Zang", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Branch and price", "Branch and cut", "Routing algorithm", "Mathematical optimization", "Algorithm", "Integer programming", "Mathematics" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2025.107104" }, { "id": "https://openalex.org/W2999980602", "doi": "10.3390/s20020515", "title": "UAV Mission Planning Resistant to Weather Uncertainty", "year": 2020, "publication_date": "2020-01-16", "type": "article", "language": "en", "authors": [ "Amila Thibbotuwawa", "Grzegorz Bocewicz", "Grzegorz Radzki", "Peter Nielsen", "Zbigniew Antoni Banaszak" ], "first_author": "Amila Thibbotuwawa", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.", "keywords": [ "Collision avoidance", "Computer science", "Key (lock)", "Process (computing)", "Wind speed", "Flight planning", "Set (abstract data type)", "Range (aeronautics)", "Operations research", "Aeronautics", "Collision", "Engineering" ], "cited_by_count": 114, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/20/2/515/pdf?version=1579682741" }, { "id": "https://openalex.org/W4399580298", "doi": "10.3390/su16124995", "title": "An Energy-Efficient Logistic Drone Routing Method Considering Dynamic Drone Speed and Payload", "year": 2024, "publication_date": "2024-06-12", "type": "article", "language": "en", "authors": [ "Kunpeng Wu", "Shaofeng Lu", "Haoqin Chen", "Minling Feng", "Zenghao Lu" ], "first_author": "Kunpeng Wu", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicles (UAVs), or drones, are recognized for their potential to improve efficiency in last-mile delivery. Unlike the vehicle routing problem, drone route design is challenging due to several operational signatures, such as speed optimization, multi-trip operation, and energy consumption estimation. Drone energy consumption is a nonlinear function of both speed and payload. Moreover, the high speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP–VFS). We propose a new model to specifically consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables. The DRP–VFS is initially formulated using mixed-integer linear programming (MILP) to minimize energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm and compare its performance with a commercial MIP solver. The experimental results demonstrate that the proposed method is capable of effectively identifying suboptimal solutions in practical scenarios. Furthermore, results indicate that operating drones at variable speeds leads to about 21% energy savings compared to fixed speeds, with advantages in cost savings and range extension.", "keywords": [ "Drone", "Payload (computing)", "Energy consumption", "Computer science", "Routing (electronic design automation)", "Range (aeronautics)", "Mathematical optimization", "Real-time computing", "Engineering", "Aerospace engineering", "Embedded system", "Mathematics" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/su16124995" }, { "id": "https://openalex.org/W4206171663", "doi": "10.3390/app12010529", "title": "Optimal Route Planning for Truck–Drone Delivery Using Variable Neighborhood Tabu Search Algorithm", "year": 2022, "publication_date": "2022-01-05", "type": "article", "language": "en", "authors": [ "Bao Tong", "Jianwei Wang", "Xue Wang", "Feihao Zhou", "Xinhua Mao", "Wenlong Zheng" ], "first_author": "Bao Tong", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. The 23 customer points are visited by three truck–drones. The results indicate that truck–drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. Sensitivity analysis of different parameters shows that increasing the number of truck–drones can effectively save the total delivery time, but gradually reduce the marginal benefits. Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Bilateral increase of truck speed and drone speed can minimize the delivery time. It can clearly be seen that the proposed method can effectively optimize the truck–drone delivery route and improve the delivery efficiency.", "keywords": [ "Drone", "Truck", "Travelling salesman problem", "Tabu search", "Computer science", "Mathematical optimization", "Engineering", "Algorithm", "Mathematics", "Automotive engineering" ], "cited_by_count": 41, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/12/1/529/pdf?version=1641392384" }, { "id": "https://openalex.org/W4285150537", "doi": "10.1007/978-3-030-98574-5_9", "title": "Strategic Deployment of Drone Centers and Fleet Size Planning for Drone Delivery", "year": 2022, "publication_date": "2022-01-01", "type": "book-chapter", "language": "en", "authors": [ "David Sacharny", "Thomas C. Henderson" ], "first_author": "David Sacharny", "venue": "Unmanned system technologies", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Software deployment", "Schedule", "Transport engineering", "Truck", "Aeronautics", "Engineering", "Population", "Operations research", "Computer science", "Aerospace engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-98574-5_9" }, { "id": "https://openalex.org/W4402949706", "doi": "10.1007/s10846-024-02129-9", "title": "Optimizing Drone Delivery Paths from Shared Bases: A Location-Routing Problem with Realistic Energy Constraints", "year": 2024, "publication_date": "2024-09-28", "type": "article", "language": "en", "authors": [ "Mahla Meskar", "Amir Ahmadi Javid" ], "first_author": "Mahla Meskar", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": "Recently, Amazon patented fulfillment centers for drones on a large scale in densely populated areas. A network of such shared centers can be used for landing and launching drones as an alternative to the traditional private bases in near future. This paper studies how a user of such a shared delivery infrastructure can optimally determine the required bases among the existing bases to perform her delivery tasks. To this end, a location-routing problem is studied where the problem determines the drone launching centers and their routes to deliver parcels. A realistic energy function is used that incorporates the effect of load weight for calculating energy consumption in all flight phases including take-off, level flight, hovering, and landing. Although the energy consumption is nonlinear, the problem is formulated as a mixed-integer linear programming model and strengthened by valid inequalities and a pre-processing algorithm that enables us to solve instances with up to 100 customers using off-the-shelf optimization solvers. Moreover, a heuristic method is presented to solve instances with up to 200 customers. Results indicate the importance of incorporating the load-dependent energy formula in contrast to using fixed flight duration constraints for drones. The value of allowing multiple visits on each trip versus only simple back-and-forth trips is also assessed. The advantage of using an integrated location-routing approach is shown over the sequential approach in which the locations of bases are decided first and the routes are constructed next. Moreover, to manage future demand uncertainty, the model is extended for the case that a number of demand scenarios are given.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Energy (signal processing)", "Mathematical optimization", "Distributed computing", "Computer network", "Mathematics", "Biology" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10846-024-02129-9.pdf" }, { "id": "https://openalex.org/W2519062619", "doi": "10.1016/j.cie.2017.10.030", "title": "Unmanned aerial vehicle routing in the presence of threats", "year": 2017, "publication_date": "2017-10-31", "type": "article", "language": "en", "authors": [ "Kamil A. Alotaibi", "Jay Michael Rosenberger", "Stephen Mattingly", "Raghavendra Kumar Punugu", "Siriwat Visoldilokpun" ], "first_author": "Kamil A. Alotaibi", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Waypoint", "Routing (electronic design automation)", "Heuristic", "Computer science", "Set (abstract data type)", "Mathematical optimization", "Path (computing)", "Enhanced Data Rates for GSM Evolution", "Drone", "Real-time computing", "Computer network", "Mathematics" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2017.10.030" }, { "id": "https://openalex.org/W2530887753", "doi": "10.1016/j.ijtst.2016.11.002", "title": "Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy", "year": 2016, "publication_date": "2016-10-01", "type": "article", "language": "en", "authors": [ "Joseph Y.J. Chow" ], "first_author": "Joseph Y.J. Chow", "venue": "International Journal of Transportation Science and Technology", "publisher": "Elsevier BV", "abstract": "Given the rapid advances in unmanned aerial vehicles, or drones, and increasing need to monitor at a city level, one of the current research gaps is how to systematically deploy drones over multiple periods. We propose a real-time data-driven approach: we formulate the first deterministic arc-inventory routing problem and derive its stochastic dynamic policy. The policy is expected to be of greatest value in scenarios where uncertainty is highest and costliest, such as city monitoring during major events. The Bellman equation for an approximation of the proposed inventory routing policy is formulated as a selective vehicle routing problem. We propose an approximate dynamic programming algorithm based on Least Squares Monte Carlo simulation to find that policy. The algorithm has been modified so that the least squares dependent variable is defined to be the “expected stock out cost upon the next replenishment”. The new algorithm is tested on 30 simulated instances of real time trajectories over 5 time periods of the selective vehicle routing problem to evaluate the proposed policy and algorithm. Computational results on the selected instances show that the algorithm on average outperforms the myopic policy by 23–28%, depending on the parametric design. Further tests are conducted on classic benchmark arc routing problem instances. The 11-link instance gdb19 (Golden et al., 1983) is expanded into a sequential 15-period stochastic dynamic example and used to demonstrate why a naïve static multi-period deployment plan would not be effective in real networks.", "keywords": [ "Computer science", "Mathematical optimization", "Benchmark (surveying)", "Routing (electronic design automation)", "Arc routing", "Stochastic programming", "Dynamic programming", "Vehicle routing problem", "Variable neighborhood search", "Static routing", "Operations research", "Metaheuristic" ], "cited_by_count": 92, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S2046043016300235/pdf" }, { "id": "https://openalex.org/W7106804829", "doi": "10.1016/j.cie.2025.111714", "title": "Multi-objective home healthcare vehicle routing problem with drone-caregiver cooperation mode", "year": 2025, "publication_date": "2025-11-27", "type": "article", "language": "en", "authors": [ "Yichen Lu", "Jun Yang", "Chao Yang", "Rui Zheng" ], "first_author": "Yichen Lu", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Task (project management)", "Workload", "Vehicle routing problem", "Service (business)", "Integer programming", "Routing (electronic design automation)", "Population", "Linear programming", "Set (abstract data type)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111714" }, { "id": "https://openalex.org/W4392349987", "doi": "10.1016/j.ejor.2024.03.002", "title": "Locating charging stations and routing drones for efficient automated stocktaking", "year": 2024, "publication_date": "2024-03-02", "type": "article", "language": "en", "authors": [ "Panupong Vichitkunakorn", "Simon Emde", "Makusee Masae", "Christoph H. Glock", "Eric H. Grosse" ], "first_author": "Panupong Vichitkunakorn", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": "Drones have received growing attention in logistics recently. One possible application is deploying drones for auditing inventory in warehouses. With the use of drones, warehouses are able to increase inventory record accuracy and decrease labor costs. In this research, we introduce the stocktaking drone routing problem (STDRP), which consists of routing a fleet of drones through a warehouse for stocktaking purposes as well as deciding on the location of charging stations on the warehouse floor, which is necessary due to the limited battery capacity of the drones. Subsequently, we develop an adaptive large neighbourhood search-based heuristic (ALNS) with novel solution encoding and decoding approaches to solve the STDRP. In a numerical study, we show that ALNS can solve realistic instances in reasonable time. We also derive recommendations regarding the ideal size of the drone fleet, the charging infrastructure, and battery capacity. Finally, we investigate the interplay between the storage assignment policy (such as the popular ABC rule) and stocktaking efficiency using drones.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Heuristic", "Operations research", "Computer network", "Artificial intelligence", "Mathematics" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2024.03.002" }, { "id": "https://openalex.org/W7117583343", "doi": "10.3934/aci.2025020", "title": "Vehicle routing problem with trucks and drones collaboration: a structured literature review", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": null, "authors": [ "Xiaoteng Han", "Jun Shen" ], "first_author": "Xiaoteng Han", "venue": "Applied Computing and Intelligence", "publisher": "American Institute of Mathematical Sciences", "abstract": "Coordinated truck-drone delivery has emerged as an important extension of the vehicle routing problem (VRP), offering new opportunities to improve logistics efficiency, accessibility, and sustainability. In this review, we synthesize the literature on truck-drone routing by examining three interrelated themes: Vehicle routing and synchronization models, stochastic and dynamic operational constraints, and payload-energy management. Early studies primarily formulate truck-drone delivery as variants of heterogeneous or two-echelon VRP using mixed-integer linear programming (MILP) to capture coupled routing decisions, drone endurance limits, and launch-retrieval feasibility, often supplemented by heuristic or metaheuristic strategies to address computational scalability. More recent research extends these formulations to stochastic and dynamic VRP settings by incorporating uncertain customer availability, time windows, vehicle delays, and weather disruptions, reflecting a shift toward adaptive routing under incomplete information. Parallel work on payload configuration and energy management investigates modular loading and battery-swapping mechanisms, demonstrating how load heterogeneity and energy constraints fundamentally reshape feasible routing structures. Extensions to multimodal VRP variants, including electric vehicle-drone systems and van-robot hybrids, further broaden applicability in dense and constrained urban environments. Despite these advances, most models remain grounded in static or weakly stochastic assumptions, with limited support for real-time updates or predictive decision-making. In this review, we identified the integration of data-driven prediction and learning-based optimization within dynamic VRP frameworks as a critical direction for advancing truck-drone delivery toward large-scale operational deployment.", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Metaheuristic", "Payload (computing)", "Heuristic", "Modular design", "Drone", "Synchronization (alternating current)" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.aimspress.com/aimspress-data/aci/2025/2/PDF/aci-05-02-020.pdf" }, { "id": "https://openalex.org/W4403063408", "doi": "10.1016/j.asoc.2024.112290", "title": "A reinforcement learning-based multiobjective heuristic algorithm for multiple-truck routing problems with heterogeneous drones", "year": 2024, "publication_date": "2024-10-02", "type": "article", "language": "en", "authors": [ "Gülçin Bektur" ], "first_author": "Gülçin Bektur", "venue": "Applied Soft Computing", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Reinforcement learning", "Computer science", "Drone", "Heuristic", "Truck", "Routing (electronic design automation)", "Mathematical optimization", "Vehicle routing problem", "Algorithm", "Routing algorithm", "Artificial intelligence", "Mathematics" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.asoc.2024.112290" }, { "id": "https://openalex.org/W4210312154", "doi": "10.1155/2022/1811288", "title": "Discrete Optimization on Truck‐Drone Collaborative Transportation System for Delivering Medical Resources", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Min Lin", "Yuming Chen", "Rui Han", "Yao Chen" ], "first_author": "Min Lin", "venue": "Discrete Dynamics in Nature and Society", "publisher": "Hindawi Publishing Corporation", "abstract": "Under the epidemic, closed management has turned a large number of communities into lonely islands, and the contactless delivery method of UAV has become the rigid demand in this special period. This paper studies a collaborative system of multi‐UAV multitruck transportation, which can deliver emergency materials such as medicine to remote areas or closed communities. In this system, delivery tasks are assigned to multiple trucks and multiple drones on each truck can perform delivery tasks in parallel, thereby improving delivery efficiency. We study the routing problem of this system specifically for medical supplying road network and establish mixed‐integer model and hybrid algorithm. We show by experiments that the number of trucks has more significant impact on the optimal solution than the number of drones and the performance of hybrid particle swarm optimization is better than the performance of the other algorithms.", "keywords": [ "Truck", "Drone", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Particle swarm optimization", "Swarm behaviour", "Integer (computer science)", "Transport engineering", "Operations research", "Computer network", "Artificial intelligence" ], "cited_by_count": 27, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/ddns/2022/1811288.pdf" }, { "id": "https://openalex.org/W4388666595", "doi": "10.1109/tits.2023.3329029", "title": "Drones in the Big City: Autonomous Collision Avoidance for Aerial Delivery Services", "year": 2023, "publication_date": "2023-11-14", "type": "article", "language": "en", "authors": [ "Fabíola Martins Campos de Oliveira", "Luiz F. Bittencourt", "Reinaldo A. C. Bianchi", "Carlos Kamienski" ], "first_author": "Fabíola Martins Campos de Oliveira", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "As delivery companies continue to explore the use of drones, the need for efficient and safe operation in urban environments becomes increasingly critical. Market-wide versions of drone delivery services will necessarily spread many drones, especially in big cities. In this scenario, avoiding collisions with other drones or typical obstacles in urban spaces is fundamental. This paper proposes and evaluates, via simulation and analytical modeling, an aerial delivery service scenario and three autonomous geometric approaches for collision avoidance. We compare our approaches with three simple methods – DoNothing (not detouring), Random, and aviation-like Rightward – and two state-of-the-art geometric approaches. Simulation experiments consider different fleet sizes with constant and Poisson drone arrival rates and drones randomly choosing one of different altitudes for the cruise flight. Contrary to our expectations, the Random and Rightward approaches increase the collisions compared with DoNothing, making the latter our baseline. Our approaches significantly reduce collisions in all experiments and deal with more drones within the detection radius, showing that collisions are more complex to avoid. Comparing the collision rate, successful trips, and the number of flying drones reveals that the efficiency in avoiding collisions reduces the number of successful trips by increasing the number of active drones. Regardless of the expected reduction in collisions, more altitudes do not eliminate them. These results indicate the need for more sophisticated approaches to reduce or eliminate collisions. The analytical modeling using Markov Chains corroborates the simulation results by shedding some light on and helping explain the simulation results.", "keywords": [ "Drone", "Computer science", "Collision avoidance", "Markov chain", "TRIPS architecture", "Trajectory", "Simulation", "Aeronautics", "Collision", "Aerospace engineering", "Computer security", "Engineering" ], "cited_by_count": 27, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2023.3329029" }, { "id": "https://openalex.org/W4366827533", "doi": "10.3390/rs15092227", "title": "UAV Network Path Planning and Optimization Using a Vehicle Routing Model", "year": 2023, "publication_date": "2023-04-22", "type": "article", "language": "en", "authors": [ "Xiaotong Chen", "Qin Li", "Ronghao Li", "Xiangyuan Cai", "Jiangnan Wei", "Hongying Zhao" ], "first_author": "Xiaotong Chen", "venue": "Remote Sensing", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications", "keywords": [ "Computer science", "Real-time computing", "Task (project management)", "Path (computing)", "Motion planning", "Routing (electronic design automation)", "Remote sensing", "Artificial intelligence", "Computer network", "Robot", "Systems engineering", "Engineering" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2072-4292/15/9/2227/pdf?version=1682268629" }, { "id": "https://openalex.org/W4366308858", "doi": "10.1109/tits.2023.3266460", "title": "A-VRPD: Automating Drone-Based Last-Mile Delivery Using Self-Driving Cars", "year": 2023, "publication_date": "2023-04-18", "type": "article", "language": "en", "authors": [ "Navid Mohammad Imran", "Sabyasachee Mishra", "Myounggyu Won" ], "first_author": "Navid Mohammad Imran", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Drone-based last-mile delivery is an emerging technology that uses drones loaded onto a truck to deliver parcels to customers. In this paper, we introduce a fully automated system for drone-based last-mile delivery through incorporation of autonomous vehicles (AVs). A novel problem called the autonomous vehicle routing problem with drones (A-VRPD) is defined. A-VRPD is to select AVs from a pool of available AVs based on crowd sourcing, assign selected AVs to customer groups, and schedule routes for selected AVs to optimize the total operational cost. We formulate A-VRPD as a Mixed Integer Linear Program (MILP) and propose an optimization framework to solve the problem. A greedy algorithm is also developed to significantly improve the running time for large-scale delivery scenarios. Extensive simulations were conducted taking into account real-world operational costs for different types of AVs, traveled distances calculated considering the real-time traffic conditions using Google Map API, and varying load capacities of AVs. We evaluated the performance in comparison with two different state-of-the-art solutions: an algorithm designed to address the traditional vehicle routing problem with drones (VRP-D), which involves human-operated trucks working in tandem with drones to deliver parcels, and an algorithm for the two echelon vehicle routing problem (2E-VRP), wherein parcels are first transported to satellite locations and subsequently delivered from those satellites to the customers. The results indicate a substantial increase in profits for both the delivery company and vehicle owners compared with the state-of-the-art algorithms.", "keywords": [ "Drone", "Vehicle routing problem", "Truck", "Last mile (transportation)", "Computer science", "Schedule", "Real-time computing", "Routing (electronic design automation)", "Integer programming", "Linear programming", "Mile", "Transport engineering" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2023.3266460" }, { "id": "https://openalex.org/W4401914792", "doi": "10.1016/j.ijdrr.2024.104776", "title": "Matheuristic approaches for multi-visit drone routing problem to prevent forest fires", "year": 2024, "publication_date": "2024-08-27", "type": "article", "language": "en", "authors": [ "İlknur Tükenmez", "Ömer Özkan" ], "first_author": "İlknur Tükenmez", "venue": "International Journal of Disaster Risk Reduction", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Transport engineering", "Aeronautics", "Environmental science", "Business", "Engineering", "Computer network", "Biology" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ijdrr.2024.104776" }, { "id": "https://openalex.org/W4410949483", "doi": "10.1109/tase.2025.3575171", "title": "Two-Echelon Arc Routing Problem With Drones in Traffic Patrol", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Jiaqi Cheng", "Tianjun Liao", "Guohua Wu", "Zhongqiang Ma", "Jianmai Shi" ], "first_author": "Jiaqi Cheng", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Traditional patrolling methods relying on ground vehicles or fixed cameras are insufficient in adapting to dynamic changes in traffic flow due to their limited mobility and fixed positions, especially during emergencies like traffic accidents occur. In this paper, we investigate a new Truck-Drone collaborative Traffic Patrolling (TDTP) mode, where multiple trucks and drones work in a collaborative manner to enhance the efficiency of performing traffic patrolling tasks. The drone can be launched and recovered by trucks at certain points along the truck route, making it possible for both trucks and drones to perform patrol tasks simultaneously. A crucial aspect of TDTP is the coordinated arc routing scheduling of multiple trucks and drones, which is referred to as the Truck-Drone Arc Routing Problem (TD-ARP). We formulate the TD-ARP as a mixed-integer linear programming model with the objective of minimizing the total patrol time of all trucks and drones. Due to the difficulty of solving large instances of TD-ARP to optimality, we construct a divide and conquer based framework TA-TDAR (Task Allocation phase and Truck Drone fleet Arc Routing phase) for TD-ARP. TA involves the assignment of tasks to different truck-drone fleets, each comprising a truck and multiple drones, while TDAR focuses on path planning for the truck-drone fleets. Two phases are iteratively performed until the stopping criteria are met. In TA phase, a Task Allocation Heuristic method (TAH) is designed. Based on the task allocation results, an Adaptive Large Neighborhood Search algorithm with Local Search (ALNSLS) is embedded into TDAR phase to achieve a satisfactory scheduling scheme of each truck-drone fleet. Extensive experiments are conducted to validate the superiority of the proposed TAH-ALNSLS. The results show that our method has a potential improvement of up to 40% compared to the comparison methods, which stems from the divide and conquer framework and problem specific operators. Furthermore, several management implications are provided to assist practitioners in making efficient decisions.", "keywords": [ "Arc routing", "Drone", "Routing (electronic design automation)", "Computer science", "Arc (geometry)", "Engineering", "Transport engineering", "Operations research", "Computer network" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tase.2025.3575171" }, { "id": "https://openalex.org/W4206099505", "doi": "10.1109/ieem50564.2021.9672856", "title": "Extending