[ { "id": "https://openalex.org/W4386917992", "doi": "10.17798/bitlisfen.1323976", "title": "A constructive heuristic for the heterogeneous drone delivery problem that considers packages' setups and battery capacity with the aim of minimizing weighted total waiting times of customers", "year": 2023, "publication_date": "2023-09-21", "type": "article", "language": "en", "authors": [ "Murat Şahin" ], "first_author": "Murat Şahin", "venue": "Bitlis Eren Üniversitesi Fen Bilimleri Dergisi", "publisher": null, "abstract": "This article considers the heterogeneous drone routing problem, which takes into account the setup times of customers’ packages and aims to minimize the weighted total waiting times of customers. Cases where drones differ from each other in terms of battery capacity, carrying capacity, speed and load capacity have been handled. The battery capacity has been associated with the payload carried by the drone as long as it stays in the air. A constructive heuristic has been suggested and many test instances have been used to show how the efficiency of the algorithm changes when different priority values are used. As a result, it has been seen that good solutions can be obtained by assigning the light customer packages to the fast drones for the given test instances by using the suggested constructive heuristic.", "keywords": [ "Drone", "Constructive", "Heuristic", "Payload (computing)", "Computer science", "Battery (electricity)", "Mathematical optimization", "Battery capacity", "Routing (electronic design automation)", "Operations research", "Process (computing)", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17798/bitlisfen.1323976" }, { "id": "https://openalex.org/W2350653188", "doi": "10.1371/journal.pone.0155176", "title": "Multi-Objective Algorithm for Blood Supply via Unmanned Aerial Vehicles to the Wounded in an Emergency Situation", "year": 2016, "publication_date": "2016-05-10", "type": "article", "language": "en", "authors": [ "Tingxi Wen", "Zhongnan Zhang", "Kelvin K. L. Wong" ], "first_author": "Tingxi Wen", "venue": "PLoS ONE", "publisher": "Public Library of Science", "abstract": "Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood's temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.", "keywords": [ "Vehicle routing problem", "Scheduling (production processes)", "Computer science", "Blood supply", "Job shop scheduling", "Decomposition", "Drone", "Algorithm", "Operations research", "Real-time computing", "Routing (electronic design automation)", "Mathematical optimization" ], "cited_by_count": 74, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0155176&type=printable" }, { "id": "https://openalex.org/W4393564679", "doi": "10.5281/zenodo.2572750", "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": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the (soon to be published) paper An Adaptive Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Drones, written by David Sacramento Lechado, David Pisinger and Stefan Røpke. 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, and more 30 instances for the sensitivity analysis, named n.m.s.t, where s stands for sensitivity. 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", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Biology", "Computer network", "Genetics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/Vehicle_Routing_Problem_with_Drones_Instances/11719365" }, { "id": "https://openalex.org/W4393807598", "doi": "10.5281/zenodo.1568853", "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": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the (soon to be published) paper An Adaptive Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Drones, written by David Sacramento Lechado, David Pisinger and Stefan Røpke. 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, and more 30 instances for the sensitivity analysis, named n.m.s.t, where s stands for sensitivity. 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. TruckCapacity: Maximum capacity of the trucks. 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", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/Vehicle_Routing_Problem_with_Drones_Instances/11521557" }, { "id": "https://openalex.org/W4393494181", "doi": "10.5281/zenodo.1403150", "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 (soon to be published) paper An Adaptive Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Drones, written by David Sacramento Lechado, David Pisinger and Stefan Røpke. 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. 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. TruckCapacity: Maximum capacity of the trucks. 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", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://zenodo.org/record/1403150" }, { "id": "https://openalex.org/W4393599051", "doi": "10.5281/zenodo.1170475", "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", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.1170475" }, { "id": "https://openalex.org/W4295992773", "doi": "10.48550/arxiv.2209.06308", "title": "Risk-aware Resource Allocation for Multiple UAVs-UGVs Recharging Rendezvous", "year": 2022, "publication_date": "2022-09-13", "type": "preprint", "language": "en", "authors": [ "Asghar, Ahmad Bilal", "Shi, Guangyao", "Nare Karapetyan", "James D. Humann", "Jean-Paul Reddinger", "James M. Dotterweich", "Pratap Tokekar" ], "first_author": "Asghar, Ahmad Bilal", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We study a resource allocation problem for the cooperative aerial-ground vehicle routing application, in which multiple Unmanned Aerial Vehicles (UAVs) with limited battery capacity and multiple Unmanned Ground Vehicles (UGVs) that can also act as a mobile recharging stations need to jointly accomplish a mission such as persistently monitoring a set of points. Due to the limited battery capacity of the UAVs, they sometimes have to deviate from their task to rendezvous with the UGVs and get recharged. Each UGV can serve a limited number of UAVs at a time. In contrast to prior work on deterministic multi-robot scheduling, we consider the challenge imposed by the stochasticity of the energy consumption of the UAV. We are interested in finding the optimal recharging schedule of the UAVs such that the travel cost is minimized and the probability that no UAV runs out of charge within the planning horizon is greater than a user-defined tolerance. We formulate this problem ({Risk-aware Recharging Rendezvous Problem (RRRP))} as an Integer Linear Program (ILP), in which the matching constraint captures the resource availability constraints and the knapsack constraint captures the success probability constraints. We propose a bicriteria approximation algorithm to solve RRRP. We demonstrate the effectiveness of our formulation and algorithm in the context of one persistent monitoring mission.", "keywords": [ "Rendezvous", "Computer science", "Knapsack problem", "Time horizon", "Schedule", "Scheduling (production processes)", "Mathematical optimization", "Real-time computing", "Resource allocation", "Integer programming", "Computer network", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2209.06308" }, { "id": "https://openalex.org/W4393492953", "doi": "10.5281/zenodo.1170476", "title": "Drone-Vehicle Routing Problem Instances", "year": 2018, "publication_date": "2018-03-07", "type": "dataset", "language": "en", "authors": [ "David Sacramento" ], "first_author": "David Sacramento", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the (soon to be published) paper A large Neighborhood Search Heuristic for the Drone-Vehicle Routing Problem, written by David Sacramento Lechado, David Pisinger and Stefan Røpke. The data correspond to 112 instances corresponding to different scenarios for the Drone-Vehicle Routing Problem. 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. 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. TruckCapacity: Maximum capacity of the trucks. 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", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/Drone-Vehicle_Routing_Problem_Instances/11590128" }, { "id": "https://openalex.org/W3207947183", "doi": "10.5281/zenodo.2572757", "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": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "The following instances are originally presented in the (soon to be published) paper An Adaptive Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Drones, written by David Sacramento Lechado, David Pisinger and Stefan Røpke. 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", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/Vehicle_Routing_Problem_with_Drones_Instances/11719368" }, { "id": "https://openalex.org/W4408100843", "doi": "10.1109/iceace63551.2024.10898944", "title": "Research on the Adaptability of Unmanned Aerial Vehicle Route Planning Based on Genetic Algorithm in Complex Environments", "year": 2024, "publication_date": "2024-12-29", "type": "conference-paper", "language": "en", "authors": [ "Jun Zhang", "Yifei Zhan", "Yuexiang Cao" ], "first_author": "Jun Zhang", "venue": null, "publisher": null, "abstract": "This paper proposes a multi-objective Unmanned Aerial Vehicle (UAV) route planning algorithm based on an improved genetic algorithm to address the challenges faced by UAVs in complex environments, such as multiple dynamic obstacles, rapid environmental changes, and difficulties in path optimization. Firstly, based on the task description of UAV route planning and the traditional genetic algorithm model, a UAV route planning model is established to obtain key information such as waypoints, obstacles, and environmental features. Secondly, in order to improve the adaptability and search efficiency of the algorithm, a strategy is proposed to introduce an environmental adaptability term and an adaptive crossover function. Furthermore, based on the characteristics of complex environmental data, an improved genetic algorithm model was constructed that comprehensively considers the impact of environmental factors and UAV performance, providing a more accurate description of the UAV's route planning problem in complex environments. Finally, a UAV route planning system based on an improved genetic algorithm was built, and through simulation and experimental verification, it was proven that the algorithm proposed in this paper can effectively improve the performance of UAV route planning in complex environments.", "keywords": [ "Adaptability", "Route planning", "Genetic algorithm", "Computer science", "Real-time computing", "Artificial intelligence", "Engineering", "Machine learning", "Transport engineering", "Ecology", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iceace63551.2024.10898944" }, { "id": "https://openalex.org/W3043959465", "doi": "10.48550/arxiv.2007.13065", "title": "Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures", "year": 2020, "publication_date": "2020-07-26", "type": "preprint", "language": "en", "authors": [ "Wei Jing", "Di Deng", "Yan Wu", "Kenji Shimada" ], "first_author": "Wei Jing", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "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", "Path (computing)", "Motion planning", "Computer science", "Vehicle routing problem", "Key (lock)", "Routing (electronic design automation)", "Set (abstract data type)", "Genetic algorithm", "Sampling (signal processing)", "Mathematical optimization", "Algorithm" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2007.13065" }, { "id": "https://openalex.org/W3140777486", "doi": "10.1007/978-981-33-4575-1_22", "title": "Simulation and Optimization of Multi-UAV Route Planning Based on Hybrid Particle Swarm Optimization", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Xuezhao Peng", "Feng Guan", "Zhongpu Wang", "Shushida Gao" ], "first_author": "Xuezhao Peng", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Particle swarm optimization", "Mathematical optimization", "Crossover", "Multi-swarm optimization", "Computer science", "Travelling salesman problem", "Genetic algorithm", "Vehicle routing problem", "Meta-optimization", "Operator (biology)", "Swarm behaviour", "Metaheuristic" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-33-4575-1_22" }, { "id": "https://openalex.org/W4297872457", "doi": "10.1080/19475705.2022.2115407", "title": "Location of disaster assessment UAVs using historical tornado data", "year": 2022, "publication_date": "2022-09-11", "type": "article", "language": "en", "authors": [ "Sean Grogan", "Nathalie Perrier", "Michel Gamache", "Robert Pellerin" ], "first_author": "Sean Grogan", "venue": "Geomatics Natural Hazards and Risk", "publisher": "Taylor & Francis", "abstract": "Preparing and responding to disasters is a complicated task. One must balance coverage of SAR resources versus preparation cost. This article presents a method and solution to prepositioning UAV damage assessment and search teams in Oklahoma using historical tornado data. The approach is based on set covering and multi-station vehicle routing models. It also presents a method to robustify the solution in the event a UAV team cannot be activated to respond to the disaster. This can simulate a team unable to respond. Results show 70% more stations and teams being required when chance of a depot failure goes from 0 to 5% and 90% more stations required when 0–10%. We find that when trying to use a solution that does not account for depot failure, the system of UAVs cannot meet search completion targets in 3–4% of cases. These results demonstrate accounting for the chance of teams not being able to respond to domestic disasters is important and failing to do so means an increased chance of not being able to respond adequately to disasters and incorporating the chance of station failure has a profound impact on the number of stations needed.", "keywords": [ "Tornado", "Event (particle physics)", "Computer science", "Operations research", "Set (abstract data type)", "Task (project management)", "Engineering", "Geography", "Meteorology" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/19475705.2022.2115407" }, { "id": "https://openalex.org/W4383264439", "doi": "10.1002/net.22172", "title": "A new MILP formulation for the flying sidekick traveling salesman problem", "year": 2023, "publication_date": "2023-07-05", "type": "article", "language": "en", "authors": [ "Maurizio Boccia", "Andrea Mancuso", "Adriano Masone", "Claudio Sterle" ], "first_author": "Maurizio Boccia", "venue": "Networks", "publisher": "Wiley", "abstract": "Abstract Nowadays, truck‐and‐drone problems represent one of the most studied classes of vehicle routing problems. The Flying Sidekick Traveling Salesman Problem ( FS‐TSP ) is the first optimization problem defined in this class. Since its definition, several variants have been proposed differing for the side constraints related to the operating conditions and for the structure of the hybrid truck‐and‐drone delivery system. However, regardless the specific problem under investigation, determining the optimal solution of most of these routing problems is a very challenging task, due to the vehicle synchronization issue. On this basis, this work provides a new arc‐based integer linear programming formulation for the FS‐TSP . The solution of such formulation required the development of a branch‐and‐cut solution approach based on new families of valid inequalities and variable fixing strategies. We tested the proposed approach on different sets of benchmark instances. The experimentation shows that the proposed method is competitive or outperforms the state‐of‐the‐art approaches, providing either the optimal solution or improved bounds for several instances unsolved before.", "keywords": [ "Travelling salesman problem", "Benchmark (surveying)", "Mathematical optimization", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Drone", "Integer programming", "Branch and cut", "Linear programming", "Class (philosophy)", "Mathematics" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/net.22172" }, { "id": "https://openalex.org/W4210541116", "doi": "10.34229/2707-451x.21.4.2", "title": "Formalization of the Problem of Optimization of Base Places and Routes of the UAV Group", "year": 2021, "publication_date": "2021-12-30", "type": "article", "language": "en", "authors": [ "Leonid Hulianytskyi", "Oleg Rybalchenko" ], "first_author": "Leonid Hulianytskyi", "venue": "Cybernetics and Computer Technologies", "publisher": null, "abstract": "Introduction. The problem of planning the mission of a set of heterogeneous unmanned aerial vehicles (UAVs)is considered, which is to survey and/or service a given set of targets in the field. A mathematical model of the problem and algorithms for its solving that is based on deterministic local search, as well as optimization by ant colonies are proposed. The efficiency of algorithms is investigated based on the results of solving problems with real objects in the field. The relative error of the results of each algorithm was obtained, which allowed to compare their efficiency. The purpose of the paper is to solve a routing problem in different ways to reduce overall mission cost and compare the efficiency. The problem statement considers multiple starting points and destinations (depots) for UAVs with determined capacity, so algorithms proposed in the paper are designed to optimize the initial placement. Each UAV has a maximum flight distance because of an energy limit, though vehicles can be recharged by visiting one of previously placed depots. The mission goal is to visit all the given targets while minimizing the overall cost, so fuel consumption over distance, depot placement, and resources needed to survey and/or service of the target by each UAV are considered as components of the final cost metric to be minimized considering a set of specific constraints. Results. To solve the given UAV routing problem, a max-min algorithm of ant systems was developed, which features step-by-step interaction of ants to form solutions, a hybrid taboo search algorithm and a deterministic local search algorithm - the decay vector method. The developed algorithms were tested both on the known travelling salesman problems, and on specially developed problems with multiple depots and additional restrictions. Conclusions. The proposed algorithms which are based on ant colony optimization are compared both in terms of accuracy and computation time. A hybrid algorithm achieved slightly better score, though computation time has increased. Keywords: routing, combinatorial optimization, UAV, local search, ant colony optimization algorithms.", "keywords": [ "Computer science", "Problem statement", "Mathematical optimization", "Set (abstract data type)", "Routing (electronic design automation)", "Tree traversal", "Metric (unit)", "Ant colony optimization algorithms", "Optimization problem", "Service (business)", "Field (mathematics)", "Operations research" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://cctech.org.ua/images/docs/Articles/2021/paper_21_4_2.pdf" }, { "id": "https://openalex.org/W4411863715", "doi": "10.2139/ssrn.5334807", "title": "Drone and Vehicle Routing Problem with Underground Logistics", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Hyeonbin Jeong", "Byung Duk Song" ], "first_author": "Hyeonbin Jeong", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Business", "Aeronautics", "Computer science", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5334807" }, { "id": "https://openalex.org/W4236555636", "doi": "10.1109/wsc.1998.745084", "title": "Reactive Tabu Search in unmanned aerial reconnaissance simulations", "year": 2002, "publication_date": "2002-11-27", "type": "conference-paper", "language": "en", "authors": [ "Joel L. Ryan", "T. Glenn Bailey", "James Tice Moore", "William B. Carlton" ], "first_author": "Joel L. Ryan", "venue": null, "publisher": null, "abstract": "We apply a Reactive Tabu Search (RTS) heuristic within a discrete-event simulation to solve routing problems for unmanned aerial vehicles (UAVs). Our formulation represents this problem as a multiple traveling salesman problem with time windows (mTSPTW), with the objective of maximizing expected target coverage. Incorporating weather and probability of UAV survival at each target as random inputs, the RTS heuristic in the simulation searches for the best solution in each realization of the problem scenario in order to identify those routes that are robust to variations in weather, threat, or target service times. We present an object-oriented implementation of this approach using CACI's simulation language MODSIM.", "keywords": [ "Tabu search", "Computer science", "Travelling salesman problem", "Heuristic", "Mathematical optimization", "Routing (electronic design automation)", "Realization (probability)", "Event (particle physics)", "Real-time computing", "Artificial intelligence", "Algorithm", "Mathematics" ], "cited_by_count": 128, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wsc.1998.745084" }, { "id": "https://openalex.org/W4408408487", "doi": "10.7307/ptt.v37i2.634", "title": "Location-Route Planning for VTOL Airport and UAV Urban Logistics Network – A Case Study of Tianjin", "year": 2025, "publication_date": "2025-03-13", "type": "article", "language": "en", "authors": [ "Hong JIANG", "Jiaxue WANG", "Xinhui Ren" ], "first_author": "Hong JIANG", "venue": "PROMET - Traffic&Transportation", "publisher": "University of Zagreb, Faculty of Transport and Traffic Sciences", "abstract": "With the potential for fast, contactless and environmentally friendly delivery, unmanned aerial vehicles (UAVs) have gained increasing attention and application due to their cost-effectiveness and convenient and rapid delivery operations. In future cities, a multi-level airport that supports vertical take-off and landing (VTOL) of UAVs and forming a delivery network is necessary to improve delivery efficiency and provide a competitive advantage. This paper proposes a multi-level airport location-routing problem for UAVs that considers UAV flight energy consumption and operational costs. The goal is to minimise the number of locations and minimise delivery path planning while meeting delivery demands within the service range. Based on the traditional distribution centre site-path problem, the UAV distribution network is constructed to solve the problem of airport location and flight path planning, and the two-layer genetic algorithm is used to solve it. Based on this, the validity of the model and algorithm is verified using the urban area of Tianjin as an example. The experimental results show that the constructed model can be used for UAV airport layout planning, which is applicable to large-scale, multi-aircraft-type and multi-level airport layout planning. Data analysis results indicate that when the location layout of the vertical hub airport is on the edge of the VTOL points, both the flight distance and the total cost of the delivery network relatively increase. Increasing the payload capacity will reduce the number of UAV operations, but the total cost shows a decreasing-then-increasing trend. This study can provide a theoretical basis for the selection of airport sites and UAV types in future UAV urban delivery networks.", "keywords": [ "Transport engineering", "City logistics", "Route planning", "Environmental planning", "Business", "Geography", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7307/ptt.v37i2.634" }, { "id": "https://openalex.org/W3182200279", "doi": "10.1007/978-3-030-78811-7_20", "title": "UAV Path Planning Based on Variable Neighborhood Search Genetic Algorithm", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Guo Zhang", "Rui Wang", "Hongtao Lei", "Tao Zhang", "Wenhua Li", "Yuanming Song" ], "first_author": "Guo Zhang", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Mathematical optimization", "Genetic algorithm", "Local search (optimization)", "Variable (mathematics)", "Algorithm", "Variable neighborhood search", "Motion planning", "Heuristic", "Mutation", "Neighbourhood (mathematics)", "Search algorithm" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-78811-7_20" }, { "id": "https://openalex.org/W4401009984", "doi": "10.1145/3654522.3654566", "title": "Modeling the Parallel Drone Scheduling Vehicle Routing Problem as a Heterogeneous Vehicle Routing Problem", "year": 2024, "publication_date": "2024-02-23", "type": "conference-paper", "language": "en", "authors": [ "Roberto Montemanni", "Mauro Dell’Amico", "Andrea Corsini" ], "first_author": "Roberto Montemanni", "venue": null, "publisher": null, "abstract": "Drones are currently seen as a viable way for improving the distribution of parcels in urban and rural environments, especially 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 visit is accomplished by a fleet of trucks performing tours and a set drones moving back and forth from a single depot. In this work we propose a simple transformation that allows mapping the problem into a heterogeneous vehicle routing problem, which is a classic problem in the literature and for which an abondant collection of solving methods is available. We finally experimentally demonstrate how freely available softwares for the heterogeneous vehicle routing problem, such as Google OR-tools, can be used to obtained results comparable to those found by more complex methods especially tailored to the parallel drone vehicle routing problem.", "keywords": [ "Vehicle routing problem", "Computer science", "Drone", "Scheduling (production processes)", "Job shop scheduling", "Routing (electronic design automation)", "Distributed computing", "Computer network", "Mathematical optimization", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3654522.3654566" }, { "id": "https://openalex.org/W2801297575", "doi": "10.1016/j.ress.2018.05.004", "title": "Joint routing and aborting optimization of cooperative unmanned aerial vehicles", "year": 2018, "publication_date": "2018-05-12", "type": "article", "language": "en", "authors": [ "Rui Peng" ], "first_author": "Rui Peng", "venue": "Reliability Engineering & System Safety", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Abort", "Computer science", "Routing (electronic design automation)", "Joint (building)", "Plan (archaeology)", "Computer network", "Engineering", "Geography" ], "cited_by_count": 96, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ress.2018.05.004" }, { "id": "https://openalex.org/W3083698083", "doi": "10.1109/isc251055.2020.9239082", "title": "Multimodal Autonomous Last-Mile Delivery System Design and Application", "year": 2020, "publication_date": "2020-09-28", "type": "conference-paper", "language": "en", "authors": [ "Farah Samouh", "Veronica Gluza", "Shadi Djavadian", "Seyed Mehdi Meshkani", "Bilal Farooq" ], "first_author": "Farah Samouh", "venue": null, "publisher": null, "abstract": "With the rapid increase in congestion, alternative solutions are needed to efficiently use the capacity of our existing networks. This paper focuses on exploring the emerging autonomous technologies for on-demand food delivery in congested urban cities. Three different last mile food delivery systems are proposed in this study employing aerial and ground autonomous vehicles technologies. The three proposed systems are: robot delivery system, drone delivery system and a hybrid delivery system. In the hybrid system the concept of hub-and-spoke network is explored in order to consolidate orders and reach more destinations in less time. To investigate the performance of the three proposed delivery systems, they are applied to the city of Mississauga network, in an in-house agent-based simulation in MATLAB. 18 Scenarios are tested differing in terms of demand and fleet size. The results show that the hybrid robot-drone delivery system performs the best with a fleet side of 25 robots and 15 drones and with an average preparation and delivery time less than the individual robot and drone system by 48 % and 42% respectively.", "keywords": [ "Drone", "Food delivery", "Last mile (transportation)", "Hybrid system", "Computer science", "Delivery system", "Robot", "Mile", "Real-time computing", "Simulation", "Transport engineering", "Engineering" ], "cited_by_count": 32, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2009.01960" }, { "id": "https://openalex.org/W169908934", "doi": null, "title": "A Genetic Algorithm for Uav Routing Integrated with a Parallel Swarm Simulation", "year": 2012, "publication_date": "2012-09-12", "type": "article", "language": "en", "authors": [ "Matthew A. Russell" ], "first_author": "Matthew A. Russell", "venue": null, "publisher": null, "abstract": "This research investigation addresses the problem of routing and simulating swarms of UAVs. Sorties are modeled as instantiations of the NP-Complete Vehicle Routing Problem, and this work uses genetic algorithms (GAs) to provide a fast and robust algorithm for a priori and dynamic routing applications. Swarms of UAVs are modeled based on extensions of Reynolds' swarm research and are simulated on a Beowulf cluster as a parallel computing application using the Synchronous Environment for Emulation and Discrete Event Simulation (SPEEDES). In a test suite, standard measures such as benchmark problems, best published results, and parallel metrics are used as performance measures. The GA consistently provides efficient and effective results for a variety of VRP benchmarks. Analysis of the solution quality over time verifies that the GA exponentially improves solution quality and is robust to changing search landscapes - making it an ideal tool for employment in UAV routing applications. Parallel computing metrics calculated from the results of a PDES show that consistent speedup (almost linear in many cases) can be obtained using SPEEDES as the communication library for this UAV routing application. Results from the routing application and parallel simulation are synthesized to produce a more advanced model for routing UAVs.", "keywords": [ "Computer science", "Benchmark (surveying)", "Routing (electronic design automation)", "Speedup", "Swarm behaviour", "Distributed computing", "Vehicle routing problem", "Parallel computing", "Algorithm", "Artificial intelligence", "Embedded system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.970.8442" }, { "id": "https://openalex.org/W4311799285", "doi": "10.1504/ijstl.2023.10052859", "title": "Vehicle Routing Problem with Drones as an Aid for Epidemic Relief", "year": 2022, "publication_date": "2022-12-15", "type": "article", "language": "en", "authors": [ "Edgar Rojas Rivera", "Nguyen Anh Tu", "Marco Fabio Benaglia", "Shih Hao Lu", "Jen Wei Cheng" ], "first_author": "Edgar Rojas Rivera", "venue": "International Journal of Shipping and Transport Logistics", "publisher": "Inderscience Publishers", "abstract": null, "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Operations research", "Business", "Computer network", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijstl.2023.10052859" }, { "id": "https://openalex.org/W4405521135", "doi": "10.1109/access.2024.3519702", "title": "Particle Swarm Optimization-Based Content Delivery Protocol for UAV VAENTs", "year": 2024, "publication_date": "2024-12-18", "type": "article", "language": "en", "authors": [ "Yongje Shin", "Hyunseok Choi", "Youngju Nam", "Euisin Lee" ], "first_author": "Yongje Shin", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "As the capabilities of mobile devices and mobile communication techniques expand, vehicular ad hoc networks (VANETs) play an increasingly significant role in transportation and traffic management, particularly in Intelligent Transportation Systems (ITS). However, the rapid topology changes, high vehicle mobility, and frequent link disconnections make routing in VANETs a challenging task, necessitating the development of efficient routing protocols. Moreover, as VANET services provided in our daily lives become more extensive and situations in which they are provided simultaneously to an increasing number of users become more frequent, numerous data delivery optimization algorithms have been researched to address these issues. Alongside this, the research related to Unnamed Aerial Vehicles (UAVs) has also been utilized as a solution to solve the physical data link disconnection problem of VANETs. In this paper, we propose a routing protocol called PSUV (Particle Swarm UAV-VANET). First, we have solved the software-related issues of VANETs by measuring data transmission availability of vehicles existing in the topology and conducting topology optimization by creating a vehicle routing table, utilizing a mathematical value calculation formula that combines Particle Swarm Optimization (PSO) from Swarm Intelligence (SI) and the directional vector information of the vehicle. Furthermore, by utilizing UAVs, we address the physical disconnectivity issues that arise during data transmission in VANETs, thereby minimizing the delay and maximizing the total data rate under realistic conditions. Finally, extensive simulations are conducted to validate the superiority of the proposed protocol in various circumstances in an NS-3 simulation. Simulation results indicate that the proposed protocol solves the challenging issues in realistic VANET with significant improvement.", "keywords": [ "Particle swarm optimization", "Protocol (science)", "Computer science", "Content delivery", "Particle (ecology)", "Content (measure theory)", "Computer network", "Algorithm" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2024.3519702" }, { "id": "https://openalex.org/W3111032549", "doi": "10.1061/9780784483053.411", "title": "Optimization of Vehicle Routing Problem with Drones in Rural Areas", "year": 2020, "publication_date": "2020-12-09", "type": "conference-paper", "language": "en", "authors": [ "Haimei Chen", "Daiwei Hu", "Tingting Zhang" ], "first_author": "Haimei Chen", "venue": "CICTP 2020", "publisher": null, "abstract": "With the development of e-commerce industry and the improvement of rural economic level, rural Internet consumption is booming. However, the “last mile” distribution of rural e-commerce logistics is facing the problems of imperfect infrastructure equipment construction, high logistics distribution cost, and low distribution efficiency. The “last mile” distribution problem has become the biggest obstacle to the development of rural e-commerce logistics. The idea of drone and truck cooperating in distribution undoubtedly provides a new way to solve the problem of “last mile” of rural e-commerce logistics. In this paper, a mathematical model is established for the vehicle routing problem in which drone and truck cooperate with each other in rural areas. The model takes minimizing total cost as the objective function, including different vehicles, multi-trucks, customer accessibility, vehicles’ load constraints, and time limitations. Finally, the optimization solver is used to solve the small-scale example, and the feasibility of the model is proved. The model can provide guidance for rural e-commerce logistics distribution.", "keywords": [ "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Computer science", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1061/9780784483053.411" }, { "id": "https://openalex.org/W4392899881", "doi": null, "title": "A Simulated annealing algorithm for the vehicle routing problem with drones", "year": 2023, "publication_date": "2023-02-20", "type": "conference-paper", "language": "fr", "authors": [ "Mahdi Moeini", "Oliver Wendt", "Marius Schummer" ], "first_author": "Mahdi Moeini", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "International audience", "keywords": [ "Simulated annealing", "Vehicle routing problem", "Drone", "Computer science", "Routing (electronic design automation)", "Algorithm", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-04297708" }, { "id": "https://openalex.org/W4389581986", "doi": "10.1007/978-3-031-49529-8_22", "title": "The Vehicle Routing Problem with Drones for Fresh Agricultural Products", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Quang Huy Vuong", "Trung-Nghia Phung", "Minh-Trien Pham" ], "first_author": "Quang Huy Vuong", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Perishability", "Drone", "Vehicle routing problem", "Integer programming", "Routing (electronic design automation)", "Agriculture", "Computer science", "Heuristic", "Mathematical optimization", "Linear programming", "Operations research", "Quality (philosophy)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-49529-8_22" }, { "id": "https://openalex.org/W4403753861", "doi": "10.48550/arxiv.2409.14173", "title": "An Evolutionary Algorithm For the Vehicle Routing Problem with Drones with Interceptions", "year": 2024, "publication_date": "2024-09-21", "type": "preprint", "language": "en", "authors": [ "Carlos Pambo", "Jacomine Grobler" ], "first_author": "Carlos Pambo", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The use of trucks and drones as a solution to address last-mile delivery challenges is a new and promising research direction explored in this paper. The variation of the problem where the drone can intercept the truck while in movement or at the customer location is part of an optimisation problem called the vehicle routing problem with drones with interception (VRPDi). This paper proposes an evolutionary algorithm to solve the VRPDi. In this variation of the VRPDi, multiple pairs of trucks and drones need to be scheduled. The pairs leave and return to a depot location together or separately to make deliveries to customer nodes. The drone can intercept the truck after the delivery or meet up with the truck at the following customer location. The algorithm was executed on the travelling salesman problem with drones (TSPD) datasets by Bouman et al. (2015), and the performance of the algorithm was compared by benchmarking the results of the VRPDi against the results of the VRP of the same dataset. This comparison showed improvements in total delivery time between 39% and 60%. Further detailed analysis of the algorithm results examined the total delivery time, distance, node delivery scheduling and the degree of diversity during the algorithm execution. This analysis also considered how the algorithm handled the VRPDi constraints. The results of the algorithm were then benchmarked against algorithms in Dillon et al. (2023) and Ernst (2024). The latter solved the problem with a maximum drone distance constraint added to the VRPDi. The analysis and benchmarking of the algorithm results showed that the algorithm satisfactorily solved 50 and 100-nodes problems in a reasonable amount of time, and the solutions found were better than those found by the algorithms in Dillon et al. (2023) and Ernst (2024) for the same problems.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Travelling salesman problem", "Routing algorithm", "Algorithm", "Mathematical optimization", "Mathematics", "Computer network", "Biology", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2409.14173" }, { "id": "https://openalex.org/W4313564944", "doi": "10.2139/ssrn.4312992", "title": "Split Delivery Application in Vehicle Routing Problem with Drones", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Yiliang Yuan" ], "first_author": "Yiliang Yuan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4312992" }, { "id": "https://openalex.org/W4313269848", "doi": "10.1109/icus55513.2022.9986599", "title": "Multi-Area Complete Coverage with Fixed-Wing UAV Swarms Based on Modified Ant Colony Algorithm", "year": 2022, "publication_date": "2022-10-28", "type": "conference-paper", "language": "en", "authors": [ "Yueqian Liang", "Yi Yang", "Yanjie Zhao" ], "first_author": "Yueqian Liang", "venue": "2022 IEEE International Conference on Unmanned Systems (ICUS)", "publisher": null, "abstract": "As a typical application, multiple unmanned aerial vehicles (UAVs) are usually sent to search for possible targets in chosen areas. In this paper, the multi-area complete coverage problem using fixed-wing UAV swarms is investigated, where the areas can be of arbitrary shape and size. For each area, its longitude direction, corresponding to the optimal sweep direction, is firstly found by the diameter function method, and is then used to compute the sweep waypoints. Observing that coverage gap and redundancy exist, we propose a waypoint adjustment procedure to achieve gap filling and redundancy eliminating. The problem can then be formulated as a multi-vehicle routing problem, and a modified ant colony algorithm is presented to address it. Finally a simulation example shows that 10 UAVs can cover 5 areas in 682.8 seconds.", "keywords": [ "Redundancy (engineering)", "Waypoint", "Computer science", "Ant colony optimization algorithms", "Algorithm", "Fixed wing", "Mathematical optimization", "Wing", "Real-time computing", "Mathematics", "Engineering", "Aerospace engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icus55513.2022.9986599" }, { "id": "https://openalex.org/W4407275539", "doi": "10.1016/j.trb.2025.103162", "title": "Interactive biobjective optimization algorithms and an application to UAV routing in continuous space", "year": 2025, "publication_date": "2025-02-08", "type": "article", "language": "en", "authors": [ "Hannan Tureci-Isik", "Murat Köksalan", "Diclehan Tezcaner Öztürk" ], "first_author": "Hannan Tureci-Isik", "venue": "Transportation Research Part B Methodological", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Space (punctuation)", "Routing (electronic design automation)", "Mathematical optimization", "Computer science", "Vehicle routing problem", "Algorithm", "Operations research", "Simulation", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trb.2025.103162" }, { "id": "https://openalex.org/W4361222360", "doi": "10.54097/jid.v2i1.5420", "title": "Review on Combined Vehicle Routing Problem of Drones and Vehicles", "year": 2023, "publication_date": "2023-02-22", "type": "article", "language": "en", "authors": [ "Chaowen Zheng" ], "first_author": "Chaowen Zheng", "venue": "Journal of innovation and development", "publisher": null, "abstract": "On the one hand, the rapid development of e-commerce has put forward higher requirements and standards for the logistics industry, which has forced the transformation of the terminal logistics delivery model; on the other hand, drones have developed fast in the field of logistics terminal delivery due to their high flexibility and high efficiency. This paper summarizes the domestic and foreign research status of vehicle route problem of four combined distribution modes, and points out the future research direction.", "keywords": [ "Drone", "Flexibility (engineering)", "Vehicle routing problem", "Terminal (telecommunication)", "Transport engineering", "Routing (electronic design automation)", "Transformation (genetics)", "Field (mathematics)", "Computer science", "Humanitarian Logistics", "Business", "Operations research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://drpress.org/ojs/index.php/jid/article/download/5420/5243" }, { "id": "https://openalex.org/W4312207705", "doi": "10.5281/zenodo.7486901", "title": "Split Delivery Application in Vehicle Routing Problem with Drones", "year": 2022, "publication_date": "2022-08-25", "type": "article", "language": "en", "authors": [ "Yiliang Yuan", "Jiahang Xie" ], "first_author": "Yiliang Yuan", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The use of drones for last-mile parcel delivery has attracted popularity of attention, especially among online retailers and food delivery platforms that pursuit a fast and high-quality one-to-one service. However, previous works disregarded or assumed the load capacity of drones as fixed values for simplification in truck-drone delivery, such that there is no split delivery assignments on drones. Yet in real-world applications, considering drone endurance and payload capacity, it is highly practical that they deliver in a split way. This paper concerns the novel variant: the split delivery vehicle routing problem with drones (VRPD-S). We deal with the cases where each truck is equipped with a single drone, for serving a set of customers with minimal delivery duration. This problem is challenging since drones can perform multiple deliveries for a single customer node, i.e., the customers' demands are allowed to be split. Whereas trucks follow the vehicle routing problem and meanwhile need to synchronize with drones at the depot or demand points. We model this problem as a mixed-integer linear program and implement a problem-guided predictive search combined with an adaptive large neighborhood search (PS-ALNS) procedure to solve it. Extensive computational experiments were carried out on benchmark instances available in the literature. According to the numerical results, our model is validated and the proposed algorithm works effectively in small and larger instances. Split deliveries can generate savings both in terms of number of vehicles and travel costs, and lead to more flexible and efficient results for the overall delivery system.", "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.7486901" }, { "id": "https://openalex.org/W3116752963", "doi": null, "title": "Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand", "year": 2020, "publication_date": "2020-01-17", "type": "preprint", "language": "en", "authors": [ "Tasnim Ibn Faiz", "Chrysafis Vogiatzis", "Md. Noor‐E‐Alam" ], "first_author": "Tasnim Ibn Faiz", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Humanitarian logistics service providers have two major responsibilities immediately after a disaster: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and telecommunications networks, which are often rendered unusable by the disaster at hand. In this work, we propose two-echelon vehicle routing frameworks for performing these operations using aerial uncrewed autonomous vehicles (UAVs or drones) to address the issues associated with these failures. In our proposed frameworks, we assume that ground vehicles cannot reach the trapped population directly, but they can only transport drones from a depot to some intermediate locations. The drones launched from these locations serve to both identify demands for medical and other aids (e.g., epi-pens, medical supplies, dry food, water) and make deliveries to satisfy them. Specifically, we present two decision frameworks, in which the resulting optimization problem is formulated as a two-echelon vehicle routing problem. The first framework addresses the problem in two stages: providing telecommunications capabilities in the first stage and satisfying the resulting demands in the second. To that end, two types of drones are considered. Hotspot drones have the capability of providing cell phone and internet reception, and hence are used to capture demands. Delivery drones are subsequently employed to satisfy the observed demand. The second framework, on the other hand, addresses the problem as a stochastic emergency aid delivery problem, which uses a two-stage robust optimization model to handle demand uncertainty. To solve the resulting models, we propose efficient and novel solution approaches.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Humanitarian aid", "Routing (electronic design automation)", "Operations research", "Humanitarian Logistics", "Population", "Phone", "Robust optimization", "Stochastic programming", "Computer security" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2001.06456.pdf" }, { "id": "https://openalex.org/W7117700187", "doi": "10.1109/ojits.2025.3650067", "title": "UAVs Missions for Sea Emergencies", "year": 2025, "publication_date": "2025-12-31", "type": "article", "language": null, "authors": [ "Sajjad Ghobadi", "Leonardo Mostarda", "Alfredo Navarra", "Francesco Piselli" ], "first_author": "Sajjad Ghobadi", "venue": "IEEE Open Journal of Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper presents the First Boat Rescue (FBR) problem, a new challenging variant of the well-known Electric Vehicle Routing problem. FBR stems from practical rescue operations where a rescue lifeboat and its medical team provide assistance to a boat close to the coast. Quite often the boat calls for unnecessary assistance which leads to a waste of resources. As an alternative and cheaper approach, this paper proposes the use of Unmanned Aerial Vehicles (UAVs) equipped with basic medical tools. More precisely, a lifeboat departure can be avoided when the UAV reaches the boat and remotely connects to the medical team which, by using the UAV’s medical tools, deems the assistance unneeded. UAVs are battery powered which may require recharging activities to accomplish the rescue operations. A buoy recharging station that uses the sea movement and provides a charging pad for a UAV can be used. When buoys are suitably disposed in the rescuing area, a UAV can assist multiple boats in need of emergency without wasting rescue lifeboat fuel and unnecessarily occupying the time of the medical team. This paper studies FBR in two scenarios with partial and full battery recharging policies and presents the Integer Linear Programming (ILP) formulations of the problems. For FBR in the partial recharging scenario, the paper proposes two heuristics. The paper also proves that there is an algorithm that approximates FBR with full recharging policy. The paper concludes by describing various simulations and evaluates the proposed algorithms on random and ad-hoc instances.", "keywords": [ "Routing (electronic design automation)", "Battery (electricity)", "Global Positioning System", "Search and rescue", "Linear programming", "Event (particle physics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ojits.2025.3650067" }, { "id": "https://openalex.org/W4214771219", "doi": "10.1109/cisai54367.2021.00127", "title": "Research on Distribution Path of Multi-Target Urban UAV (unmanned aerial vehicle) based on epsilon-Constraint Method", "year": 2021, "publication_date": "2021-09-01", "type": "conference-paper", "language": "en", "authors": [ "Mengcheng Xiong", "Haotong Fei", "Weixi Yan" ], "first_author": "Mengcheng Xiong", "venue": "2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI)", "publisher": null, "abstract": "UAV delivery has the advantage of low distribution cost, but the scheduling scheme of their routing problems is complex and challenging to solve. This paper mainly studies the distribution scheme of UAVs under actual conditions. The multi-objective nonlinear programming problem is established by taking the minimum total flight distance and the highest average customer satisfaction as the objective functions, starting from the distribution centre and returning as the constraint condition. A heuristic algorithm that is different from the traditional algorithm mode is adopted. Epsilon constraint method transforms the objective function of average customer satisfaction into constraint, and finally, the optimal distribution path is obtained. The algorithm is analyzed and verified by a numerical study and compared with other algorithms, such as adaptive large-scale neighbour search algorithm(ALNS) and genetic algorithm(GA). The results show that the proposed algorithm in this paper can effectively improve the computing speed under multiple distribution points and has a significant ability to optimize results. Specifically, the larger the case size is, the more efficient the Method performs.", "keywords": [ "Mathematical optimization", "Constraint (computer-aided design)", "Computer science", "Path (computing)", "Vehicle routing problem", "Heuristic", "Genetic algorithm", "Algorithm", "Scheduling (production processes)", "Hybrid algorithm (constraint satisfaction)", "Mathematics", "Routing (electronic design automation)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cisai54367.2021.00127" }, { "id": "https://openalex.org/W4410309564", "doi": "10.26650/jtl.2025.1607328", "title": "Unmanned Aerial Vehicle Routing and Facility Location Selection for Healthcare Supply Chain Management: A Case Study in Türkiye", "year": 2025, "publication_date": "2025-05-12", "type": "article", "language": "en", "authors": [ "Mevlüt Evci", "Celal Özçelik", "Tarık Ataman", "Yıldız Köse" ], "first_author": "Mevlüt Evci", "venue": "Journal of Transportation and Logistics", "publisher": "Istanbul University", "abstract": "Healthcare supply chain management is of great importance, particularly for hospitals. To manage medical supply chains quickly and effectively requires new and optimized logistics strategies. The use of an unmanned aerial vehicle (UAV) enables the swift delivery of medical supplies, overcoming obstacles like traffic, natural disasters, or infrastructure damage, and saving lives in critical situations. This study focuses on the distribution phase of emergency medical supply chains using electric UAVs, aiming to save lives during crises by establishing an efficient, technological, and sustainable transportation system in healthcare. The strategic locations of the charging stations, which can also serve as medical supply depots, and the optimal routes for the vehicles are integrated. Using Multi-Criteria Decision-Making (MCDM) techniques, specifically the AHP and TOPSIS methods, the optimal charging points for electric unmanned aerial vehicles are determined by considering various criteria. From these identified facilities, a multi-depot vehicle routing model was developed for supplying emergency medical supplies to hospitals, considering vehicle characteristics, hospital demand quantities, and facility capacities. This integrated methodology was applied to the province of Trabzon in Türkiye. Three different routes covering a total distance of 29.59 kilometres were generated to supply medical materials from five identified facilities to ten hospitals.", "keywords": [ "Supply chain management", "Health care", "Supply chain", "Business", "Selection (genetic algorithm)", "Vehicle routing problem", "Computer science", "Operations management", "Routing (electronic design automation)", "Engineering", "Marketing", "Artificial intelligence" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/801DE56A73174C208123CAB3A3425FA0" }, { "id": "https://openalex.org/W4312198924", "doi": "10.1109/ieem55944.2022.9989729", "title": "Multi-UAV Route Planning for Data Collection from Heterogeneous IoT Devices", "year": 2022, "publication_date": "2022-12-07", "type": "conference-paper", "language": "en", "authors": [ "Shukang Wang", "Yuying Long", "Yaoming Zhou", "Gangyan Xu" ], "first_author": "Shukang Wang", "venue": "2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)", "publisher": null, "abstract": "IoT devices are widely adopted in various scenarios to realize real-time data collection. However, it is challenging to efficiently collect the sensing data when IoT devices are physically distributed in remote areas. This paper proposes to use Unmanned Aerial Vehicles (UAVs) to realize efficient data collection from these IoT devices. Specifically, the heterogeneity of IoT devices in terms of data generation rates is considered, which is modelled as different time windows and service time requirements into the UAV routing problem. Besides, a multi-UAV multi-trip route planning model is proposed and a Tabu-Search based solution algorithm is developed. Finally, an experimental case study is conducted that verifies the effectiveness and performance of the proposed method.", "keywords": [ "Computer science", "Data collection", "Route planning", "Tabu search", "Internet of Things", "Real-time computing", "Routing (electronic design automation)", "Distributed computing", "Service (business)", "Computer network", "Embedded system", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ieem55944.2022.9989729" }, { "id": "https://openalex.org/W2791598186", "doi": "10.1371/journal.pone.0194690", "title": "Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind", "year": 2018, "publication_date": "2018-03-21", "type": "article", "language": "en", "authors": [ "He Luo", "Zhengzheng Liang", "Moning Zhu", "Xiaoxuan Hu", "Guoqiang Wang" ], "first_author": "He Luo", "venue": "PLoS ONE", "publisher": "Public Library of Science", "abstract": "Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided.", "keywords": [ "Turning radius", "Airspeed", "Crossover", "Wind speed", "Computer science", "Genetic algorithm", "Motion planning", "Control theory (sociology)", "Path (computing)", "Operator (biology)", "Mathematical optimization", "Simulation" ], "cited_by_count": 43, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0194690&type=printable" }, { "id": "https://openalex.org/W4390746704", "doi": "10.1016/j.eswa.2023.123106", "title": "New features for customer classification in the Flying Sidekick Traveling Salesman Problem", "year": 2024, "publication_date": "2024-01-11", "type": "article", "language": "en", "authors": [ "Maurizio Boccia", "Andrea Mancuso", "Adriano Masone", "Teresa Murino", "Claudio Sterle" ], "first_author": "Maurizio Boccia", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": "The increasing interest in the deployment of truck and drone delivery systems leads to the definition of new and complex vehicle routing problems. In this context, the flying sidekick traveling salesman problem (FS-TSP) is the first truck-and-drone routing problem defined in the literature. Several variants appeared in the last years differing in the operating conditions and the structure of the hybrid truck-and-drone delivery system. Exact and heuristic solution methods have been proposed in the literature to solve these problems effectively. However, the exact solution methods can generally solve only small-size instances due to the complexity of these problems. On the other hand, heuristic solution methods are able to find feasible solutions with an acceptable computational burden but without any guarantee of the quality of the solution. This work aims to investigate the possibility of using data science and machine learning techniques to reduce the complexity of solving an FS-TSP instance. The idea is to determine a good/optimal customer-to-vehicle assignment apriori to reduce the number of decisions involved in the FS-TSP solution. The assignment is determined through the classification of customers based on a subset of features specifically defined for the FS-TSP. This information can be exploited by existing solution approaches for the FS-TSP to improve their performance. An extensive computational campaign on benchmark instances is carried out with a twofold objective. On the one hand, we aim to evaluate the impact of the features on customer classification. On the other hand, we intend to show the relationship between classification and combinatorial optimization results by observing the effect of the customer classification on the FS-TSP solution.", "keywords": [ "Travelling salesman problem", "Computer science", "Vehicle routing problem", "Drone", "Truck", "Benchmark (surveying)", "Context (archaeology)", "Heuristic", "Routing (electronic design automation)", "A priori and a posteriori", "Mathematical optimization", "Hyper-heuristic" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2023.123106" }, { "id": "https://openalex.org/W2140202248", "doi": "10.1109/vppc.2008.4677752", "title": "Digraph matrix reliability analysis for fault assessment for A UAV platform application. A fault-tree analysis approach", "year": 2008, "publication_date": "2008-09-01", "type": "conference-paper", "language": "en", "authors": [ "Georgios P. Kladis", "J.T. Economou", "K. Knowles", "Antonios Tsourdos", "Brian A. White" ], "first_author": "Georgios P. Kladis", "venue": null, "publisher": null, "abstract": "In this paper potential causes that lead to a failure are investigated through a digraph analysis utilizing graph theory tools and pseudo boolean expressions. A generic scenario of a multi-layered scheme for fault assessment for a UAV application, illustrates the proposed methodology. The resulting solution is obtained by the use of shortest path algorithms due to the means the problem is posed and its similarity to routing problems. In addition mathematical formulation and its relevance is also addressed.", "keywords": [ "Digraph", "Fault tree analysis", "Computer science", "Graph theory", "Reliability (semiconductor)", "Directed graph", "Shortest path problem", "Routing (electronic design automation)", "Similarity (geometry)", "Theoretical computer science", "Graph", "Path (computing)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/vppc.2008.4677752" }, { "id": "https://openalex.org/W4321488646", "doi": "10.1109/icoin56518.2023.10048936", "title": "A Collaborative UAV Routing Algorithm for Time Sensitive Surveillance Tasks", "year": 2023, "publication_date": "2023-01-11", "type": "conference-paper", "language": "en", "authors": [ "Yang Yu", "Sanghwan Lee" ], "first_author": "Yang Yu", "venue": null, "publisher": null, "abstract": "Surveillance and monitoring of an area are fundamental to a variety of missions, especially in the military field, where quadrotor UAV swarms can be used for area reconnaissance to build awareness of the environment and targets of interest. Aiming at the problem of surveillance and monitoring of a given area, this paper proposes a distributed cooperative vehicle routing algorithm for multi-quadrotor UAV swarms called MUCS-BS. In order to reduce the complexity of the algorithm, we use sweep search to search the area with a \"square wave signal\" path. In this algorithm, each UAV covers a segment of the \"square wave signal\" path in such a way that the surveillance time can be minimized.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Search and rescue", "Real-time computing", "Path (computing)", "Routing algorithm", "Field (mathematics)", "Variety (cybernetics)", "Square (algebra)", "SIGNAL (programming language)", "Algorithm", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/icoin56518.2023.10048936" }, { "id": "https://openalex.org/W4382052828", "doi": "10.1109/icccbda56900.2023.10154746", "title": "Swarm Intelligence Optimization of UAV Routing with Simultaneously Stochastic Pick-up and Delivery during COVID-19", "year": 2023, "publication_date": "2023-04-26", "type": "conference-paper", "language": "en", "authors": [ "Haoyan Yang", "Huihui Zhang", "Yuao Qin", "Tian Tang" ], "first_author": "Haoyan Yang", "venue": null, "publisher": null, "abstract": "During the COVID-19 pandemic, it is imperative to distribute daily supplies to residents in lockdown communities and collect their household garbage. Unmanned Aerial Vehicle (UAV) logistics offers a contactless solution, but the limitations of load and maintenance costs must be considered. Therefore, it is crucial to plan UAV transportation routes effectively to minimize overall transportation costs. Additionally, the stochasticity of household garbage quality further complicates UAV delivery. We address this issue by formulating the problem as vehicle routing problem with simultaneous stochastic pickups and deliveries (VRPSSPD) and develop a range of swarm intelligent optimization algorithms (SIOAs) to solve it. Our experiments demonstrate that the Artificial Bee Colony (ABC) algorithm and Shuffled Frog Leaping Algorithm (SFLA) are well-suited to this practical problem. Moreover, to address the long convergence time of SFLA, we propose the General Center SFLA based on Roulette Wheel Selection (RWS-GC-SFLA) to improve it. Our experimental results show that RWS-GC-SFLA can improve the convergence time while ensuring solution quality. Finally, we apply RWS-GCSFLA to a practical instance case of UAV logistics in Guangzhou communities and achieved satisfactory results.", "keywords": [ "Computer science", "Convergence (economics)", "Swarm behaviour", "Vehicle routing problem", "Heuristic", "Coronavirus disease 2019 (COVID-19)", "Fitness proportionate selection", "Range (aeronautics)", "Routing (electronic design automation)", "Mathematical optimization", "Genetic algorithm", "Operations research" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/icccbda56900.2023.10154746" }, { "id": "https://openalex.org/W4413556801", "doi": "10.1109/jiot.2025.3602147", "title": "A Dual-Layer Deep Reinforcement Learning Routing Approach for Integrated UAV and Satellite IoT Networks", "year": 2025, "publication_date": "2025-08-25", "type": "article", "language": "en", "authors": [ "Fei Zheng", "Juzhong Wei", "Shichao Li", "Peng Yu", "Shaoyong Guo", "Jilong Zhao", "Yunhai Huang" ], "first_author": "Fei Zheng", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Internet of Things Devices (IoTDs) in remote areas connect to the Internet through Low Earth Orbit (LEO) satellites. The transmission of massive IoTDs data volumes leveraging satellites faces a critical challenge, as constrained bandwidth onboard significantly impacts the performance of time-sensitive applications. Moreover, due to the limited number of satellite-ground links and the capacity of satellite access devices, it is challenging to serve all IoTDs within the satellite network coverage. To tackle these challenges, we propose a dual-layer network architecture that integrates Satellite Internet of Things (SIoT) and Unmanned Aerial Vehicles (UAVs), where UAVs serve as transmission units and satellites serve as computing units to overcome the limitations of conventional SIoT. In the dual-layer network architecture, a dual-layer routing problem is formulated to ensure the rapid transmission of tasks, and the problem is further decomposed into UAV layer routing and LEO satellite layer routing. At the UAV layer, we propose a multitask concurrent routing strategy based on task segmentation according to path bandwidth to reduce link load and improve transmission efficiency. At the LEO satellite layer, an integrated computation-transmission routing strategy is introduced to mitigate onboard bandwidth constraints through computational capabilities, thereby reducing transmitted data volume and significantly enhancing transmission efficiency. We transform the problem into a Markov Decision Process (MDP) for each layer and solve the problem using Deep Reinforcement Learning (DRL). To enhance the performance, we introduce improvements to the route algorithm. In the UAV layer, we introduce channel state averaging to reduce algorithmic complexity. In the LEO layer, we employ Prioritized Trajectory Replay (PTR) to improve learning efficiency, while a loss constraint is introduced to enhance training stability. Simulation results demonstrate that the proposed algorithm outperforms other algorithms in terms of convergence performance and overall delay.", "keywords": [ "Computer science", "Reinforcement learning", "Satellite", "Routing (electronic design automation)", "Dual (grammatical number)", "Layer (electronics)", "Computer network", "Communications satellite", "Artificial intelligence", "Distributed computing", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2025.3602147" }, { "id": "https://openalex.org/W4391306548", "doi": "10.1109/smc53992.2023.10394106", "title": "Joint UAV Trajectory Scheduling and Network Routing for FANETs: A Reinforcement Learning Approach", "year": 2023, "publication_date": "2023-10-01", "type": "conference-paper", "language": "en", "authors": [ "Junyi Gan", "Bing Li", "Shengjie Zhao" ], "first_author": "Junyi Gan", "venue": null, "publisher": null, "abstract": "Flying Ad-hoc Networks (FANETs) consisting of multiple flexible unmanned aerial vehicles (UAVs) have gained ever-increasing attention due to their advantages in flexible deployment and enhanced data link connectivity. However, existing FANET schemes do not consider the mutual influence between data routing and UAV trajectory scheduling, resulting in limited network performance especially when the number of UAVs in FANET is large. This paper proposes a joint optimization framework for routing and UAV trajectory scheduling in FANET to enhance communication reliability and efficiency. Unlike previous studies that consider routing and trajectory scheduling separately, this framework formulates the routing problem as a Markov Decision Process (MDP) and employs trajectory schedule to guide the state transition probability, addressing the limitation of routing algorithms that passively perceive network topology. In addition, we exploit reinforcement learning to iteratively determine the optimal routing and trajectory scheduling strategy with the support of network topology prediction. Simulation results demonstrate that our proposed approach can significantly reduce latency, communication overhead, and packet loss in FANET.", "keywords": [ "Computer science", "Reinforcement learning", "Computer network", "Distributed computing", "Scheduling (production processes)", "Static routing", "Markov decision process", "Wireless ad hoc network", "Network topology", "Network packet", "Markov process", "Routing protocol" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/smc53992.2023.10394106" }, { "id": "https://openalex.org/W4393115262", "doi": "10.1007/978-3-031-53161-3_6", "title": "A Method with Roulette Selection Strategy for Path Planning in UAV-Based Waste Monitoring Systems", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Dalibor Dobrilović", "Jelena Stojanov", "Dragan Peraković", "Gordana Jotanović", "Goran Jauševac" ], "first_author": "Dalibor Dobrilović", "venue": "EAI/Springer Innovations in Communication and Computing", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Roulette", "Selection (genetic algorithm)", "Fitness proportionate selection", "Computer science", "Motion planning", "Path (computing)", "Operations research", "Environmental science", "Real-time computing", "Engineering", "Artificial intelligence", "Machine learning" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-53161-3_6" }, { "id": "https://openalex.org/W2765528332", "doi": "10.1016/j.ifacol.2017.08.1229", "title": "Use of Co-operative UAVs to Support/Augment UGV Situational Awareness and/or Inter-Vehicle Communications", "year": 2017, "publication_date": "2017-07-01", "type": "article", "language": "en", "authors": [ "F. Quintin", "Serena Iovino", "Al Savvaris", "Antonios Tsourdos" ], "first_author": "F. Quintin", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "This paper presents the development of a path-planning algorithm for Unmanned Aerial Vehicles (UAVs) in order to increase the situational awareness for platooning vehicles. The scenario considers a team of cooperative UAVs, initially docked on moving Unmanned Ground Vehicles (UGVs). In particular, the goal consists in finding the best routing plan for the UAVs in order to visit some designated targets in a wide search area to augment the UGVs’ situational awareness. Taking into account the maximal endurance constraint of the UAVs, this problem becomes equivalent to a time-constrained multiple depot vehicle routing problem with moving depots. To tackle this variant of the well-known vehicle routing problem, a methodology based on a TABU search meta-heuristic is implemented. While respecting the endurance constraint, this methodology tempts to optimize multiple objectives: the number of UAVs required, the total length of the routing and the balance of the lengths of the different routes within the routing plan. The proposed approach based on a meta-heuristic gives relevant results in a relatively short period of time.", "keywords": [ "Situation awareness", "Routing (electronic design automation)", "Tabu search", "Vehicle routing problem", "Computer science", "Heuristic", "Constraint (computer-aided design)", "Operations research", "Real-time computing", "Engineering", "Artificial intelligence", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2017.08.1229" }, { "id": "https://openalex.org/W3198874840", "doi": "10.1007/978-3-030-85906-0_5", "title": "Drone Delivery Vehicle Routing Problem with Multi-flight Level", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Yonggab Kim", "Hoyoung Jung", "Seokcheon Lee" ], "first_author": "Yonggab Kim", "venue": "IFIP advances in information and communication technology", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Obstacle", "Computer science", "Metropolitan area", "Transport engineering", "Aeronautics", "Engineering", "Computer network", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://inria.hal.science/hal-04022113" }, { "id": "https://openalex.org/W4404875780", "doi": "10.1007/978-981-97-9243-6_8", "title": "Iterated Local Search Metaheuristic for the Vehicle Routing Problem with Drones", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Tomoya Iseki", "Shun Ito", "Kengo Katayama" ], "first_author": "Tomoya Iseki", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Metaheuristic", "Iterated local search", "Vehicle routing problem", "Local search (optimization)", "Computer science", "Mathematical optimization", "Routing (electronic design automation)", "Iterated function", "Mathematics", "Artificial intelligence", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-97-9243-6_8" }, { "id": "https://openalex.org/W7212435473", "doi": "10.1007/978-981-92-3570-4_16", "title": "Hybrid Differential Evolution Algorithm for the Vehicle Routing Problem with Drones", "year": 2026, "publication_date": "2026-09-13", "type": "conference-paper", "language": "en", "authors": [ "Yuandong Chen", "Aidi Xiao" ], "first_author": "Yuandong Chen", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Differential evolution", "Routing (electronic design automation)", "Vehicle routing problem", "Hybrid system", "Hybrid algorithm (constraint satisfaction)", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-92-3570-4_16" }, { "id": "https://openalex.org/W4415885612", "doi": "10.1016/j.swevo.2026.102402", "title": "Co-evolution with hierarchical decomposition for Vehicle Routing Problem with Drones", "year": 2026, "publication_date": "2026-04-27", "type": "article", "language": "en", "authors": [ "Ruonan Zhai", "Xuejun Zhang", "Yi Mei", "Tong Guo", "Wenbo Du" ], "first_author": "Ruonan Zhai", "venue": "Swarm and Evolutionary Computation", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Scalability", "Curse of dimensionality", "Decomposition", "Payload (computing)", "Benchmark (surveying)", "Cluster analysis", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5704006" }, { "id": "https://openalex.org/W4394714276", "doi": "10.21203/rs.3.rs-4217850/v1", "title": "Multi-depot vehicle routing problem with drones in emergency logistics", "year": 2024, "publication_date": "2024-04-11", "type": "preprint", "language": "en", "authors": [ "Xun Weng", "Wenke She", "Hong-Qiang Fan", "Jingtian Zhang", "Lifen Yun" ], "first_author": "Xun Weng", "venue": "Research Square", "publisher": null, "abstract": "Abstract In order to expand the application of truck-drone cooperative delivery in emergency logistics, this paper investigates the multi-depot vehicle routing problem with drones for emergency logistics scheduling. A mixed-integer programming model with the objective of minimizing the maximum expected rescue time from urban distribution centres to temporary distribution stations is proposed. To address this problem, a new two-stage hybrid heuristic algorithm is proposed. In the first stage, we employ an improved K-means clustering to cluster the temporary distribution stations. In the second stage, a combination of Tabu search (TS), enhanced genetic algorithm (GA), and simulated annealing (SA) is applied to optimize the routes of both trucks and drones. The numerical study involves the validation of the model using real-world cases. Through comparison experiments with the Gurobi solver, we demonstrate the significant advantages of the proposed heuristic algorithm in terms of solution quality and efficiency.", "keywords": [ "Vehicle routing problem", "Drone", "Depot", "Routing (electronic design automation)", "Business", "Transport engineering", "Computer science", "Operations research", "Engineering", "Computer network", "Political science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-4217850/latest.pdf" }, { "id": "https://openalex.org/W3125951091", "doi": "10.1109/syscon48628.2021.9447142", "title": "Flying Robots for Safe and Efficient Parcel Delivery Within the COVID-19 Pandemic", "year": 2021, "publication_date": "2021-04-15", "type": "conference-paper", "language": "en", "authors": [ "Manuel Patchou", "Benjamin Sliwa", "Christian Wietfeld" ], "first_author": "Manuel Patchou", "venue": null, "publisher": null, "abstract": "The integration of small-scale Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITSs) will empower novel smart-city applications and services. After the unforeseen outbreak of the COVID-19 pandemic, the public demand for delivery services has increased. Mobile robotic systems inherently offer the potential for minimizing the amount of direct human-to-human interactions within the parcel delivery process. In this paper, we apply a system-level perspective for identifying key challenges and promising solution approaches for modeling, analysis, and optimization of UAV-aided parcel delivery. We present a system-of-systems model for UAV-enhanced parcel delivery to cope with higher capacity requirements induced by the COVID-19. The proposed system-of-systems consists of various complex aspects such as assigning and distributing delivery jobs, establishing and maintaining reliable communication links between the vehicles, as well as path planning and mobility control. In a proof of concept evaluation, we simulate the logistics performance and network communications of a hybrid delivery fleet composed of ground and aerial vehicles operating under pandemic induced constraints, such as the prioritization of time-critical deliveries like medical goods. Results indicate that network requirements for reliable communications within the delivery fleet can be met by existing network communications approaches. Furthermore, gains in delivery capacity induced by drone usage and a trade-off caused by prioritizing time-critical goods, therefore, resulting in a penalty for regular deliveries, are observed.", "keywords": [ "Drone", "Software deployment", "Computer science", "Process (computing)", "Prioritization", "Coronavirus disease 2019 (COVID-19)", "Risk analysis (engineering)", "Business", "Process management" ], "cited_by_count": 23, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2101.07877" }, { "id": "https://openalex.org/W3124066600", "doi": "10.1109/globecom42002.2020.9322226", "title": "Cooperative Path Planning of a UAV Swarm to Meet Temporal-Spatial User Demands", "year": 2020, "publication_date": "2020-12-01", "type": "conference-paper", "language": "en", "authors": [ "Kai You Wang", "Xiao Zhang", "Lingjie Duan" ], "first_author": "Kai You Wang", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing, fast Internet connection, and local caching) to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing demands locally. It is necessary for different UAVs to cooperate with each other for servicing many users, and how to determine their cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question. This paper is the first to design and analyze cooperative path-planning algorithms of a UAV swarm for optimally servicing many spatial locations with dynamic user arrivals and waiting deadlines in the time horizon. For each UAV, it needs to decide whether to wait at the current location or chase a newly released demand in another location, under upper coordination with the other UAVs in the swarm. For each UAV's routing problem even without coordinating with the rest UAVs, it follows dynamic programming structure and is difficult to solve directly given many user demands. We manage to simplify and propose an optimal algorithm of fast computation time (only polynomial with respect to both the numbers of user locations and user demands) for returning the UAV's optimal path-planning. When a large number |K| of UAVs are coordinating, the dynamic programming simplification becomes intractable. Alternatively, we present an iterative cooperation algorithm with approximation ratio 1 - (1 - 1/|K| )|K|in the worst case, which is proved to obviously outperform the traditional idea of partitioning UAVs to serve different user/location clusters separately. Finally, we conduct simulation experiments to show that our algorithm's average performance is close to the optimum.", "keywords": [ "Computer science", "Path (computing)", "Swarm behaviour", "Routing (electronic design automation)", "Dynamic programming", "Motion planning", "Time horizon", "Distributed computing", "Service (business)", "Computation", "Enhanced Data Rates for GSM Evolution", "Shortest path problem" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/globecom42002.2020.9322226" }, { "id": "https://openalex.org/W4410206522", "doi": "10.3390/drones9050359", "title": "Stochastic Planning of Synergetic Conventional Vehicle and UAV Delivery Operations", "year": 2025, "publication_date": "2025-05-08", "type": "article", "language": "en", "authors": [ "Κωνσταντίνος Κουρέτας", "Konstantinos L. Kepaptsoglou" ], "first_author": "Κωνσταντίνος Κουρέτας", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Synergetic transportation schemes are extensively used in package delivery operations, exploiting the best features of different modes. This paper proposes a methodology to solve the mode assignment and routing problem for the case of combined conventional vehicle and unmanned aerial vehicle (CV–UAV) parcel deliveries under uncertainty for next-day operations. This research incorporates ground and air uncertainties: travel times are assumed for conventional vehicles, while