the Last Mile Delivery Routing Problem for Enhancing Sustainability by Drones Using a Sentiment Analysis Approach", "year": 2021, "publication_date": "2021-12-13", "type": "conference-paper", "language": "en", "authors": [ "Edy Anas Ahmadi", "Hendro Wicaksono", "Omid Fatahi Valilai" ], "first_author": "Edy Anas Ahmadi", "venue": "2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)", "publisher": null, "abstract": "Last mile delivery is of important stages through supply chains and logistics, which covers the final stage of delivering products to the end customers. With the growth of e-commerce and the increasing desire of people to shopping online, logistics and consequently last mile delivery became very important. One of the biggest challenges is the issue of sustainability and reducing pollution when delivering products to customers, especially in urban surroundings. For this reason, this study presents a new model that considers the issue of sustainability in optimising the problem of vehicle routing problem with drones. For this purpose, a sentiment analysis is used considering Twitter to determine the alignment of customer sentiments on environmental protection and to calculate the net promoter score index. This index is considered as a coefficient in the penalty function added to the base model. According to the results of this study, this new model will be applied to logistics companies that are responsible for the issue of sustainability and can help customers who are willing to participate for improving the sustainability of the supply chain.", "keywords": [ "Sustainability", "Last mile (transportation)", "Drone", "Reverse logistics", "Supply chain", "Index (typography)", "Business", "Computer science", "Environmental economics", "Mile", "Marketing", "Economics" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ieem50564.2021.9672856" }, { "id": "https://openalex.org/W2974277811", "doi": "10.1109/tits.2019.2939094", "title": "Unmanned Aerial Vehicle Location Routing Problem With Charging Stations for Belt Conveyor Inspection System in the Mining Industry", "year": 2019, "publication_date": "2019-09-17", "type": "article", "language": "en", "authors": [ "Roberto Gomes Ribeiro", "Jose R. C. Junior", "Luciano Perdig�ão Cota", "Thiago A. M. Euzébio", "Frederico Gadelha Guimarães" ], "first_author": "Roberto Gomes Ribeiro", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Technological advances have opened up the possibility of using unmanned aerial vehicles (UAVs) in diverse environments. The mining industry has been looking for solutions to handle periodic inspections of the belt conveyors that transport iron ore. The state of the art indicates the use of UAVs for this task as an attractive, low-cost and safe alternative, allowing for a significant increase in security. A new concise mixed-integer linear programming (MILP) model is developed to address UAV routing and charging station planning for belt conveyor inspection. We conduct computational tests covering a real conveyor belt system in Brazil to validate the model in practical applications. The loading terminal possesses approximately 120 km of belt conveyors, leading to 230 inspection points. Instances of different sizes were generated by randomly sampling a subset of these points and using two different drone specifications. The results show that the new optimization modeling satisfies the problem requirements and is a significant contribution to the automation of inspection in the mining industry.", "keywords": [ "Automation", "Conveyor belt", "Drone", "Routing (electronic design automation)", "Integer programming", "Engineering", "Belt conveyor", "Linear programming", "Task (project management)", "Real-time computing", "Computer science", "Systems engineering" ], "cited_by_count": 65, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2019.2939094" }, { "id": "https://openalex.org/W3043811452", "doi": null, "title": "Vehicle Routing Problem for Drones", "year": 2018, "publication_date": "2018-01-01", "type": "dissertation", "language": "en", "authors": [ "M.S.P. Oosterhaven" ], "first_author": "M.S.P. Oosterhaven", "venue": null, "publisher": null, "abstract": "This research is about the vehicle routing problem with drones, where a single drone is considered. The drone, which is an Unmanned Aerial Vehicle (UAV), is able to carry more than one package. There is not enough research available at the moment on the multi-package drone delivery. To ensure that the drone is able to deliver two packages in a row, multiple warehouses are evenly\r\ndistributed in the environment. At those warehouses drones are able to pick up packages and to swap the battery. The starting point for the drone is at the base station which is at the boundary of the city. At the same time this base station will also be the ending point for the drone. The UAV is able to carry a maximum of two packages. The fly range of the drone is limited and different for\r\nevery payload. The price is also different for every payload since with more load on the drone the battery will be saturated faster. Multiple customers are randomly distributed in different\r\nenvironments and are in need of service. An algorithm is constructed based on the nearest neighboring principle to find the path the UAV has to travel to serve all customers. The objective is to minimize the total costs. This algorithm is simulated with different sizes of area, different number of warehouses and with different capacities. The influence on the number of warehouses, the size of the environment, the fly range and the capacity of the drone are investigated during this research.\r\nThe results show that all of these factors have influence on the total costs. The factor with the largest influence on the total costs is the capacity.", "keywords": [ "Drone", "Payload (computing)", "Swap (finance)", "Computer science", "Routing (electronic design automation)", "Service (business)", "Range (aeronautics)", "Real-time computing", "Engineering", "Computer network", "Network packet", "Aerospace engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://fse.studenttheses.ub.rug.nl/16419/" }, { "id": "https://openalex.org/W3193516070", "doi": "10.21070/prozima.v5i1.1398", "title": "Development of Particle Swarm Optimization and Simulated Annealing Algorithms to Solve Vehicle Routing Problems with Drones", "year": 2021, "publication_date": "2021-07-06", "type": "article", "language": "en", "authors": [ "Hasan Aji Prawira", "Budi Santosa" ], "first_author": "Hasan Aji Prawira", "venue": "PROZIMA (Productivity Optimization and Manufacturing System Engineering)", "publisher": "Universitas Muhammadiyah Sidoarjo", "abstract": "Vehicle Routing Problem with Drone (VRPD) is a problem of determining the number of routes for delivery of goods from the depot to a number of customers using trucks and drones. Drones are an alternative delivery tool besides trucks, each truck can be equipped with a support drone. Drones can be used to make a delivery while the truck is making others. By combining a truck and a drone, the truck can act as a tool for drone launch and landing so that the drones can reach long distances from the depot. The purpose of this problem is to minimize the cost of sending goods by trucks and drones. In this study, the Particle Swarm Optimization (PSO) and the Simulated Annealing (SA) are proposed to solve these problems. The Route Drone algorithm are used to help change the structure of the PSO and SA solutions into a VRPD solution. The proposed algorithm has been applied to 24 different scenarios ranging from 6 customers to 100 customers. The PSO and SA algorithms are able to find solutions that are close to optimal. The SA is able to find a better solution than the PSO.", "keywords": [ "Drone", "Truck", "Simulated annealing", "Particle swarm optimization", "Vehicle routing problem", "Computer science", "Swarm behaviour", "Routing (electronic design automation)", "Mathematical optimization", "Algorithm", "Engineering", "Automotive engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://prozima.umsida.ac.id/index.php/prozima/article/download/1398/1684" }, { "id": "https://openalex.org/W4394726112", "doi": "10.1080/19427867.2024.2336339", "title": "A hybrid multi-objective solution approach for a reliable truck-drone routing problem integrated with pickup and delivery services", "year": 2024, "publication_date": "2024-04-11", "type": "article", "language": "en", "authors": [ "Sanaz Khalaj Rahimi", "Donya Rahmani" ], "first_author": "Sanaz Khalaj Rahimi", "venue": "Transportation Letters", "publisher": "Taylor & Francis", "abstract": "Humanitarian logistics aims to reduce the arrival time of relief goods and optimize services in disaster-affected regions. This paper introduces a multi-objective hybrid truck-drone routing problem with pickup and delivery services, considering reliability (MHTDRP-PD-R) to enhance relief operations within humanitarian logistics. The model is formulated as a mixed-integer linear programming problem with the simultaneous goals of minimizing transportation time and maximizing reliability through routing and inventory decisions made by multiple trucks and drones. An adaptive large neighborhood search (ALNS) algorithm, coupled with heuristic technique, is developed to tackle the problem. The ALNS algorithm refines initial solutions through destroy and repair operators, integrated with multi-objective optimization methods to create a unified objective function. Small and large-scale test problems are then employed to evaluate solution quality using multi-objective optimization. The calculation results show the superiority of the developed ALNS integrated with the ε-constraint method regarding the objective value and solution time.", "keywords": [ "Pickup", "Drone", "Truck", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Operations research", "Transport engineering", "Computer network", "Engineering", "Automotive engineering", "Artificial intelligence" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/19427867.2024.2336339" }, { "id": "https://openalex.org/W4413353913", "doi": "10.1016/j.tre.2025.104375", "title": "Multi-objective optimization of truck-drone cooperative routing problem based on customer classification and fuzzy time windows", "year": 2025, "publication_date": "2025-08-20", "type": "article", "language": "en", "authors": [ "Xianlong Ge", "Qiushuang Yin", "Md. Abdul Moktadir", "Jingzheng Ren" ], "first_author": "Xianlong Ge", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Drone", "Vehicle routing problem", "Fuzzy logic", "Routing (electronic design automation)", "Computer science", "Engineering", "Operations research", "Transport engineering", "Automotive engineering", "Artificial intelligence", "Computer network" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104375" }, { "id": "https://openalex.org/W2154966075", "doi": "10.1109/acc.2009.5160144", "title": "Dynamic optimal control of Multiple Depot Vehicle Routing Problem with Metric Temporal Logic", "year": 2009, "publication_date": "2009-01-01", "type": "conference-paper", "language": "en", "authors": [ "Mariam Faied", "Ahmed Mostafa", "Anouck Girard" ], "first_author": "Mariam Faied", "venue": null, "publisher": null, "abstract": "This paper discusses a class of mission planning problems that generalizes the standard Multiple Depot Vehicle Routing Problem with Time Windows (MDVRPTW) to incorporate complicated technical constraints. These constraints are specified via Metric Temporal Logic (MTL). A tree search algorithm is provided to solve that novel MDVRPTW with MTL specifications (MDVRPMTL) to optimality. In this work, we use tree search algorithm to seek the optimal flyable trajectories for teams of UAVs starting from different depots to complete missions with complicated time and technical requirements. Then a Stochastic Dynamic Programming (SDP) based algorithm is proposed to dynamically tune the UAV teams in order to secure them against adversarial actions. Examples for practical mission planning problems, in which MTL is used as a high level language to specify complex mission tasks, are presented and discussed in the paper.", "keywords": [ "Computer science", "Metric (unit)", "Routing (electronic design automation)", "Temporal logic", "Dynamic programming", "Mathematical optimization", "Vehicle routing problem", "Tree (set theory)", "Vehicle dynamics", "Class (philosophy)", "Theoretical computer science", "Artificial intelligence" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/acc.2009.5160144" }, { "id": "https://openalex.org/W2981203694", "doi": "10.1109/etfa.2019.8869278", "title": "Optimal Indoor Goods Delivery Using Drones", "year": 2019, "publication_date": "2019-09-01", "type": "conference-paper", "language": "en", "authors": [ "Marius Kloetzer", "Adrian Burlacu", "Gabriel Enescu", "Simona Caraiman", "Cristian Mahulea" ], "first_author": "Marius Kloetzer", "venue": null, "publisher": null, "abstract": "During the last few years, the field of micro aerial vehicles (drones) has encountered a significant focus among the robotics research community. Autonomous maneuvering for indoor environments is highly challenging on one hand due to space topology that can include different storage facilities, and on the other hand due to need for optimal planning that saves drone battery and task accomplishment time. In this paper we study a type of vehicle routing problem applied to an indoor warehouse. In particular, some goods should be transported from storage areas to delivery areas by limited energy drones with finite capacities for transporting goods. The presentation includes a planning solution based on mathematical programming and presents supporting simulations and real-time experiments.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Robotics", "Robot", "Task (project management)", "Field (mathematics)", "Architectural engineering", "Simulation", "Systems engineering", "Artificial intelligence", "Engineering" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/etfa.2019.8869278" }, { "id": "https://openalex.org/W3096458579", "doi": "10.1155/2020/8860646", "title": "IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization", "year": 2020, "publication_date": "2020-11-07", "type": "article", "language": "en", "authors": [ "Rehan Tariq", "Zeshan Iqbal", "Farhan Aadil" ], "first_author": "Rehan Tariq", "venue": "Wireless Communications and Mobile Computing", "publisher": "Wiley", "abstract": "Technology advancement in the field of vehicular ad hoc networks (VANETs) improves smart transportation along with its many other applications. Routing in VANETs is difficult as compared to mobile ad hoc networks (MANETs); topological constraints such as high mobility, node density, and frequent path failure make the VANET routing more challenging. To scale complex routing problems, where static and dynamic routings do not work well, AI-based clustering techniques are introduced. Evolutionary algorithm-based clustering techniques are used to solve such routing problems; moth flame optimization is one of them. In this work, an intelligent moth flame optimization-based clustering (IMOC) for a drone-assisted vehicular network is proposed. This technique is used to provide maximum coverage for the vehicular node with minimum cluster heads (CHs) required for routing. Delivering optimal route by providing end-to-end connectivity with minimum overhead is the core issue addressed in this article. Node density, grid size, and transmission ranges are the performance metrics used for comparative analysis. These parameters were varied during simulations for each algorithm, and the results were recorded. A comparison was done with state-of-the-art clustering algorithms for routing such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Gray Wolf Optimization (GWO). Experimental outcomes for IMOC consistently outperformed the state-of-the-art techniques for each scenario. A framework is also proposed with the support of a commercial Unmanned Aerial Vehicle (UAV) to improve routing by minimizing path creation overhead in VANETs. UAV support for clustering improved end-to-end connectivity by keeping the routing cost constant for intercluster communication in the same grid.", "keywords": [ "Computer science", "Cluster analysis", "Ant colony optimization algorithms", "Overhead (engineering)", "Node (physics)", "Computer network", "Routing (electronic design automation)", "Particle swarm optimization", "Scalability", "Distributed computing", "Dynamic Source Routing", "Routing protocol" ], "cited_by_count": 27, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/wcmc/2020/8860646.pdf" }, { "id": "https://openalex.org/W2082327552", "doi": "10.1109/tsmca.2012.2216871", "title": "Incorporating Human Factor Considerations in Unmanned Aerial Vehicle Routing", "year": 2012, "publication_date": "2012-12-08", "type": "article", "language": "en", "authors": [ "Chase C. Murray", "Woojin Park" ], "first_author": "Chase C. Murray", "venue": "IEEE Transactions on Systems Man and Cybernetics Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Unmanned aerial vehicles (UAVs) have become increasingly valuable military assets, and reliance upon them will continue to increase. Despite lacking an onboard pilot, UAVs require crews of up to three human operators. These crews are already experiencing high workload levels, which is a problem that will be likely compounded as the military envisions a future where a single operator controls multiple UAVs. To accomplish this goal, effective scheduling of UAVs and human operators is crucial to future mission success. We present a mathematical model for simultaneously routing UAVs and scheduling human operators, subject to operator workload considerations. This model is thought to be the first of its kind. Numerical examples demonstrate the dangers of ignoring the human element in UAV routing and scheduling.", "keywords": [ "Workload", "Scheduling (production processes)", "Operator (biology)", "Computer science", "Operations research", "Routing (electronic design automation)", "Vehicle routing problem", "Aeronautics", "Engineering", "Computer network", "Operations management" ], "cited_by_count": 39, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tsmca.2012.2216871" }, { "id": "https://openalex.org/W4401534062", "doi": "10.1016/j.swevo.2024.101699", "title": "An improved variable neighborhood search algorithm embedded temporal and spatial synchronization for vehicle and drone cooperative routing problem with pre-reconnaissance", "year": 2024, "publication_date": "2024-08-13", "type": "article", "language": "en", "authors": [ "Weichang Sun", "Zhihao Luo", "Xingchen Hu", "Witold Pedrycz", "Jianmai Shi" ], "first_author": "Weichang Sun", "venue": "Swarm and Evolutionary Computation", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Drone", "Variable (mathematics)", "Vehicle routing problem", "Synchronization (alternating current)", "Routing (electronic design automation)", "Variable neighborhood search", "Algorithm", "Real-time computing", "Metaheuristic", "Computer network" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.swevo.2024.101699" }, { "id": "https://openalex.org/W4392719414", "doi": "10.1109/tits.2024.3371189", "title": "Communication-Aware Drone Delivery Problem", "year": 2024, "publication_date": "2024-03-12", "type": "article", "language": "en", "authors": [ "Cihan Tugrul Cicek", "Çağrı Koç", "Hakan Gültekin", "Güneş Erdoğan" ], "first_author": "Cihan Tugrul Cicek", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The drone delivery problem (DDP) has been introduced to include aerial vehicles in last-mile delivery operations to increase efficiency. However, the existing studies have not incorporated the communication quality requirements of such a delivery operation. This study introduces the communication-aware DDP (C-DDP), which incorporates handover and outage constraints into the conventional multi-depot multi-trip green vehicle routing problem with time windows. In particular, any trip of a drone to deliver a customer package must require less than a certain number of handover operations and cannot exceed a predefined outage duration threshold. A mixed integer programming (MIP) model is developed to minimize the total flight distance while satisfying communication constraints. We present a genetic algorithm (GA) that can solve large instances and compare its performance with an off-the-shelf MIP solver. Computational study shows that the GA and MIP solver performances are equivalent to solving smaller instances. We also compare the GA performance against another evolutionary algorithm, particle swarm optimization (PSO), for larger instances and find that the GA outperforms the PSO with slightly longer CPU times. The results indicate that ignoring the communication constraints would cause significant operational disruption risk and this risk can be easily mitigated with a slight sacrifice from flight distances by incorporating the proposed communication constraints. In particular, the communication performance can be improved by up to 28.9% when the flight distance is increased by 19.1% at most on average.", "keywords": [ "Drone", "Computer science", "Engineering", "Aeronautics" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://researchportal.bath.ac.uk/en/publications/6d231b3b-bf92-4c68-b2d2-3d10eeb2528e" }, { "id": "https://openalex.org/W4403182844", "doi": "10.1109/tits.2024.3467219", "title": "An Adaptive Memetic Algorithm for a Cost-Optimal Electric Vehicle-Drone Routing Problem", "year": 2024, "publication_date": "2024-10-07", "type": "article", "language": "en", "authors": [ "Setyo Tri Windras Mara", "Ruhul Amin Sarker", "Daryl Essam", "Saber Mohammed Elsayed" ], "first_author": "Setyo Tri Windras Mara", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper considers a fleet of electric vehicles and drones that deliver goods collaboratively. To determine the optimal routes of this electric vehicle-drone routing problem, the problem is formulated as a mixed-integer linear program to minimize the total operational costs. To solve the model, we develop an adaptive memetic algorithm that employs a multi-operator concept with a Q-learning-based selection mechanism and a set of local search operators for exploring the complex search space of the problem. Using extensive numerical experiments, we prove the effectiveness of our proposal and reveal some interesting managerial insights.", "keywords": [ "Memetic algorithm", "Drone", "Vehicle routing problem", "Computer science", "Electric vehicle", "Mathematical optimization", "Routing (electronic design automation)", "Genetic algorithm", "Mathematics", "Computer network", "Physics", "Biology" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2024.3467219" }, { "id": "https://openalex.org/W4297542746", "doi": "10.3390/math10193527", "title": "Multi-Trip Time-Dependent Vehicle Routing Problem with Split Delivery", "year": 2022, "publication_date": "2022-09-27", "type": "article", "language": "en", "authors": [ "Jie Zhang", "Yifan Zhu", "Xiaobo Li", "Mengjun Ming", "Weiping Wang", "Tao Wang" ], "first_author": "Jie Zhang", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Motivated by some practical applications of post-disaster supply delivery, we study a multi-trip time-dependent vehicle routing problem with split delivery (MTTDVRP-SD) with an unmanned aerial vehicle (UAV). This is a variant of the VRP that allows the UAV to travel multiple times; the task nodes’ demands are splittable, and the information is time-dependent. We propose a mathematical formulation of the MTTDVRP-SD and analyze the pattern of the solution, including the delivery routing and delivery quantity. We developed an algorithm based on the simulation anneal (SA) framework. First, the initial solution is generated by an improved intelligent auction algorithm; then, the stochastic neighborhood of the delivery route is generated based on the SA algorithm. Based on this, the model is simplified to a mixed-integer linear programming model (MILP), and the CPLEX optimizer is used to solve for the delivery quantity. The proposed algorithm is compared with random–simulation anneal–CPLEX (R-SA-CPLEX), auction–genetic algorithm–CPLEX (A-GA-CPLEX), and auction–simulation anneal–CPLEX (A-SA) on 30 instances at three scales, and its effectiveness and efficiency are statistically verified. The proposed algorithm significantly differs from R-SA-CPLEX at a 99% confidence level and outperforms R-SA-CPLEX by about 30%. In the large-scale case, the computation time of the proposed algorithm is about 30 min shorter than that of A-SA. Compared to the A-GA-CPLEX algorithm, the performance and efficiency of the proposed algorithm are improved. Furthermore, compared to a model that does not allow split delivery, the objective function values of the solution of the MTTDVRP-SD model are reduced by 52.67%, 48.22%, and 34.11% for the three scaled instances, respectively.", "keywords": [ "Vehicle routing problem", "Computer science", "Mathematical optimization", "Genetic algorithm", "Simulated annealing", "Routing (electronic design automation)", "Algorithm", "Computation", "Integer programming", "Mathematics" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2227-7390/10/19/3527/pdf?version=1665484281" }, { "id": "https://openalex.org/W4256196971", "doi": "10.1109/cac53003.2021.9728454", "title": "A new artificial bee colony for vehicle routing problem with drones", "year": 2021, "publication_date": "2021-10-22", "type": "conference-paper", "language": "en", "authors": [ "Zhengzhi Cui", "Deming Lei" ], "first_author": "Zhengzhi Cui", "venue": "2021 China Automation Congress (CAC)", "publisher": null, "abstract": "Truck-drone delivery is a hybrid one combining the advantages of both vehicles including large capacity of truck and high travel speed of drone together and leads to some generalized routing problems such as vehicle routing problem with drones (VRP-D). In this paper, VRP-D is addressed and a new artificial bee colony (ABC) is applied to minimize routing cost. Two bee swarms are produced and an effective evaluation process is used to determine employed bee swarm and onlooker bee swarm dynamically. Variable neighborhood descent is constructed by using 15 neighborhood structures and adopted in employed bee phase and onlooker bee phase in different ways. Computational results reveal that the proposed ABC can obtain better solutions than the compared algorithm and has promising advantages on VRP-D.", "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Swarm behaviour", "Computer science", "Metaheuristic", "Truck", "Artificial bee colony algorithm", "Mathematical optimization", "Artificial intelligence", "Engineering", "Mathematics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cac53003.2021.9728454" }, { "id": "https://openalex.org/W4416750786", "doi": "10.1109/iros60139.2025.11247477", "title": "Adapt-VRPD: Vehicle Routing Problem with Drones Under Dynamically Changing Traffic Conditions", "year": 2025, "publication_date": "2025-10-19", "type": "conference-paper", "language": null, "authors": [ "Navid Mohammad