UAV paths are affected by weather conditions and restricted flying zones. A nested genetic algorithm is initially used to solve the problem under fixed conditions. Then, a robust optimization approach is employed to propose the best solution that will perform well in a stochastic environment. The framework is applied to a case study of realistic urban–suburban size, and results are discussed. The entire platform is useful for strategic decisions on infrastructure and for operation planning with satisfactory performance and less risk.", "keywords": [ "Computer science", "Operations research", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/5/359/pdf?version=1746717981" }, { "id": "https://openalex.org/W2891521608", "doi": "10.48550/arxiv.1809.07863", "title": "Decentralized dynamic task allocation for UAVs with limited communication range", "year": 2018, "publication_date": "2018-08-31", "type": "preprint", "language": "en", "authors": [ "Marc Pujol-González", "Jesús Cerquides", "Pedro Meseguer", "Juan A. Rodríguez-Aguilar", "Milind Tambe" ], "first_author": "Marc Pujol-González", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workload-based solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the best-case scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem.", "keywords": [ "Computer science", "Task (project management)", "Workload", "Range (aeronautics)", "Distributed computing", "Routing (electronic design automation)", "Service (business)", "Mathematical optimization", "Markov decision process", "Markov chain", "Independence (probability theory)", "Operations research" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1809.07863" }, { "id": "https://openalex.org/W7149255813", "doi": "10.2139/ssrn.6517904", "title": "Robust Vehicle Routing Problem with Flexible Docking Drones in Humanitarian Logistics", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Mingze Wu", "Xinpeng Geng", "Chun Cheng", "Hang Zhou" ], "first_author": "Mingze Wu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Exploit", "Vehicle routing problem", "Flexibility (engineering)", "Robustness (evolution)", "Benchmark (surveying)", "Linear programming", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6517904" }, { "id": "https://openalex.org/W349820065", "doi": null, "title": "A Java Based Human Computer Interface For a UAV Decision Support Tool Using Conformal Mapping.", "year": 1999, "publication_date": "1999-03-01", "type": "article", "language": "en", "authors": [ "Randy A. Flood" ], "first_author": "Randy A. Flood", "venue": null, "publisher": null, "abstract": "Abstract : This paper describes the development of the Human Computer Interface (HCI) for a Decision Support System for routing Unmanned Aerial Vehicles (UAVs). This problem is a multi-vehicle routing problem with time-windows. Because of the unique nature of UAVs, a tool is needed to support dynamic re-routing. We solve the problem in two ways. First, we create a UAV Decision Support Tool (UAV DST) that uses a set of Java software objects to display maps and convert between latitude-longitude coordinates and x-y coordinates. Secondly, this library provides the ability for the user to dynamically reoptimize large UAV routing problems through a simple graphical interface. The library is built on top of a Java implementation of the tabu search algorithm written by O'Rourke (1999). This library provides the basis for future simulation and analysis of the Kenney Battlelab Initiatives by providing the interface to routing decision support and simulation modules.", "keywords": [ "Java", "Computer science", "Routing (electronic design automation)", "Interface (matter)", "Graphical user interface", "Decision support system", "Software", "Set (abstract data type)", "Geographic coordinate system", "Distributed computing", "Operating system", "Data mining" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://apps.dtic.mil/sti/pdfs/ADA361562.pdf" }, { "id": "https://openalex.org/W4414918615", "doi": "10.1007/s13177-025-00558-w", "title": "A Hybrid Metaheuristic Algorithm for Solving Vehicle Routing Problem with Drone (VRPD)", "year": 2025, "publication_date": "2025-10-07", "type": "article", "language": "en", "authors": [ "Ahmad Rahimi Denjkolaei", "Rasul Enayatifar", "Mahdi Golsorkhtabaramiri", "Kamal Jadidy Aval" ], "first_author": "Ahmad Rahimi Denjkolaei", "venue": "International Journal of Intelligent Transportation Systems Research", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Benchmark (surveying)", "Metaheuristic", "Heuristic", "Variable neighborhood search", "Greedy algorithm", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s13177-025-00558-w" }, { "id": "https://openalex.org/W4287658175", "doi": "10.48550/arxiv.2009.14446", "title": "Joint Mobility-Aware UAV Placement and Routing in Multi-Hop UAV Relaying\\n Systems", "year": 2020, "publication_date": "2020-09-30", "type": "preprint", "language": "en", "authors": [ "Anousheh Gholami", "Nariman Torkzaban", "John S. Baras", "Chrysa Papagianni" ], "first_author": "Anousheh Gholami", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned Aerial Vehicles (UAVs) have been extensively utilized to provide\\nwireless connectivity in rural and under-developed areas, enhance network\\ncapacity and provide support for peaks or unexpected surges in user demand,\\nmainly due to their fast deployment, cost-efficiency and superior communication\\nperformance resulting from Line of Sight (LoS)-dominated wireless channels. In\\norder to exploit the benefits of UAVs as base stations or relays in a mobile\\nnetwork, a major challenge is to determine the optimal UAV placement and\\nrelocation strategy with respect to the mobility and traffic patterns of the\\nground network nodes. Moreover, considering that the UAVs form a multi-hop\\naerial network, capacity and connectivity constraints have significant impacts\\non the end-to-end network performance. To this end, we formulate the joint UAV\\nplacement and routing problem as a Mixed Integer Linear Program (MILP) and\\npropose an approximation that leads to a LP rounding algorithm and achieves a\\nbalance between time-complexity and optimality.\\n", "keywords": [ "Computer science", "Computer network", "Exploit", "Hop (telecommunications)", "Software deployment", "Wireless network", "Base station", "Distributed computing", "Wireless", "Telecommunications" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2009.14446" }, { "id": "https://openalex.org/W4403325897", "doi": "10.1145/3688636.3688667", "title": "Graph Neural Network Enhanced Dynamic Routing for UAV-Assisted Delay Tolerant Network", "year": 2024, "publication_date": "2024-07-24", "type": "conference-paper", "language": "en", "authors": [ "Ran Gao", "Can Tan", "Honglin Fang", "Peng Yu" ], "first_author": "Ran Gao", "venue": null, "publisher": null, "abstract": "In certain extreme environments where infrastructure such as base stations is absent, connections between nodes can be intermittent. Traditional networks struggle to function in these conditions. Delay Tolerant Network (DTN) are a network architecture designed for such scenarios. Utilizing Unmanned Aerial Vehicle (UAV) as nodes within DTN can enhance the network’s transmission capabilities and compensate for the deficiencies of ground nodes. However, previous routing methods have shortcomings. Given the high speed and greater freedom of movement of UAVs compared to ground nodes, the network topology is subject to constant and significant change. Traditional routing methods, which typically rely on predetermined rules for selecting forwarding nodes, struggle to adapt to such variations. This paper introduces Graph Neural Network (GNN) into routing decisions for DTN to learn network structural information and assist with routing decisions. In this paper, we first model the routing problem within DTN, accounting for the dynamic changes in network topology and limited resources. Subsequently, we propose a GNN-based dynamic routing method. This method utilizes a graph neural network model that takes as input the feature s of nodes at the current moment and the network’s connectivity information to generate optimal forwarding nodes for each data packet. The proposed method has been implemented in the Opportunistic Networking Environment (ONE) simulator. Experimental results indicate that our method achieves higher delivery ratio, lower latency, and reduced overhead compared to traditional methods.", "keywords": [ "Computer science", "Computer network", "Routing (electronic design automation)", "Artificial neural network", "Distributed computing", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3688636.3688667" }, { "id": "https://openalex.org/W3012234865", "doi": "10.1109/nana.2019.00017", "title": "RBDR: A Distributed Routing Algorithm in High Mobility UAV Network", "year": 2019, "publication_date": "2019-10-01", "type": "conference-paper", "language": "en", "authors": [ "Wenchao Pi", "Hao Hao", "Shujie Yang", "Liao Yong", "Changqiao Xu" ], "first_author": "Wenchao Pi", "venue": null, "publisher": null, "abstract": "High Mobility Unmanned Aerial Vehicle(UAV) network is a wireless mobile network with strong mobility of nodes, limited space and energy of nodes and real-time changed of network topology. At present, UAV networks are widely used in military, disaster reconstruction, agricultural and other scenarios. The routing problem as a focus in the research of UAV network has also aroused researcher's extensive attention. The routing algotithm in UAV network mainly include proactive routing algorithm, reactive routing algorithm, auxiliary routing algorithm, etc. However, most of these routing algorithms are centralized, deployment difculties, low stability and poor scalability. And they also require each node to maintain a global routing information table in order to obtain global node information in real time to network flooding, which undoubtedly wastes a lot of storage space for uav nodes with limited space. This paper, a novel and effective distributed routing algorithm is proposed which named reputation-based distributed routing algorithm(RBDR), and deploy it to the high mobility UAV network. In RBDR, we introduce a realtime quota-controlled strategy (which can control the number of message replicates in real time) and defne the concept of reputation for each node, this can avoid network congestion and improve the success rate of messages transmission. Since each node only needs to maintain its own friend list without obtaining the global network status, so the distributed RBDR algorithm can avoid the problem of difcult deployment and difcult information exchange of centralized routing algorithm in the high mobility UAV network. The experimental results show that the RBDR algorithm have higher success rate of message transmission and lower network resource overhead in high mobility UAV network.", "keywords": [ "Computer science", "Computer network", "Static routing", "Policy-based routing", "Distributed computing", "Routing table", "Geographic routing", "Dynamic Source Routing", "Destination-Sequenced Distance Vector routing", "Link-state routing protocol", "Multipath routing", "Routing domain" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/nana.2019.00017" }, { "id": "https://openalex.org/W7133471611", "doi": null, "title": "An ALNS algorithm for vehicle routing problem with drones in the last mile distribution*", "year": 2020, "publication_date": "2020-01-01", "type": "dissertation", "language": "es", "authors": [ "Jhon Alexander Jiménez Romero", "Jeamy Sebastián Tarazona Jiménez" ], "first_author": "Jhon Alexander Jiménez Romero", "venue": "Universidad Industrial de Santander", "publisher": null, "abstract": "Los vehículos aéreos no tripulados o más conocidos como drones han despertado gran interés en los últimos años, teniendo aplicaciones en operaciones militares y civiles, recientemente se ha investigado acerca de las ventajas de su uso en la distribución de paquetes. En el presente trabajo de investigación se formula un problema similar a el Flying Sidekick Traveling Salesman Problem (FSTSP), en referencia a que los drones y camiones pueden trabajar en conjunto; mientras el dron hace una entrega, el camión puede visitar varios clientes. La diferencia del FSTSP es que este modelo considera que los camiones tienen un límite de capacidad y además se tienen restricciones que limitan el tiempo de la ruta, dicha formulación se conoce como el Problema de Ruteo de Vehículos con Drones (VRPD); esta investigación se centra en la distribución de última milla. Para resolver esta formulación se presenta el algoritmo Adaptive Large Neighborhood Search (ALNS), el cual es validado usando instancias propuestas en la literatura. Se verifica cómo varía la función objetivo de la solución inicial mediante el uso de heurísticas destrucción y reparación, finalmente se realiza un análisis de sensibilidad para algunos parámetros del algoritmo y características de los drones; realizando conclusiones de los resultados arrojados y efectuando recomendaciones para futuras investigaciones.", "keywords": [ "Drone", "Travelling salesman problem", "Vehicle routing problem", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://noesis.uis.edu.co/bitstreams/ee507bd8-5074-4311-9edd-a4348bcda2cf/download" }, { "id": "https://openalex.org/W7202293633", "doi": "10.3390/drones10080616", "title": "A Reinforcement Learning Framework for Traveling Salesman and Vehicle Routing Problem with Drones", "year": 2026, "publication_date": "2026-08-12", "type": "article", "language": "en", "authors": [ "Qi Li", "Tad Gonsalves" ], "first_author": "Qi Li", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two classical combinatorial optimization problems. In recent years, their drone-assisted variants, the Traveling Salesman Problem with Drones (TSP-D) and the Vehicle Routing Problem with Drones (VRP-D) have attracted growing attention. Generally, these problems are solved using exact algorithms or metaheuristic algorithms. However, as the problem complexity increases and the scale of instances grows, these approaches often become less efficient. In this paper, we propose a reinforcement learning method with a shared attention encoder and a hierarchical dual-decoder architecture, where truck–drone coordination is achieved by first decoding the truck’s next node and then conditionally decoding the drone action. To further explore the solution space of large-scale instances, the proposed method adopts a multi-rollout learning strategy. We conducted experiments on large-scale TSP-D and VRP-D instances, and the results show that this model outperforms traditional metaheuristic algorithms in terms of both solution quality and computational efficiency.", "keywords": [ "Travelling salesman problem", "Drone", "Reinforcement learning", "Vehicle routing problem", "2-opt", "Routing (electronic design automation)", "Metaheuristic", "Node (physics)", "Decoding methods" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10080616" }, { "id": "https://openalex.org/W7113714349", "doi": null, "title": "Vehicle routing problems with drones equipped with multi-package payload compartments", "year": null, "publication_date": null, "type": "article", "language": null, "authors": [ "Amine Masmoudi, M.", "Mancini, Simona", "Baldacci, Roberto", "Kuo, Yong-Hong" ], "first_author": "Amine Masmoudi, M.", "venue": "RePEc: Research Papers in Economics", "publisher": "Federal Reserve Bank of St. Louis", "abstract": "The vehicle routing problem with drones (VRP-D) consists of designing combined truck-drone routes and schedules to serve a set of customers with specific requests and time constraints. In this paper, VRP-D is extended to include a fleet of drones equipped with multi-package payload compartments to serve more customers on a single trip. Moreover, a drone can return to a truck, different from the one from which it started, to swap its depleted battery and/or to pick up more packages. This problem, denoted as VRP-D equipped with multi-package payload compartments (VRP-D-MC), aims to maximize total profit. In this work, an adaptive multi-start simulated annealing (AMS-SA) metaheuristic algorithm is proposed to efficiently solve this problem. Experimental results show that the proposed algorithm outperforms the current state-of-the-art algorithms for VRP-D in terms of solution quality. Extensive analyses have been conducted to provide managerial insights. The analyses carried out show (i) the benefits of using drones equipped with different compartment configurations, (ii) the incremental total profit obtainable using a combined truck-drone fleet rather than a fleet of trucks, (iii) the benefit of swapping drone battery while picking up the items to deliver, and (iv) the impact of the packages load on the consumption energy of battery drone. It is also demonstrated that the different intensification and diversification mechanisms adopted improve the convergence of the traditional SA.", "keywords": [ "Drone", "Payload (computing)", "Swap (finance)", "Fuel efficiency", "Energy consumption", "Simulated annealing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.sciencedirect.com/science/article/pii/S136655452200148X" }, { "id": "https://openalex.org/W4407506210", "doi": "10.1007/978-3-031-80775-6_1", "title": "Last-Mile Delivery Optimization Using Mixed Electric Vehicles, UAVs and Full Truck Delivery Based on Artificial Intelligence Algorithms", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Bouhia Hanaa", "Jamal Benhra", "Wissal Ed-dehbi" ], "first_author": "Bouhia Hanaa", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Truck", "Mile", "Computer science", "Last mile (transportation)", "Algorithm", "Artificial intelligence", "Automotive engineering", "Engineering", "Physics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-80775-6_1" }, { "id": "https://openalex.org/W3114845371", "doi": "10.1109/itsc45102.2020.9294353", "title": "Automated and Connected Unmanned Aerial Vehicles (AC-UAV) for Service Patrol: System Design and Field Experiments", "year": 2020, "publication_date": "2020-09-20", "type": "conference-paper", "language": "en", "authors": [ "Kaiping Wang", "Rong Jun Yang", "Xi Lin", "Fang He", "Meng Li" ], "first_author": "Kaiping Wang", "venue": null, "publisher": null, "abstract": "With the recent development of Unmanned Aerial Vehicles (UAV) applications, traffic police might utilize UAV to conduct Service Patrol (SP) tasks. However, a major limitation of existing UAV systems is their limited flight endurance. To address this issue, by implementing the auto-rechargeable mechanism, we explicitly optimize hardware setting and system strategy required for regional SP with predefined initial tasks and stochastic incidents by solving a heuristic facility location problem and multi-objective path planning problem based on cooperative auto-recharging facilities, and fleet management center. The proposed fleet size and system performance are leveraged in a grid network with respect to different infrastructure settings and service coverage. The field experiments were conducted in Xi'an for SP tasks in complete vehicle coverage trajectory reconstruction, and results show that the proposed system is capable of unmanned SP tasks and large-scale application in urban scenarios.", "keywords": [ "Computer science", "Trajectory", "Service (business)", "Grid", "Heuristic", "Field (mathematics)", "Real-time computing", "Drone", "Scale (ratio)", "Simulation", "Operations research", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itsc45102.2020.9294353" }, { "id": "https://openalex.org/W4411599851", "doi": "10.1109/cec65147.2025.11042983", "title": "An Enhanced Multi-Objective Evolutionary Algorithm for Adaptive-Formation Multi-UAV Task Allocation with Load-Dependent Constraints", "year": 2025, "publication_date": "2025-06-08", "type": "conference-paper", "language": "en", "authors": [ "Hu Tuo", "Xiaoying Yang", "Fuhua Jia", "Songcen Xu", "Kai Hua Yang", "Tianxiang Cui" ], "first_author": "Hu Tuo", "venue": null, "publisher": null, "abstract": "As the demand for logistics continues to surge, traditional ground delivery methods fall short, prompting the advent of low-altitude unmanned aerial vehicle (UAV) delivery as a revolutionary solution for rapid and adaptable logistics services. This paper introduces the Adaptive-Formation Multi-UAV Task Allocation with Load-Dependent Constraints (AF-MUTALC), an approach that transcends the Capacitated Vehicle Routing Problem (CVRP) framework with the distinctive operational constraints of UAVs, addressing the burgeoning need for efficient low-altitude delivery systems. Our proposed model integrates the impact of payload on UAV performance, encompassing constraints such as limited power, endurance, payload capacity, and the intricate interplay between flight dynamics and energy consumption. Additionally, the model contemplates the inherent uncertainties in transit times during actual UAV operations. To effectively manage this multi-objective optimization challenge, we employ an Enhanced Multi-Objective Evolutionary Algorithm (MOEA) that combines the strengths of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a refined local search strategy. This approach aims to optimize resource utilization by minimizing the UAV fleet size and reducing the maximum flight time of a single UAV, thus ensuring an optimal balance between operational efficiency and resource allocation. Our algorithm has undergone rigorous validation through simulations, showcasing its ability to surpass conventional methodologies in delivering high-quality solutions. Furthermore, corroborated by empirical trials in real-world settings, we attest to the feasibility of the algorithm for real-life implementation.", "keywords": [ "Computer science", "Task (project management)", "Evolutionary algorithm", "Mathematical optimization", "Evolutionary computation", "Algorithm", "Artificial intelligence", "Mathematics", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cec65147.2025.11042983" }, { "id": "https://openalex.org/W4401160108", "doi": "10.5875/p25fht10", "title": "UAV parcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization", "year": 2024, "publication_date": "2024-07-31", "type": "article", "language": "en", "authors": [ "Chung‐Hsien Kuo" ], "first_author": "Chung‐Hsien Kuo", "venue": "International Journal of Automation and Smart Technology", "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.", "keywords": [ "Reinforcement learning", "Payload (computing)", "Collision avoidance", "Computer science", "Drone", "Genetic algorithm", "Path (computing)", "Collision", "Software", "Simulation", "Real-time computing", "Artificial intelligence" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.jausmt.org/index.php/jausmt/article/download/4/20" }, { "id": "https://openalex.org/W4288055345", "doi": "10.48550/arxiv.2207.11879", "title": "A Robust Optimization Framework for Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Logistics Operations", "year": 2022, "publication_date": "2022-07-25", "type": "preprint", "language": "en", "authors": [ "Tasnim Ibn Faiz", "Chrysafis Vogiatzis", "Md. Noor‐E‐Alam" ], "first_author": "Tasnim Ibn Faiz", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Providing first aid and other supplies (e.g., epi-pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emerging technologies like uncrewed autonomous vehicles can help humanitarian logistics providers reach otherwise stranded populations after transportation network failures. However, due to the failures in telecommunication infrastructure, demand for emergency aid can become uncertain. To address the challenges of delivering emergency aid to trapped populations with failing infrastructure networks, we propose a novel robust computational framework for a two-echelon vehicle routing problem that uses uncrewed autonomous vehicles, or drones, for the deliveries. We formulate the problem as a two-stage robust optimization model to handle demand uncertainty. Then, we propose a column-and-constraint generation approach for worst-case demand scenario generation for a given set of truck and drone routes. Moreover, we develop a decomposition scheme inspired by the column generation approach to heuristically generate drone routes for a set of demand scenarios. Finally, we combine the heuristic decomposition scheme within the column-andconstraint generation approach to determine robust routes for both trucks and drones, the time that affected communities are served, and the quantities of aid materials delivered. To validate our proposed computational framework, we use a simulated dataset that aims to recreate emergency aid requests in different areas of Puerto Rico after Hurricane Maria in 2017.", "keywords": [ "Column generation", "Vehicle routing problem", "Drone", "Truck", "Computer science", "Robust optimization", "Humanitarian Logistics", "Routing (electronic design automation)", "Heuristic", "Operations research", "Decomposition", "Set (abstract data type)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2207.11879" }, { "id": "https://openalex.org/W4405846158", "doi": "10.1109/icmra62519.2024.10809315", "title": "Multi-UAV 3D Hierarchical Coverage Path Planning in Dynamic Environments for Large and Complex Structures", "year": 2024, "publication_date": "2024-09-20", "type": "conference-paper", "language": "en", "authors": [ "Pranav P Dave", "Chundru Jayanth Krishna", "Rakshithvihaan P Badiger", "N Neha", "Shikha Tripathi", "D. Sethuram" ], "first_author": "Pranav P Dave", "venue": null, "publisher": null, "abstract": "In the field of structural inspection methodologies, Unmanned Aerial Vehicles have emerged as a suitable alternative to conventional inspections led by technicians due to their various advantages. However, suboptimal paths can pose risks to the structure under inspection, personnel, and nearby equipment. This paper addresses the challenge of path planning in dynamic environments, where people and equipment move near the structure during drone inspections, with a focus on minimizing inspection time, a hierarchical approach is proposed, combining Reinforcement Learning (RL) for the Vehicle Routing Problem (VRP) and OR Tools for the Travelling Salesman Problem (TSP). This method leverages Deep Reinforcement Learning (DRL) to adapt to various scenarios by adjusting environmental parameters, ensuring efficiency through parallelizability. The approach shows an average performance gap of 4.82% compared to Google Operations Research (OR) Tools in static settings and is evaluated for adaptability through experiments in various practical scenarios.", "keywords": [ "Computer science", "Motion planning", "Path (computing)", "Artificial intelligence", "Distributed computing", "Computer network", "Robot" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icmra62519.2024.10809315" }, { "id": "https://openalex.org/W4401391049", "doi": "10.3390/drones8080379", "title": "A Heuristic Routing Algorithm for Heterogeneous UAVs in Time-Constrained MEC Systems", "year": 2024, "publication_date": "2024-08-06", "type": "article", "language": "en", "authors": [ "Long Chen", "Guangrui Liu", "Xia Zhu", "Xin