Imran", "Myounggyu Won" ], "first_author": "Navid Mohammad Imran", "venue": null, "publisher": null, "abstract": "The vehicle routing problem with drones (VRPD) involves determining the optimal routes for trucks and drones to collaboratively deliver parcels to customers, aiming to minimize total operational costs. While various heuristic algorithms have been developed to address the problem, existing solutions are built based on simplistic cost models, overlooking the temporal dynamics of the costs, which fluctuate depending on the dynamically changing traffic conditions. In this paper, we present a novel problem called the vehicle routing problem with drones under dynamically changing traffic conditions (Adapt-VRPD) to address the limitation of existing VRPD solutions. We design a novel cost model that factors in the actual travel distance and projected travel time, computed using a machine learning-driven travel time prediction algorithm. A variable neighborhood descent (VND) algorithm is developed to find the optimal truck-drone routes under the dynamics of traffic conditions through incorporation of the travel time prediction model. A simulation study was performed to compare our algorithm with a state-of-the-art VRPD heuristic. Our algorithm outperformed the benchmark, reducing the average and maximum discrepancies from the actual cost by 37.6% and 27.6%, respectively, across various delivery scenarios.", "keywords": [ "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Heuristic", "Variable (mathematics)", "Truck" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros60139.2025.11247477" }, { "id": "https://openalex.org/W2147033906", "doi": "10.1109/mcdm.2007.369410", "title": "UAV Swarm Mission Planning and Routing using Multi-Objective Evolutionary Algorithms", "year": 2007, "publication_date": "2007-04-01", "type": "conference-paper", "language": "en", "authors": [ "Gary B. Lamont", "James N. Slear", "Kenneth Melendez" ], "first_author": "Gary B. Lamont", "venue": null, "publisher": null, "abstract": "The purpose of this research is to design and implement a comprehensive mission planning system for swarms of autonomous aerial vehicles (UAV). The system integrates several problem domains including path planning, vehicle routing, and swarm behavior as based upon a hierarchical architecture. The developed system consists of a parallel, multi-objective evolutionary algorithm-based terrain-following parallel path planner and an evolutionary algorithm-based vehicle router. Objectives include minimizing cost and risk generally associated with a three dimensional vehicle routing problem (VRP). The culmination of this effort is the development of an extensible developmental path planning model integrated with swarm behavior and tested with a parallel UAV simulation. Discussions on the system's capabilities are presented along with recommendations for further development.", "keywords": [ "Computer science", "Motion planning", "Routing (electronic design automation)", "Evolutionary algorithm", "Vehicle routing problem", "Swarm behaviour", "Router", "Terrain", "Distributed computing", "Path (computing)", "Real-time computing", "Robot" ], "cited_by_count": 94, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mcdm.2007.369410" }, { "id": "https://openalex.org/W3083026720", "doi": "10.35329/jiik.v6i2.150", "title": "Desain Antarmuka Pada Vehicle Routing Problem Untuk Manajemen Armada Multi-Drone", "year": 2020, "publication_date": "2020-09-02", "type": "article", "language": "id", "authors": [ "Setyawan Ajie Sukarno", "Yuliadi Erdani" ], "first_author": "Setyawan Ajie Sukarno", "venue": "JURNAL ILMIAH ILMU KOMPUTER", "publisher": null, "abstract": "Artikel ini membahas desain antarmuka pada vehicle routing problem (VRP) 3-dimensi untuk armada multi-drone. Armada ini melakukan perjalanan untuk mengunjungi serangkaian titik dengan memperhatikan batasan tertentu. Karena VRP diklasifikasikan sebagai masalah optimasi NP-hard, algoritma aproksimasi seperti Algoritma Genetika diterapkan untuk menemukan solusi terbaik untuk masalah optimisasi kombinatorial ini. Dalam merancang GUI ini, kami menggunakan Netlogo sebagai alat untuk membangun antarmuka, dan juga untuk eksperimen dan simulasi. Hasil penelitian ini menunjukkan bahwa dengan menggunakan Netlogo, kita dapat mendesain antarmuka untuk mensimulasikan algoritma aproksimasi dalam penyelesaian permasalahan optimasi kombinatorial, yang mudah dioperasikan oleh pengguna", "keywords": [ "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Computer network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://ejournal.fikom-unasman.ac.id/index.php/jikom/article/download/150/82" }, { "id": "https://openalex.org/W4318344232", "doi": "10.1016/j.engappai.2023.105876", "title": "A mixed closed-open multi-depot routing and scheduling problem for homemade meal delivery incorporating drone and crowd-sourced fleet: A self-adaptive hyper-heuristic approach", "year": 2023, "publication_date": "2023-01-28", "type": "article", "language": "en", "authors": [ "Mahdi Hamid", "Mohammad Mahdi Nasiri", "Masoud Rabbani" ], "first_author": "Mahdi Hamid", "venue": "Engineering Applications of Artificial Intelligence", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Vehicle routing problem", "Heuristic", "Scheduling (production processes)", "Customer satisfaction", "Crowdsourcing", "Operations research", "Context (archaeology)", "Mathematical optimization", "Routing (electronic design automation)", "Computer network", "Artificial intelligence" ], "cited_by_count": 53, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.engappai.2023.105876" }, { "id": "https://openalex.org/W4399724243", "doi": "10.1016/j.trc.2024.104698", "title": "Reliable truck-drone routing with dynamic synchronization: A high-dimensional network programming approach", "year": 2024, "publication_date": "2024-06-17", "type": "article", "language": "en", "authors": [ "Jiahao Xing", "Tong Guo", "Lu Carol Tong" ], "first_author": "Jiahao Xing", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Truck", "Synchronization (alternating current)", "Routing (electronic design automation)", "Time synchronization", "Computer network", "Engineering", "Automotive engineering" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2024.104698" }, { "id": "https://openalex.org/W2619389213", "doi": null, "title": "Stochastic and Dynamic Routing Problems for Multiple UAVs", "year": 2009, "publication_date": "2009-01-01", "type": "article", "language": "en", "authors": [ "John J. Enright", "Ketan Savla", "Emilio Frazzoli", "Francesco Bullo" ], "first_author": "John J. Enright", "venue": null, "publisher": null, "abstract": "Consider a routing problem for a team of vehicles in the plane: target points appear randomly over time in a bounded environment and must be visited by one of the vehicles. It is desired to minimize the expected system time for the targets, i.e., the expected time elapsed between the appearance of a target point, and the instant it is visited. In this paper, such a routing problem is considered for a team of Uninhabited Aerial Vehicles (UAVs), modeled as vehicles moving with constant forward speed along paths of bounded curvature. distinct set of operating conditions. Three algorithms are presented, each designed for a Each is proven to provide a system time within a constant factor of the optimal when operating under the appropriate conditions. It is shown that the optimal routing policy depends on problem parameters such as the workload per vehicle and the vehicle density in the environment. Finally, there is discussion of a phase transition between two of the policies as the problem parameters are varied. In particular, for the case in which targets appear sporadically, a dimensionless parameter is identified which completely captures this phase transition and an estimate of the critical value of the parameter is provided.", "keywords": [ "Bounded function", "Constant (computer programming)", "Routing (electronic design automation)", "Mathematical optimization", "Vehicle routing problem", "Computer science", "Workload", "Point (geometry)", "Dimension (graph theory)", "Set (abstract data type)", "Control theory (sociology)", "Mathematics" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.325.5120" }, { "id": "https://openalex.org/W3037445508", "doi": "10.1007/978-3-030-49889-4_3", "title": "The Vehicle Routing Problem with Drone for the Minimum $$\\text {CO}_2$$ Emissions", "year": 2020, "publication_date": "2020-06-29", "type": "conference-paper", "language": "en", "authors": [ "Xinying Peng", "Deguo Sun", "Zhiyi Meng" ], "first_author": "Xinying Peng", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Truck", "Drone", "Fuel efficiency", "Automotive engineering", "Routing (electronic design automation)", "Environmental science", "Vehicle routing problem", "Computer science", "Battery (electricity)", "Integer programming", "Power (physics)", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-49889-4_3" }, { "id": "https://openalex.org/W4283760409", "doi": "10.1016/j.asoc.2022.109225", "title": "Routing and scheduling optimization for UAV assisted delivery system: A hybrid approach", "year": 2022, "publication_date": "2022-07-01", "type": "article", "language": "en", "authors": [ "Mohammad Sajid", "Himanshu Mittal", "Shreya Pare", "Mukesh Prasad" ], "first_author": "Mohammad Sajid", "venue": "Applied Soft Computing", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Mathematical optimization", "Simulated annealing", "Job shop scheduling", "Particle swarm optimization", "Scheduling (production processes)", "Crossover", "Differential evolution", "Algorithm", "Routing (electronic design automation)", "Mathematics", "Artificial intelligence" ], "cited_by_count": 130, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.asoc.2022.109225" }, { "id": "https://openalex.org/W3131727203", "doi": "10.1109/iros45743.2020.9341089", "title": "Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures", "year": 2020, "publication_date": "2020-10-24", "type": "conference-paper", "language": "en", "authors": [ "Wei Jing", "Di Deng", "Yan Jun Wu", "Kenji Shimada" ], "first_author": "Wei Jing", "venue": null, "publisher": null, "abstract": "We present a multi-UAV Coverage Path Planning (CPP) framework for the inspection of large-scale, complex 3D structures. In the proposed sampling-based coverage path planning method, we formulate the multi-UAV inspection applications as a multi-agent coverage path planning problem. By combining two NP-hard problems: Set Covering Problem (SCP) and Vehicle Routing Problem (VRP), a Set-Covering Vehicle Routing Problem (SC-VRP) is formulated and subsequently solved by a modified Biased Random Key Genetic Algorithm (BRKGA) with novel, efficient encoding strategies and local improvement heuristics. We test our proposed method for several complex 3D structures with the 3D model extracted from OpenStreetMap. The proposed method outperforms previous methods, by reducing the length of the planned inspection path by up to 48%.", "keywords": [ "Heuristics", "Motion planning", "Computer science", "Path (computing)", "Routing (electronic design automation)", "Vehicle routing problem", "Key (lock)", "Set (abstract data type)", "Genetic algorithm", "Mathematical optimization", "Sampling (signal processing)", "Encoding (memory)" ], "cited_by_count": 82, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros45743.2020.9341089" }, { "id": "https://openalex.org/W4396609487", "doi": "10.1016/j.jclepro.2024.142429", "title": "Collaborative orchard pesticide spraying routing problem with multi-vehicles supported multi-UAVs", "year": 2024, "publication_date": "2024-05-03", "type": "article", "language": "en", "authors": [ "Jinhua Xu", "Chenghao Liu", "Jin Shao", "Yubing Xue", "Yan Li" ], "first_author": "Jinhua Xu", "venue": "Journal of Cleaner Production", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pesticide", "Vehicle routing problem", "Orchard", "Routing (electronic design automation)", "Environmental science", "Transport engineering", "Computer science", "Engineering", "Embedded system" ], "cited_by_count": 22, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.jclepro.2024.142429" }, { "id": "https://openalex.org/W4398793435", "doi": "10.1016/j.tre.2024.103573", "title": "The multi-fleet delivery problem combined with trucks, tricycles, and drones for last-mile logistics efficiency requirements under multiple budget constraints", "year": 2024, "publication_date": "2024-05-24", "type": "article", "language": "en", "authors": [ "Enming Chen", "Zhongbao Zhou", "Ruiyang Li", "Zhongxiang Chang", "Jianmai Shi" ], "first_author": "Enming Chen", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Mile", "Last mile (transportation)", "Transport engineering", "Drone", "Operations management", "Business", "Environmental science", "Operations research", "Engineering", "Automotive engineering", "Geography" ], "cited_by_count": 35, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2024.103573" }, { "id": "https://openalex.org/W4415707248", "doi": "10.1109/tsmc.2025.3623697", "title": "The Cooperative Vehicle Routing Problem With Drones", "year": 2025, "publication_date": "2025-10-30", "type": "article", "language": null, "authors": [ "Jie Jiang", "Ying Dai", "Fei Yang", "Ruijuan Zhang", "Zujun Ma" ], "first_author": "Jie Jiang", "venue": "IEEE Transactions on Systems Man and Cybernetics Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Given the sparse demands and poor traffic conditions, rural logistics is still challenging in providing efficient yet cost-effective pickup and delivery service. To realize cost reduction and efficiency enhancement, we study a novel cooperative vehicle routing problem with drones (CoVRPDs) to address operational optimization and cost allocation among a coalition with multiple heterogeneous players and investigate how their heterogeneous operating modes (i.e., operating in a truck-drone or truck-only mode) influence cooperation efficiency and cost sharing. This problem is formulated to formally represent highly interactive and complex decisions, including online participation choice, customer assignment among copartners, and collaborative truck-drone routing scheduling involving vehicle matching, intersection scheduling, and transshipment management. To tackle this, we customize an adaptive neighborhood search metaheuristic by introducing a series of customer-level and route-level destroy and repair operators to solve operational optimization efficiently, then apply both the Shapley value and the equal profit method for fair cost allocation. Then, numerical studies demonstrate that the emerging truck-drone delivery mode within a cooperative framework offers substantial economic advantages. From coalition partners, those utilizing a truck-drone delivery can significantly improve the coalition’s efficiency and thereby share smaller coalition costs, encouraging more partners to adopt an efficient truck-drone mode. Further insights on the coalition efficiency and cost allocation are also offered.", "keywords": [ "Shapley value", "Vehicle routing problem", "Scheduling (production processes)", "Transshipment (information security)", "Bilevel optimization", "Profit (economics)", "Drone", "Metaheuristic", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tsmc.2025.3623697" }, { "id": "https://openalex.org/W4393765599", "doi": "10.5281/zenodo.2572764", "title": "Vehicle Routing Problem with Drones Instances", "year": 2018, "publication_date": "2018-08-24", "type": "dataset", "language": "en", "authors": [ "David Sacramento" ], "first_author": "David Sacramento", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The following instances are originally presented in the paper An Adaptive Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Drones, written by David Sacramento Lechado, David Pisinger and Stefan Røpke, and published in Transportation Research Part C: Emerging Technologies. The data correspond to 112 instances corresponding to different scenarios for the Vehicle Routing Problem with Drones. Each instance is named n.m.t, where n is the number of customers in the scenario, m is the dimension of the grid, and t is the generic name of the scenario. Moreover, the data additionally contains 10 clustered instances, named n.m.c.t, where c refers to the cluster-feature of the instance, 100 instances for the sensitivity analysis, named n.m.s.t, where s stands for sensitivity, and 375 instances for the experiments with drone savings as function of grid size, named n.m.g.t. The first line of each instance file indicates the number of customers in the specific instance. The second line is the header for the characteristics of each customer, where it is written Coordinate X, Coordinate Y and Demand. The following provides the previous information for each customer in the instance. Finally, there is an extra file, named RouteData, which provides information about the value of the parameters in the main configuration of the problem. These parameters are: TruckSpeed: Speed in mpm (miles per minute) of the trucks. DroneSpeed: Speed in mpm of the drones. TruckCapacityWithDrones: Maximum capacity of the trucks for the drone-truck scenario. TruckCapacityWithoutDrones: Maximum capacity of the trucks for the truck-only scenario. DroneCapacity: Maximum allowed weight a drone can carry. ServiceTimeTruck: Required service time for a truck to service a customer. ServiceTimeDrone: Required service time for a drone to service a customer. Endurance: Maximum flight endurance of the battery of the drone. MaximumDriveTime: Maximum duration time of the routes. LaunchTime: Required time for launching a drone. RecoveryTime: Required time for recovering a drone. CostFactor: Corresponding parameter for computing the cost for a truck for traversing arc (i,j), given by fuel price (euro/liter), consumption rate (liter/km) and miles converter (km/miles). DroneFactor: Corresponding parameter for computing the cost for a drone for traversing arc (i,j) with respect to the truck cost.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Computer network", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://zenodo.org/record/2572764" }, { "id": "https://openalex.org/W4390618379", "doi": "10.1016/j.eswa.2023.123115", "title": "A new evolutionary optimization algorithm with hybrid guidance mechanism for truck-multi drone delivery system", "year": 2024, "publication_date": "2024-01-05", "type": "article", "language": "en", "authors": [ "Cemal Yılmaz", "Enes Cengiz", "Hamdi Tolga Kahraman" ], "first_author": "Cemal Yılmaz", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Travelling salesman problem", "Computer science", "Local search (optimization)", "Mathematical optimization", "Selection (genetic algorithm)", "Drone", "Evolutionary algorithm", "Process (computing)", "Greedy algorithm", "Vehicle routing problem", "Routing (electronic design automation)", "Algorithm" ], "cited_by_count": 30, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/11486/6619" }, { "id": "https://openalex.org/W4417531820", "doi": "10.1016/j.tre.2025.104601", "title": "A two-stage adaptive robust optimization model for the location-routing problem with drone delivery and uncertainty in humanitarian relief", "year": 2025, "publication_date": "2025-12-21", "type": "article", "language": "en", "authors": [ "Yongjian Yang", "Chenglong Li", "Dujuan Wang", "Yunqiang Yin", "T.C.E. Cheng" ], "first_author": "Yongjian Yang", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Solver", "Robustness (evolution)", "Robust optimization", "Drone", "Scheme (mathematics)", "Integer programming", "Key (lock)", "Vehicle routing problem", "Set (abstract data type)" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104601" }, { "id": "https://openalex.org/W2943545481", "doi": "10.1016/j.cor.2019.04.022", "title": "Minimizing dispersion in multiple drone routing", "year": 2019, "publication_date": "2019-04-29", "type": "article", "language": "en", "authors": [ "Guilherme Dhein", "Marcelo Serrano Zanetti", "Olinto César Bassi de Araújo", "Ghendy Cardoso" ], "first_author": "Guilherme Dhein", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Benchmark (surveying)", "Metric (unit)", "Vehicle routing problem", "Routing (electronic design automation)", "Dispersion (optics)", "Mathematical optimization", "Drone", "Mathematics", "Computer network", "Engineering", "Geography" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2019.04.022" }, { "id": "https://openalex.org/W4294990639", "doi": "10.48550/arxiv.2103.01528", "title": "Nested Vehicle Routing Problem: Optimizing Drone-Truck Surveillance Operations", "year": 2021, "publication_date": "2021-03-02", "type": "preprint", "language": "en", "authors": [ "Fanruiqi Zeng", "Zaiwei Chen", "John-Paul Clarke", "David M. Goldsman" ], "first_author": "Fanruiqi Zeng", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned aerial vehicles or drones are becoming increasingly popular due to their low cost and high mobility. In this paper we address the routing and coordination of a drone-truck pairing where the drone travels to multiple locations to perform specified observation tasks and rendezvous periodically with the truck to swap its batteries. We refer to this as the Nested-Vehicle Routing Problem (Nested-VRP) and develop a Mixed Integer Quadratically Constrained Programming (MIQCP) formulation with critical operational constraints, including drone battery capacity and synchronization of both vehicles during scheduled rendezvous. An enhancement of the MIQCP model for the Nested-VRP is achieved by deriving the equivalent Mixed Integer Linear Programming (MILP) formulation as well as leveraging lifting and Reformulation-Linearization techniques to strengthen the subtour elimination constraints of the drone. Given the NP-hard nature of the Nested-VRP, we further propose an efficient neighborhood search (NS) heuristic where we generate and improve on a good initial solution by iteratively solving the Nested-VRP on a local scale. We provide comparisons of both the exact approaches based on MIQCP or its enhanced formulations and NS heuristic methods with a relaxation lower bound in the cases of small and large problem sizes, and present the results of a computational study to show the effectiveness of the MIQCP model and its variants as well as the efficiency of the NS heuristic, including for a real-life instance with 631 locations. We envision that this framework will facilitate the planning and operations of combined drone-truck missions.", "keywords": [ "Drone", "Computer science", "Heuristic", "Truck", "Vehicle routing problem", "Rendezvous", "Routing (electronic design automation)", "Integer programming", "Mathematical optimization", "Linear programming", "Linearization", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2103.01528" }, { "id": "https://openalex.org/W4413780601", "doi": "10.1016/j.trc.2025.105322", "title": "Dynamic truck-drone collaborative transportation during hurricanes", "year": 2025, "publication_date": "2025-08-28", "type": "article", "language": "en", "authors": [ "Keyu Li", "Haipeng Cui", "Qiang Meng", "Xuxin Zhang" ], "first_author": "Keyu Li", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Aeronautics", "Transport engineering", "Engineering", "Poison control", "Computer science", "Aerospace engineering", "Medical emergency", "Medicine" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2025.105322" }, { "id": "https://openalex.org/W4405787520", "doi": "10.1109/iros58592.2024.10801949", "title": "SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle Routing Problem with Drones Using Reinforcement Learning", "year": 2024, "publication_date": "2024-10-14", "type": "conference-paper", "language": "en", "authors": [ "Navid Mohammad Imran", "Myounggyu Won" ], "first_author": "Navid Mohammad Imran", "venue": null, "publisher": null, "abstract": "The Vehicle Routing Problem with Drones (VRPD) seeks to optimize the routing paths for both trucks and drones, where the trucks are responsible for delivering parcels to customer locations, and the drones are dispatched from these trucks for parcel delivery, subsequently being retrieved by the trucks. Given the NP-Hard complexity of VRPD, numerous heuristic approaches have been introduced. However, improving solution quality, the definition of which can vary depending on various heuristic approaches, e.g., the total operation time, remain significant challenges. In this paper, we conduct a comprehensive examination of heuristic methods designed for solving VRPD, distilling and standardizing them into core elements. We then develop a novel reinforcement learning (RL) framework that is seamlessly integrated with the heuristic solution components, establishing a set of universal principles for incorporating the RL framework with heuristic strategies in an aim to improve both the solution quality and computation speed, regardless of how the solution quality is defined. This integration has been applied to a state-of-the-art heuristic solution for VRPD, showcasing the substantial benefits of incorporating the RL framework. Our evaluation results demonstrated that the heuristic solution incorporated with our RL framework not only elevated the quality of solutions but also achieved rapid computation speeds, especially when dealing with extensive customer locations.", "keywords": [ "Drone", "Reinforcement learning", "Heuristic", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Mathematical optimization", "Artificial intelligence", "Computer network", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros58592.2024.10801949" }, { "id": "https://openalex.org/W4386820358", "doi": "10.1016/j.trc.2023.104333", "title": "When drone delivery meets human courier: A co-sourcing perspective", "year": 2023, "publication_date": "2023-09-18", "type": "article", "language": "en", "authors": [ "Zhi Pei", "Yunduo Liu", "Xu Dai", "Yilun Yuan", "Changchun Liu" ], "first_author": "Zhi Pei", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Profitability index", "Queue", "Operations research", "Profit (economics)", "Queueing theory", "Mathematical optimization", "Revenue", "Business", "Economics", "Microeconomics" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2023.104333" }, { "id": "https://openalex.org/W4401098706", "doi": null, "title": "MULTI-DEPOT VEHICLE ROUTING PROBLEM WITH DRONE COLLABORATION IN HUMANITARIAN LOGISTIC", "year": 2023, "publication_date": "2023-12-23", "type": "article", "language": "en", "authors": [ "Zeynep Yüksel", "Dursun Emre Epcim", "Süleyman Mete" ], "first_author": "Zeynep Yüksel", "venue": "DergiPark (Istanbul University)", "publisher": "Istanbul University", "abstract": "Routing problems are used in many areas to obtain the most appropriate results in terms of time and cost. An attempt is made to address the issue by formulating mathematical models that incorporate multiple variables such as capacity, time, cost, and demand, tailored to the specific area of application. Natural disasters are one of these applications. In natural disasters, especially time management is a critical issue. For this reason, routing models play a crucial role in delivering aid to disaster