Li" ], "first_author": "Long Chen", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The rapid proliferation of Internet of Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation and storage closer to the data sources. However, UAV heterogeneity and the time window constraints for task execution pose a significant challenge. This paper addresses the multiple heterogeneity UAV routing problem in MEC environments, modeling it as a multi-traveling salesman problem (MTSP) with soft time constraints. We propose a two-stage heuristic algorithm, heterogeneous multiple UAV routing (HMUR). The approach first identifies task areas (TAs) and optimal hovering positions for the UAVs and defines an effective fitness measurement to handle UAV heterogeneity. A novel scoring function further refines the path determination, prioritizing real-time task compliance to enhance Quality of Service (QoS). The simulation results demonstrate that our proposed HMUR method surpasses the existing baseline algorithms on multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments.", "keywords": [ "Computer science", "Heuristic", "Routing (electronic design automation)", "Routing algorithm", "Algorithm", "Meta heuristic", "Mathematical optimization", "Distributed computing", "Computer network", "Artificial intelligence", "Mathematics", "Routing protocol" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/8/8/379/pdf?version=1722957005" }, { "id": "https://openalex.org/W2154175082", "doi": "10.5772/56286", "title": "Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning", "year": 2013, "publication_date": "2013-01-01", "type": "article", "language": "en", "authors": [ "Durdana Habib", "Habibullah Jamal", "Shoab Ahmed Khan" ], "first_author": "Durdana Habib", "venue": "International Journal of Advanced Robotic Systems", "publisher": "SAGE Publishing", "abstract": "Abstract In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a Mixed Integer Linear Programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP). The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1) each target is visited exactly by one of the chosen UAVs (2) the total distance travelled by the group is minimized and (3) the number of targets that each UAV visits may not be less than K or greater than L.", "keywords": [ "Computer science", "Motion planning", "Path (computing)", "Integer programming", "Routing (electronic design automation)", "Set (abstract data type)", "Linear programming", "Operations research", "Real-time computing", "Mathematical optimization", "Robot", "Artificial intelligence" ], "cited_by_count": 36, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journals.sagepub.com/doi/pdf/10.5772/56286" }, { "id": "https://openalex.org/W7203456038", "doi": "10.1007/978-981-92-2597-2_15", "title": "The Min-Makespan Vehicle Routing Problem with Drones Under Multiple Trips and Visits", "year": 2026, "publication_date": "2026-08-14", "type": "conference-paper", "language": "en", "authors": [ "Nguyen Khanh Phuong", "Tran Thi Hue", "Thai Hoa Nguyen" ], "first_author": "Nguyen Khanh Phuong", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "TRIPS architecture", "Benchmark (surveying)", "Pickup", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-92-2597-2_15" }, { "id": "https://openalex.org/W4398229958", "doi": "10.2139/ssrn.4865970", "title": "A Branch-Cut-And-Price Approach for the Two-Echelon Vehicle Routing Problem with Drones", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Sylvain Lichau", "Ruslan Sadykov", "Julien François", "Rémy Dupas" ], "first_author": "Sylvain Lichau", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Branch and cut", "Computer science", "Mathematical optimization", "Operations research", "Business", "Mathematics", "Integer programming", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.2139/ssrn.4865970" }, { "id": "https://openalex.org/W4407538984", "doi": "10.1007/978-981-96-1535-3_18", "title": "Performance Investigation of a New Chaotic Search Method for Vehicle Routing Problem with Drones", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Kazuma Nakajima", "Takafumi Matsuura", "Takayuki Kimura" ], "first_author": "Kazuma Nakajima", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Chaotic", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Mathematical optimization", "Artificial intelligence", "Computer network", "Mathematics", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-96-1535-3_18" }, { "id": "https://openalex.org/W7135302385", "doi": null, "title": "A hybrid learning model for the vehicle routing problem with drones in the era of industry 4.0", "year": 2022, "publication_date": "2022-04-29", "type": "conference-abstract", "language": "en", "authors": [ "Ali Arishi" ], "first_author": "Ali Arishi", "venue": "Holmes Museum Of Anthropology (Wichita State University)", "publisher": "Wichita State University", "abstract": "Presented to the 18th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 29, 2022", "keywords": [ "Drone", "State (computer science)", "Routing (electronic design automation)", "Key (lock)", "Feature (linguistics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://soar.wichita.edu/handle/10057/23486" }, { "id": "https://openalex.org/W3199137983", "doi": null, "title": "Truck-Drone Routing Problem with a Drone Station", "year": 2017, "publication_date": "2017-11-01", "type": "article", "language": "ko", "authors": [ "김성우", "문일경" ], "first_author": "김성우", "venue": "대한산업공학회 추계학술대회 논문집", "publisher": null, "abstract": "드론을 활용한 서비스의 수요는 계속해서 증가하고 있다. 그러나 드론을 활용한 택배 서비스에서 운용적인 측면에 대한 연구는 제한적으로만 이루어지고 있다. 그중 트럭과 드론을 동시에 이용하는 트럭-드론 배송시스템의 경우, 드론의 제한적인 가용 범위로 인해 드론 관련 시설을 이용한 운용 방법론에 대한 연구는 더욱 등한시 되고 있다. 본 연구에서는 기존의 드론 가용 범위에 한계를 극복하기 위한 새로운 트럭-드론 시스템을 제안한다. 이를 위해 물류 센터와 독립적으로 운용되고 드론 저장 및 드론 충전 설비를 갖춘 시설을 드론 정거장으로 정의한다. 본 연구는 드론 정거장을 활용한 외판원 문제(TDRP-DS)를 제시하고 본질적인 특성을 분석한다. 그리고 TDRP-DS가 독립적인 외판원 문제와 평행 머신 스케줄링으로 분해가 가능한 것을 보인다. 실험 결과 및 분석을 통해 TDRP-DS의 특징을 재확인할 수 있고, 본 연구에서 제안하는 분해 방법이 효과적으로 TDRP-DS의 문제 복잡도를 낮출 수 있음을 보인다.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Aeronautics", "Computer science", "Engineering", "Computer network", "Automotive engineering", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dbpia.co.kr/journal/articleDetail?nodeId=NODE07262459" }, { "id": "https://openalex.org/W4415299178", "doi": "10.3390/sym17101758", "title": "An Efficient Task Scheduling Framework for Large-Scale 3D Reconstruction in Multi-UAV Edge-Intelligence Systems", "year": 2025, "publication_date": "2025-10-17", "type": "article", "language": "en", "authors": [ "Xia Yu", "Xueyong Xu", "Yuhang Xu", "An-Min Li", "Jinchen Wang", "Chenchen Fu", "Weiwei Wu" ], "first_author": "Xia Yu", "venue": "Symmetry", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid development of edge-intelligence systems, multi-UAV platforms have become vital for large-scale 3D reconstruction. However, efficient task scheduling remains a critical challenge due to constraints on UAV energy, communication range, and the need for balanced workload distribution. To address these issues, this paper presents a novel, centralized two-stage task scheduling framework. In the first stage, the framework partitions the target area into communication-feasible subregions by applying cell decomposition that accounts for no-fly zones and workload. It then models the subregion allocation as a Capacitated Vehicle Routing Problem (CVRP) with an added balancing constraint to optimize the traversal sequence for each operational sortie. In the second stage, a time-efficient, scan-based heuristic algorithm allocates viewpoints among UAVs to ensure workload balance, minimizing the mission completion time. Extensive simulations demonstrate that our proposed approach achieves superior performance in workload balance, path efficiency, and reconstruction quality. Overall, this work provides a scalable and energy-aware solution for centralized multi-UAV 3D reconstruction, highlighting an effective approach to ensure cooperation and efficiency in complex multi-agent systems.", "keywords": [ "Workload", "Scheduling (production processes)", "Scalability", "Task (project management)", "Tree traversal", "Job shop scheduling", "Bottleneck", "Critical path method" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2073-8994/17/10/1758/pdf?version=1760688222" }, { "id": "https://openalex.org/W4413925511", "doi": "10.1109/icra55743.2025.11128262", "title": "Risk-Aware Energy-Constrained UAV-UGV Cooperative Routing Using Attention-Guided Reinforcement Learning", "year": 2025, "publication_date": "2025-05-19", "type": "conference-paper", "language": "en", "authors": [ "Md Safwan Mondal", "R. Subramanian", "Ragib Rownak", "Luca Russo", "James D. Humann", "James M. Dotterweich", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": null, "publisher": null, "abstract": "Maximizing the endurance of unmanned aerial vehicles (UAVs) in large-scale monitoring missions spanning over large areas requires addressing their limited battery capacity. Deploying unmanned ground vehicles (UGVs) as mobile recharging stations offers a practical solution, extending UAVs' operational range. This introduces the challenge of optimizing UAV-UGV routes for efficient mission point coverage and seamless recharging coordination. In this paper, we present a risk-aware deep reinforcement learning (Ra-DRL) framework with a multi-head attention mechanism within an encoder-decoder transformer architecture to solve this cooperative routing problem for a UAV-UGV team. Our model minimizes mission time while accounting for the stochastic fuel consumption of the UAV, influenced by environmental factors like wind velocity, ensuring adherence to a risk threshold to avoid mid-mission energy depletion. Extensive evaluations on various problem sizes show that our method significantly outperforms nearest-neighbor heuristics in both solution quality and risk management. We validate the Ra-DRL policy in a Gazebo-ROS SITL environment with a PX4-based custom UAV and Clearpath Husky UGV. The results demonstrate the robustness and adaptability of our policy, making it highly effective for mission planning in dynamic, uncertain scenarios.", "keywords": [ "Reinforcement learning", "Computer science", "Routing (electronic design automation)", "Energy (signal processing)", "Artificial intelligence", "Computer network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icra55743.2025.11128262" }, { "id": "https://openalex.org/W4409662020", "doi": "10.4018/979-8-3693-8532-6.ch016", "title": "Drone Integration in Vehicle Routing", "year": 2025, "publication_date": "2025-04-22", "type": "book-chapter", "language": "en", "authors": [ "Tejinder Singh Lakhwani", "Anuj Pal Kapoor", "Yerasani Sinjana" ], "first_author": "Tejinder Singh Lakhwani", "venue": "Advances in environmental engineering and green technologies book series", "publisher": "IGI Global", "abstract": "This chapter examines the integration of drones into Vehicle Routing Problems (VRPs) and their impact on e-commerce and urban logistics. It highlights the benefits of drone delivery, focusing on last-mile logistics, with advantages such as reduced delivery times, cost-effectiveness, and expanded regional coverage. Drones also support environmental sustainability by lowering carbon emissions and promoting energy-efficient operations, aligning with ecological goals. The chapter reviews advancements in technology, optimization models, and routing techniques that enhance drone utility in congested urban areas. However, successful adoption faces challenges, including technical issues, security risks, public acceptance, and legal constraints. By analyzing these limitations, the chapter provides an overview of the current state of drone delivery in VRPs and offers practical recommendations to address these barriers, enabling improved integration of drones into urban logistics while advancing sustainability and reducing environmental impacts.", "keywords": [ "Drone", "Computer science", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3693-8532-6.ch016" }, { "id": "https://openalex.org/W4210707613", "doi": "10.34229/2707-451x.21.4.3", "title": "Vehicle Routing Problem When Using UAVs", "year": 2021, "publication_date": "2021-12-30", "type": "article", "language": "en", "authors": [ "Iryna Norba" ], "first_author": "Iryna Norba", "venue": "Cybernetics and Computer Technologies", "publisher": null, "abstract": "Introduction. In recent years, the use of unmanned aerial vehicles (UAVs) is growing rapidly. Initially introduced for military purposes, drones and related technologies have been successfully switched to a number of new civilian applications in the last few years, such as delivery, logistics, surveillance, entertainment, and more. They also opened up new opportunities, such as working in difficult or dangerous areas. The UAV has the potential to solve the problem of air mobility, allowing to change transport and logistics in the future. Combining UAVs with traditional land vehicles can solve the last-mile delivery problem by achieving significant improvements in distribution costs and speed of vehicle delivery. One of the biggest challenges is to plan UAV routes with a number of constraints, including time, distance or energy costs, cargo weight, environmental and environmental conditions (such as wind direction or obstacles), UAV battery life, and demand requirements. users you want to visit. Thus, it revealed the need to classify different types of research and study the general characteristics of the study area. This article aims to help identify the main topics and new areas of research, as well as provides a published overview of the current state and contribution to the problem of UAV routing, as well as a general categorization of the problem of vehicle routing (VRP). The purpose of the paper is to analyze the scientific contributions to the problem of UAV routing to determine the main characteristics of these problems, as well as trends in research and recent improvements. Results. Sources are classified according to the areas of application of UAVs; methods that include exact, heuristic, metaheuristic, and mixed algorithms are mentioned. Conclusions. An overview of the work on routing problems using UAVs and the tasks they generate, trends in research and recent developments. Keywords: Unmanned aerial vehicle, routing, vehicle, optimization.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Transport engineering", "Routing (electronic design automation)", "Plan (archaeology)", "Operations research", "Last mile (transportation)", "Engineering", "Computer network", "Geography", "Mile" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.34229/2707-451x.21.4.3" }, { "id": "https://openalex.org/W4360585750", "doi": "10.1007/978-3-030-54621-2_766-1", "title": "Drone-Truck Routing Problems", "year": 2022, "publication_date": "2022-09-21", "type": "reference-entry", "language": "en", "authors": [ "Sung Hoon Chung", "Changhyun Kwon" ], "first_author": "Sung Hoon Chung", "venue": "Encyclopedia of Optimization", "publisher": null, "abstract": null, "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Computer network", "Engineering", "Aerospace engineering", "Biology", "Genetics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-54621-2_766-1" }, { "id": "https://openalex.org/W7128370507", "doi": "10.5281/zenodo.18561880", "title": "Hybrid Vehicle Routing Problem with Truck and Drone Using Mixed Integer Linear Programming", "year": 2024, "publication_date": "2024-12-01", "type": "other", "language": "en", "authors": [ "Suhaib Kaissi", "Bethel Mbakaogu", "Nabin Hitang", "Prajaya Pathak", "Valencia Eder", "Le Caroline" ], "first_author": "Suhaib Kaissi", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Hybrid Vehicle Routing Problem with Truck and Drone Using Mixed Integer Linear Programming", "keywords": [ "Drone", "Integer programming", "Truck", "Linear programming", "Vehicle routing problem", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18561880" }, { "id": "https://openalex.org/W4406174644", "doi": "10.61186/engt.5.2.2924", "title": "Hybrid Vehicle Routing Problem with Truck and Drone Using Mixed Integer Linear Programming", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Suhaib Kaissi", "Nabin Hitang", "Prajaya Pathak", "Eder Valencia", "Caroline Le", "Nya McCoy", "Bethel Mbakaogu", "Yashas Mysore Ravishkumar" ], "first_author": "Suhaib Kaissi", "venue": "ENG Transactions", "publisher": null, "abstract": null, "keywords": [ "Drone", "Integer programming", "Truck", "Vehicle routing problem", "Linear programming", "Branch and price", "Routing (electronic design automation)", "Computer science", "Integer (computer science)", "Mathematical optimization", "Mathematics", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.61186/engt.5.2.2924" }, { "id": "https://openalex.org/W7128360299", "doi": "10.5281/zenodo.18561879", "title": "Hybrid Vehicle Routing Problem with Truck and Drone Using Mixed Integer Linear Programming", "year": 2024, "publication_date": "2024-12-01", "type": "other", "language": "en", "authors": [ "Suhaib Kaissi", "Bethel Mbakaogu", "Nabin Hitang", "Prajaya Pathak", "Valencia Eder", "Le Caroline" ], "first_author": "Suhaib Kaissi", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Hybrid Vehicle Routing Problem with Truck and Drone Using Mixed Integer Linear Programming", "keywords": [ "Drone", "Integer programming", "Truck", "Linear programming", "Vehicle routing problem", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18561879" }, { "id": "https://openalex.org/W4292814075", "doi": "10.1117/12.2645488", "title": "A two-layer heuristic optimization algorithm for large-scale vehicle routing problem with drones", "year": 2022, "publication_date": "2022-08-23", "type": "conference-paper", "language": "en", "authors": [ "Jiajing Tang", "Shuaipeng Jia", "Zhongyu Ma" ], "first_author": "Jiajing Tang", "venue": null, "publisher": null, "abstract": "With the increasing application in real life, drones have received more and more attention in recent years. In particular, drones show great potential when it comes to solving the last-mile delivery problems. Large-scale vehicle routing problems with drones (LSVRPDs) are challenging because most real-world problems typically involve thousands of delivery nodes. In this article, we proposed a two-layer heuristic optimization algorithm called THOA to address them. In the upper layer, three local search operators are used to generate different routes, each of which consists of several nodes. In the lower layer, a Branch-and-Bound method with special constraint is suggested to optimize the routes obtained from the upper layer. The proposed THOA is compared with a simulated annealing algorithm in six scenarios with different number of nodes. The experimental results demonstrate the effectiveness and efficiency of THOA, especially in VRPDs with a large number of nodes.", "keywords": [ "Drone", "Simulated annealing", "Computer science", "Routing (electronic design automation)", "Heuristic", "Mathematical optimization", "Upper and lower bounds", "Layer (electronics)", "Vehicle routing problem", "Branch and bound", "Algorithm", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2645488" }, { "id": "https://openalex.org/W4415445503", "doi": "10.36227/techrxiv.176118428.82525838/v1", "title": "Genetic Algorithm vs Firefly for a Single-Truck, Single-Drone Vehicle Routing Problem", "year": 2025, "publication_date": "2025-10-23", "type": "preprint", "language": null, "authors": [ "Janith Thenura Akuratiya Gamage" ], "first_author": "Janith Thenura Akuratiya Gamage", "venue": null, "publisher": null, "abstract": "We study a practically motivated variant of the Vehicle Routing Problem with Drones (VRP-D): a single truck collaborates with a single drone to serve customers. On each truck edge (i → j), at most one drone sortie may be launched at i, visit exactly one customer k, and rendezvous at j. The edge time is the maximum of the truck's travel and service time versus the drone's sortie time (including launch and recovery). We compare two popular metaheuristics-Genetic Algorithm (GA) and Firefly Algorithm (FF)-against a simple baseline (Nearest-Neighbor + 2-opt with greedy drone assignment). We implement a reproducible experiment suite, parse parameters and a 20-customer instance from provided files, and report best-time (makespan) performance over multiple random seeds. On the 20-customer instance, FF tends to achieve lower total time than GA (Mann-Whitney: z ≈ −1.73, p ≈ 0.083; Cliff's δ ≈ −0.531, large, negative favors FF). The baseline offers a fast, competitive reference. We release code, datasets, and plots to support replication. Code & data (Zenodo, v1.0)", "keywords": [ "Drone", "Genetic algorithm", "Routing (electronic design automation)", "Truck", "Vehicle routing problem", "Enhanced Data Rates for GSM Evolution" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.176118428.82525838/v1" }, { "id": "https://openalex.org/W4313203001", "doi": "10.5281/zenodo.7488489", "title": "Time Dependent Drone-aided Vehicle Routing Problem with Time Windows and Multi-visits", "year": 2022, "publication_date": "2022-10-12", "type": "article", "language": "en", "authors": [ "Yiliang Yuan", "Jiahang Xie" ], "first_author": "Yiliang Yuan", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The continuous development of e-commerce and ongoing pandemic in recent years leads to a large increase in the demand for last-mile delivery with higher standards of service quality. The usage of unmanned aerial vehicles, also known as drones, is an ideal solution for such complex delivery problems. When designing the delivery system with the drone, we cannot omit the following features of drones: drones have the unique ability to avoid traffic congestion compared to truck traveling; and the dispatched drones need customer assistance to retrieve goods in parcel releasing procedures. In this paper, the time-dependent vehicle routing problem with drones and time windows (TDVRPDTW) is introduced. For practical application scenarios, the parcel releasing operation of drones needs customer assistance. Thus, the drone visits are fulfilled in given time windows for each demand point. Besides, the time-dependent traveling routes of trucks are also considered to capture road congestion. The objective is to minimize total route makespan, and vehicles’ dispatch times at the depot are crucial to avoid urban congestion. We present a branch-and-price algorithm to solve the optimization. The master problem is solved using a customized column generation algorithm and the pricing problem is solved by a tailored labeling algorithm with a novel dominance rule that allows more label dominance. For our numerous experimental results, we modified Solomon’s data sets by adding time dependency. Our algorithm can solve all the small-scale and medium-scale instances optimally. In terms of large-size instances, more than 90\\% are solved optimally within 2 hours.", "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://zenodo.org/record/7488489" }, { "id": "https://openalex.org/W4295837674", "doi": "10.1007/978-3-031-16579-5_14", "title": "Integrated Path Planning and Task Assignment Model for On-Demand Last-Mile UAV-Based Delivery", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Jose Javier Escribano Macias", "Huan Chang", "Panagiotis Angeloudis" ], "first_author": "Jose Javier Escribano Macias", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Task (project management)", "Integer programming", "Heuristic", "Operations research", "Path (computing)", "Dynamic programming", "Assignment problem", "Reinforcement learning", "Stochastic programming", "Mathematical optimization", "Baseline (sea)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-16579-5_14" }, { "id": "https://openalex.org/W7125628226", "doi": "10.1109/iscon65210.2025.11340847", "title": "Fast Heuristics for Solving a Multi-Drone Integrated Constrained Electric Vehicle Routing Problem", "year": 2025, "publication_date": "2025-09-05", "type": "conference-paper", "language": null, "authors": [ "Ritesh Kumari", "Prem Prakash Vuppuluri", "Chinmay Patvardhan" ], "first_author": "Ritesh Kumari", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Vehicle routing problem", "Heuristics", "Electric vehicle", "Routing (electronic design automation)", "Work (physics)", "Constraint (computer-aided design)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iscon65210.2025.11340847" }, { "id": "https://openalex.org/W7138125021", "doi": "10.1016/j.trpro.2026.02.069", "title": "Autonomous electric vehicle routing problem with battery recharging by drones using public transit", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Giusy Macrina", "Luigi Di Puglia Pugliese", "Francesca Guerriero" ], "first_author": "Giusy Macrina", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "The autonomous vehicle routing problem with battery recharging by drones, using public transit consists of routing a fleet of autonomous electric vehicles (AEVs) with limited battery capacity to deliver orders to customers in the last mile. In order to prevent service disruptions, resulting from the limited battery capacity of the AEVs, it is possible to recharge them during the trip when they are at customer locations for the delivery tasks. This can be achieved through drones equipped with wireless energy transmission technology, which can be viewed as mobile chargers. Furthermore, drones can be launched from the depot as well as from some dedicated public stations. Each drone will then complete part of its journey on the roof of the public lines. We mathematically represent the problem as a Mixed-Integer Linear Programming (MILP) model, considering capacity, time windows, and synchronization constraints. We solve the MILP for studying the framework and analyzing the potential of the new paradigm.", "keywords": [ "Drone", "Battery (electricity)", "Transit (satellite)", "Public transport", "Electric vehicle", "Routing (electronic design automation)", "Urban transit" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2026.02.069" }, { "id": "https://openalex.org/W7154743285", "doi": "10.17632/3yyt8wtkhx.5", "title": "Computational Study Instances of the Electric Vehicle Routing Problem with Heterogeneous Drones and Time Windows", "year": 2026, "publication_date": "2026-04-17", "type": "dataset", "language": null, "authors": [ "Shanshan Meng" ], "first_author": "Shanshan Meng", "venue": "Mendeley Data", "publisher": null, "abstract": "The set includes all instances used in the electric vehicle routing problem with heterogeneous drones and time windows: 1. Small instances 2. Large instances 3. Instances for sensitivity analysis", "keywords": [ "Drone", "Vehicle routing problem", "Electric vehicle", "Routing (electronic design automation)", "Set (abstract data type)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/3yyt8wtkhx.5" }, { "id": "https://openalex.org/W4362464693", "doi": "10.5281/zenodo.7794613", "title": "Multi-Depot Vehicle Routing Problem with Drones: Mathematical Formulation, Solution Algorithm and Experiments", "year": 2024, "publication_date": "2024-05-01", "type": "article", "language": "en", "authors": [ "Petr Stodola", "Libor Kutěj" ], "first_author": "Petr Stodola", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": "Data to the article \"Multi-Depot Vehicle Routing Problem with Drones: Mathematical formulation, solution algorithm and experiments\".", "keywords": [ "Depot", "Vehicle routing problem", "Computer science", "Mathematical optimization", "Drone", "Routing (electronic design automation)", "Algorithm", "Mathematics", "Computer network", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.7794613" }, { "id": "https://openalex.org/W3045690643", "doi": "10.1007/978-3-030-53829-3_4", "title": "UAV Fleet Mission Planning Subject to Robustness Constraints", "year": 2020, "publication_date": "2020-07-28", "type": "conference-paper", "language": "en", "authors": [ "Grzegorz Radzki", "Peter Nielsen", "Grzegorz Bocewicz", "Zbigniew Antoni Banaszak" ], "first_author": "Grzegorz Radzki", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Computer science", "Robustness (evolution)", "Time horizon", "Flight planning", "Wind power", "Operations research", "Aeronautics", "Aerospace engineering", "Engineering", "Mathematical optimization" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-53829-3_4" }, { "id": "https://openalex.org/W4401387718", "doi": "10.1117/12.3034872", "title": "Adaptive large neighborhood search algorithm for vehicle routing problem with drones with delivery options", "year": 2024, "publication_date": "2024-08-07", "type": "conference-paper", "language": "en", "authors": [ "Jiapu Sheng", "Lifeng Mu" ], "first_author": "Jiapu Sheng", "venue": null, "publisher": null, "abstract": "Compared to traditional urban delivery systems, the last-mile delivery has become a complex issue due to lower daily customer demands, significant delivery time variations, and a widespread distribution of locations. The delivery time of customer orders is associated with the choice of delivery locations, where considerations of capacity and operating hours arise, especially when utilizing shared delivery spots such as parcel lockers. To address these issues and emphasize the advantages of shared parcel lockers, we propose a delivery problem with delivery options and preference levels. We define a routing scheme for the coordinated delivery of vehicles and drones, integrating various types of delivery locations. We solve this problem using an adaptive large neighborhood search algorithm with simulated annealing, introducing specific ruin and repair operators. These operators, combined with various operators from the literature, undergo numerical experiments. In the specific context of this problem, our proposed method outperforms conventional algorithms, such as large neighborhood search and a single-parent genetic algorithm.", "keywords": [ "Vehicle routing problem", "Simulated annealing", "Computer science", "Routing (electronic design automation)", "Drone", "Scheme (mathematics)", "Context (archaeology)", "Mathematical optimization", "Local search (optimization)", "Delivery Performance", "Food delivery", "Algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3034872" }, { "id": "https://openalex.org/W4402543091", "doi": "10.1007/s10846-024-02169-1", "title": "UAV Routing for Enhancing the Performance of a Classifier-in-the-loop", "year": 2024, "publication_date": "2024-09-14", "type": "article", "language": "en", "authors": [ "Deepak Prakash Kumar", "Pranav Rajbhandari", "Loy McGuire", "Swaroop Darbha", "Donald Sofge" ], "first_author": "Deepak Prakash Kumar", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": "Abstract Some human-machine systems are designed so that machines (robots) gather and deliver data to remotely located operators (humans) through an interface to aid them in classification. The performance of a human as a (binary) classifier-in-the-loop is characterized by probabilities of correctly classifying objects (or points of interest) as a true target or a false target. These two probabilities depend on the time spent collecting information at a point of interest (POI), known as dwell time. The information gain associated with collecting information at a POI is then a function of dwell time and discounted by the revisit time, i.e., the duration between consecutive revisits to the same POI, to ensure that the vehicle covers all POIs in a timely manner. The objective of the routing problem for classification is to route the vehicles optimally, which is a discrete problem, and determine the optimal dwell time at each POI, which is a continuous optimization problem, to maximize the total discounted information gain while visiting every POI at least once. Due to the coupled discrete and continuous problem, which makes the problem hard to solve, we make a simplifying assumption that the information gain is discounted exponentially by the revisit time; this assumption enables one to decouple the problem of routing with the problem of determining optimal dwell time at each POI for a single vehicle problem. For the multi-vehicle problem, since the problem involves task partitioning between vehicles in addition to routing and dwell time computation, we provide a fast heuristic to obtain high-quality feasible solutions.", "keywords": [ "Computer science", "Classifier (UML)", "Computer network", "Artificial intelligence" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10846-024-02169-1.pdf" }, { "id": "https://openalex.org/W4393902859", "doi": "10.3390/drones8040138", "title": "Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue", "year": 2024, "publication_date": "2024-04-03", "type": "article", "language": "en", "authors": [ "Dan Han", "Hao Jiang", "Lifang Wang", "Xinyu Zhu", "Yaqing Chen", "Qizhou Yu" ], "first_author": "Dan Han", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.", "keywords": [ "Search and rescue", "Task (project management)", "Aeronautics", "Computer science", "Rescue therapy", "Disaster response", "Operations research", "Artificial intelligence", "Engineering", "Emergency management", "Systems engineering", "Medicine" ], "cited_by_count": 41, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/8/4/138/pdf?version=1712131411" }, { "id": "https://openalex.org/W4387780905", "doi": "10.1108/jhlscm-01-2023-0004", "title": "A multiobjective mathematical model for a humanitarian logistics multimodal transportation problem", "year": 2023, "publication_date": "2023-10-19", "type": "article", "language": "en", "authors": [ "Marisol S. Romero‐Mancilla", "Kenneth Edgar Hernandez-Ruiz", "Diana L. Huerta‐Muñoz" ], "first_author": "Marisol S. Romero‐Mancilla", "venue": "Journal of Humanitarian Logistics and Supply Chain Management", "publisher": "Emerald Publishing Limited", "abstract": "Purpose The purpose of this paper is to introduce a three-echelon multimodal transportation problem applied to a humanitarian logistic case study that occurred in Mexico. Design/methodology/approach This study develops a methodology combining a transshipment problem and an adaptation of the multidepot heterogeneous fleet vehicle routing problem to construct a mathematical model that incorporates the use of land-based vehicles and drones. The model was applied to the case study of the Earthquake on September 19, 2017, in Mexico, using the Gurobi optimization solver. Findings The results ratified the relevance of the study, showing an inverse relationship between transportation costs and delivery time; on the flip side, the model performed in a shorter CPU time with medium and small instances than with large instances. Research limitations/implications While the size of the instances limits the use of the model for big-scale problems, this approach manages to provide a good representation of a transportation network during a natural disaster using drones in the last-mile deliveries. Originality/value The present study contributes to a model that combines a vehicle routing problem with transshipment, multiple depots and a heterogeneous fleet including land-based vehicles and drones. There are multiple models present in the literature for these types of problems that incorporate the use of these transportation modes; however, to the best of the authors’ knowledge, there are still no proposals similar to this study.", "keywords": [ "Transshipment (information security)", "Computer science", "Solver", "Vehicle routing problem", "Operations research", "Drone", "Construct (python library)", "Routing (electronic design automation)", "Relevance (law)", "Paratransit", "Scale (ratio)", "Mathematical optimization" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.emerald.com/insight/content/doi/10.1108/JHLSCM-01-2023-0004/full/pdf?title=a-multiobjective-mathematical-model-for-a-humanitarian-logistics-multimodal-transportation-problem" }, { "id": "https://openalex.org/W2373079984", "doi": null, "title": "UAV Route Planning Dynamic Estimation Method for Multi-constraints", "year": 2012, "publication_date": "2012-01-01", "type": "article", "language": "en", "authors": [ "HU Shou-song" ], "first_author": "HU Shou-song", "venue": "Command Control & Simulation", "publisher": "Command Control and Simulation", "abstract": "Aiming at unmanned air vehicle route planning estimation method lacks rationality and dynamic characteristics with multiple constraint condition,the route planning estimation method based on 6-DOF flight simulation is proposed.The multiple constraint condition of unmanned air vehicle route planning and several factors affect the route estimation are analyzed.Unmanned air vehicle flight simulation model based on an unmanned air vehicle 6-DOF flight dynamics model and flight control system model is established.The software of route estimation method is designed through an organic combination of route planning and estimation.By simulation,the dynamic performance of actual flying parameters obtained when aircraft follows the path planned are analyzed,general estimation for planning effectiveness with multiple constraint condition that comes from simulation is achieved.Simulation result indicated that the estimation method can actualize the validation of planned paths dynamic characteristic and meet the engineering requirements.", "keywords": [ "Constraint (computer-aided design)", "Route planning", "Estimation", "Computer science", "Simulation", "Vehicle dynamics", "Control (management)", "Control engineering", "Control theory (sociology)", "Engineering", "Automotive engineering", "Transport engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://en.cnki.com.cn/Article_en/CJFDTOTAL-QBZH201202008.htm" }, { "id": "https://openalex.org/W7162247530", "doi": "10.65109/hxaj9978", "title": "Drone Routing Problems Challenge", "year": 2026, "publication_date": "2026-05-24", "type": "conference-paper", "language": null, "authors": [ "Shiyao Ding", "Fumito Uwano", "Hideki Aoyama", "Donghui Lin" ], "first_author": "Shiyao Ding", "venue": null, "publisher": null, "abstract": "Drone routing problems (DRP) focus on optimizing routes for a fleet of drones to ensure efficient, timely, and cost-effective delivery of goods. The problem is formulated as a multi-agent path finding (MAPF) problem, aiming to compute collision-free, optimal paths that minimize the total travel time of all agents. While traditional MAPF research is typically conducted on 4-connected grid graphs, DRP extends this to a non-grid environment suited for drone delivery. This competition incorporates non-grid maps based on real-world environments and a gym-standard environment for benchmarking various pathfinding algorithms.", "keywords": [ "Drone", "Focus (optics)", "Routing (electronic design automation)", "Benchmarking", "Grid", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.65109/hxaj9978" }, { "id": "https://openalex.org/W3067636645", "doi": "10.1007/s00291-020-00602-z", "title": "Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles", "year": 2020, "publication_date": "2020-08-18", "type": "article", "language": "en", "authors": [ "Jose Javier Escribano Macias", "Nils Goldbeck", "Pei-Yuan Hsu", "Panagiotis Angeloudis", "Washington Ochieng" ], "first_author": "Jose Javier Escribano Macias", "venue": "OR Spectrum", "publisher": "Springer Science+Business Media", "abstract": "Abstract Unmanned aerial vehicles (UAVs) have been increasingly viewed as useful tools to assist humanitarian response in recent years. While organisations already employ UAVs for damage assessment during relief delivery, there is a lack of research into formalising a problem that considers both aspects simultaneously. This paper presents a novel endogenous stochastic vehicle routing problem that coordinates UAV and relief vehicle deployments to minimise overall mission cost. The algorithm considers stochastic damage levels in a transport network, with UAVs surveying the network to determine the actual network damages. Ground vehicles are simultaneously routed based on the information gathered by the UAVs. A case study based on the Haiti road network is solved using a greedy solution approach and an adapted genetic algorithm. Both methods provide a significant improvement in vehicle travel time compared to a deterministic approach and a non-assisted relief delivery operation, demonstrating the benefits of UAV-assisted response.", "keywords": [ "Damages", "Computer science", "Vehicle routing problem", "Operations research", "Routing (electronic design automation)", "Genetic algorithm", "Computer network", "Engineering", "Machine learning" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s00291-020-00602-z.pdf" }, { "id": "https://openalex.org/W2786260278", "doi": "10.1109/ssci.2017.8280873", "title": "Rescue path optimization using ant colony systems", "year": 2017, "publication_date": "2017-11-01", "type": "conference-paper", "language": "en", "authors": [ "Manuela Graf", "Marc Poy", "Simon Bischof", "Rolf Dornberger", "Thomas Hanne" ], "first_author": "Manuela Graf", "venue": null, "publisher": null, "abstract": "The vehicle routing problem is a well-researched problem with various solutions. Although this problem has been solved many times, there is rarely a case where a solution covers a real-life situation completely. In this paper, a particular dynamic vehicle routing problem is being analyzed. It consists of rescue path planning independently flying drones or driving vehicles in a disaster simulation. The scenario includes continuous updates of injured people that need to be picked up by rescue drones, limited operation range, prioritization of the pickup order depending on the injury level, and a number of people at a certain rescue location. This paper discusses how the dynamic vehicle routing problem can be broken down into a set of static vehicle routing problems. Those problems are then optimized with the help of a particular ant algorithm - in this particular case, an adapted Ant Colony System (ACS). This paper discusses the modelling of the rescue problem as a particular vehicle routing problem and the adaptation of the Ant Colony Optimization (ACO) to solve it.", "keywords": [ "Ant colony optimization algorithms", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Ant colony", "Drone", "Path (computing)", "Adaptation (eye)", "Pickup", "Set (abstract data type)", "Mathematical optimization", "Operations research" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ssci.2017.8280873" }, { "id": "https://openalex.org/W7118160202", "doi": "10.1007/978-3-032-15621-1_10", "title": "Bayesian Optimization for Two Echelon Vehicle Routing Problem Using Drone for Last Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Preetam Kumar Sur", "Anubrata Naskar", "Sunirmal Khatua" ], "first_author": "Preetam Kumar Sur", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Cluster analysis", "Benchmark (surveying)", "Hyperparameter", "Truck", "Routing (electronic design automation)", "Bayesian optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-15621-1_10" }, { "id": "https://openalex.org/W4387390066", "doi": "10.48550/arxiv.2310.02726", "title": "Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms", "year": 2023, "publication_date": "2023-10-04", "type": "preprint", "language": "en", "authors": [ "Akash Kopparam Sreedhara", "Deepesh Padala", "Shashank Mahesh", "Kai Cui", "Mengguang Li", "Heinz Koeppl" ], "first_author": "Akash Kopparam Sreedhara", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "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", "Swarm behaviour", "Computer science", "Vehicle routing problem", "Payload (computing)", "Generalization", "Routing (electronic design automation)", "Distributed computing", "Computer network", "Artificial intelligence", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2310.02726" }, { "id": "https://openalex.org/W4225541625", "doi": "10.1007/978-3-031-03502-9_7", "title": "Multi Depot UAVs Routing Subject to Changing Weather and Time Windows Variation", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Grzegorz Radzki", "Grzegorz Bocewicz", "Jarosław Wikarek", "Peter Nielsen", "Zbigniew Antoni Banaszak" ], "first_author": "Grzegorz Radzki", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Constraint (computer-aided design)", "Route planning", "Operations research", "Time constraint", "Mathematical optimization", "Transport engineering", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-03502-9_7" }, { "id": "https://openalex.org/W7133425262", "doi": null, "title": "VNS algorithm for the solution of a mothership system in a drone vehicle routing problem in package delivery and pickup", "year": 2023, "publication_date": "2023-11-05", "type": "dissertation", "language": "es", "authors": [ "Lina Maria Higuera Ayala", "Luisa Fernanda Peña Jara" ], "first_author": "Lina Maria Higuera Ayala", "venue": "Universidad Industrial de Santander", "publisher": null, "abstract": "Este documento presenta un modelo matemático y una metodología de solución eficiente para el problema de ruteo de vehículos con drones (VDRP) en el cual el objetivo es cumplir la demanda de servicios de recogidas y entregas en una red minimizando el costo total de la operación. para esto el problema es formulado como programa lineal de enteros mixtos (MILP). El modelo formulado contempla la mayoría de las condiciones e interacciones del sistema de nave nodriza (vehículo-dron) como las restricciones que se generan por la forma de operación de cada uno de ellos, a saberse restricciones de capacidad de carga y rango de vuelo. Así mismo, se tienen en cuenta algunas suposiciones con el fin de simplificar un poco el problema. La metodología de solución se basa en la utilización de una extensión del clásico algoritmo de Clarke and Wright para resolver el VDRP con la cual se genera una solución inicial que sea buena y se busca su optimización mediante un algoritmo de búsqueda de vecindario variable (VNS). Se llevan a cabo algunos experimentos numéricos con el fin de medir el rendimiento de las heurísticas y dar respuesta al problema planteado considerando diferentes instancias. Al ser un tema poco explorado por la literatura se considera que este documento amplía tanto preguntas como respuestas en este sistema de nave nodriza que se considera aun innovador en la planificación de las tareas de entrega y recogida de paquetes.", "keywords": [ "Variable (mathematics)", "Artificiality" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://noesis.uis.edu.co/bitstreams/351a374c-87d7-4117-954d-8964230c06a6/download" }, { "id": "https://openalex.org/W3021408769", "doi": "10.1109/dasc43569.2019.9081655", "title": "Integrated Routing and Charging Scheduling for Autonomous Electric Aerial Vehicle System", "year": 2019, "publication_date": "2019-09-01", "type": "conference-paper", "language": "en", "authors": [ "Jun Chen" ], "first_author": "Jun Chen", "venue": null, "publisher": null, "abstract": "Autonomous electric aerial vehicles (EAVs) are expected to bring fundamental changes to city infrastructures and daily commutes. Currently, the EAVs, including the delivery drones and the electric vertical takeoff and landing (eVTOL) air taxi, are all under limited battery endurance and vertiport capacity, which is insufficient for long-range commutes. In this paper, we propose a joint scheduling methodology to handle the optimal routing and charging tasks for the Autonomous Electric Aerial Vehicle System. By considering the unique characteristics of on-demand EAVs, the joint optimization problem can effectively utilize the system's resources. The problem is formulated by integrating charging features into the classic vehicle routing problem with time windows. To make it easy to solve, we further transform the nonlinear problem to a mixed integer linear program. The problem formulation and the operational constraints are both well validated through comprehensive simulations.", "keywords": [ "Computer science", "Scheduling (production processes)", "Electric vehicle", "Routing (electronic design automation)", "Real-time computing", "Computer network", "Engineering" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dasc43569.2019.9081655" }, { "id": "https://openalex.org/W2981484461", "doi": "10.1109/icmsao.2019.8880379", "title": "Autonomous Vehicles Delivery Systems Classification: Introducing a TSP With a Moving Depot", "year": 2019, "publication_date": "2019-04-01", "type": "conference-paper", "language": "en", "authors": [ "Batool Madani", "Malick Ndiaye" ], "first_author": "Batool Madani", "venue": null, "publisher": null, "abstract": "The vast growth in the e-commerce market has increased the attention to resolving the problem of conveying goods from distribution hubs to final destinations (customers) in the supply chain. The process known as the Last Mile Delivery (LMD) problem has significant challenges such as reducing operational cost or ecological impact and increasing supply chain performance. The inclusion of new technologies such as drones, robots, and the internet of things help tackle these challenges by developing new distribution systems to improve from traditional deliveries methods (delivery person, postal/delivery boxes). However, the use of these technologies brings new operational challenges. For instance, in a combined truck-drone delivery system, the truck serves as a depot from where we load the product to the drone for final delivery to the customers. The depot is now moving unlike in a traditional Vehicle Routing Problem (VRP) for which we always assume a fixed depot. This research paper deals with the impact of using Autonomous Vehicles (AV) on the logistics of the LMD. We first present a technological review of the use of AVs in logistics and use it to introduce a classification of the delivery systems based on the parcel/product handover at the time of the last handling before delivery to customers. We describe three categories of handovers, namely, machine-to-person, machine-to-machine, and person-to-machine. We present a new class of vehicle routing problems with a moving depot and formulate two variants of a Discrete Traveling Salesman Problem (TSP) with a Moving Depot (TSP-MD). We solve the problem using the General Algebraic Modeling System (GAMS) software and discuss some of the problem characteristics.", "keywords": [ "Truck", "Vehicle routing problem", "Travelling salesman problem", "Computer science", "Supply chain", "Drone", "Last mile (transportation)", "Routing (electronic design automation)", "Product (mathematics)", "Depot", "Operations research", "Process (computing)" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icmsao.2019.8880379" }, { "id": "https://openalex.org/W4395475010", "doi": "10.1016/j.trc.2024.104615", "title": "Sustainable last-mile distribution with autonomous delivery robots and public transportation", "year": 2024, "publication_date": "2024-04-25", "type": "article", "language": "en", "authors": [ "Annarita De Maio", "Gianpaolo Ghiani", "Demetrio Laganà", "Emanuele Manni" ], "first_author": "Annarita De Maio", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": "In recent years, the rapid growth of e-commerce and the need to make last-mile logistics more sustainable have stimulated the development of new distribution paradigms, based on air drones and autonomous delivery robots, which are less sensitive to traffic congestion. In this article we deal with a routing problem in which a fleet of autonomous delivery robots can travel not only on the road network but also on the public transportation system (or part of it) to extend their range of action with a given battery capacity. The problem entails building delivery robot routes synchronized with the rides of the public transportation lines, enabling robots to drop on/off public vehicles, where they use dedicated compartments, to reach customers that would be otherwise out of reach. To this purpose, we develop a tailored destroy-and-repair mechanism that, embedded into a neighborhood search algorithm, allows to effectively explore the feasibility region of large-scale instances. Extensive computational results on instances resembling the distribution of drugs to pharmacies in Rome (Italy) show that the proposed algorithmic mechanism allows to obtain a cost reduction up to about 7.5% with respect to a more traditional approach. Moreover, from a managerial point of view, our experiments show that autonomous delivery robots combined with public transportation can provide huge benefits in terms of costs and emissions reduction, when compared to both traditional and electric vans.", "keywords": [ "Mile", "Last mile (transportation)", "Public transport", "Business", "Transport engineering", "Robot", "Distribution (mathematics)", "Computer science", "Engineering", "Geography", "Artificial intelligence" ], "cited_by_count": 67, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2024.104615" }, { "id": "https://openalex.org/W2893812993", "doi": "10.1155/2019/9063232", "title": "Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance, and Reconnaissance", "year": 2019, "publication_date": "2019-01-23", "type": "article", "language": "en", "authors": [ "Yao Liu", "Zhong Liu", "Jianmai Shi", "Guohua Wu", "Chao Chen" ], "first_author": "Yao Liu", "venue": "Journal of Advanced Transportation", "publisher": "Hindawi Publishing Corporation", "abstract": "The location-routing problem (LRP) of unmanned aerial vehicles (UAV) in border patrol for Intelligence, Surveillance, and Reconnaissance is investigated, where the locations of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the practical border in Guangxi is presented to illustrate the problem and the solution approach. The performance of the two algorithms is analysed and compared through randomly generated instances.", "keywords": [ "Heuristic", "Base station", "Integer programming", "Computer science", "Base (topology)", "Drone", "Military Base", "Routing (electronic design automation)", "Location model", "Real-time computing", "Operations research", "Artificial intelligence" ], "cited_by_count": 56, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/jat/2019/9063232.pdf" }, { "id": "https://openalex.org/W4403514239", "doi": "10.1177/09544100241288067", "title": "A hybrid evolutionary algorithm integrated with specific neighborhood search for collaborative task planning of heterogeneous multi-UAVs under SEAD scenarios", "year": 2024, "publication_date": "2024-10-17", "type": "article", "language": "en", "authors": [ "Jianfeng Wang", "Gaowei Jia", "Ke Yu", "Yue Zhang", "Zheng Xin Guo", "Zhongxi Hou" ], "first_author": "Jianfeng Wang", "venue": "Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering", "publisher": "SAGE Publishing", "abstract": "To overcome the challenges posed by diversified task combinations and strict temporal constraints in the Suppression of Enemy Air Defenses (SEAD), this paper investigates the task planning problem. The task correlation in SEAD evolves from chained correlations within individual units to a web of interdependencies across all UAVs, making the task planning problem significantly more complex than traditional vehicle routing problems. This complexity ensures that any alteration to a single task will produce a cascading effect on the overall plan. To address this issue, the paper designs a matrix-based task dependency analysis method that quantitatively evaluates the logical interdependencies between tasks. Additionally, the concept of dynamic time windows is introduced to precisely measure the temporal impact of task adjustments on the overall plan. On the basis of these analyses, an adaptive hybrid evolutionary algorithm is designed, which employs a neighborhood search strategy to precisely adjust task assignments and execution sequences based on task dependencies and dynamic time windows. Finally, simulation results suggest that the algorithm proposed in this paper consistently produces task plans with a high number of completed tasks and short completion time across scenarios involving varying task quantities, task combinations, time windows, regional distributions, and UAV configurations.", "keywords": [ "Task (project management)", "Computer science", "Evolutionary algorithm", "Artificial intelligence", "Systems engineering", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/09544100241288067" }, { "id": "https://openalex.org/W4312971493", "doi": "10.23919/ccc55666.2022.9902609", "title": "Route Planning Based on Deep Reinforcement Learning to Minimize Energy Consumption in UAV Photogrammetry", "year": 2022, "publication_date": "2022-07-25", "type": "conference-paper", "language": "en", "authors": [ "Hongjie Wang", "Shengxuan Xu", "Linlin Qin", "Gang Wu" ], "first_author": "Hongjie Wang", "venue": "2022 41st Chinese Control Conference (CCC)", "publisher": null, "abstract": "In photogrammetry, limited battery energy and storage capacity require unmanned aerial vehicles (UAVs) to travel frequently to and from depots. Therefore, route planning is necessary to ensure safety and efficiency. In this paper, the traditional iterative search algorithm is improved through neural network-assisted deep reinforcement learning with an attention layer and the strategy of separating improvement and reconstruction operators, and applied to the vehicle route problem with energy and capacity constraints. Simulation and practice show that energy consumption of the route generated by the improved algorithm is reduced by 10.63%, and the calculation time is only 8.92% that of the optimal traditional algorithm. Therefore, deep reinforcement learning can be applied in the simulation and practice of UAV photogrammetry, and it has been verified to be reliable and efficient, which lays the foundation for minimizing the energy consumption of photogrammetry with multi-aircraft cooperation.", "keywords": [ "Reinforcement learning", "Energy consumption", "Photogrammetry", "Computer science", "Deep learning", "Artificial intelligence", "Simulation", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/ccc55666.2022.9902609" }, { "id": "https://openalex.org/W4221107933", "doi": "10.36227/techrxiv.19365389", "title": "Q-Learning Aided Intelligent Routing with Maximum Utility in Cognitive UAV Swarm for Emergency Communications", "year": 2022, "publication_date": "2022-03-17", "type": "preprint", "language": "en", "authors": [ "Long Zhang", "Xiaozheng Ma", "Zirui Zhuang", "Haitao Xu", "Vishal Sharma", "Zhu Han" ], "first_author": "Long Zhang", "venue": null, "publisher": null, "abstract": "In this paper, we attempt to deal with the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which applies the cognitive radio into a swarm of UAVs within a three-hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a Q-learning framework to achieve the intelligent routing with maximum utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric is determined by maximizing the utility, which jointly captures the achievable rate of UAV pair and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for the UAV. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update Q-values during the training process for achieving a fast convergence speed. Extensive simulation results are provided to assess the potential of the Q-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm can significantly increase the accumulated rewards significantly with practical complexity compared to other benchmark schemes with fixed and decaying learning rates.", "keywords": [ "Swarm behaviour", "Computer science", "Benchmark (surveying)", "Routing (electronic design automation)", "Swarm intelligence", "Metric (unit)", "Real-time computing", "Artificial intelligence", "Algorithm", "Particle swarm optimization", "Computer network", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.techrxiv.org/articles/preprint/Q-Learning_Aided_Intelligent_Routing_with_Maximum_Utility_in_Cognitive_UAV_Swarm_for_Emergency_Communications/19365389/3/files/37504264.pdf" }, { "id": "https://openalex.org/W4408432428", "doi": "10.2139/ssrn.5179422", "title": "A Constraint Relaxation Co-Evolutionary Algorithm for the Two-Echelon Vehicle Routing Problem with Load-Dependent Drones", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Fan Yu", "Qun Chen", "Jinlong Zhou", "Chao Li", "Sheng Qi", "Weixiong Huang" ], "first_author": "Fan Yu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Vehicle routing problem", "Drone", "Constraint (computer-aided design)", "Mathematical optimization", "Relaxation (psychology)", "Computer science", "Evolutionary algorithm", "Algorithm", "Routing (electronic design automation)", "Mathematics", "Biology", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5179422" }, { "id": "https://openalex.org/W7160874791", "doi": "10.17632/2vn4mw5zdt.1", "title": "Multi-objective Multi-depot Vehicle Routing Problem with Drone considering Time Windows and Resource Sharing", "year": 2026, "publication_date": "2026-05-11", "type": "dataset", "language": null, "authors": [ "yanru Cui" ], "first_author": "yanru Cui", "venue": "Mendeley Data", "publisher": null, "abstract": "This dataset is suitable for VRP problems involving multiple depots and time windows. The first row of the file specifies the number of customers and the number of stops; the subsequent columns represent the customer number, x-coordinate, y-coordinate, service duration, demand, start of the time window, and end of the time window, respectively.", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Drone", "Service (business)", "Resource (disambiguation)", "Resource allocation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/2vn4mw5zdt.1" }, { "id": "https://openalex.org/W7202299796", "doi": "10.48084/etasr.19139", "title": "An Optimization of the Two-Echelon Vehicle Routing Problem with Drones Considering Truck Travel Time and Carbon Emissions", "year": 2026, "publication_date": "2026-08-11", "type": "article", "language": null, "authors": [ "Santoso Santoso", "Nurhadi Siswanto", "Budi Santosa", "Budisantoso Wirjodirdjo" ], "first_author": "Santoso Santoso", "venue": "Engineering Technology & Applied Science Research", "publisher": "Engineering, Technology & Applied Science Research", "abstract": "The Two-Echelon Vehicle Routing Problem with Drones (2E VRP-D) model can initiate flights from the truck, complete several deliveries to different customer locations, and then rendezvous with the truck again. In addition to economic benefits, logistics providers must consider the environmental impacts of the order-fulfillment process. A novel multi-objective optimization framework is established in this study to simultaneously minimize the total time required for truck travel while also reducing total carbon emissions. Due to restrictions in payload capacity and battery energy limits, drones need to work alongside trucks to deliver services effectively. A dynamic energy consumption model is applied for the drone, where energy use changes based on the loading rate, enabling a more realistic representation of actual operations. This complex problem is addressed using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) with two approaches: the Giant Chromosome (GC) and K-means methods. Routing plans for both trucks and drones are then constructed using a novel heuristic algorithm. Overall, the K-means method delivers better average objective values, reflecting enhanced exploitation performance. Conversely, the GC method produces a higher Hypervolume (HV), indicating superior convergence and coverage of the Pareto front, supported by a lower spacing value, while K-means achieves a slightly better spread. These outcomes contribute to improving logistics operations and informing government policy decisions.", "keywords": [ "Truck", "Drone", "Genetic algorithm", "Routing (electronic design automation)", "Sorting", "Payload (computing)", "Pareto principle", "Energy consumption", "Multi-objective optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://etasr.com/index.php/ETASR/article/download/19139/6928" }, { "id": "https://openalex.org/W4394206722", "doi": "10.6084/m9.figshare.17697209", "title": "A novel multi-objective optimization model for vehicle routing problem with drone delivery and dynamic flight endurance", "year": 2022, "publication_date": "2022-01-01", "type": "dataset", "language": "en", "authors": [ "Anonymous authors" ], "first_author": "Anonymous authors", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "In these instances, the number of customers is set to 40, 80, 120, and 160 respectively. All customers and depot are located in a square area. The vertical distance from the edge of the area to the center of the area is set to 20 km, 30 km, and 40 km, which represent the three different sizes of square areas respectively. Because the maximum load capacity of drones can not satisfy the demand of all customers in the real world, the percentage of customers that can be served by drones is assumed to be 30%, 50%, and 70% respectively in these instances. Since these instances involve the different types of combinations (the number of customers, the vertical distance from the edges of square area to its center, and the percentage of customers who can be served by drones), they can be simply named intuitively. For example, the instance \"40-20-30\" means that there are 40 customers located in a square area, the vertical distance from the edge of the area to its center is 20 km, and only 30% of the customers can be served by the drone.", "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Simulation", "Embedded system", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.17697209" }, { "id": "https://openalex.org/W7160847683", "doi": "10.17632/2vn4mw5zdt", "title": "Multi-objective Multi-depot Vehicle Routing Problem with Drone considering Time Windows and Resource Sharing", "year": 2026, "publication_date": "2026-05-11", "type": "dataset", "language": null, "authors": [ "yanru Cui" ], "first_author": "yanru Cui", "venue": "Mendeley Data", "publisher": null, "abstract": "This dataset is suitable for VRP problems involving multiple depots and time windows. The first row of the file specifies the number of customers and the number of stops; the subsequent columns represent the customer number, x-coordinate, y-coordinate, service duration, demand, start of the time window, and end of the time window, respectively.", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Drone", "Service (business)", "Resource (disambiguation)", "Resource allocation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/2vn4mw5zdt" }, { "id": "https://openalex.org/W7152396760", "doi": "10.70675/b90f39c1z06aez479ez8948z62d4786c3e87", "title": "Solving vehicle routing problems with drones in urban logistics : an approach based on exact and approximate optimization methods", "year": 2026, "publication_date": "2026-02-04", "type": "dissertation", "language": "en", "authors": [ "Sylvain Lichau" ], "first_author": "Sylvain Lichau", "venue": null, "publisher": null, "abstract": "Résolution de problèmes de tournées de véhicules avec drones en logistique urbaine : une approche basée sur les méthodes d’optimisation exactes et approchées Les drones constituent un complément prometteur pour la logistique de livraison. Ils présentent des caractéristiques fondamentalement différentes de celles des véhicules classiques : des coûts d’utilisation plus faibles et des vitesses plus élevées, au détriment d’une autonomie et d’une capacité réduite. Pour les applications où ces contraintes sont acceptables, les drones peuvent remplacer entièrement les véhicules de livraison. Cependant, dans la plupart des cas, il est nécessaire de combiner stratégiquement ces types de véhicules complémentaires afin de tirer le meilleur parti de leurs atouts opérationnels respectifs. Cette thèse propose principalement des méthodes mathématiques d’optimisation exactes pour les problèmes de routage avec drones. Le premier chapitre propose une revue de littérature consacrée aux problèmes de routage avec drones dans le domaine de la recherche opérationnelle, en mettant l’accent sur les méthodes d’optimisation exactes. Nous soulignons la diversité des problèmes abordés par les chercheurs ainsi que la nécessité d’augmenter la taille des instances résolues à l’optimale. Le deuxième chapitre rappelle brièvement les concepts de programmation mathématique nécessaires à la compréhension des algorithmes de «branch-cut-and-price». Nous décrivons également l’algorithme utilisé dans cette thèse, en détaillant ses composantes avancées ainsi que l’algorithme de «labeling» employé pour résoudre les sous-problèmes. Le troisième chapitre traite du «Two-Echelon Vehicle Routing Problem with Drones». Nous proposons un nouveau modèle de partitionnement d’ensembles, dans lequel les chemins partiels correspondant aux mouvements des drones sont énumérés à l’aide d’un programme dynamique. Nous présentons également une adaptation des «rounded capacity cuts», spécialement conçue pour résoudre ce problème. Par ailleurs, nous développons une heuristique basée sur l’algorithme exact. Les expériences numériques montrent que l’algorithme exact augmente considérablement la taille des instances résolues de manière optimale. Pour évaluer le modèle, l’algorithme est ensuite étendu pour traiter le «Park-and-Loop Routing Problem». Le quatrième chapitre traite du «Truck-based Drone Delivery Routing Problem with Time Windows». Nous développons un algorithme exact de branch-cut-and-price ainsi qu’un algorithme de «labeling» pour résoudre le sous-problème. Nous comparons deux modèles de sous-problème alternatifs définis sur des graphes distincts et montrons qu’aucun des deux ne domine l’autre : le meilleur choix dépend des caractéristiques de l’instance considérée. Les expériences numériques démontrent l’efficacité des approches proposées. Nous introduisons également de nouvelles instances plus complexes, où la capacité des drones est augmentée. Le cinquième chapitre étudie une variante du «Vehicle Routing Problem with Time Windows», qui intègre deux caractéristiques pertinentes sur le plan opérationnel : des heures de départ flexibles depuis le dépôt et des limites strictes sur la durée des routes. Bien que plus générique, ce problème peut s’apparenter à un problème de routage de drones dans lequel la portée est limitée par la durée du trajet. Nous développons un algorithme de branch-cut-and-price, dont la composante principale est un algorithme de «labeling» bidirectionnel résolvant le sous-problème. Nous montrons également comment traiter le problème connexe de minimisation de la durée totale du trajet. Les expériences numériques confirment les performances de l’algorithme.", "keywords": [ "Drone", "Vehicle routing problem", "Routing algorithm", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.theses.fr/2026BORD0010/document" }, { "id": "https://openalex.org/W2981738698", "doi": "10.31590/ejosat.637816", "title": "Clarke & Wright's Savings Algorithm and Genetic Algorithms Based Hybrid Approach for Flying Sidekick Traveling Salesman Problem", "year": 2019, "publication_date": "2019-10-24", "type": "article", "language": "en", "authors": [ "Büşra Özoğlu", "Emre Çakmak", "Tuğçe Koç" ], "first_author": "Büşra Özoğlu", "venue": "European Journal of Science and Technology", "publisher": "European Journal of Science and Technology", "abstract": "Over the past few years, drones also known as unmanned aerial vehicles (UAV), have been adopted as a part of transportation activities in logistic sector. This paper investigates a new version of traveling salesman problem called as flying sidekick traveling salesman problem(FSTSP) in which trucks and drones serve the customers in coordination with the objective of minimizing the total delivery distance of trucks at the depot after completing the deliveries. Clarke & Wright's savings algorithm is a well-known heuristics approach in literature, which gives better solution for classical vehicle routing problem. In this paper, a hybrid approach based on Clarke & Wright's savings algorithm and genetic algorithm is proposed for solving the new version of travelling salesman problem. In the proposed hybrid algorithm, which is the sequential use of genetic algorithm and Clarke & Wright’s savings algorithm, is used for assignment of the truck, drone or both of them to serve the customer. The solution of the genetic algorithm, which is the well-known metaheuristic approach, is enhanced with Clarke & Wright's savings algorithm. The aim of the problem is to minimize the total delivery distance according to the assignment decisions. This is the first hybrid approach in the literature including Clarke & Wright’s savings algorithm and genetic algorithm that applies for FSTSP problem. The hypothetical experiments conducted on various instances and results confirm the efficiency of the approach and give some insights on this drone delivery system.", "keywords": [ "Travelling salesman problem", "Algorithm", "Drone", "Wright", "Computer science", "Vehicle routing problem", "Genetic algorithm", "Heuristics", "Hybrid algorithm (constraint satisfaction)", "Truck", "Mathematical optimization", "Metaheuristic" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/en/download/article-file/839098" }, { "id": "https://openalex.org/W7128483867", "doi": "10.1016/j.tre.2026.104726", "title": "Bidirectional energy supply logistics using uncrewed electric aerial and ground vehicles: A two-echelon location-routing problem with resource-constrained demand allocation and time windows", "year": 2026, "publication_date": "2026-02-10", "type": "article", "language": "en", "authors": [ "Hyunhwa Kim", "Denissa Sari Darmawi Purba", "Eleftheria Kontou" ], "first_author": "Hyunhwa Kim", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "In the aftermath of hazards, energy supply to demand nodes is constrained due to power outages caused by damaged infrastructure. Electric uncrewed ground vehicles (UGVs) and aerial vehicles (UAVs) can play a crucial role in providing backup power, discharging energy directly to meet demand, and recharging en-route if necessary. However, charging infrastructure that replenishes backup power has limited capacity and can be inoperable due to disruptions of the main power system. Since certain energy demands are urgent in post-disaster contexts (i.e., healthcare loads), UGVs and UAVs prioritize these critical needs. As a result, UGVs and UAVs may meet only portion of the energy needs due to constrained resources. This research aims to design a bidirectional energy supply logistics model, as a two-echelon electric vehicle location-routing problem with resource-constrained demand allocation and time windows, employing UGVs and UAVs. The first echelon involves deploying UGVs and UAVs from a depot to satellite charging locations. In the second echelon, these vehicles travel from satellites to serve local energy demand, either fully or partially within the constrained resource budget discharging their batteries and recharging en-route as needed. We formulate this model and propose a metaheuristic solution which consists of a two-stage approach based on the adaptive large neighborhood search. Numerical experiments on benchmark instances were conducted to evaluate the novel heuristic’s performance compared to a commercial solver. Sensitivity analysis was carried out to examine the impact of UAV and UGV batteries capacity, budget for charging infrastructure, and the amount of energy resources. We applied our model to a real-world case of post-wildfire humanitarian aid in California’s counties aiming to supply energy during large-scale power loss in the region.", "keywords": [ "Backup", "Energy supply", "Energy (signal processing)", "Power (physics)", "Resource (disambiguation)", "Resource allocation", "Supply chain", "Energy storage" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104726" }, { "id": "https://openalex.org/W4403532758", "doi": "10.1016/j.adhoc.2024.103686", "title": "Approximation schemes for age of information minimization in UAV grid patrols", "year": 2024, "publication_date": "2024-10-18", "type": "article", "language": "en", "authors": [ "Weiqi Wang", "Jin Xu" ], "first_author": "Weiqi Wang", "venue": "Ad Hoc Networks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Minification", "Grid", "Computer science", "Mathematical optimization", "Operations research", "Mathematics", "Geometry" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2412.14273" }, { "id": "https://openalex.org/W2178009456", "doi": "10.5139/jksas.2015.43.6.488", "title": "Optimal path planning and analysis for the maximization of multi UAVs survivability for missions involving multiple threats and locations", "year": 2015, "publication_date": "2015-06-01", "type": "article", "language": "en", "authors": [ "Seongsik Jeong", "Dae-Sung Jang", "Hyunjin Park", "Taehyun Seong", "Jaemyung Ahn" ], "first_author": "Seongsik Jeong", "venue": "Journal of the Korean Society for Aeronautical & Space Sciences", "publisher": "Korean Society for Aeronautical and Space Science", "abstract": "본 연구에서는 각각 다른 목적지에서 다수의 임무를 수행해야하는 복수 무인항공기(Unmanned Aerial Vehicle, UAVs)의 경로를 결정할 때, 무인항공기의 생존가능성을 고려하여 경로를 결정하는 프레임워크를 제안하였다. 본 라우팅 문제는 무인항공기 안전과 임무 완료시간 간의 trade-off 를 나타내는 비용 매트릭스를 이용한 차량경로문제(Vehicle Routing Problem, VRP)로 정의할 수 있다. 특정위치에서 무인항공기의 위험 레벨은 감지될 확률과 격추될 확률을 고려하여 모델링 하였고, 위협 레벨과 비행거리를 고려한 두 지역간의 최소비용경로는 육각형격자(Hexagonal cells)에서 Dijkstra 알고리듬을 사용하여 결정하였다. 또한, 지속적으로 다수의 적을 감시 정찰하는 임무를 수행하는 복수 무인항공기의 최적경로를 결정하는 case study를 수행하였으며, 그 결과를 논의하였다. This paper proposes a framework to determine the routes of multiple unmanned aerial vehicles (UAVs) to conduct multiple tasks in different locations considering the survivability of the vehicles. The routing problem can be formulated as the vehicle routing problem (VRP) with different cost matrices representing the trade-off between the safety of the UAVs and the mission completion time. The threat level for a UAV at a certain location was modeled considering the detection probability and the shoot-down probability. The minimal-cost path connecting two locations considering the threat level and the flight distance was obtained using the Dijkstra algorithm in hexagonal cells. A case study for determining the optimal routes for a persistent multi-UAVs surveillance and reconnaissance missions given multiple enemy bases was conducted and its results were discussed.", "keywords": [ "Dijkstra's algorithm", "Survivability", "Computer science", "Routing (electronic design automation)", "Maximization", "Shortest path problem", "Path (computing)", "Motion planning", "Vehicle routing problem", "Real-time computing", "Mathematical optimization", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://koreascience.or.kr:80/article/JAKO201519262930576.pdf" }, { "id": "https://openalex.org/W4391486815", "doi": "10.1016/j.eng.2023.10.014", "title": "Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management", "year": 2024, "publication_date": "2024-02-02", "type": "article", "language": "en", "authors": [ "Lu Zhen", "Zhiyuan Yang", "Gilbert Laporte", "Wen Yi", "Tianyi Fan" ], "first_author": "Lu Zhen", "venue": "Engineering", "publisher": "Elsevier BV", "abstract": "Technological advancements in unmanned aerial vehicles (UAVs) have revolutionized various industries, enabling the widespread adoption of UAV-based solutions. In engineering management, UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments, surpassing traditional inspection techniques. Building on this foundation, this paper delves into the optimization of UAV inspection routing and scheduling, addressing the complexity introduced by factors such as no-fly zones, monitoring-interval time windows, and multiple monitoring rounds. To tackle this challenging problem, we propose a mixed-integer linear programming (MILP) model that optimizes inspection task assignments, monitoring sequence schedules, and charging decisions. The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem (VRP), leading to a mathematically intractable model for commercial solvers in the case of large-scale instances. To overcome this limitation, we design a tailored variable neighborhood search (VNS) metaheuristic, customizing the algorithm to efficiently solve our model. Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm, demonstrating its scalability for both large-scale and real-scale instances. Sensitivity experiments and a case study based on an actual engineering project are also conducted, providing valuable insights for engineering managers to enhance inspection work efficiency.", "keywords": [ "Scalability", "Computer science", "Scheduling (production processes)", "Vehicle routing problem", "Integer programming", "Metaheuristic", "Routing (electronic design automation)", "Variable neighborhood search", "Real-time computing", "Engineering", "Artificial intelligence", "Algorithm" ], "cited_by_count": 33, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eng.2023.10.014" }, { "id": "https://openalex.org/W2998095266", "doi": "10.2514/6.2020-2296", "title": "Nested Vehicle Routing Problem: Combined Operations of UAV and Ground Vehicle", "year": 2020, "publication_date": "2020-01-05", "type": "conference-paper", "language": "en", "authors": [ "Fanruiqi Zeng", "Zaiwei Chen", "Harold Nikoue", "Jose M. Magalhaes", "John-Paul Clarke" ], "first_author": "Fanruiqi Zeng", "venue": "AIAA Scitech 2020 Forum", "publisher": null, "abstract": "In this paper, we study the Nested Vehicle Routing Problem (Nested-VRP) in which a truck and a drone are coordinated to perform an aerial reconnaissance mission over a large geographical area. Motivated by two key constraints, battery life and line-of-sight operations, we develop a flexible framework for generating an efficient, compliant operations plan. This plan includes synchronized routing and drone recharging plans utilizing the truck as a mobile recharge point. Two multi-stage strategies for coordination are proposed: a drone-centered strategy and a truck-centered strategy. Finally, we demonstrate the performance of both approaches applied to the scenario of post-disaster assessment in Santa Rosa during 2017 California Wildfires. The resulting solution leads to a considerable time savings for completion of the assessment, lowering the time taken from days to hours.