victims and transporting disaster victims to hospitals. In this study, a mathematical model is proposed to be applied in post-disaster humanitarian aid logistics. The model, which aims to minimize the total distribution time, also considers the distribution of perishable commodities. Drones are integrated into this operational framework to facilitate the dissemination of perishable commodities. Thus, a new mathematical model for the multi-depot vehicle routing problem, which includes both truck-drone collaboration and perishable commodities, has been introduced to the literature. The proposed model is solved with an illustrative example using GAMS/CPLEX software and the results are tested.", "keywords": [ "Drone", "Vehicle routing problem", "Depot", "Routing (electronic design automation)", "Operations research", "Computer science", "Aeronautics", "Engineering", "Computer network", "Political science", "Law" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/en/download/article-file/3616334" }, { "id": "https://openalex.org/W4383860621", "doi": "10.48550/arxiv.2307.03523", "title": "Parallel drone scheduling vehicle routing problems with collective drones", "year": 2023, "publication_date": "2023-07-07", "type": "preprint", "language": "en", "authors": [ "Roberto Montemanni", "Mauro Dell’Amico", "Corsini, Andrea" ], "first_author": "Roberto Montemanni", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We study last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers. In particular, we focus on problem settings where either a single truck or a fleet with several homogeneous trucks work in parallel to drones, and drones have the capability of collaborating for delivering missions. This cooperative behaviour of the drones, which are able to connect to each other and work together for some delivery tasks, enhance their potential, since connected drone has increased lifting capabilities and can fly at higher speed, overcoming the main limitations of the setting where the drones can only work independently. In this work, we contribute a Constraint Programming model and a valid inequality for the version of the problem with one truck, namely the \\emph{Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones} and we introduce for the first time the variant with multiple trucks, called the \\emph{Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones}. For the latter variant, we propose two Constraint Programming models and a Mixed Integer Linear Programming model. An extensive experimental campaign leads to state-of-the-art results for the problem with one truck and some understanding of the presented models' behaviour on the version with multiple trucks. Some insights about future research are finally discussed.", "keywords": [ "Drone", "Truck", "Travelling salesman problem", "Vehicle routing problem", "Computer science", "Scheduling (production processes)", "Integer programming", "Routing (electronic design automation)", "Job shop scheduling", "Constraint programming", "Operations research", "Mathematical optimization" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2307.03523" }, { "id": "https://openalex.org/W3138110645", "doi": "10.1177/03611981211036685", "title": "Case Study of Drone Delivery Reliability for Time-Sensitive Medical Supplies With Stochastic Demand and Meteorological Conditions", "year": 2021, "publication_date": "2021-08-27", "type": "article", "language": "en", "authors": [ "Travis B. Glick", "Miguel Figliozzi", "Avinash Unnikrishnan" ], "first_author": "Travis B. Glick", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "Drones are increasingly being utilized to deliver medical supplies, and the COVID-19 pandemic has accelerated this trend. Drones arrive quickly by taking more direct paths and avoiding ground-based obstructions. However, drones are not completely reliable and may also experience failures and delays. For consumer products, delivery delays are an inconvenience, but for some medical supplies, delays may be fatal. This research focused on the drone reliability of one particular type of supply and event: automatic defibrillators for out-of-hospital cardiac arrests. A modeling framework was developed to analyze drone delivery reliability with stochastic demands and meteorological conditions. Using probability distributions based on real data from Portland, OR, this research quantified the failure rates as a function of drone range and meteorological conditions that included temperature, precipitation, and wind. Tradeoffs among drone reliability, fleet size, population size, and meteorological conditions were analyzed.", "keywords": [ "Drone", "Reliability (semiconductor)", "Environmental science", "Population", "Computer science", "Reliability engineering", "Operations research", "Engineering", "Medicine", "Environmental health" ], "cited_by_count": 29, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journals.sagepub.com/doi/pdf/10.1177/03611981211036685" }, { "id": "https://openalex.org/W4391769280", "doi": "10.1109/itsc57777.2023.10422505", "title": "Electric Vehicle-Drone Routing Problem with Optional Drone Availability", "year": 2023, "publication_date": "2023-09-24", "type": "conference-paper", "language": "en", "authors": [ "Setyo Tri Windras Mara", "Ruhul Amin Sarker", "Daryl Essam", "Saber Mohammed Elsayed" ], "first_author": "Setyo Tri Windras Mara", "venue": null, "publisher": null, "abstract": "This paper considers a last-mile distribution system consisting of a fleet of electric vehicles, where each vehicle has the option to be equipped with a single drone. We formulate the system as a mixed-fleet electric vehicle-drone routing problem, with the objective of minimizing the total cost of the system. Subsequently, an adaptive memetic algorithm with a Q-learning-based operator selection mechanism is employed to solve the model. A set of extensive numerical experiments show that the proposed algorithm performs better than the state-of-the-art approaches. Further, we also demonstrate the importance of economies of scale in drone-aided routing problems.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Aeronautics", "Computer network", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itsc57777.2023.10422505" }, { "id": "https://openalex.org/W2809492836", "doi": "10.1109/icccc.2018.8390444", "title": "Using drones in a warehouse with minimum energy consumption", "year": 2018, "publication_date": "2018-05-01", "type": "conference-paper", "language": "en", "authors": [ "Iván Dérpich", "Daniela Miranda", "Juan M. Sepúlveda" ], "first_author": "Iván Dérpich", "venue": null, "publisher": null, "abstract": "In this paper we analyze the feasibility of using drones in a manufacturing factory that requires multiple transports from the warehouse to the workstations. Considering low cost drones it is understood that they have little autonomy and low load capacity. To minimize energy consumption a mathematical model of vehicle routing type is used, in three dimensions, which is solved using a heuristic method based on the structure of the problem. The problem is complex because the routes must be planned in order to avoid collisions, therefore they must follow time windows in the deliveries.", "keywords": [ "Drone", "Energy consumption", "Computer science", "Routing (electronic design automation)", "Warehouse", "Heuristic", "Workstation", "Factory (object-oriented programming)", "Consumption (sociology)", "Production (economics)", "Energy (signal processing)", "Mathematical optimization" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icccc.2018.8390444" }, { "id": "https://openalex.org/W2606432390", "doi": null, "title": "Filming Sport Events with Mobile Camera Drones: Mathematical Modeling and Algorithms", "year": 2012, "publication_date": "2012-12-15", "type": "report", "language": "en", "authors": [ "Enrico Natalizio", "Rosario Surace", "Valéria Loscrì", "Francesca Guerriero", "Tommaso Melodia" ], "first_author": "Enrico Natalizio", "venue": "INRIA a CCSD electronic archive server", "publisher": null, "abstract": "We introduce and formulate the Sport Event Filming (SEF) problem. We are interested in controlling the movement of a set of camera drones that are filming the event by moving over the field where the event takes place. The objective of the problem is to maximize the satisfaction of event viewers while minimizing the distance traveled by the camera-drones. We model the static version of the SEF problem as a Vehicle Routing Problem with Soft Time Window (VRP-STW), where the whole sequence of actions in the event are known a priori. Since this assumption is unrealistic for real sport events, we propose two families of heuristics to solve the dynamic version of the problem, where the camera-drones do not have any knowledge of the input sequence and move in reaction to the movements of the protagonists of the event. The first family (Nearest Neighbor) is based on a technique used in robotic systems, whereas the second family (Ball Movement Interception) is designed based on specific characteristics of the SEF problem. We present extensive simulation results for both families in terms of average viewer satisfaction and traveled distance for the camera-drones, when several parameters vary.", "keywords": [ "Drone", "Event (particle physics)", "Heuristics", "Computer science", "A priori and a posteriori", "Computer vision", "Artificial intelligence", "Algorithm", "Sequence (biology)" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://inria.hal.science/hal-00801126" }, { "id": "https://openalex.org/W2050205301", "doi": "10.1145/1068009.1068249", "title": "A genetic algorithm for unmanned aerial vehicle routing", "year": 2005, "publication_date": "2005-06-25", "type": "conference-paper", "language": "en", "authors": [ "Matthew A. Russell", "Gary B. Lamont" ], "first_author": "Matthew A. Russell", "venue": null, "publisher": null, "abstract": "Genetic Algorithms (GAs) can efficiently produce high quality results for hard combinatorial real world problems such as the Vehicle Routing Problem (VRP). Genetic Vehicle Representation (GVR), a recent approach to solving instances of the VRP with a GA, produces competitive or superior results to the standard benchmark problems. This work extends GVR research by presenting a more precise mathematical model of GVR than in previous works and a thorough comparison of GVR to Path Based Representation approaches. A suite of metrics that measures GVR's efficiency and effectiveness provides an adequate characterization of the jagged search landscape. A new variation of a crossover operator is introduced. A previously unmentioned insight about the convergence rate of the search is also noted that is especially important to the application of a priori and dynamic routing for swarms of Unmanned Aerial Vehicles (UAVs). Results indicate that the search is robust, and it exponentially drives toward high quality solutions in relatively short time. Consequently, a GA with GVR encoding is capable of providing a state-of-the-art engine for a UAV routing system or related application.", "keywords": [ "Benchmark (surveying)", "Computer science", "Vehicle routing problem", "Crossover", "Path (computing)", "Routing (electronic design automation)", "Representation (politics)", "Genetic algorithm", "Convergence (economics)", "Mathematical optimization", "Algorithm", "A priori and a posteriori" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/1068009.1068249" }, { "id": "https://openalex.org/W2156107305", "doi": "10.1109/acc.2008.4587156", "title": "Multi-UAV dynamic routing with partial observations using restless bandit allocation indices", "year": 2008, "publication_date": "2008-06-01", "type": "conference-paper", "language": "en", "authors": [ "Jérôme Le Ny", "Munther A. Dahleh", "Éric Féron" ], "first_author": "Jérôme Le Ny", "venue": null, "publisher": null, "abstract": "Motivated by the type of missions currently performed by unmanned aerial vehicles, we investigate a discrete dynamic vehicle routing problem with a potentially large number of targets and vehicles. Each target is modeled as an independent two-state Markov chain, whose state is not observed if the target is not visited by some vehicle. The goal for the vehicles is to collect rewards obtained when they visit the targets in a particular state. This problem can be seen as a type of restless bandits problem with partial information. We compute an upper bound on the achievable performance and obtain in closed form an index policy proposed by Whittle. Simulation results provide evidence for the outstanding performance of this index heuristic and for the quality of the upper bound.", "keywords": [ "Heuristic", "Markov chain", "Routing (electronic design automation)", "Computer science", "Upper and lower bounds", "Markov process", "Mathematical optimization", "State (computer science)", "Index (typography)", "Vehicle routing problem", "State information", "Markov decision process" ], "cited_by_count": 101, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/acc.2008.4587156" }, { "id": "https://openalex.org/W3184239945", "doi": "10.1109/access.2021.3098863", "title": "A Hybrid Large Neighborhood Search Algorithm for Solving the Multi Depot UAV Swarm Routing Problem", "year": 2021, "publication_date": "2021-01-01", "type": "article", "language": "en", "authors": [ "Xiaohui Li", "Peifan Li", "Yi Zhao", "Lu Zhang", "Dong Yuan" ], "first_author": "Xiaohui Li", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper focuses on a modified Multi-Depot Unmanned Aerial Vehicle Routing Problem (MMDUAVRP). Comparing to classic multi-depot vehicle routing problem, our studied problem has no constraints to restrict the depot where the Unmanned Aerial Vehicle (UAV) departs and returns. This work aims to minimize the number of UAVs and total distance traveled by all UAVs. This problem is mathematically formulated in this paper and a heuristic-assignment based hybrid large neighborhood search(HLNS) is proposed to solve it. Extensive computational experiments are conducted to verify the performance of HLNS. The HLNS algorithm was first tested on Multi Traveling Salesman Problem which is a simplified version of the MMDUAVRP, and high quality solutions have been obtained. Experimental results by compared with CPLEX and other well-known algorithms suggest that our proposed algorithm provides better solutions within a comparatively shorter period of time. In addition, we also conduct sensitivity analysis on the location of depots and task points that may affect the total cost of solution.", "keywords": [ "Vehicle routing problem", "Travelling salesman problem", "Computer science", "Mathematical optimization", "Heuristic", "Routing (electronic design automation)", "Particle swarm optimization", "Sensitivity (control systems)", "Swarm behaviour", "Depot", "Task (project management)", "Algorithm" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/6287639/9312710/09492093.pdf" }, { "id": "https://openalex.org/W4401477194", "doi": "10.1016/j.omega.2024.103172", "title": "Optimal allocation and route design for station-based drone inspection of large-scale facilities", "year": 2024, "publication_date": "2024-08-10", "type": "article", "language": "en", "authors": [ "Lei Cai", "Jiliu Li", "Kai Wang", "Zhixing Luo", "Hu Qin" ], "first_author": "Lei Cai", "venue": "Omega", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Column generation", "Mathematical optimization", "Lagrangian relaxation", "Computer science", "Integer programming", "Swap (finance)", "Benders' decomposition", "Facility location problem", "Reliability engineering", "Engineering", "Mathematics" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.omega.2024.103172" }, { "id": "https://openalex.org/W4286987239", "doi": "10.48550/arxiv.2109.01447", "title": "Coordinating drones with mothership vehicles: The mothership and\\n multiple drones routing problem with Graphs", "year": 2021, "publication_date": "2021-09-03", "type": "preprint", "language": null, "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\\ntandem between a mothership vehicle and a fleet of drones. %more than one\\ndrone. Each drone can be launched from the mothership to perform an operation.\\nAfter completing the operations the drones return to the mothership to recharge\\nbatteries and to be ready for a new operation. Operations consist on\\n(partially) visiting graphs with given lengths to deliver some service or to\\nperform some surveillance/inspection activity. The goal is to minimize the\\noverall weighted distance traveled by the mothership while satisfying some\\nrequirements in terms of percentages of visits to the target graphs. In all\\ncases, we develop exact formulations resorting to mixed integer second order\\ncone programs that are compared on a testbed of instances to assess their\\nperformance. We also develop a matheuristic algorithm that provides reasonable\\nsolutions. Computational experiments show the usefulness of our methods in\\ndifferent scenarios.\\n", "keywords": [ "Drone", "Computer science", "Testbed", "Column generation", "Operations research", "Real-time computing", "Mathematical optimization", "Engineering", "Computer network", "Mathematics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2109.01447" }, { "id": "https://openalex.org/W4372329771", "doi": "10.1016/j.trc.2023.104147", "title": "Routing battery-constrained delivery drones in a depot network: A business model and its optimization–simulation assessment", "year": 2023, "publication_date": "2023-05-06", "type": "article", "language": "en", "authors": [ "Yanchao Liu" ], "first_author": "Yanchao Liu", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Scalability", "Mathematical optimization", "Vehicle routing problem", "Column generation", "Time horizon", "Payload (computing)", "Routing (electronic design automation)", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 29, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2023.104147" }, { "id": "https://openalex.org/W4200525079", "doi": "10.1007/s10479-021-04459-5", "title": "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing", "year": 2021, "publication_date": "2021-12-10", "type": "article", "language": "en", "authors": [ "Yang Xia", "Wenjia Zeng", "Xinjie Xing", "Yuanzhu Zhan", "Kim Hua Tan", "Ajay Kumar" ], "first_author": "Yang Xia", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": "Abstract Alongside the rise of ‘last-mile’ delivery in contemporary urban logistics, drones have demonstrate commercial potential, given their outstanding triple-bottom-line performance. However, as a lithium-ion battery-powered device, drones’ social and environmental merits can be overturned by battery recycling and disposal. To maintain economic performance, yet minimise environmental negatives, fleet sharing is widely applied in the transportation field, with the aim of creating synergies within industry and increasing overall fleet use. However, if a sharing platform’s transparency is doubted, the sharing ability of the platform will be discounted. Known for its transparent and secure merits, blockchain technology provides new opportunities to improve existing sharing solutions. In particular, the decentralised structure and data encryption algorithm offered by blockchain allow every participant equal access to shared resources without undermining security issues. Therefore, this study explores the implementation of a blockchain-enabled fleet sharing solution to optimise drone operations, with consideration of battery wear and disposal effects. Unlike classical vehicle routing with fleet sharing problems, this research is more challenging, with multiple objectives (i.e., shortest path and fewest charging times), and considers different levels of sharing abilities. In this study, we propose a mixed-integer programming model to formulate the intended problem and solve the problem with a tailored branch-and-price algorithm. Through extensive experiments, the computational performance of our proposed solution is first articulated, and then the effectiveness of using blockchain to improve overall optimisation is reflected, and a series of critical influential factors with managerial significance are demonstrated.", "keywords": [ "Drone", "Computer science", "Integer programming", "Supply chain", "Blockchain", "Operations research", "Routing (electronic design automation)", "Computer security", "Business", "Computer network", "Engineering", "Marketing" ], "cited_by_count": 47, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10479-021-04459-5.pdf" }, { "id": "https://openalex.org/W4401527999", "doi": "10.1016/j.cie.2024.110468", "title": "Collaborative truck–drone–motorcycle delivery of emergency supplies for mountain wildfire suppression", "year": 2024, "publication_date": "2024-08-13", "type": "article", "language": "en", "authors": [ "Jiahao Cheng", "Yufeng Zhou", "Changzhi Wu", "Zhiguo Li" ], "first_author": "Jiahao Cheng", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Aeronautics", "Transport engineering", "Environmental science", "Engineering", "Automotive engineering", "Biology" ], "cited_by_count": 22, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2024.110468" }, { "id": "https://openalex.org/W2606990251", "doi": "10.7737/jkorms.2016.41.3.075", "title": "Usefulness of Drones in the Urban Delivery System: Solving the Vehicle and Drone Routing Problem with Time Window", "year": 2016, "publication_date": "2016-08-31", "type": "article", "language": "en", "authors": [ "Yerim Chung", "Taejoon Park", "Yunhong Min" ], "first_author": "Yerim Chung", "venue": "한국경영과학회지", "publisher": null, "abstract": "This paper investigates the usefulness of drones in an urban delivery system. We define the vehicle and drone routing problem with time window (VDRPTW) and present a model that can describe a dual mode delivery system consisting of drones and vehicles in the metropolitan area. Drones are relatively free from traffic congestion but have limited flight range and capacity. Vehicles are not free from traffic congestion, and the complexity of urban road network reduces the efficiency of vehicles. Using drones and vehicles together can reduce inefficiency of the urban delivery system because of their complementary cooperation. In this paper, we assume that drones operate in a point-to-point manner between the depot and customers, and that customers in the need of fast delivery are willing to pay additional charges. For the experiment datasets, we use instances of Solomon (1987), which are well known in the Vehicle Routing Problem society. Moreover, to mirror the urban logistics demand trend, customers who want fast delivery are added to the Solomon's instances. We propose a hybrid evolutionary algorithm for solving VDRPTW. The experiment results provide different useful insights according to the geographical distributions of customers. In the instances where customers are randomly located and in instances where some customers are randomly located while others form some clusters, the dual mode delivery system displays lower total cost and higher customer satisfaction. In instances with clustered customers, the dual mode delivery system exhibits narrow competition for the total cost with the delivery system that uses only vehicles. In this case, using drones and vehicles together can reduce the level of dissatisfaction of customers who take their cargo over the time-window. From the view point of strategic flexibility, the dual mode delivery system appears to be more interesting. In meeting the objective of maximizing customer satisfaction, the use of drones and vehicles incurs less cost and requires fewer resources.", "keywords": [ "Drone", "Computer science", "Metropolitan area", "Vehicle routing problem", "Routing (electronic design automation)", "Traffic congestion", "Inefficiency", "Mode (computer interface)", "Operations research", "Transport engineering", "Computer network", "Economics" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://koreascience.or.kr:80/article/JAKO201609633505758.pdf" }, { "id": "https://openalex.org/W2906697459", "doi": "10.1109/liss.2018.8593233", "title": "Truck and Drone in Tandem Route Scheduling under Sparse Demand Distribution", "year": 2018, "publication_date": "2018-08-01", "type": "conference-paper", "language": "en", "authors": [ "Jiashi Liu", "Zhongliang Guan", "Xiang Xie" ], "first_author": "Jiashi Liu", "venue": null, "publisher": null, "abstract": "Once the drone's use area is focus on military area, now it is being broadened. The drone is more frequently used in logistic distribution, due to its low cost in delivery. However, the delivery range capacity of drone is limited. This problem can be solved by truck and drone complement delivery. This research is about drone truck in tandem routing modeling. We focus on solving drone and truck routing problem under sparse demand condition.", "keywords": [ "Drone", "Truck", "Scheduling (production processes)", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Focus (optics)", "Operations research", "Engineering", "Computer network", "Operations management", "Aerospace engineering" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/liss.2018.8593233" }, { "id": "https://openalex.org/W2985101306", "doi": "10.1016/j.comcom.2019.10.026", "title": "GeoUAVs: A new geocast routing protocol for fleet of UAVs", "year": 2019, "publication_date": "2019-11-01", "type": "article", "language": "en", "authors": [ "Fatima Zohra Bousbaa", "Chaker Abdelaziz Kerrache", "Zohra Mahi", "Abdou El Karim Tahari", "Nasreddine Lagraa", "Mohamed Bachir Yagoubi" ], "first_author": "Fatima Zohra Bousbaa", "venue": "Computer Communications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Routing protocol", "Computer network", "Geocast", "Wireless Routing Protocol", "Zone Routing Protocol", "Reliability (semiconductor)", "Routing (electronic design automation)", "Link-state routing protocol", "Interior gateway protocol", "Network packet", "Dynamic Source Routing" ], "cited_by_count": 66, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.comcom.2019.10.026" }, { "id": "https://openalex.org/W2916024482", "doi": "10.5505/pajes.2018.19052", "title": "A mathematical model for the delivery routing problem via drones", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "Sinem Han", "Begüm Özer", "Barış Alioğlu", "Özgür Polat", "Tülin Aktin" ], "first_author": "Sinem Han", "venue": "Pamukkale University Journal of Engineering Sciences", "publisher": "Pamukkale University", "abstract": "ÖzLogistics sector aims to distribute merchandise to designated places on time, while focusing on the entire process that starts from storing the goods at warehouses.Transportation by land is the most common and preferred way in logistics in terms of cost and service.Studies have been going on for drone transportation with the purpose of reducing the service time and making the process automated.Advancement of this technology has led to an increase in transportation with drones.Traffic is a huge problem in land transportation causing delays in delivery time.Furthermore, nature and economy are affected because of the CO2 emission, which is a consequence of intense traffic.In this study, utilizing drones for delivery is proposed with the aim of increasing customer satisfaction and minimizing transportation cost per delivery in a green way.İstanbul's Bakırköy district is selected as the implementation region, and daily drug delivery to pharmacies with drones is examined.In the first stage, a mathematical model that uses pharmacy coordinates as an input is developed for clustering analysis.In the second stage, one of these clusters is selected, and the location of the drone center that will serve pharmacies in the cluster is obtained by center of gravity method.Then, a vehicle routing model is proposed for finding the drone routes and calculating the total distance travelled within this cluster.Mathematical models are solved with GAMS/CPLEX solver.It is believed that this study will trigger new studies in \"green delivery\" literature in near future.Lojistik, bir ürünün nakliyesinin yanı sıra, depolanması, ambalajlanması ve dağıtımı gibi süreçleri de kapsamakta ve tüm sürecin, ilk üreticiden nihai tüketiciye kadar doğru bir biçimde çalışmasını hedeflemektedir.Maliyet ve hizmet bakımından uygun bulunan karayolu taşımacılığı, günümüzde tercih edilen ulaşım sistemlerinin başında gelmektedir.Ancak gelişen teknolojiyle birlikte, birçok ürünün dağıtımında drone'lardan da yararlanıldığı görülmektedir.Bekleme zamanlarını azaltmayı ve insangücü maliyetini ortadan kaldırmayı amaçlayan bu sistem, yeni bir teslimat aracı olarak lojistik sürecinde yerini almıştır.Karayolunda oluşan trafik yoğunluğu, ürünlerin müşteriye ulaşmasını geciktirmekte, bu sırada artan CO2 salımı da çevre ve ekonomi üzerinde olumsuz etkilere neden olmaktadır.Bu çalışmanın amacı, drone kullanımı ile hızlı ve doğa dostu bir teslimat gerçekleştirmek suretiyle müşteri memnuniyetinde artış ve taşıma maliyetlerinde azalma sağlamak ve yukarıda bahsedilen problemlere bir çözüm sunmaktır.Çalışmada, eczanelerin günlük ilaç gereksinimlerinin drone'lar yardımıyla karşılanması ele alınmış ve bu amaçla, İstanbul'un Bakırköy ilçesi pilot bölge olarak seçilmiştir.Önerilen iki aşamalı yöntemin ilk aşamasındaki matematiksel model, kümeleme analizi yapmaktadır.Bölgedeki eczanelerin koordinatları modele girdi olarak verilmiş ve eczane kümeleri oluşturulmuştur.İkinci aşamada ise, oluşturulan kümelerden birisi seçilerek, ağırlık merkezi yöntemiyle, burada bulunan eczanelere hizmet verecek olan drone merkezinin yeri bulunmuştur.Daha sonra, bu kümeye ait eczanelere ilaç dağıtımı yapılırken, aralarındaki toplam mesafeyi enküçükleyecek şekilde drone'ların teslimat rotalarını belirleyen bir araç rotalama modeli geliştirilmiştir.Modeller, GAMS/CPLEX çözücüsü ile çözülmüştür.Bu çalışmanın yakın gelecekte literatürdeki \"yeşil teslimat\" alanındaki yeni araştırmaları tetikleyeceğine inanılmaktadır.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Operations research", "Mathematical optimization", "Computer network", "Mathematics", "Biology" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://jag.journalagent.com/z4/download_fulltext.asp?pdir=pajes&plng=eng&un=PAJES-19052" }, { "id": "https://openalex.org/W4401416087", "doi": "10.1109/cec60901.2024.10611902", "title": "The Vehicle Routing Problem with Drones and Flexibility Demands", "year": 2024, "publication_date": "2024-06-30", "type": "conference-paper", "language": "en", "authors": [ "Tran Thi Hue", "Nguyen Bao Ngoc", "Quoc Cuong Nguyen", "Pham Phu Manh", "Nguyen Khanh Phuong", "Huỳnh Thị Thanh Bình", "Dang Quang Thang" ], "first_author": "Tran Thi Hue", "venue": null, "publisher": null, "abstract": "In recent years, the Vehicle Routing Problem, with its variants handling complex realistic constraints, continues to be a key area of research, reflecting its enduring importance in modernized societies. Nevertheless, less attention has been paid to the variant with the flexibility of customer demands. This paper introduces the problem of addressing the integration of drones into the existing transportation system to maximize the profit of distributing freight during a limited working time by allowing flexibility in the delivered quantity. We propose a Mixed Integer Linear Programming (MILP) formulation and a genetic algorithm (GA) to solve the problem. To assess the proposed algorithm, we create the benchmark instances of the problem from published data sources. Experimental results on the instances show a significantly better performance of our proposal than MILP. Moreover, we evaluate our approach by comparing its performance with existing solutions in published closely related works.", "keywords": [ "Drone", "Flexibility (engineering)", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Aeronautics", "Computer network", "Engineering", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/cec60901.2024.10611902" }, { "id": "https://openalex.org/W4387010663", "doi": "10.1109/jiot.2023.3318524", "title": "Collaborative Delivery Optimization With Multiple Drones via Constrained Hybrid Pointer Network", "year": 2023, "publication_date": "2023-09-25", "type": "article", "language": "en", "authors": [ "Fanhui Kong", "Bin Jiang", "Jian Wang", "Huihui Helen Wang", "Houbing Herbert Song" ], "first_author": "Fanhui Kong", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Drone participation in truck delivery is a potential booster for the last-mile logistics system, which has been an emerging hot research field. Among that, how to arrange a fleet of drones from the truck and optimize the vehicle routing problem with drones (VRPDs) is a key issue. However, most existing studies fail to derive the feasible solutions due to unordered customer distributions and multivariant drone feature constraints. In this article, we propose a novel self-driven reinforcement learning structure, named constraint-based hybrid pointer network (CH-Ptr-Net) model, which is a hybrid pointer network approach composed of graph neural network (GNN) embedding and attention decoder. We go into developing the simpler embedding version for multiple drones-assisted truck delivery. The CH-Ptr-Net model tends to generate a set of optimal delivery sequence, after constructing the mixed-integer linear program (MILP) formulation. Extensive numerical testing indicates that the proposed method performs better than recent exact and heuristic approaches for collaborative delivery routing optimization with the truck carrying multiple drones.", "keywords": [ "Drone", "Computer science", "Pointer (user interface)", "Vehicle routing problem", "Integer programming", "Truck", "Mathematical optimization", "Linear programming", "Network packet", "Distributed computing", "Routing (electronic design automation)", "Computer network" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://mdsoar.org/bitstreams/79a8f351-50ac-4310-b648-6bce572fb5e2/download" }, { "id": "https://openalex.org/W4252308085", "doi": "10.1109/icst50505.2020.9732812", "title": "Relief Mapping Assessment using Two-Echelon Vehicle Routing Problem with Drone", "year": 2020, "publication_date": "2020-09-07", "type": "conference-paper", "language": "en", "authors": [ "Anak Agung Ngurah Perwira Redi", "Rahmad Inca Liperda", "Bertha Maya Sopha", "Anna Maria Sri Asih", "Nandini N. Sekaringtyas", "Handina B. Astiana" ], "first_author": "Anak Agung Ngurah Perwira Redi", "venue": null, "publisher": null, "abstract": "This study considers the two-echelon vehicle routing problem using drones in the postdisaster situation. The problem takes into account using a collaboration between a drone with a ground vehicle to conduct an information gathering so-called mapping operation. Each ground vehicle is associated with a drone. The first echelon depicts the routes travelled from depot to stopover point and from a stopover point to another stopover point. The first echelon is travelled by a ground vehicle to extend the limitation of drone's range coverage to gather information. The second echelon is the assignment of the drone to the target point. A target point is an area being mapped or in this case that is affected by the disaster. The problem is modelled as an integer linear programming problem denoted as 2EVRP-MOD. It is assumed that drone can only be released from a stopover point. The mapping operation is associated with the amount of area being covered at each target area. The objective is to minimize the total mapping operation time. The entire mapping operation time is limited by the drone flying capacity limit. The model is tested on a real-case dataset in Bekasi, Indonesia. The computational results show that the model can effectively provide a solution for the 2EVRP-MOD.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Point (geometry)", "Integer programming", "Operations research", "Vehicle routing problem", "Linear programming", "Range (aeronautics)", "Integer (computer science)", "Ground station", "Simultaneous localization and mapping" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icst50505.2020.9732812" }, { "id": "https://openalex.org/W2970900800", "doi": "10.1007/978-3-030-18500-8_3", "title": "Integration of Drones in Last-Mile Delivery: The Vehicle Routing Problem with Drones", "year": 2019, "publication_date": "2019-01-01", "type": "book-chapter", "language": "en", "authors": [ "Daniel Schermer" ], "first_author": "Daniel Schermer", "venue": "Operations research proceedings", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Solver", "Integer programming", "Vehicle routing problem", "Computer science", "Job shop scheduling", "Routing (electronic design automation)", "Set (abstract data type)", "Mathematical optimization", "Operations research", "Engineering", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-18500-8_3" }, { "id": "https://openalex.org/W2793005893", "doi": "10.1016/j.adhoc.2018.02.008", "title": "Quality-aware UAV coverage and path planning in geometrically complex environments", "year": 2018, "publication_date": "2018-02-27", "type": "article", "language": "en", "authors": [ "Rui Dai", "Sneha Fotedar", "Mohammadreza Radmanesh", "Manish Kumar" ], "first_author": "Rui Dai", "venue": "Ad Hoc Networks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Waypoint", "Computer science", "Motion planning", "Real-time computing", "Path (computing)", "Terrain", "Energy consumption", "Energy (signal processing)", "Routing (electronic design automation)", "Scheme (mathematics)", "Mathematical optimization", "Artificial intelligence" ], "cited_by_count": 78, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.adhoc.2018.02.008" }, { "id": "https://openalex.org/W3197610991", "doi": "10.1007/978-3-030-85906-0_4", "title": "Collaborative Hybrid Delivery System: Drone Routing Problem Assisted by Truck", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Ho Young Jeong", "Seokcheon Lee" ], "first_author": "Ho Young Jeong", "venue": "IFIP advances in information and communication technology", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Integer programming", "Vehicle routing problem", "Exploit", "Operations research", "Engineering", "Computer network", "Automotive engineering", "Computer security" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://inria.hal.science/hal-04022164" }, { "id": "https://openalex.org/W4390103296", "doi": "10.5267/j.ijiec.2023.9.011", "title": "Heterogeneous multi-drone and helicopter routing problem for reconnaissance", "year": 2023, "publication_date": "2023-12-22", "type": "article", "language": "en", "authors": [ "Peixin Zhao", "Xiaoyue Zeng", "Chenchen Du" ], "first_author": "Peixin Zhao", "venue": "International Journal of Industrial Engineering Computations", "publisher": "Growing Science", "abstract": "Helicopters and drones are widely used in military and post-disaster reconnaissance. But less attention has been paid to collaborative reconnaissance between the two, especially when drones can be launched and retrieved multiple times. We propose a synchronous routing problem of helicopter and heterogeneous multi-drone for reconnaissance, which is a new variant of the orienteering problem (OP), where the drones can visit multiple mission nodes and can reconnoiter the retrieval nodes in a single trip, with the goal of maximizing the information collected. The problem is formulated as a mixed integer linear programming (MILP) model, and then an adaptive simulated annealing algorithm (A-SA) is designed to solve the problem. Specifically, a universal high-efficiency heuristics solution evaluation method based on segment sorting is proposed. The time complexity of this method is O(n). The numerical experiments illustrate the accuracy and efficiency of the algorithm. The results also show that allowing the drones to conduct reconnaissance on the retrieval nodes can positively impact the solution.", "keywords": [ "Drone", "Heuristics", "Computer science", "Orienteering", "Routing (electronic design automation)", "Simulated annealing", "Integer programming", "Linear programming", "Sorting", "Mathematical optimization", "Real-time computing", "Operations research" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5267/j.ijiec.2023.9.011" }, { "id": "https://openalex.org/W4412480669", "doi": "10.3390/drones9070501", "title": "A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA", "year": 2025, "publication_date": "2025-07-16", "type": "article", "language": "en", "authors": [ "Yongchao Zhang", "Wei Xu", "Hongchen Ye", "Zhuoyong Shi" ], "first_author": "Yongchao Zhang", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optimizers, which can lead to suboptimal performance. To address this gap, this paper proposes a novel two-stage hierarchical framework that integrates the Grey Wolf Optimizer (GWO) with the Consensus-Based Bundle Algorithm (CBBA). At the strategic level, the GWO determines the optimal number of UAVs by minimizing a comprehensive cost function that balances mission efficiency and operational costs. Subsequently, at the tactical level, the CBBA performs decentralized, real-time task allocation for the optimally sized fleet. We validated our GWO-CBBA framework through extensive simulations against three benchmarks: a standard CBBA with a fixed fleet, a centralized Particle Swarm Optimization (PSO) approach, and a Greedy Heuristic algorithm. The results are compelling: our framework demonstrates superior performance across all key metrics, reducing the overall scheduling cost by 13.2–36.5%, minimizing UAV mileage cost and significantly decreasing total task waiting time. This work provides a robust and efficient solution that effectively balances operational costs with service quality for dynamic multi-UAV scheduling problems.", "keywords": [ "Sizing", "Computer science", "Scheduling (production processes)", "Particle swarm optimization", "Key (lock)", "Operations research", "Mathematical optimization", "Engineering", "Operations management" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/7/501/pdf?version=1752677722" }, { "id": "https://openalex.org/W7153024469", "doi": "10.1016/j.swevo.2026.102382", "title": "Solving electric vehicle routing problem with heterogeneous drones and no-fly zones using enhanced adaptive large neighborhood search algorithm", "year": 2026, "publication_date": "2026-04-01", "type": "article", "language": "en", "authors": [ "Wanting Chen", "Shuai Zhang", "Kang Jiang" ], "first_author": "Wanting Chen", "venue": "Swarm and Evolutionary Computation", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Drone", "Electric vehicle", "Routing (electronic design automation)", "Search algorithm", "Local search (optimization)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.swevo.2026.102382" }, { "id": "https://openalex.org/W4283769270", "doi": "10.1177/03611981221103872", "title": "Multiple Drone Routing Problem for On-Demand Hyperlocal Market", "year": 2022, "publication_date": "2022-07-02", "type": "article", "language": "en", "authors": [ "Murugaiyan Pachayappan" ], "first_author": "Murugaiyan Pachayappan", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "Deploying drones for rapid pickup and delivery for on-demand customers in the hyperlocal market is unexplored in literature and demands attention. Maximizing customer pickup and deliveries with a limited drone flight endurance is essential but is hard to achieve in practice because of many service requests by on-demand customers. It mandates that drones cover extra mileage to visit docking stations in between services for recharge. Optimally allocating the services on available flight endurance and minimizing the docking station visits to complete all scheduled services is a predominant requirement for effective drone operations in a hyperlocal market. This problem is formulated as a mixed-integer linear programming model, and a heuristic algorithm is proposed to attempt various practical size problems, with near-optimal solutions reported. The paper offers valuable insights for practitioners and future researchers wishing to analyze the performance of drone operations and determine the appropriate number of drones required for the hyperlocal market based on service demand.", "keywords": [ "Drone", "Operations research", "Computer science", "Integer programming", "Pickup", "Service (business)", "Business", "Marketing", "Engineering", "Artificial intelligence" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981221103872" }, { "id": "https://openalex.org/W3014244852", "doi": "10.48550/arxiv.2004.02275", "title": "The two-echelon routing problem with truck and drones", "year": 2020, "publication_date": "2020-04-05", "type": "preprint", "language": "en", "authors": [ "Minh Hoàng Hà", "Lam Vu", "Duy Manh Vu" ], "first_author": "Minh Hoàng Hà", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In this paper, we study novel variants of the well-known two-echelon vehicle routing problem in which a truck works on the first echelon to transport parcels and a fleet of drones to intermediate depots while in the second echelon, the drones are used to deliver parcels from intermediate depots to customers. The objective is to minimize the completion time instead of the transportation cost as in classical 2-echelon vehicle routing problems. Depending on the context, a drone can be launched from the truck at an intermediate depot once (single trip drone) or several times (multiple trip drone). Mixed Integer Linear Programming (MILP) models are first proposed to formulate mathematically the problems and solve to optimality small-size instances. To handle larger instances, a metaheuristic based on the idea of Greedy Randomized Adaptive Search Procedure (GRASP) is introduced. Experimental results obtained on instances of different contexts are reported and analyzed.", "keywords": [ "Drone", "Vehicle routing problem", "Truck", "Context (archaeology)", "Computer science", "Integer programming", "Routing (electronic design automation)", "Mathematical optimization", "Linear programming", "GRASP", "Metaheuristic", "Heuristic" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2004.02275" }, { "id": "https://openalex.org/W7127585695", "doi": "10.1016/j.trb.2026.103410", "title": "Drone routing problem for shore-to-ship delivery services considering non-linear energy consumption", "year": 2026, "publication_date": "2026-02-04", "type": "article", "language": "en", "authors": [ "Mengtong Wang", "Shukai Chen", "Qiang Meng" ], "first_author": "Mengtong Wang", "venue": "Transportation Research Part B Methodological", "publisher": "Elsevier BV", "abstract": "This study investigates the emerging application of unmanned aerial vehicles (UAVs), or drones, for shore-to-ship delivery services between onshore and offshore locations. However, deploying drones for shore-to-ship delivery can encounter unique operational challenges, including constantly moving target vessels and non-linear drone energy consumption. To address these issues, we propose a novel and practical drone routing problem for shore-to-ship delivery services (DRP-SSDS) considering the non-linear energy consumption related to payload, flight phase, and flight time. The proposed DRP-SSDS is formulated as a mixed-integer second-order cone programming (MISOCP) model that integrates continuous decisions on both time and location to realistically capture vessel movements within port waters. We then develop a tailored branch-and-price algorithm that can solve DRP-SSDS exactly and efficiently for medium-scale instances. Additionally, we design an effective heuristic method that can provide high-quality solutions in a reasonable time limit for large-scale instances. Extensive numerical experiments demonstrate the superiority of the proposed solution methods over the off-the-shelf optimization solver and a benchmark method across all tested instances.", "keywords": [ "Drone", "Energy consumption", "Benchmark (surveying)", "Solver", "Routing (electronic design automation)", "Heuristic", "Limit (mathematics)", "Vehicle routing problem" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trb.2026.103410" }, { "id": "https://openalex.org/W2332837215", "doi": "10.2514/6.2007-158", "title": "Optimal Static Soaring of UAVs Using Vehicle Routing with Time Windows", "year": 2007, "publication_date": "2007-01-08", "type": "conference-paper", "language": "en", "authors": [ "Nazli E. Kahveci", "Pétros Ioannou", "Maj Mirmirani" ], "first_author": "Nazli E. Kahveci", "venue": "45th AIAA Aerospace Sciences Meeting and Exhibit", "publisher": null, "abstract": null, "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Real-time computing", "Simulation", "Computer network" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2007-158" }, { "id": "https://openalex.org/W4389398323", "doi": "10.1016/j.eswa.2023.122809", "title": "A bi-criteria approach to the truck-multidrone routing problem", "year": 2023, "publication_date": "2023-12-06", "type": "article", "language": "en", "authors": [ "Pedro Luis González Rodríguez", "David Sánchez-Wells", "José Luis Andrade-Pineda" ], "first_author": "Pedro Luis González Rodríguez", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": "In the rapidly expanding field of e-commerce logistics, the optimisation of last-mile delivery solutions is paramount. This paper introduces a novel methodology that addresses this challenge by generating approximate Pareto fronts in a hybrid truck-drone delivery system. Specifically, we examine a generalized single truck multi-drone problem that allows multi-visit flight missions and rendezvous points distinct from launch locations. Our goal is providing decision-makers with a portfolio of optimal routing solutions that balance service time and environmental impact, criteria that are increasingly shaping decision-making in this domain. To achieve this, we introduce a bivector coding scheme inspired by flow-shop scheduling problems and implement a Simulated Annealing algorithm. This algorithm features an advanced stopping mechanism, negating the need for manual adjustments by utilizing a distinctive blend of a domination rate and a Kalman filter. Importantly, our framework employs an iterated greedy search algorithm to evolve from initial solutions towards identifying non-dominated solutions sets, which are then ranked using a hypervolume coefficient. To validate our methodology, we conduct a sensitivity analysis on two different size instances using a full factorial design of experiments. Our analysis reveals crucial insights into the impact of the number of drones, their autonomy, and their flight speed settings. From it, we conclude that it is a robust and adaptable framework for its practical application for obtaining Pareto fronts solutions among which picking the ultimate routing to be implemented.", "keywords": [ "Computer science", "Drone", "Mathematical optimization", "Pareto principle", "Iterated local search", "Truck", "Operations research", "Vehicle routing problem", "Routing (electronic design automation)", "Metaheuristic", "Algorithm", "Mathematics" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2023.122809" }, { "id": "https://openalex.org/W2804132163", "doi": "10.1155/2018/7318207", "title": "Multidepot UAV Routing Problem with Weapon Configuration and Time Window", "year": 2018, "publication_date": "2018-01-01", "type": "article", "language": "en", "authors": [ "Tianren Zhou", "Jiaming Zhang", "Jianmai Shi", "Zhong Liu", "Jincai Huang" ], "first_author": "Tianren Zhou", "venue": "Journal of Advanced Transportation", "publisher": "Hindawi Publishing Corporation", "abstract": "In recent wars, there is an increasing trend that unmanned aerial vehicles (UAVs) are utilized to conduct military attacking missions. In this paper, we investigate a novel multidepot UAV routing problem with consideration of weapon configuration in the UAV and the attacking time window of the target. A mixed-integer linear programming model is developed to jointly optimize three kinds of decisions: the weapon configuration strategy in the UAV, the routing strategy of target, and the allocation strategy of weapons to targets. An adaptive large neighborhood search (ALNS) algorithm is proposed for solving the problem, which is tested by randomly generated instances covering the small, medium, and large sizes. Experimental results confirm the effectiveness and robustness of the proposed ALNS algorithm.", "keywords": [ "Robustness (evolution)", "Integer programming", "Computer science", "Linear programming", "Routing (electronic design automation)", "Window (computing)", "Mathematical optimization", "Vehicle routing problem", "Operations research", "Real-time computing", "Engineering", "Algorithm" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "http://downloads.hindawi.com/journals/jat/2018/7318207.pdf" }, { "id": "https://openalex.org/W3126308699", "doi": "10.1016/j.ejtl.2021.100070", "title": "A branch-and-price algorithm for a team orienteering problem with fixed-wing drones", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Kaarthik Sundar", "Sujeevraja Sanjeevi", "Christopher Montez" ], "first_author": "Kaarthik Sundar", "venue": "EURO Journal on Transportation and Logistics", "publisher": "Springer Science+Business Media", "abstract": "This paper formulates a team orienteering problem with multiple fixed-wing drones and develops a branch-and-price algorithm to solve the problem to optimality. Fixed-wing drones, unlike rotary drones, have kinematic constraints associated with them, thereby preventing them to make on-the-spot turns and restricting them to a minimum turn radius. This paper presents the implications of these constraints on the drone routing problem formulation and proposes a systematic technique to address them in the context of the team orienteering problem. Furthermore, a novel branch-and-price algorithm with branching techniques specific to the constraints imposed due to fixed-wing drones are proposed. Extensive computational experiments on benchmark instances corroborating the effectiveness of the algorithms are also presented.", "keywords": [ "Orienteering", "Drone", "Context (archaeology)", "Mathematical optimization", "Turning radius", "Computer science", "Kinematics", "Benchmark (surveying)", "Mathematics", "Engineering", "Aerospace engineering", "Geography" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S2192437621000352/pdf" }, { "id": "https://openalex.org/W4391524931", "doi": "10.1002/net.22214", "title": "Solving the probabilistic drone routing problem: Searching for victims in the aftermath of disasters", "year": 2024, "publication_date": "2024-02-13", "type": "article", "language": "en", "authors": [ "Amadeu Almeida Coco", "Christophe Didier Duhamel", "Andréa Cynthia Santos", "Matheus Nohra Haddad" ], "first_author": "Amadeu Almeida Coco", "venue": "Networks", "publisher": "Wiley", "abstract": "Abstract Several major industrial disasters happen each year around the world. They usually involve limited accessibility, poor ground conditions, and toxic wastes. As a consequence, this reduces the efficiency of humanitarian operations. In such a context, flying drones may be a viable alternative: faster, no dependency on ground conditions, and larger areas scanned. They are also better suited for following the population and the crisis dynamic. For such a purpose, various issues have to be addressed such as defining and optimizing the drone's routes, their energy consumption, choosing the relay points for recharging equipment, among others. In this study, several additional features from existing works are considered: first, a probability of identifying individuals is defined. Thus, each node can be scanned several times in order to improve the observation. In addition, the nodes are prioritized according to a given heatmap. The probabilistic drone routing problem (PDRP) consists of finding a route, that is, a sequence of trips, for each drone such that the sum of the expected number of identified individuals on all routes is maximized. Constraints on energy consumption, collision avoidance and drone‐base assignment are considered. We propose a heuristic and metaheuristics based on the adaptive large neighborhood search for the PDRP. The methods are tested on theoretical instances, as well as on a case study of the Beirut Port explosion on August 4, 2020, in order to analyze the performance of the proposed methods.", "keywords": [ "Drone", "Probabilistic logic", "Routing (electronic design automation)", "Computer science", "Computer security", "Artificial intelligence", "Computer network" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/net.22214" }, { "id": "https://openalex.org/W4399704353", "doi": "10.1007/s40747-024-01472-6", "title": "An iterative two-phase optimization method for heterogeneous multi-drone routing problem considering differentiated demands", "year": 2024, "publication_date": "2024-06-15", "type": "article", "language": "en", "authors": [ "Huan Liu", "Guohua Wu", "Yufei Yuan", "Dezhi Wang", "Long Gen Zheng", "Wei Zhou" ], "first_author": "Huan Liu", "venue": "Complex & Intelligent Systems", "publisher": "Springer Science+Business Media", "abstract": "Abstract Owing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of “last-mile” delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a pioneering heterogeneous multi-drone delivery system. In this system, the mother drone handles the delivery of large and heavy parcels, releasing small drones to manage the delivery of smaller and lighter parcels. To address the complexities of this multi-drone delivery system, we introduce a divide-and-conquer framework consisting of two integral phases. The first phase, the task allocation phase, generates multiple task allocation schemes, while the second phase, the single-drone route planning phase, produces high-quality routes for each individual drone. Two phases are performed in an iterative manner until the predefined stopping criteria are satisfied. In the task allocation phase, we propose a simulated annealing algorithm (SA) to facilitate task allocation among multiple drones, utilizing transfer and recombination operators to generate high-quality solutions. After obtaining the task allocation scheme, a satisfactory route of a mother drone is generated by a variable neighborhood descent algorithm (VND). A desirable route for each single small drone is produced by dynamic programming (DP).Extensive experiments are conducted, demonstrating the outstanding optimization and time efficiency of the proposed two-phase optimization method by the fact that it obtains within a 4.89% gap from the optimal solution generated by CPLEX in 15.48 s for instance up to 125 nodes.", "keywords": [ "Drone", "Computer science", "Simulated annealing", "Task (project management)", "Mathematical optimization", "Payload (computing)", "Routing (electronic design automation)", "Optimization problem", "Distributed computing", "Network packet", "Computer network", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s40747-024-01472-6" }, { "id": "https://openalex.org/W3208367865", "doi": "10.1016/j.asoc.2021.108015", "title": "Optimization of the distance-constrained multi-based multi-UAV routing problem with simulated annealing and local search-based matheuristic to detect forest fires: The case of Turkey", "year": 2021, "publication_date": "2021-10-29", "type": "article", "language": "en", "authors": [ "Ömer Özkan" ], "first_author": "Ömer Özkan", "venue": "Applied Soft Computing", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Simulated annealing", "Metaheuristic", "Mathematical optimization", "Integer programming", "Evolutionary algorithm", "Algorithm", "Artificial intelligence", "Mathematics" ], "cited_by_count": 38, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.asoc.2021.108015" }, { "id": "https://openalex.org/W4320728916", "doi": "10.3390/atmos14020373", "title": "A Drone Routing Problem for Ship Emission Detection Considering Simultaneous Movements", "year": 2023, "publication_date": "2023-02-14", "type": "article", "language": "en", "authors": [ "Zhi‐Hua Hu", "Tian-Ci Liu", "Xi-Dan Tian" ], "first_author": "Zhi‐Hua Hu", "venue": "Atmosphere", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Offshore ships’ emission has a tremendous environmental and healthy impact on the port cities and citizens, even though the Emission Control Area (ECA) policy imposes legislative constraints on the ships. It is challenging to detect ships with illegal emissions using traditional administrative and enforcement methods. In addition to a system of ships’ emission detection, a drone-based detection system is investigated, and a drone routing problem is formulated considering the distinct feature: the drone flying while ships move simultaneously. A nonlinear program is devised, and heuristics algorithms are developed to solve the test instances. The numerical experiments demonstrate the feasibility and advantages of using drone routing solutions. The solution algorithm can solve large samples with 50 ships within 2 s, and the computing time is almost linear to the number of ships. The proposed model and algorithms should contribute to drone-based ship emission detection and a featured routing problem.", "keywords": [ "Drone", "Routing (electronic design automation)", "Heuristics", "Computer science", "Enforcement", "Port (circuit theory)", "Operations research", "Marine engineering", "Engineering", "Computer network" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2073-4433/14/2/373/pdf?version=1676361692" }, { "id": "https://openalex.org/W7126158577", "doi": "10.1016/j.tre.2026.104678", "title": "Enabling the multi-LR ability of drones in the multi-visit truck-drone routing problem with pickup and delivery", "year": 2026, "publication_date": "2026-01-31", "type": "article", "language": "en", "authors": [ "Sepehr Pasha", "S. Mehdi Sajadifar" ], "first_author": "Sepehr Pasha", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "The use of drones alongside trucks for parcel delivery has received considerable research attention, further stimulated by advancements in drone capacity and range that enhance operational viability relative to traditional methods. In this paper, we address a variant of the combined truck-drone routing problem, entailing multiple trucks collaborating with drones to meet the pickup and delivery demands of customers. In the proposed problem, drone energy consumption depends on the carried load; drones may serve multiple customers per flight, and each truck can launch and retrieve its drone multiple times at each customer node (multi-LR) to enhance overall utilization. We propose a mixed-integer linear programming model to minimize total cost, enhanced with problem-specific cuts, which are demonstrated through extensive computational experiments to effectively reduce runtime. The model includes flexible features that allow it to handle diverse operational constraints, such as restrictions on the number of flights performed and high-traffic areas. Given the complexity of the model, we develop an adapted algorithm from the literature, incorporating significant modifications along with a new acceleration strategy. The approach combines a maximum payload method in the first stage with an improved simulated annealing algorithm using problem-specific neighborhood operators in the second stage. Although our findings show that the multi-LR feature increases the number of flights performed, both the model and the adapted algorithm demonstrate its cost efficiency, achieving average transportation cost reductions of 14.51% compared to the system without multi-LR and 45.62% compared to the traditional truck-only system.", "keywords": [ "Drone", "Payload (computing)", "Pickup", "Linear programming", "Routing (electronic design automation)", "Truck", "Energy consumption", "Node (physics)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104678" }, { "id": "https://openalex.org/W4394867680", "doi": "10.48550/arxiv.2404.09065", "title": "VRPD-DT: Vehicle Routing Problem with Drones Under Dynamically Changing Traffic Conditions", "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 (VRP-D) is to determine the optimal routes of trucks and drones such that the total operational cost is minimized in a scenario where the trucks work in tandem with the drones to deliver parcels to customers. While various heuristic algorithms have been developed to address the problem, existing solutions are built based on simplistic cost models, overlooking the temporal dynamics of the costs, which fluctuate depending on the dynamically changing traffic conditions. In this paper, we present a novel problem called the vehicle routing problem with drones under dynamically changing traffic conditions (VRPD-DT) to address the limitation of existing VRP-D solutions. We design a novel cost model that factors in the actual travel distance and projected travel time, computed using a machine learning-driven travel time prediction algorithm. A variable neighborhood descent (VND) algorithm is developed to find the optimal truck-drone routes under the dynamics of traffic conditions through incorporation of the travel time prediction model. A simulation study was performed to evaluate the performance compared with a state-of-the-art VRP-D heuristic solution. The results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithm in various delivery scenarios.", "keywords": [ "Drone", "Vehicle routing problem", "Truck", "Heuristic", "Computer science", "Routing (electronic design automation)", "Variable (mathematics)", "State (computer science)", "Toll", "Mathematical optimization", "Real-time computing", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2404.09065" }, { "id": "https://openalex.org/W4401460846", "doi": "10.1111/itor.13527", "title": "Optimal drone deployment for cost‐effective and sustainable last‐mile delivery operations", "year": 2024, "publication_date": "2024-08-09", "type": "article", "language": "en", "authors": [ "Gaurav Kumar", "Oqais Tanvir", "Akhilesh Kumar", "Mohit Goswami" ], "first_author": "Gaurav Kumar", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Abstract Delivery by drones holds significant potential to solve issues (such as high costs, access to remote areas, etc.) faced in last‐mile delivery operations, particularly in the e‐commerce industry. Still, it involves complex issues such as multi‐trip operations, energy estimation, and battery recharge planning. A sound drone delivery problem entails an optimal drone deployment plan with routing details at the lowest possible cost. To this end, this study focuses on formulating a delivery problem that involves multi‐trip drone routing, energy optimization, and travel time optimization problems where energy consumption by drones is modeled as a non‐linear function. We develop a mixed integer non‐linear programming model as an integrated optimization model. This model aims to: (a) maximize revenue by meeting demand completely without leaving idle drones, (b) optimize energy use by drones, and (c) minimize the required drone fleet size for an optimal plan. The proposed model is solved using the Gurobi Solver, which employs data supplied by a well‐known e‐commerce firm. We introduce a two‐phase heuristic solution methodology to tackle larger networks’ complexities. This method consists of the clustering phase (K‐means clustering method) and the optimization phase. The robustness of the developed mathematical modeling is demonstrated by testing with varied large problem instances. The evaluation shows that expanding destination options boosts drone demand until saturation, necessitating more drones. Efficient route planning and fleet adjustments are crucial for meeting rising demand and satisfying customers amidst dense clustering. This model helps e‐commerce manage daily last‐mile drone deliveries and anticipate future growth.", "keywords": [ "Drone", "Computer science", "Last mile (transportation)", "Software deployment", "Operations research", "Cluster analysis", "Linear programming", "Solver", "Integer programming", "Mathematical optimization", "Optimization problem", "Heuristic" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13527" }, { "id": "https://openalex.org/W4400944545", "doi": "10.1007/978-3-031-58919-5_8", "title": "A Simulated Annealing Heuristic Approach for the Energy Minimizing Electric Vehicle Routing Problem with Drones", "year": 2024, "publication_date": "2024-01-01", "type": "book-chapter", "language": "en", "authors": [ "Nikolaos A. Kyriakakis", "Themistoklis Stamadianos", "Magdalene Marinaki", "Yannis Marinakis" ], "first_author": "Nikolaos A. Kyriakakis", "venue": "Springer optimization and its applications", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Simulated annealing", "Drone", "Heuristic", "Mathematical optimization", "Computer science", "Vehicle routing problem", "Electric vehicle", "Routing (electronic design automation)", "Mathematics", "Computer network", "Physics", "Power (physics)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-58919-5_8" }, { "id": "https://openalex.org/W2886728770", "doi": "10.1007/s10732-018-9389-6", "title": "A UAV location and routing problem with spatio-temporal synchronization constraints solved by ant colony optimization", "year": 2018, "publication_date": "2018-08-09", "type": "article", "language": "en", "authors": [ "Oktay Yılmaz", "Ertan Yakıcı", "Mümtaz Karataş" ], "first_author": "Oktay Yılmaz", "venue": "Journal of Heuristics", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Ant colony optimization algorithms", "Computer science", "Synchronization (alternating current)", "Mathematical optimization", "Travelling salesman problem", "Routing (electronic design automation)", "Ant colony", "ANT", "Artificial intelligence", "Mathematics", "Algorithm", "Computer network" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10732-018-9389-6" }, { "id": "https://openalex.org/W4403355727", "doi": "10.1080/15568318.2024.2407150", "title": "Last mile delivery with drones: A carbon emissions comparison", "year": 2024, "publication_date": "2024-09-01", "type": "article", "language": "en", "authors": [ "Jay R. Brown", "Maxim A. Bushuev" ], "first_author": "Jay R. Brown", "venue": "International Journal of Sustainable Transportation", "publisher": "Taylor & Francis", "abstract": "The development and potential adoption of drones or unmanned aerial vehicles as delivery vehicles creates incredible opportunities and unique challenges for last mile delivery. This research first presents a last mile delivery fleet model with drones that can be further modified and expanded over time. The model shows the optimal number of drones needed based on deterministic or stochastic demand using both traditional charging and battery swapping. The research then compares the carbon emissions of four delivery modes: traditional internal combustion delivery vehicles, all-electric vehicles, plug-in hybrids, and drones within the context of last mile delivery. Findings reveal that the breakdown of carbon emissions by delivery modality depends on parameter assumptions, ambient temperature, delivery radius, electric grid pollution rate, and number of customers.", "keywords": [ "Drone", "Last mile (transportation)", "Mile", "Environmental science", "Greenhouse gas", "Business", "Environmental planning", "Environmental engineering", "Geography" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/15568318.2024.2407150" }, { "id": "https://openalex.org/W4393376871", "doi": "10.35378/gujs.1340189", "title": "Capacitated Multi Drone Assisted Vehicle Routing Problem", "year": 2024, "publication_date": "2024-04-01", "type": "article", "language": "en", "authors": [ "Hasan Kavlak", "Selçuk Kürşat İşleyen", "Bilal Toklu" ], "first_author": "Hasan Kavlak", "venue": "GAZI UNIVERSITY JOURNAL OF SCIENCE", "publisher": null, "abstract": "This research delves into the dynamic landscape of transportation systems, with a specific focus on the integration of drones and conventional vehicles. The study presents a Mixed Integer Programming (MIP) model for the Capacitated Multi-Drone Assisted Vehicle Routing Problem (mDroneCVRP), aiming to minimize the time of the last vehicle's arrival at the warehouse. It is essential to highlight that the proposed model was effectively solved using the CPLEX algorithm within the GAMS framework, underscoring the sophistication of the solution approach. The integration of multiple drones into the routing process proves to be instrumental in significantly reducing service time, demonstrating the efficacy of synergizing drone and truck operations. As the number of nodes escalates, emphasizing the necessity for heuristic approaches to address larger instances, the study provides valuable insights into the judicious use of drones in synchronized routing operations. Furthermore, the research challenges conventional assumptions by permitting drones to take off from and land on different vehicles, thereby augmenting operational capabilities and adeptly tackling contemporary transportation challenges.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/en/download/article-file/3318878" }, { "id": "https://openalex.org/W4285193894", "doi": "10.21638/11701/spbu10.2022.111", "title": "Applied routing problem for a fleet of delivery drones using a modified parallel genetic algorithm", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Anastasia Y. Markelova", "Alexander Allahverdyan", "Alexey Martemyanov", "Inga S. Sokolova", "Ovanes Petrosian", "Михаил Владимирович Свиркин" ], "first_author": "Anastasia Y. Markelova", "venue": "Vestnik of Saint Petersburg University Applied Mathematics Computer Science Control Processes", "publisher": "Saint Petersburg State University", "abstract": "More and more experts agree that in the near future, most freight traffic will be carried out using automated systems, and of them drone delivery is considered to be the most promising. Drone delivery would benefit by independence from the limitations of transport infrastructure and road conditions and would ensure cargo delivery with rapid turnaround times, as well as a significant reduction of environmental impact. The technical capabilities of unmanned aerial vehicles improve year by year, so the task of coordinating drones and effectively planning routes is relevant and in great demand. The development of such technologies will help reduce transportation costs and improve customer service through faster delivery. This article discusses the applied routing problem for a fleet of drones with limited load capacity for the delivery of heterogeneous goods with the possibility of loading in multiple warehouses from an international optimization competition. The solution includes new approach based on a mixed dimensional parallel genetic algorithm (MDPGA) for finding rational routes for delivering goods to various customers and an assignment problem to reduce the dimension depending on the number of warehouses.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Genetic algorithm", "Operations research", "Service (business)", "Truck", "Dimension (graph theory)", "Transport engineering", "Business", "Engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dspace.spbu.ru/bitstream/11701/36938/1/135-148.pdf" }, { "id": "https://openalex.org/W4407169724", "doi": "10.1109/cbd65573.2024.00059", "title": "Two- Echelon Vehicle Routing Problem with Trucks and Drones", "year": 2024, "publication_date": "2024-11-28", "type": "conference-paper", "language": "en", "authors": [ "Xiaoteng Han", "Quan Spring Zhou", "Bo Du", "Jun Shen" ], "first_author": "Xiaoteng Han", "venue": null, "publisher": null, "abstract": "Over the years, the Vehicle Routing Problem (VRP) has been extensively studied. As real-world scenarios and technol-ogy evolve, researchers are increasingly integrating drones with trucks, leading to the development of the Two-Echelon Vehicle Routing Problem (2EVRP). The 2EVRP involves dividing the route plan into two segments: the first echelon operated by trucks and the second by drones. With the introduction of lightweight, cost-effective delivery solutions like drones, the 2EVRP faces challenges such as limited drone battery life and adherence to public safety policies. This survey examines the literature on traditional exact and heuristic algorithms alongside contem-porary machine learning approaches, including supervised and reinforcement learning methods. Additionally, we review current real-world applications of drone delivery. Finally, we highlight several future research directions for 2EVRP with truck and drone collaborations.", "keywords": [ "Drone", "Truck", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Computer network", "Engineering", "Automotive engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cbd65573.2024.00059" }, { "id": "https://openalex.org/W4407777942", "doi": "10.1016/j.pdisas.2025.100411", "title": "A combined fleet size and mix vehicle routing model for last-mile distribution in disaster relief", "year": 2025, "publication_date": "2025-02-20", "type": "article", "language": "en", "authors": [ "Katharina Eberhardt", "Florian Diehlmann", "Markus Lüttenberg", "Florian Klaus Kaiser", "Frank Schultmann" ], "first_author": "Katharina Eberhardt", "venue": "Progress in Disaster Science", "publisher": "Elsevier BV", "abstract": "Disasters pose a significant challenge for last-mile operations, straining emergency logistics systems' ability to provide efficient aid and support. In this context, a Fleet Size and Mix Vehicle Routing Problem for Disaster Management (FSMVRP-DM) is formulated, incorporating a fleet composition decision tailored to the specifics of disaster relief logistics. The model aims to optimize routing and analyze fleet decisions to minimize the sum of operating costs and population deprivation costs. Moreover, a prioritization approach is introduced to monitor deprivation time during transport resource scarcity, adjusting routes periodically to prevent extended supply gaps and minimize suffering costs. In addition, a case study is conducted in the German state of Baden-Württemberg to illustrate the potential applicability of the model. The findings highlight the advantages of integrating diverse and innovative fleet types, such as drones, and prioritizing the supply of multiple demand points when resources are scarce. Overall, the research offers decision support for authorities by enhancing information transparency, facilitating resource management, strengthening the effectiveness of disaster response capabilities, and providing resilient and adaptive strategies for last-mile distribution. • Preparedness and adaptive strategies increase resilience in last-mile operations. • Optimal fleet decisions ensure timely response and strengthen delivery performance. • Diversifying fleet composition enhances cost efficiency and operational capability. • Strategic prioritization minimizes human suffering through improved demand coverage.", "keywords": [ "Mile", "Vehicle routing problem", "Transport engineering", "Distribution (mathematics)", "Last mile (transportation)", "Routing (electronic design automation)", "Business", "Computer science", "Engineering", "Geography", "Computer network", "Mathematics" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.pdisas.2025.100411" }, { "id": "https://openalex.org/W1491630879", "doi": "10.1007/978-3-540-72696-8_6", "title": "Algorithms for Routing Problems Involving UAVs", "year": 2007, "publication_date": "2007-01-01", "type": "book-chapter", "language": "en", "authors": [ "Sivakumar Rathinam", "Raja Sengupta" ], "first_author": "Sivakumar Rathinam", "venue": "Studies in computational intelligence", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Class (philosophy)", "Operations research", "Distributed computing", "Computer network", "Artificial intelligence", "Engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-540-72696-8_6" }, { "id": "https://openalex.org/W3121894342", "doi": "10.20944/preprints201902.0072.v1", "title": "Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone", "year": 2019, "publication_date": "2019-02-07", "type": "preprint", "language": "en", "authors": [ "Yao Liu", "Jianmai Shi", "Zhong Liu", "Jincai Huang", "Tianren Zhou" ], "first_author": "Yao Liu", "venue": "Preprints.org", "publisher": null, "abstract": "A novel high-voltage powerline inspection system is investigated, which consists of the cooperated ground vehicle and drone. The ground vehicle acts as a mobile platform that can launch and recycle the drone, while the drone can fly over the powerline for inspection within limited endurance. This inspection system enables the drone to inspect powerline networks in a very large area. Both vehicle’ route in the road network and drone’s routes along the powerline network have to be optimized for improving the inspection efficiency, which generates a new two-layer point-arc routing problem. Two constructive heuristics are designed based on “Cluster First, Rank Second” and “Rank First, Split Second”. Then local search strategies are developed to further improve the quality of the solution. To test the performance of the proposed algorithms, practical cases with different-scale are designed based on the road network and powerline network of Ji’an, China. Sensitivity analysis on the parameters related with the drone’s inspection speed and battery capacity is conducted. Computational results indicate that technical improvement on the inspection sensor is more important for the cooperated ground vehicle and drone system.", "keywords": [ "Drone", "Routing (electronic design automation)", "Heuristics", "Computer science", "Engineering", "Real-time computing", "Embedded system" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/manuscript/201902.0072/v1/download" }, { "id": "https://openalex.org/W7118158341", "doi": "10.1080/00207543.2025.2609948", "title": "A decomposition-based evolutionary algorithm for drone-assisted electric routing problem with drone stations", "year": 2026, "publication_date": "2026-01-04", "type": "article", "language": "en", "authors": [ "Setyo Tri Windras Mara", "Ruhul Amin Sarker", "Saber Mohammed Elsayed", "Daryl Essam" ], "first_author": "Setyo Tri Windras Mara", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "This paper focuses on an integrated routing problem incorporating electric vehicles (EVs), drones, and drone stations. Despite the considerable interest in employing drones in logistics as a tandem, previous studies indicated that adopting EV-drone tandems has a significant barrier due to the upfront investments to procure drones. To tackle this issue, this study explores the prospect of drone-as-a-service, employing drones owned by third-party providers to enhance logistics operations. The proposed system is formulated as a mixed-integer linear program with an objective function that minimises the system's makespan, which represents the completion time of the delivery mission. Considering the size and complexity of the model, we propose an evolutionary algorithm as a solver. The algorithm incorporates a customised solution representation, a specialised initialisation technique, and a local search technique to improve the algorithm's searching capability. Numerical experiments demonstrate the superior performance of our algorithm, showing gaps of 5.35–6.32%; compared to baseline algorithms. The results also indicate that incorporating DaaS providers can shorten the operations' makespan with up to 18.71%; gaps. Finally, the paper provides managerial insights into the proposed logistics system's benefits, challenges, and prospects.", "keywords": [ "Drone", "Evolutionary algorithm", "Routing (electronic design automation)", "Genetic algorithm", "Evolutionary computation" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2025.2609948" }, { "id": "https://openalex.org/W1988933932", "doi": "10.1177/154193120905300408", "title": "Effect of Level of Automation on Unmanned Aerial Vehicle Routing Task", "year": 2009, "publication_date": "2009-10-01", "type": "article", "language": "en", "authors": [ "Gloria L. Calhoun", "Mark H. Draper", "Heath A. Ruff" ], "first_author": "Gloria L. Calhoun", "venue": "Proceedings of the Human Factors and Ergonomics Society Annual Meeting", "publisher": "SAGE Publishing", "abstract": "Supervisory control of multiple unmanned aerial vehicles (UAVs) raises many issues concerning the balance of system autonomy with human interaction for optimal operator situation awareness and system performance. A UAV simulation environment designed to manipulate the application of automation was used to evaluate participants' performance on routing tasks under three levels of automation. Trials also involved completion of several mission-related secondary tasks as participants supervised either one or three UAVs. Both objective and subjective data were collected. The results showed that participants took longer to complete the routing task when automation was high due to the time they spent verifying the accuracy of the imperfect decision aid. These results show the importance of designing an interface that provides an efficient means of interacting with the automation and communicates the automation's rationale, especially under high automation levels.", "keywords": [ "Automation", "Task (project management)", "Routing (electronic design automation)", "Computer science", "Autonomy", "Supervisory control", "Human–computer interaction", "Control (management)", "Simulation", "Real-time computing", "Engineering", "Embedded system" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/154193120905300408" }, { "id": "https://openalex.org/W4413906490", "doi": "10.1051/ro/2025119", "title": "A literature review of drone-truck routing problems: challenges and future research directions", "year": 2025, "publication_date": "2025-09-01", "type": "article", "language": "en", "authors": [ "Enes Cengiz", "Cemal Yılmaz", "Hamdi Tolga Kahraman" ], "first_author": "Enes Cengiz", "venue": "RAIRO - Operations Research", "publisher": "EDP Sciences", "abstract": "In recent years, new routing problems with high complexity have emerged in parallel with the diversification of collaborative operational activities carried out by drones with ground vehicles. There are two main factors that affect the complexity level of these new generation routing problems. The first factor is the synchronization of vehicles with very different characteristics to optimally perform operational tasks. The second factor is that unlike traditional routing problems such as transportation and logistics, healthcare, military and emergency operations, new generation problems have more complex objective and constraint spaces. In traditional routing problems, customers are represented by fixed points in a two-dimensional search space. In contrast, in agricultural spraying, which is a new generation routing problem, the areas to be sprayed are represented by irregular areas. The objective and constraint functions in next generation routing problems are specified as area-based rather than point-based because of this difference. This increases the geometric complexity of the objective and constraint spaces in next generation routing problems. This paper analyzes 108 publications on traditional and next generation routing problems published between 2015 and 2024. A comprehensive review of routing problems based on drone-truck cooperation is provided according to their application areas, mathematical models, and solution methods. Unlike the review studies in the literature, new routing problems in the field of agriculture are also included in this research and the mathematical complexity of these problems is presented for the first time. Current trends for future research on drone-truck based problems are discussed.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Operations research", "Aeronautics", "Engineering", "Aerospace engineering", "Computer network" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1051/ro/2025119" }, { "id": "https://openalex.org/W4405931602", "doi": "10.1016/j.jairtraman.2024.102736", "title": "MILP-based cost and time-competitive vehicle routing problem for last-mile delivery service using a swarm of UAVs and UGVs", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Sunghun Jung" ], "first_author": "Sunghun Jung", "venue": "Journal of Air Transport Management", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Last mile (transportation)", "Mile", "Routing (electronic design automation)", "Computer science", "Swarm behaviour", "Service (business)", "Mathematical optimization", "Operations research", "Transport engineering", "Engineering", "Business" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.jairtraman.2024.102736" }, { "id": "https://openalex.org/W1982075318", "doi": "10.1109/sysose.2010.5544109", "title": "Unmanned Aerial Vehicle route optimization using ant system algorithm", "year": 2010, "publication_date": "2010-06-01", "type": "conference-paper", "language": "en", "authors": [ "Aleksandar Jevtić", "Diego Andina", "Aldo S. Jaimes", "Jose Gomez", "Mo M. Jamshidi" ], "first_author": "Aleksandar Jevtić", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicle (UAV) is defined as aircraft without the onboard presence of pilots. UAVs have been used to perform intelligence, surveillance, and reconnaissance missions. The UAVs are not limited to military operations, they can also be used in commercial applications such as telecommunications, ground traffic control, search and rescue operations, crop monitoring, etc. In this paper, we propose a swarm intelligence-based method for UAVs' route optimization. The team of UAVs is used for area coverage with the defined set of waypoints. The problem can be interpreted as a well-known Traveling Salesman Problem where the task is to find the route of minimal length such that all the waypoints are visited only once. We applied the Ant System algorithm and compared it with the Nearest Neighbor Search. The experimental results confirm the effectiveness of our method, especially for a large number of waypoints.", "keywords": [ "Travelling salesman problem", "Ant colony optimization algorithms", "Swarm intelligence", "Computer science", "Task (project management)", "Search and rescue", "Set (abstract data type)", "Real-time computing", "Artificial intelligence", "Algorithm", "Particle swarm optimization", "Robot" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/sysose.2010.5544109" }, { "id": "https://openalex.org/W3168448590", "doi": "10.29122/mipi.v15i1.4732", "title": "A VEHICLE ROUTING PROBLEM OPTIMIZATION WITH DRONE USING TABU SEARCH ALGORITHM AND ANALYTICAL HIERARCHY PROCESS", "year": 2021, "publication_date": "2021-06-02", "type": "article", "language": "en", "authors": [ "Linda Nuryanti" ], "first_author": "Linda Nuryanti", "venue": "Majalah ilmiah Pengkajian Industri", "publisher": null, "abstract": "This research presents optimization of the fastest route using Drone. Vehicle Routing Problem with Drones (VRPD) is used for planning the mapping of areas with minimal makespan. Drone point route design aims to mapping the area with considering the boundaries. In this paper, an optimization method using the Tabu Search algorithm and Analytical Hierarchy Process (AHP) to resolve VRPD problems is proposed. Tabu Search algorithm is suitable for the implementation of Vehicle Routing Problem because it is able to find the closest distance optimally with guiding other processes using a series of movements to change one solution to another. The optimization process using Tabu Search can find a suitable pair of points so that the closest route can be found. AHP is used for the weighting process and determining the hierarchy of route selection by prioritizing routes that meet the criteria and the appropriate weights. The optimization resulted in a 7.08% reduction in the distance and significantly reduce the makespan as well as the metaheuristic approach. Experimental processes and performance analysis are carried out to find that this method can consistently produce better and optimal solutions.", "keywords": [ "Tabu search", "Metaheuristic", "Mathematical optimization", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Job shop scheduling", "Guided Local Search", "Analytic hierarchy process", "Process (computing)", "Drone", "Algorithm" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ejurnal.bppt.go.id/index.php/MIPI/article/download/4732/4063" }, { "id": "https://openalex.org/W4400617204", "doi": "10.1016/j.jfranklin.2024.107060", "title": "An improved ant colony algorithm for multiple unmanned aerial vehicles route planning", "year": 2024, "publication_date": "2024-07-14", "type": "article", "language": "en", "authors": [ "Yibing Li", "Zitang Zhang", "Qian Sun", "Yujie Huang" ], "first_author": "Yibing Li", "venue": "Journal of the Franklin Institute", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Ant colony optimization algorithms", "Route planning", "Computer science", "ANT", "Ant colony", "Artificial intelligence", "Algorithm", "Engineering", "Transport engineering", "Computer network" ], "cited_by_count": 32, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.jfranklin.2024.107060" }, { "id": "https://openalex.org/W4382983543", "doi": "10.3390/drones7070435", "title": "Drone Optimization in Factory: Exploring the Minimal Level Vehicle Routing Problem for Efficient Material Distribution", "year": 2023, "publication_date": "2023-06-30", "type": "article", "language": "en", "authors": [ "Iván Dérpich", "Carlos Rey" ], "first_author": "Iván Dérpich", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The efficient movement of raw materials within organizations is fundamental to maintaining the seamless progression of production processes. However, these logistical operations can inadvertently compromise overall company efficiency, primarily due to the substantial time invested in transporting materials. This paper introduces an innovative mathematical model specifically designed to optimize the transport of raw materials via drones across multiple workstations. This model employs a novel modification of the traditional multi-level Vehicle Routing Problem by incorporating an additional index and accounting for the drone’s energy consumption. We employ a widely-recognized solver for practical resolution and compare it with a heuristic algorithm. The resultant strategies offer promising prospects for the organization studied, introducing robust solutions for elevating the efficiency of raw material transportation.", "keywords": [ "Drone", "Solver", "Computer science", "Factory (object-oriented programming)", "Workstation", "Heuristic", "Routing (electronic design automation)", "Vehicle routing problem", "Raw material", "Production (economics)", "Industrial engineering", "Mathematical optimization" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/7/7/435/pdf?version=1688371755" }, { "id": "https://openalex.org/W4391449396", "doi": "10.1287/ijoc.2022.0390", "title": "Decremental State-Space Relaxations for the Basic Traveling Salesman Problem with a Drone", "year": 2024, "publication_date": "2024-02-01", "type": "article", "language": "en", "authors": [ "Marcos Blufstein", "Gonzalo Lera‐Romero", "Francisco J. Soulignac" ], "first_author": "Marcos Blufstein", "venue": "INFORMS journal on computing", "publisher": "Institute for Operations Research and the Management Sciences", "abstract": "Truck-and-drone routing problems have become an important research topic in the last decade because of their applications for last-mile deliveries. Despite the many publications in this area, the most efficient exact algorithms designed thus far struggle to solve the benchmark instances with 39 or more customers. This fact holds even for one of the simplest variants involving one truck and one drone whose routes must synchronize at customers’ locations: the basic traveling salesman problem with a drone (TSP-D). In this article, we devise a new algorithm for the TSP-D that solves every benchmark instance with up to 59 customers, and it scales up to 99 customers when the drone is much faster than the truck. The core of our method is a dynamic programming algorithm that is applied for column generation and variable fixing within tailored decremental state-space relaxation strategies. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Fondo para la Investigación Científica y Tecnológica [Grant PICT-2018-2961] (Ministry of Science, Argentina). Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0390 .", "keywords": [ "Travelling salesman problem", "Drone", "Traveling purchaser problem", "2-opt", "State space", "Mathematical optimization", "Bottleneck traveling salesman problem", "Space (punctuation)", "Computer science", "State (computer science)", "Mathematics", "Algorithm" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1287/ijoc.2022.0390" }, { "id": "https://openalex.org/W4280545495", "doi": "10.3390/s22103702", "title": "A Coordinated Vehicle–Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search", "year": 2022, "publication_date": "2022-05-12", "type": "article", "language": "en", "authors": [ "Guohua Wu", "Kexin Zhao", "Jiaqi Cheng", "Manhao Ma" ], "first_author": "Guohua Wu", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Through urban traffic patrols, problems such as traffic congestion and accidents can be found and dealt with in time to maintain the stability of the urban traffic system. The most common way to patrol is using ground vehicles, which may be inflexible and inefficient. The vehicle-drone coordination maximizes utilizing the flexibility of drones and addresses their limited battery capacity issue. This paper studied a vehicle-drone arc routing problem (VD-ARP), consisting of one vehicle and multiple drones. Considering the coordination mode and constraints of the vehicle-drone system, a mathematical model of VD-ARP that minimized the total patrol time was constructed. To solve this problem, an improved, adaptive, large neighborhood search algorithm (IALNS) was proposed. First, the initial route planning scheme was generated by the heuristic rule of \"Drone-First, Vehicle-Then\". Then, several problem-based neighborhood search strategies were embedded into the improved, adaptive, large neighborhood search framework to improve the quality of the solution. The superiority of IALNS is verified by numerical experiments on instances with different scales. Several critical factors were tested to determine the effects of coordinated traffic patrol; an example based on a real road network verifies the feasibility and applicability of the algorithm.", "keywords": [ "Drone", "Vehicle routing problem", "Arc routing", "Arc (geometry)", "Computer science", "Routing (electronic design automation)", "Engineering", "Computer network", "Biology" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/22/10/3702/pdf?version=1652691273" }, { "id": "https://openalex.org/W4404226368", "doi": "10.1016/j.engappai.2024.109608", "title": "Elite-based multi-objective improved iterative local search algorithm for time-dependent vehicle-drone collaborative routing problem with simultaneous pickup and delivery", "year": 2024, "publication_date": "2024-11-11", "type": "article", "language": "en", "authors": [ "Haohao Duan", "Xiaoling Li", "Guanghui Zhang", "Yanxiang Feng", "Qing-Chang Lu" ], "first_author": "Haohao Duan", "venue": "Engineering Applications of Artificial Intelligence", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pickup", "Computer science", "Drone", "Vehicle routing problem", "Local search (optimization)", "Routing (electronic design automation)", "Mathematical optimization", "Algorithm", "Operations research", "Simulation", "Artificial intelligence", "Embedded system" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.engappai.2024.109608" }, { "id": "https://openalex.org/W4382366469", "doi": "10.1007/978-3-031-37111-0_21", "title": "A Bi-objective Routing Problem with Trucks and Drones: Minimizing Mission Time and Energy Consumption", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Mahdi Moeini", "Oliver Wendt", "Marius Schummer" ], "first_author": "Mahdi Moeini", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Computer science", "Energy consumption", "Job shop scheduling", "Integer programming", "Benchmark (surveying)", "Simulated annealing", "Vehicle routing problem", "Routing (electronic design automation)", "Mathematical optimization", "Minification" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-37111-0_21" }, { "id": "https://openalex.org/W2593112539", "doi": "10.1109/icuas.2017.7991314", "title": "Multi-UAV routing for persistent intelligence surveillance & reconnaissance missions", "year": 2017, "publication_date": "2017-06-01", "type": "conference-paper", "language": "en", "authors": [ "Satyanarayana G. Manyam", "Steven A. Rasmussen", "David W. Casbeer", "Krishna Kalyanam", "Suresh Manickam" ], "first_author": "Satyanarayana G. Manyam", "venue": null, "publisher": null, "abstract": "We consider a Persistent Intelligence, Surveillance and Reconnaissance (PISR) routing problem, which includes collecting data from a set of specified task locations and delivering that data to a control station. Each task is assigned a refresh rate based on its priority, where higher priority tasks require higher refresh rates. The UAV team's objective is to minimize the maximum of the delivery times of all the tasks' data to the control station, while simultaneously, satisfying each task's revisit period constraint. The centralized path planning problem for this PISR routing problem is formulated using mixed integer linear programming and solved using a branch-and-cut algorithm. Heuristics are presented to find suboptimal feasible solutions that require much less computation time. The algorithms are tested on several instances and their performance is compared with respect to the optimal cost and computation time.", "keywords": [ "Heuristics", "Computer science", "Routing (electronic design automation)", "Task (project management)", "Integer programming", "Real-time computing", "Computation", "Set (abstract data type)", "Linear programming", "Integer (computer science)", "Constraint (computer-aided design)", "Control (management)" ], "cited_by_count": 76, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas.2017.7991314" }, { "id": "https://openalex.org/W4389238259", "doi": "10.3390/s23239540", "title": "Intelligent Drone Swarms to Search for Victims in Post-Disaster Areas", "year": 2023, "publication_date": "2023-11-30", "type": "article", "language": "en", "authors": [ "Matheus Nohra Haddad", "Andréa Cynthia Santos", "Christophe Didier Duhamel", "Amadeu Almeida Coco" ], "first_author": "Matheus Nohra Haddad", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This study presents the Drone Swarms Routing Problem (DSRP), which consists of identifying the maximum number of victims in post-disaster areas. The post-disaster area is modeled in a complete graph, where each search location is represented by a vertex, and the edges are the shortest paths between destinations, with an associated weight, corresponding to the battery consumption to fly to a location. In addition, in the DSRP addressed here, a set of drones are deployed in a cooperative drone swarms approach to boost the search. In this context, a V-shaped formation is applied with leader replacements, which allows energy saving. We propose a computation model for the DSRP that considers each drone as an agent that selects the next search location to visit through a simple and efficient method, the Drone Swarm Heuristic. In order to evaluate the proposed model, scenarios based on the Beirut port explosion in 2020 are used. Numerical experiments are presented in the offline and online versions of the proposed method. The results from such scenarios showed the efficiency of the proposed approach, attesting not only the coverage capacity of the computational model but also the advantage of adopting the V-shaped formation flight with leader replacements.", "keywords": [ "Drone", "Computer science", "Search and rescue", "Swarm behaviour", "Context (archaeology)", "Heuristic", "Set (abstract data type)", "Computation", "Vertex (graph theory)", "Shortest path problem", "Mathematical optimization", "Operations research" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/23/23/9540/pdf?version=1701419693" }, { "id": "https://openalex.org/W3005867904", "doi": "10.3390/s20041080", "title": "UAV Mission Planning with SAR Application", "year": 2020, "publication_date": "2020-02-17", "type": "article", "language": "en", "authors": [ "Wojciech Stecz", "Krzysztof Gromada" ], "first_author": "Wojciech Stecz", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors' capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm-Vehicle Route Planning with Time Window (VRPTW)-using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.", "keywords": [ "Flight planning", "Flight plan", "Computer science", "Scheduling (production processes)", "Task (project management)", "Real-time computing", "Plan (archaeology)", "Adversary", "Radar", "Route planning", "Key (lock)", "Artillery" ], "cited_by_count": 60, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/s20041080" }, { "id": "https://openalex.org/W4404756526", "doi": "10.1016/j.tre.2024.103859", "title": "Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search", "year": 2024, "publication_date": "2024-11-27", "type": "article", "language": "en", "authors": [ "Xinyu He", "Lishuai Li", "Yanfang Mo", "Zhankun Sun", "Shiliang Qin" ], "first_author": "Xinyu He", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Flow network", "Computer science", "Graph", "Transport engineering", "Flow (mathematics)", "Commodity", "Operations research", "Mathematical optimization", "Engineering", "Business", "Theoretical computer science" ], "cited_by_count": 31, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2024.103859" }, { "id": "https://openalex.org/W3014932913", "doi": "10.1109/rtss46320.2019.00054", "title": "Work-in-Progress: Routing of Delivery Drones with Load-Dependent Flight Speed", "year": 2019, "publication_date": "2019-12-01", "type": "conference-paper", "language": "en", "authors": [ "Yusuke Funabashi", "Ittetsu Taniguchi", "Hiroyuki Tomiyama" ], "first_author": "Yusuke Funabashi", "venue": null, "publisher": null, "abstract": "Drones draws increasing attention as vehicles for home delivery services. Delivery time is one of the most critical concerns of both customers and delivery service providers. The delivery time depends not only on the flight distance but also on the flight speed, and the flight speed depends on the payload. This paper studies a routing problem for delivery drones considering load-dependent flight speed. This paper formally defines Flight Speed-aware Vehicle Routing Problem (FSVRP) and proposes a dynamic programming algorithm to efficiently solve the problem. Experiments show the effectiveness of the proposed algorithm in terms of quality of results and algorithm runtime.", "keywords": [ "Payload (computing)", "Drone", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Quality of service", "Dynamic programming", "Distributed computing", "Computer network", "Algorithm" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/rtss46320.2019.00054" }, { "id": "https://openalex.org/W4386314082", "doi": "10.1016/j.tre.2023.103266", "title": "Integrated ground vehicle and drone routing with simultaneous surveillance coverage for evading intentional disruption", "year": 2023, "publication_date": "2023-08-31", "type": "article", "language": "en", "authors": [ "Fatemeh Zandieh", "Seyed Farid Ghannadpour", "Mohammad Mahdavi Mazdeh" ], "first_author": "Fatemeh Zandieh", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Simulated annealing", "Computer science", "Process (computing)", "Vehicle routing problem", "Computer security", "Operations research", "Transport engineering", "Engineering", "Computer network", "Machine learning" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2023.103266" }, { "id": "https://openalex.org/W4388303316", "doi": "10.1080/25765299.2023.2275505", "title": "Ant colony algorithm to solve a drone routing problem for hazardous waste collection", "year": 2023, "publication_date": "2023-11-02", "type": "article", "language": "en", "authors": [ "Khadija Abdulsattar", "Youssef Harrath", "Jihene Kaabi" ], "first_author": "Khadija Abdulsattar", "venue": "Arab Journal of Basic and Applied Sciences", "publisher": "Taylor & Francis", "abstract": "Waste management issues are affecting the economic and environmental aspects of modern societies.Thus, growing the interest of academic and industrial research and development in optimizing the process of waste management.As these issues greatly impact human health and environmental aspects and impose a threat, hazardous waste management requires even much more attention.The problem studied in this research is a variant of the vehicle routing problem using an unmanned aerial vehicle (UAV).The focus of this research is on planning the routes for waste collection and disposal using a UAV.The aim is to collect all the waste as early as possible respecting two constraints; the maximum flying and load capacities of the UAV.A two-phase approach has been proposed to solve the investigated problem.This approach is a hybridization of a developed heuristic (IMWMTT) and an Ant Colony Optimization (ACO) algorithm.The experimental study showed that the hybrid approach outperforms a recently published heuristic MWMTT for all tested instances of various sizes.", "keywords": [ "Hazardous waste", "Ant colony optimization algorithms", "Heuristic", "Vehicle routing problem", "Computer science", "Process (computing)", "Waste collection", "Drone", "Routing (electronic design automation)", "Operations research", "Ant colony", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.tandfonline.com/doi/pdf/10.1080/25765299.2023.2275505?needAccess=true" }, { "id": "https://openalex.org/W2169937161", "doi": "10.1109/itaic.2011.6030358", "title": "A bee evolutionary particle swarm optimization algorithm for vehicle routing problem", "year": 2011, "publication_date": "2011-08-01", "type": "conference-paper", "language": "en", "authors": [ "Mingshun Kou", "Chunming Ye", "Zihao Chen" ], "first_author": "Mingshun Kou", "venue": null, "publisher": null, "abstract": "For vehicle routing problem (VRP), an algorithm based on bee evolutionary particle swarm (BEPSO) was presented and applied to VRP. In this algorithm, the best particle which was regarded as queen cross with selected drones in a random probability, enhancing application ability of the best individual's information. At the same time, some drones were randomly generated and crossed with the queen, which increased diversity of the algorithm. A simulation study showed that this algorithm has better global search ability, verifying the feasibility of the algorithm.", "keywords": [ "Vehicle routing problem", "Particle swarm optimization", "Metaheuristic", "Drone", "Mathematical optimization", "Computer science", "Routing (electronic design automation)", "Algorithm", "Evolutionary algorithm", "Mathematics", "Biology" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itaic.2011.6030358" }, { "id": "https://openalex.org/W7149032244", "doi": "10.70675/7a049146zb15dz4121z9923z649a9b8c339f", "title": "Approximation methods to vehicle routing problem for a drone fleet management", "year": 2019, "publication_date": "2019-12-04", "type": "dissertation", "language": "fr", "authors": [ "Setyawan Ajie Sukarno" ], "first_author": "Setyawan Ajie Sukarno", "venue": null, "publisher": null, "abstract": "Méthodes d'approximation au problème de routage de véhicule pour une gestion de flotte de drones Aujourd’hui, le drone joue un rôle important dans les activités civiles et deviendra de plus en plus important à l’avenir. Récemment, de nouvelles tendances se dirigent vers la gestion d’une flotte de drones afin de réaliser les missions données. Ce problème ouvre de nombreuses idées de recherche, et notre projet est fait pour répondre au défi, développer une plateforme de gestion de flotte de drones. Entre plusieurs approches, le problème de routage de véhicule (VRP) est une étude parfaite pour relever ce défi, afin de répartir les tâches et de trouver le meilleur chemin pour chaque drone, en tenant compte de plusieurs contraintes. Comme les VRP sont classés comme un problème d’optimisation NP-hard, une méthode d’approximation est considérée comme mise en œuvre dans ce projet. L’algorithme génétique (GA), est appliqué dans ce projet, puisqu’il s’agit de l’un des algorithmes les plus utilisés pour résoudre le VRP parmi plusieurs méthodes d’approximation. Nous avons observé que l’AG convient pour être utilisé dans ce projet, mais lorsque le nombre de points à traiter serait considérablement accru, le nombre d’itérations pour obtenir un résultat satisfaisant sera extrêmement augmenté. Ce problème nous a amenés à hybrider GA avec l’algorithme de sauvegarde (SA) afin de générer la population initiale, pour qu’elle ne soit plus générée aléatoirement comme d’habitude. Comme nous l’avons testé, cette méthode proposée peut améliorer les performances de l’algorithme de manière très satisfaisante et réduire le nombre d’itérations de plus de 90%. De plus, un scénario dynamique dans le VRP est pris en compte dans ce travail, c’est-à-dire l’émergence d’un ou plusieurs nouveaux points qui apparaissent quand la mission a déjà été lancée et qui nécessitent une visite d’un seul drone. Pour faire face à ce scénario dynamique, un problème de routage de véhicule ouvert en sens inverse (ROVRP) est considéré. Le ROVRP est utilisé pour définir un ensemble d’itinéraires de véhicules de retour au dépôt, lors de la construction de nouveaux chemins en raison d’un scénario dynamique. Nous décidons de choisir une méthode heuristique pour résoudre le ROVRP, puisqu’il s’agit d’un problème d’optimisation NP-hard, et nous préférons appliquer l’algorithme de sauvegarde (SA) à ce projet, en raison de leur rapidité et de leur simplicité. A notre point de vue, la vitesse est l’un des aspects les plus importants du choix d’un algorithme pour résoudre un scénario dynamique dans un VRP. Notre méthode proposée est divisée en deux phases : regroupement et routage. Et nos résultats expérimentaux montrent que notre méthode proposée peut donner plus de 95% de précision. Afin de simuler et d’examiner les méthodes proposées, une interface utilisateur graphique (GUI) est développée. Il existe un cadre disponible pour développer cet outil, et Netlogo est considéré comme le cadre choisi.", "keywords": [ "Vehicle routing problem", "Drone", "Network routing", "Population" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.theses.fr/2019UPHF0022/document" }, { "id": "https://openalex.org/W4388820311", "doi": "10.1109/icnsc58704.2023.10319053", "title": "A hybrid algorithm for drone routing problem with time windows", "year": 2023, "publication_date": "2023-10-25", "type": "conference-paper", "language": "en", "authors": [ "Jiaxin Qu", "Jia Tao", "Dong Lei" ], "first_author": "Jiaxin Qu", "venue": null, "publisher": null, "abstract": "Drone Routing Problem with Time Window (DRPTW) is an important extension of the logistics service routing optimization problem and has been proved to be an NP-hard problem. In this paper, DRPTW is formulated as a mixed integer nonlinear programming model considering nonlinear energy constraints. To solve this problem efficiently, we propose a hybrid metaheuristic algorithm combined with a genetic algorithm (GA) to generate a set of feasible routes. In addition, we incorporate a tabu search algorithm (TS) that uses a taboo table to store previously encountered solutions, ensuring that the best solution is not overlooked. To evaluate its performance, we test it with Solomon's VRPTW benchmark instances. The results show that a good solution can be obtained within an acceptable computation time. Furthermore, for the solution of the same delivery sequence route, we compared the impact on delivery completion with and without energy constraints, and found that the original delivery solution would be ineffective if the nonlinear energy constraints of the drone are not considered or approximated.", "keywords": [ "Mathematical optimization", "Computer science", "Benchmark (surveying)", "Metaheuristic", "Tabu search", "Vehicle routing problem", "Nonlinear programming", "Routing (electronic design automation)", "Integer programming", "Drone", "Genetic algorithm", "Algorithm" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icnsc58704.2023.10319053" }, { "id": "https://openalex.org/W4406193149", "doi": "10.1007/s10846-024-02209-w", "title": "Cooperative Multi-Agent Planning Framework for Fuel Constrained UAV-UGV Routing Problem", "year": 2025, "publication_date": "2025-01-09", "type": "article", "language": "en", "authors": [ "Md Safwan Mondal", "R. Subramanian", "James D. Humann", "James M. Dotterweich", "Jean-Paul Reddinger", "Marshal Childers", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": "Unmanned Aerial Vehicles (UAVs), adept at aerial surveillance, are often constrained by their limited battery capacity. Refueling on slow-moving Unmanned Ground Vehicles (UGVs) can significantly enhance UAVs’ operational endurance. This paper explores the computationally complex problem of cooperative UAV-UGV routing for vast area surveillance, considering speed and fuel constraints. It presents a sequential multi-agent planning framework aimed at achieving feasible and optimally satisfactory solutions. By considering the UAV fuel limit and utilizing a minimum set cover algorithm, we determine UGV refueling stops. This, in turn, facilitates UGV route planning as the first step. Through a task allocation technique and energy-constrained vehicle routing problem modeling with time windows (E-VRPTW), we then achieve the UAV route in the second step of the framework. The effectiveness of our multi-agent strategy is demonstrated through the implementation on 30 different task scenarios across three different scales. This work provides significant insight into the collaborative advantages of UAV-UGV systems and introduces heuristic approaches to bypass computational challenges and swiftly reach high-quality solutions.", "keywords": [ "Routing (electronic design automation)", "Computer science", "Unmanned ground vehicle", "Distributed computing", "Mathematical optimization", "Operations research", "Engineering", "Artificial intelligence", "Computer network", "Mathematics" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10846-024-02209-w.pdf" }, { "id": "https://openalex.org/W3007886788", "doi": "10.1109/access.2020.2976122", "title": "Node and Edge Drone Surveillance Problem With Consideration of Required Observation Quality and Battery Replacement", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Ivan Kristianto Singgih", "Jong-Hwa Lee", "Byung‐In Kim" ], "first_author": "Ivan Kristianto Singgih", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Our study introduces a drone routing problem in which drones fly to capture photos for surveillance purposes after a disaster. The drones perform observations on nodes and edges representing populated areas and road segments of a network from multiple altitudes. Each target node and edge requires observation at least once with a certain required quality. When the drones fly at a relatively high altitude, they can simultaneously capture low-quality photos and a large number of observed target nodes and edges. However, high-quality photos and narrow observation areas can be captured from a relatively low altitude. Each drone has a limited battery capacity and thus must return to the depot for battery replacement. This study routes the drones to satisfy the required photo quality of all target nodes and edges while minimizing the makespan of the surveillance by all drones. Our study is the first to examine a multiple-drone routing problem while considering flight altitude-dependent observation quality, battery replacement, node and edge combination, and minimizing the makespan. Our problem is formulated as a mixed integer linear programming (MILP) model. Firefly and adaptive-reactive tabu search algorithms are proposed. The latter outperforms the former and obtains better solutions than those in the MILP model for small-sized instances within a given short computation time.", "keywords": [ "Drone", "Computer science", "Enhanced Data Rates for GSM Evolution", "Integer programming", "Real-time computing", "Routing (electronic design automation)", "Node (physics)", "Mathematical optimization", "Computer network", "Artificial intelligence", "Algorithm", "Engineering" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieeexplore.ieee.org/ielx7/6287639/8948470/09007713.pdf" }, { "id": "https://openalex.org/W7115697749", "doi": "10.1016/j.trb.2025.103378", "title": "Robust optimization for truck-and-drone collaboration with travel time uncertainties", "year": 2025, "publication_date": "2025-12-17", "type": "article", "language": "en", "authors": [ "Wenbo Sun", "Lingxiao Wu", "Fangni Zhang" ], "first_author": "Wenbo Sun", "venue": "Transportation Research Part B Methodological", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Solver", "Drone", "Robust optimization", "Vehicle routing problem", "Routing (electronic design automation)", "Integer programming", "Linear programming" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trb.2025.103378" }, { "id": "https://openalex.org/W3107919152", "doi": "10.1007/978-3-030-45308-4_7", "title": "When Green Technology Meets Optimization Modeling: The Case of Routing Drones in Logistics, Agriculture, and Healthcare", "year": 2020, "publication_date": "2020-01-01", "type": "book-chapter", "language": "en", "authors": [ "Malick Ndiaye", "Saı̈d Salhi", "Batool Madani" ], "first_author": "Malick Ndiaye", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Agriculture", "Emerging technologies", "Work (physics)", "Last mile (transportation)", "Business", "Engineering", "Computer science", "Mile", "Geography" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-45308-4_7" }, { "id": "https://openalex.org/W4399714938", "doi": "10.1007/978-3-031-62912-9_13", "title": "Combining Heuristics and Constraint Programming for the Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Roberto Montemanni", "Mauro Dell’Amico", "Andrea Corsini" ], "first_author": "Roberto Montemanni", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Heuristics", "Scheduling (production processes)", "Constraint programming", "Job shop scheduling", "Routing (electronic design automation)", "Mathematical optimization", "Computer network", "Mathematics", "Operating system" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://iris.unimore.it/bitstream/11380/1342368/2/chapter.pdf" }, { "id": "https://openalex.org/W4400119952", "doi": "10.52693/jsas.1474515", "title": "Collaborative Truck/Drone Routing Problem: An Application to Disaster Logistics", "year": 2024, "publication_date": "2024-06-28", "type": "article", "language": "en", "authors": [ "Alparslan Güzey", "Mehmet Hakan Satman" ], "first_author": "Alparslan Güzey", "venue": "İstatistik ve Uygulamalı Bilimler Dergisi", "publisher": null, "abstract": "This study focuses on collaborative UAV/UGV routing problem in emergency logistics aiming to optimize the coordinated delivery efforts of both vehicles during disaster situations. By using a mixed integer model the study identifies the best delivery points and assigns specific drones to transport medical supplies to designated emergency assembly points. The research methodically assesses how UAV speed, UGV stops and cluster numbers affect delivery time. The results show that increasing UAV speeds and reducing stops and clusters generally lead to deliveries but achieving the time requires a careful balance due to their complex interactions. The study suggests that strategic coordination between UAVs and UGVs can significantly improve the efficiency of emergency logistics systems potentially reducing response times in disaster relief and medical supply deliveries. In conclusion the model highlights the potential for enhancements, in emergency response capabilities that could help save lives and lessen disaster impacts. Future studies should consider adapting the model for conditions and unpredictable scenarios to ensure resilience against demands.", "keywords": [ "Drone", "Resilience (materials science)", "Truck", "Routing (electronic design automation)", "Computer science", "Humanitarian Logistics", "Vehicle routing problem", "Operations research", "Emergency response", "Emergency management", "Transport engineering", "Risk analysis (engineering)" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/en/download/article-file/3889289" }, { "id": "https://openalex.org/W7125417823", "doi": "10.1080/00207543.2026.2616664", "title": "Multi-commodity vehicle-drone routing problem with simultaneous delivery and pickup in humanitarian logistics", "year": 2026, "publication_date": "2026-01-20", "type": "article", "language": "en", "authors": [ "Wenhao Peng", "Dujuan Wang", "Yunqiang Yin", "Yugang Yu", "T.C.E. Cheng", "Mohd Kamal Mohd Nawawi" ], "first_author": "Wenhao Peng", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "We address a novel vehicle-drone routing problem involving multiple categories of relief commodities in the context of humanitarian logistics. Unlike existing studies, the transfer and redistribution of relief commodities from one affected area to other affected areas is allowed. A set of homogeneous vehicle-drone tandems, where each vehicle is paired with a dedicated drone, is exploited to cooperatively perform the pickup and delivery services to minimise an optimality criterion that simultaneously involves cost-effectiveness, delivery timeliness, and delivery equity. To solve the problem, we propose a tailored adaptive large neighbourhood search with deep Q-network (ALNS-DQN) algorithm, where some structural properties-related removal and insertion operator pairs are introduced to enhance the exploration of the solution space. A DQN algorithm is developed to learn how to select suitable removal and insertion operator pairs, and some local search operators are explored to further improve the obtained solutions. Extensive numerical experiments demonstrate the superior performance and adaptability of the proposed ALNS-DQN algorithm compared with two benchmarks, highlighting the superiority of the vehicle-drone transport pattern over the vehicle/helicopter-only transport pattern, as well as the advantages of allowing commodity transfer and redistribution among affected areas.", "keywords": [ "Pickup", "Humanitarian Logistics", "Vehicle routing problem", "Routing (electronic design automation)", "Supply chain", "Key (lock)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2026.2616664" }, { "id": "https://openalex.org/W4382050689", "doi": "10.1109/icuas57906.2023.10156585", "title": "Solving Vehicle Routing Problem for Unmanned Heterogeneous Vehicle Systems using Asynchronous Multi-Agent Architecture (A-teams)", "year": 2023, "publication_date": "2023-06-06", "type": "conference-paper", "language": "en", "authors": [ "R. Subramanian", "Md Safwan Mondal", "Jean-Paul Reddinger", "James M. Dotterweich", "James D. Humann", "Marshal Childers", "Pranav A. Bhounsule" ], "first_author": "R. Subramanian", "venue": null, "publisher": null, "abstract": "Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to cooperatively perform tasks over wide areas. Such a cooperation can be achieved efficiently by solving a path planning problem. On top of solving a path planning problem, the problem of routing an heterogeneous set of vehicles in an optimal fashion is quite challenging. In order to solve the computationally expensive path-planning problem in a reasonable time, we created a two-level optimization approach with heuristics. At the outer level, the UGV route is parameterized by considering which set of locations to visit in the scenario and the UGV wait times to recharge UAVs and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judiciously in order to create high quality solutions. We explore two methods for tuning the free UGV parameters: (1) a Genetic Algorithm (GA), and (2) Asynchronous Multi-agent architecture (A-teams). The A-teams uses multiple agents to create, improve, and destroy solutions. The parallel asynchronous architecture enables A-teams to quickly optimize the parameters. Our results on test cases show that the A-teams produces similar solutions as GA but is 2-3 times faster.", "keywords": [ "Computer science", "Asynchronous communication", "Heuristics", "Routing (electronic design automation)", "Unmanned ground vehicle", "Motion planning", "Set (abstract data type)", "Parameterized complexity", "Vehicle routing problem", "Path (computing)", "Distributed computing", "Architecture" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas57906.2023.10156585" }, { "id": "https://openalex.org/W4403794757", "doi": "10.1080/15568318.2024.2419378", "title": "Sustainable last mile logistics employing drones and e-bikes", "year": 2024, "publication_date": "2024-10-01", "type": "article", "language": "en", "authors": [ "Stephanie Santiago-Montaño", "Daniel F. Silva", "Alice E. Smith" ], "first_author": "Stephanie Santiago-Montaño", "venue": "International Journal of Sustainable Transportation", "publisher": "Taylor & Francis", "abstract": "Last mile delivery is an important and growing part of the supply chain that has a sizeable negative environmental impact. This paper considers more sustainable approaches to home-delivery that are pragmatic for many non-rural environments. We address the two-echelon, multi-trip, capacitated vehicle routing problem with home-delivery and optional self-pickup services using different combinations of drones, trucks, and electric-assisted bikes (i.e. e-bikes). In the proposed approach, parcels are transported from a depot to parcel lockers by either drones or trucks and are then delivered to customer locations by either e-bikes or trucks. The four approaches range from the most sustainable (drones then e-bikes) to partly green (drone then truck or truck then e-bike) to finally the traditional (truck then truck). We formulate a mathematical model that determines the vehicle routes to minimize the total cost, which consists of vehicle operational cost and operator wages. The four types of delivery networks are assessed and compared for both costs and emissions. Experimental results suggest that with a modest increase in total cost (as little as 13%), emission reductions of up to 92%, on average, can be achieved when using the greenest delivery strategy of drones and e-bikes. The other delivery options have varying tradeoffs between costs and environmental impact. The percentage of self-pickup customers is an influencing factor to consider when choosing the delivery strategy that best meets the organization’s budget and environmental goals, especially when using e-bikes in the second echelon.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Transport engineering", "Aeronautics", "Business", "Sustainable transport", "Engineering", "Sustainability", "Geography" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/15568318.2024.2419378" }, { "id": "https://openalex.org/W4391169979", "doi": "10.1111/itor.13429", "title": "A predictive multistage postdisaster damage assessment framework for drone routing", "year": 2024, "publication_date": "2024-01-24", "type": "article", "language": "en", "authors": [ "Birce Adsanver", "Elvin Çoban", "Burcu Balcik" ], "first_author": "Birce Adsanver", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Abstract This study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an assessment period. In the initial phase, the problem involves determining areas to be scanned by each drone and the optimal sequence for visiting these selected areas. We have adapted an electric vehicle routing formulation and devised a variable neighborhood descent heuristic for this phase. In the second phase, information collected from the scanned areas is employed to predict the damage status of the unscanned areas. We have introduced a novel, fast, and easily implementable imputation policy for this purpose. To evaluate the performance of our approach in real‐life disasters, we develop a case study for the expected 7.5 magnitude earthquake in Istanbul, Turkey. Our numerical study demonstrates a significant improvement in response time and priority‐based metrics.", "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Operations research", "Aeronautics", "Computer network", "Engineering", "Biology" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13429" }, { "id": "https://openalex.org/W4291340492", "doi": "10.2139/ssrn.4189789", "title": "A Grasp Approach for the Energy Minimizing Electric Vehicle Routing Problem with Drones", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Nikolaos A. Kyriakakis", "Themistoklis Stamadianos", "Magdalene Marinaki", "Ioannis Marinakis" ], "first_author": "Nikolaos A. Kyriakakis", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "GRASP", "Drone", "Electric vehicle", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Energy (signal processing)", "Mathematical optimization", "Mathematics", "Computer network", "Physics", "Power (physics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4189789" }, { "id": "https://openalex.org/W4383604999", "doi": "10.48550/arxiv.2307.02980", "title": "Constraint Programming models for the parallel drone scheduling vehicle routing problem", "year": 2023, "publication_date": "2023-07-06", "type": "preprint", "language": "en", "authors": [ "Roberto Montemanni", "Mauro Dell’Amico" ], "first_author": "Roberto Montemanni", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Drones are currently seen as a viable way for improving the distribution of parcels in urban and rural environments, while working in coordination with traditional vehicles like trucks. In this paper we consider the parallel drone scheduling vehicle routing problem, where the service of a set of customers requiring a delivery is split between a fleet of trucks and a fleet of drones. We consider two variations of the problem. In the first one the problem is more theoretical, and the target is the minimization of the time required to complete the service and have all the vehicles back to the depot. In the second variant more realistic constraints involving operating costs, capacity limitation and workload balance, are considered, and the target is to minimize the total operational costs. We propose several constraint programming models to deal with the two problems. An experimental champaign on the instances previously adopted in the literature is presented to validate the new solving methods. The results show that on top of being a viable way to solve problems to optimality, the models can also be used to derive effective heuristic solutions and high-quality lower bounds for the optimal cost, if the execution is interrupted after its natural end.", "keywords": [ "Computer science", "Vehicle routing problem", "Truck", "Scheduling (production processes)", "Mathematical optimization", "Drone", "Workload", "Constraint programming", "Job shop scheduling", "Operations research", "Heuristic", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2307.02980" }, { "id": "https://openalex.org/W7130652024", "doi": "10.1016/j.tre.2026.104760", "title": "LLM-based automatic heuristic design for vehicle-drone collaborative routing problems", "year": 2026, "publication_date": "2026-02-20", "type": "article", "language": "en", "authors": [ "Haiyang Shi", "Lu Zhen" ], "first_author": "Haiyang Shi", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Heuristics", "Heuristic", "Robustness (evolution)", "Adaptability", "Routing (electronic design automation)", "Computation", "Key (lock)", "Evolutionary algorithm" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104760" } ]