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Plan (archaeology)", "Key (lock)", "Real-time computing", "Operations research", "Computer network", "Engineering", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2020-2296" }, { "id": "https://openalex.org/W4221031533", "doi": "10.2139/ssrn.4062871", "title": "Modeling Optimal Drone Fleet Size and Analyzing Drone-Truck Fleet Allocation Sustainability Tradeoffs", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Yuval Hadas", "Miguel Figliozzi" ], "first_author": "Yuval Hadas", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Aeronautics", "Fleet management", "Sustainability", "Transport engineering", "Computer science", "Operations research", "Engineering", "Aerospace engineering", "Environmental science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4062871" }, { "id": "https://openalex.org/W4413267632", "doi": "10.1109/tce.2025.3593383", "title": "Dynamic Path Planning for Space-Time Optimization Cooperative Tasks of Multiple Unmanned Aerial Vehicles in Uncertain Environment", "year": 2025, "publication_date": "2025-07-28", "type": "article", "language": "en", "authors": [ "Huimin Zhao", "M. Gu", "Shaopeng Qiu", "Ang Zhao", "Wu Deng" ], "first_author": "Huimin Zhao", "venue": "IEEE Transactions on Consumer Electronics", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Traditional path planning methods face significant challenges in addressing the high task density and complex airspace requirements of multi-UAV systems in uncertain environments, particularly in mitigating collision risks. This paper proposes a novel dynamic space-time optimization method that integrates an enhanced multi-ant colony system for vehicle routing problems with time windows with a proactive collision avoidance strategy. The approach begins by formulating a multi-UAV cooperative path optimization model that simultaneously maximizes node coverage and minimizes path conflicts for multi-depot routing scenarios with time constraints. The core methodology combines a space-time optimization algorithm with node weight quantification to detect and resolve path conflicts, along with an innovative node selection strategy that constructs a probabilistic conflict resolution model. Experimental validation using real-world task data demonstrates the method’s effectiveness, showing a 95.23% conflict resolution rate while significantly reducing isolated nodes and improving path planning efficiency compared to conventional approaches. The proposed solution provides a robust framework for safe and efficient multi-UAV operations in dense uncertain deployment scenarios.", "keywords": [ "Motion planning", "Computer science", "Path (computing)", "Remotely operated underwater vehicle", "Space (punctuation)", "Mobile robot", "Engineering", "Real-time computing", "Control engineering", "Robot", "Artificial intelligence", "Computer network" ], "cited_by_count": 30, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tce.2025.3593383" }, { "id": "https://openalex.org/W7161707104", "doi": "10.1016/j.epsr.2026.113309", "title": "UAV-vehicle coordinated routing for power inspection: A wind-aware optimization with terminal pickup strategy", "year": 2026, "publication_date": "2026-05-19", "type": "article", "language": "en", "authors": [ "Jinhua Xu", "Huiting Cheng", "Chenghao Liu", "Yikai Luo", "Wenbo Lu", "Longjian Wang" ], "first_author": "Jinhua Xu", "venue": "Electric Power Systems Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pickup", "Terminal (telecommunication)", "Power (physics)", "Routing (electronic design automation)", "Power consumption" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.epsr.2026.113309" }, { "id": "https://openalex.org/W2585826190", "doi": "10.1109/glocom.2016.7842033", "title": "Occlusion-Aware Coverage for Efficient Visual Sensing in Unmanned Aerial Vehicle Networks", "year": 2016, "publication_date": "2016-12-01", "type": "conference-paper", "language": "en", "authors": [ "Kevon Scott", "Rui Dai", "Manish Kumar" ], "first_author": "Kevon Scott", "venue": null, "publisher": null, "abstract": "Networks of unmanned aerial vehicles (UAVs), capable of providing flexible aerial views over large areas, are playing important roles in today's distributed sensing systems. Since camera sensors are sensitive to occlusions, it is more challenging to deploy UAVs for sensing in geometrically complex environments, such as dense urban areas and mountainous terrains. This paper proposes a new occlusion-aware UAV coverage technique with the objective of sensing a target area with satisfactory spatial resolution subject to the energy constraints of UAVs. An occlusion- aware waypoint generation algorithm is first designed to find the best set of waypoints for taking pictures in a target area. The selected waypoints are then assigned to multiple UAVs by solving a vehicle routing problem (VRP), which is formulated to minimize the maximum energy for the UAVs to travel through the waypoints. A genetic algorithm is designed to solve the VRP problem. Evaluation results show that the proposed coverage technique can reduce energy consumption by 16.1% while achieving better coverage than traditional coverage path planning techniques for UAVs.", "keywords": [ "Waypoint", "Computer science", "Terrain", "Energy consumption", "Real-time computing", "Motion planning", "Artificial intelligence", "Set (abstract data type)", "Computer vision", "Robot", "Geography", "Engineering" ], "cited_by_count": 22, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/glocom.2016.7842033" }, { "id": "https://openalex.org/W7135191271", "doi": "10.3389/frai.2026.1759688", "title": "An improved adaptive large neighborhood search algorithm for the flexible two-tier vehicle routing problem with drone stations", "year": 2026, "publication_date": "2026-03-13", "type": "article", "language": "en", "authors": [ "Qi Li", "Wenhao Yan", "Tad Gonsalves" ], "first_author": "Qi Li", "venue": "Frontiers in Artificial Intelligence", "publisher": "Frontiers Media", "abstract": "The two-tier vehicle routing problem (2T-VRP) represents a novel variant differing from the traditional VRPs. It can be applied to urban logistics operations system, offering significant potential for mitigating last-mile traffic congestion and reducing delivering costs. Distinct from traditional VRPs, this scenario contains a hierarchical two-tier structure, with trucks operating on the first tier and drones at stations on the second tier. In this study, we first extend the 2T-VRP framework by relaxing the hierarchical constraints, enabling trucks to transport goods not only to robot stations but also directly to customers. This new variant is referred to as the flexible two-tier vehicle routing problem with drone stations (F2T-VRP-DS). Then we formulate the problem as a mixed-integer linear programming (MILP) model. Finally, given the complexity of this problem, an improved adaptive large neighborhood search heuristic algorithm (IALNS) is proposed. The algorithm incorporates an adapted Clark and Wright saving algorithm as the initial solution, and a simulated annealing scheme is employed as the acceptance criterion. The experimental results show that our algorithm can provide high-quality solutions. In particular, compared with the MILP method, our algorithm demonstrates strong competitiveness on large-scale instances, offering smaller time consumption and better solution quality compared to commercial solver like Gurobi. In addition, based on the results of the sensitivity analysis, we further assessed the influence of several key parameters within the F2T-VRP-DS framework.", "keywords": [ "Simulated annealing", "Vehicle routing problem", "Solver", "Key (lock)", "Routing (electronic design automation)", "Drone", "Variable neighborhood search", "Traffic congestion" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://public-pages-files-2025.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1759688/pdf" }, { "id": "https://openalex.org/W4396685718", "doi": "10.2139/ssrn.4819017", "title": "The Load-Dependent Drone General Routing Problem", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Isaac Plana", "José María Sanchis", "Paula Segura" ], "first_author": "Isaac Plana", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Mathematical optimization", "Computer network", "Mathematics", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4819017" }, { "id": "https://openalex.org/W4393446776", "doi": "10.5281/zenodo.2600808", "title": "Instances of the Truck-Drone-ATV Routing Problem", "year": 2019, "publication_date": "2019-03-25", "type": "dataset", "language": "en", "authors": [ "Hagen Salewski", "Mahdi Moeini" ], "first_author": "Hagen Salewski", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This dataset contains the test instances of the Tuck-Drone-ATV Routing Problem as described in our Article \"A Genetic Algorithm for Solving the Truck-Drone-ATV Routing Problem\" written for the 6th World Congress on Global Optimization (WCGO 2019).", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Aeronautics", "Operations research", "Engineering", "Automotive engineering", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.2600808" }, { "id": "https://openalex.org/W7138919291", "doi": "10.2139/ssrn.6348258", "title": "The Single Truck and Drone Trailer Routing Problem", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Alexander Rave" ], "first_author": "Alexander Rave", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Trailer", "Drone", "Truck", "Flexibility (engineering)", "Routing (electronic design automation)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6348258" }, { "id": "https://openalex.org/W4407208849", "doi": "10.1109/tiv.2026.3696943", "title": "Deep Reinforcement Learning Enabled Persistent Surveillance With Energy-Aware UAV-UGV Systems for Disaster Management Applications", "year": 2026, "publication_date": "2026-05-26", "type": "article", "language": "en", "authors": [ "Md Safwan Mondal", "R. Subramanian", "James D. Humann", "James M. Dotterweich", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": "IEEE Transactions on Intelligent Vehicles", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Integrating Unmanned Aerial Vehicles (UAVs) with Unmanned Ground Vehicles (UGVs) provides an effective solution for persistent surveillance in disaster management. UAVs excel at covering large areas rapidly, but their range is limited by battery capacity. UGVs, though slower, can carry larger batteries for extended missions. By using UGVs as mobile recharging stations, UAVs can extend mission duration through periodic refueling, leveraging the complementary strengths of both systems. To optimize this energy-aware UAV-UGV cooperative routing problem, we propose a planning framework that determines optimal routes and recharging points between a UAV and a UGV. Our solution employs a deep reinforcement learning (DRL) framework built on an encoder-decoder transformer architecture with multi-head attention mechanisms. This architecture enables the model to sequentially select actions for visiting mission points and coordinating recharging rendezvous between the UAV and UGV. The DRL model is trained to minimize theage periods(the time gap between consecutive visits) of mission points, ensuring effective surveillance. We evaluate the framework across various problem sizes and distributions, comparing its performance against heuristic methods and an existing learning-based model. Results show that our approach consistently outperforms these baselines in both solution quality and runtime. Additionally, we demonstrate the DRL policy's applicability in a real-world disaster scenario as a case study and explore its potential for online mission planning to handle dynamic changes. Adapting the DRL policy for priority-driven surveillance highlights the model's generalizability for real-time disaster response. More details are available on our website:https://sites.google.com/view/ecucps.", "keywords": [ "Reinforcement learning", "Computer science", "Reinforcement", "Aeronautics", "Artificial intelligence", "Computer security", "Human–computer interaction", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tiv.2026.3696943" }, { "id": "https://openalex.org/W7141014638", "doi": "10.6084/m9.figshare.31867687", "title": "A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance", "year": 2026, "publication_date": "2026-03-27", "type": "dataset", "language": null, "authors": [ "Anonymous Authors (for blind review purpose)" ], "first_author": "Anonymous Authors (for blind review purpose)", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data for the manuscript titled \"A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance\".", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Work (physics)", "Optimization problem", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31867687" }, { "id": "https://openalex.org/W7128709410", "doi": "10.1109/taes.2026.3664046", "title": "Nutcracker Genetic Algorithm for Task Scheduling in UAV Swarm MEC With Adaptive Caching", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Baofeng Ji", "Zifan Chen", "Ji Zhang", "Hao Li", "Huitao Fan", "Pei Xiao" ], "first_author": "Baofeng Ji", "venue": "IEEE Transactions on Aerospace and Electronic Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The effectiveness of autonomous Unmanned Aerial Vehicle (UAV) swarms in providing reliable Mobile Edge Computing (MEC) services is critically dependent on two conflicting performance metrics: the overall task completion time, which dictates service responsiveness, and the swarm's total energy consumption, which determines the network's operational longevity. This optimization challenge is further complicated by the unique operational constraints of aerospace systems, such as maintaining safe separation distances and managing finite onboard battery energy. To address this core challenge, we propose the Nutcracker-Inspired Hybrid Genetic Algorithm, hereinafter the Nutcracker Genetic Algorithm (NGA), a scheduling framework designed to find superior solutions for this trade-off. Unlike traditional approaches that can prematurely converge on inefficient schedules, NGA employs a unique cache-and-recovery mechanism, reinforced by specialized crossover and mutation operators, to autonomously optimize the fleet size and repair bottleneck flight trajectories. We rigorously validate NGA's performance on large-scale mission scenarios incorporating rigorous rotary-wing aerodynamic power models against the original NOA and other state-of-the-art algorithms. The results demonstrate that our framework can reduce task completion time by up to 42.8% while ensuring physical feasibility. This substantial performance gain highlights NGA's potential as a key enabling method for enhancing the scheduling efficiency and operational sustainability of resource-constrained UAV-assisted MEC networks.", "keywords": [ "Bottleneck", "Scheduling (production processes)", "Genetic algorithm", "Key (lock)", "Crossover", "Particle swarm optimization", "Cluster analysis", "Swarm behaviour", "Job shop scheduling" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/taes.2026.3664046" }, { "id": "https://openalex.org/W7140271831", "doi": "10.6084/m9.figshare.31852156.v1", "title": "A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance", "year": 2026, "publication_date": "2026-03-25", "type": "dataset", "language": null, "authors": [ "Anonymous Authors (for blind review purpose)" ], "first_author": "Anonymous Authors (for blind review purpose)", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data for the manuscript titled \"A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance\".", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Work (physics)", "Optimization problem", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31852156.v1" }, { "id": "https://openalex.org/W4411271816", "doi": "10.1109/temscon-aspac62480.2024.11024639", "title": "Development of A Vehicle Routing Problem with Multiple Cross-Dockings (VRPMCD) Model with Truck-Drone Fleet Combination Operating on the Highway", "year": 2024, "publication_date": "2024-09-25", "type": "conference-paper", "language": "en", "authors": [ "Naudur Marito Pane", "Fadila Isnaini", "Ahmad Rusdiansyah" ], "first_author": "Naudur Marito Pane", "venue": null, "publisher": null, "abstract": "This study aims to explore the use of drones in logistics delivery, particularly for e-commerce businesses requiring rapid services such as same-day delivery. We present a new vehicle routing problem model that integrates truck-drone fleets to simulate highway operations. In this model, trucks travel on highways to transport packages between cities, using rest areas as cross-docking points or hubs for load transfers between trucks and drones. Drones operate off-highway to pick up and deliver packages to and from these hubs, and must arrive at the hubs within specific time windows determined by the trucks' arrival schedules. This research contributes by combining multiple cross-docking operations with two different transportation modes. We refer to the proposed model as the Vehicle Routing Problem with Multiple Cross-docking (VRPMCD). The model effectively schedules drone pickups and deliveries at each hub, taking truck schedules into account. To implement this model, we develop heuristic algorithms and conduct numerical experiments to validate the proposed model and algorithms.", "keywords": [ "Drone", "Truck", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Aeronautics", "Automotive engineering", "Transport engineering", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/temscon-aspac62480.2024.11024639" }, { "id": "https://openalex.org/W7140996786", "doi": "10.6084/m9.figshare.31868992.v1", "title": "A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance", "year": 2026, "publication_date": "2026-03-27", "type": "dataset", "language": null, "authors": [ "Yue Wu" ], "first_author": "Yue Wu", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data for the manuscript titled \"A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance\".", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Work (physics)", "Optimization problem", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31868992.v1" }, { "id": "https://openalex.org/W4416749434", "doi": "10.1109/access.2025.3638144", "title": "Proposal for a Model to Optimize the Vehicle Routing Problem With Drones That Perform Last-Mile Parcel Deliveries Focusing on Customer Satisfaction", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "V. Rosi", "Paulo Sérgio Cugnasca" ], "first_author": "V. Rosi", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The integration of drones for last-mile parcel deliveries tends to be a key factor in improving logistics services. Therefore, this work aims to define a Mixed Integer Linear Programming (MILP) model in order to study the Vehicle Routing Problem with Drones (VRP-D). The mathematical formulation is based on the Simultaneous Traveling Repairman Problem with Drone (STRPD), in which a vehicle with one or more drones departs from a depot to deliver parcels, aiming to reduce customer waiting times. Whereas in the STRPD the drone can only serve one customer per trip, in the proposed study the drone can serve multiple customers per trip. However, its endurance is limited by battery energy consumption, which depends on the weight carried by the drone. This problem is called the Simultaneous Traveling Repairman Problem with Drone with Multiple Visits (STRPDMV), and this work validated the mathematical model through the analysis of nineteen instances. Due to the inability to obtain results for some instances with twenty or more customers, a heuristic was developed to find feasible solutions within reduced processing times. Then, the results provided by the new model and the heuristic were compared and the analyses were presented. Additionally, another analysis performed was the impact of varying drone speed compared to vehicle speed, and vice-versa.", "keywords": [ "Drone", "Heuristic", "Routing (electronic design automation)", "Customer satisfaction", "Key (lock)", "Linear programming", "Integer programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2025.3638144" }, { "id": "https://openalex.org/W7141098492", "doi": "10.6084/m9.figshare.31868992", "title": "A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance", "year": 2026, "publication_date": "2026-03-27", "type": "dataset", "language": null, "authors": [ "Yue Wu" ], "first_author": "Yue Wu", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data for the manuscript titled \"A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance\".", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Work (physics)", "Optimization problem", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31868992" }, { "id": "https://openalex.org/W7141515631", "doi": "10.6084/m9.figshare.31867687.v1", "title": "A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance", "year": 2026, "publication_date": "2026-03-27", "type": "dataset", "language": null, "authors": [ "Anonymous Authors (for blind review purpose)" ], "first_author": "Anonymous Authors (for blind review purpose)", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data for the manuscript titled \"A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance\".", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Work (physics)", "Optimization problem", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31867687.v1" }, { "id": "https://openalex.org/W7140307297", "doi": "10.6084/m9.figshare.31852156", "title": "A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance", "year": 2026, "publication_date": "2026-03-25", "type": "dataset", "language": null, "authors": [ "Anonymous Authors (for blind review purpose)" ], "first_author": "Anonymous Authors (for blind review purpose)", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data for the manuscript titled \"A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance\".", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Work (physics)", "Optimization problem", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31852156" }, { "id": "https://openalex.org/W7141155767", "doi": "10.6084/m9.figshare.31852156.v2", "title": "A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance", "year": 2026, "publication_date": "2026-03-25", "type": "dataset", "language": null, "authors": [ "Anonymous Authors (for blind review purpose)" ], "first_author": "Anonymous Authors (for blind review purpose)", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data for the manuscript titled \"A novel multi-visit truck-drone collaborative optimization model for the green vehicle routing problem with dynamic flight endurance\".", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Work (physics)", "Optimization problem", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31852156.v2" }, { "id": "https://openalex.org/W7161597225", "doi": "10.3390/a19050403", "title": "A Guided Collaborative Optimization Framework for the Stability-Constrained UAV Routing and Three-Dimensional Loading Problem", "year": 2026, "publication_date": "2026-05-18", "type": "article", "language": "en", "authors": [ "Changhui Han", "Mengmeng Zhang", "Jie Zhang", "Xiaolong Ma" ], "first_author": "Changhui Han", "venue": "Algorithms", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The joint optimization of routing and three-dimensional loading is a highly complex NP-hard combinatorial problem, particularly when stringent center-of-gravity (CoG) stability constraints are required for unmanned aerial vehicle (UAV) operations. Existing algorithms typically adopt a route-first, load-second evaluation strategy for these interconnected components, often yielding distance-optimal yet physically infeasible solutions. To address this bottleneck, this paper formulates the Three-Dimensional Loading-Constrained UAV Routing Problem (3DLC-UAVRP), integrating unloading sequence consistency, spatial packing feasibility, and CoG deviation control into the routing decision process. A guided collaborative optimization framework, GLS-WSCPA, is proposed, coupling an Improved White Shark Optimization (IWSO) algorithm for global route exploration with a Human-like Divide-and-Conquer Packing Strategy (HLDCPS) for spatial arrangement. Unlike conventional decoupled approaches that treat loading feasibility as a post hoc filter, a Center-of-Gravity-Guided Path Adjustment (CGPA) and Local Loading Repair (LLR) mechanism is introduced to establish a dynamic feedback loop between routing search and loading evaluation, so that CoG violations are actively translated into guided routing perturbations rather than simply triggering solution rejection. Experimental results demonstrate that GLS-WSCPA generally achieves better solutions than the compared algorithms across the tested problem scales, with the performance gap tending to widen as the instance size increases within the tested range. Ablation studies verify the complementary roles of CGPA and LLR, and sensitivity analysis confirms that moderately relaxing payload and CoG constraints reduces routing distance within safety boundaries. Case analysis shows that the proposed method reduces fleet size by 20% and total delivery distance by 6.85% compared to traditional decoupled strategies.", "keywords": [ "Payload (computing)", "Routing (electronic design automation)", "Vehicle routing problem", "Stability (learning theory)", "Sensitivity (control systems)", "Optimization problem", "Path (computing)", "Coupling (piping)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/a19050403" }, { "id": "https://openalex.org/W7131387031", "doi": "10.48550/arxiv.2602.20310", "title": "A unified solution framework for truck-and-drone routing problems", "year": 2026, "publication_date": "2026-02-23", "type": "preprint", "language": null, "authors": [ "Ke Xu", "John Gunnar Carlsson" ], "first_author": "Ke Xu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Coordinated truck-and-drone routing integrates the high capacity and range of ground vehicles with the flexible routing and speed of drones, enabling simultaneous service. Increasingly applied in last-mile delivery, this synchronization helps reduce completion time and operational costs. To improve its efficiency, various coordination modes between trucks and drones have been proposed. Each mode accommodates diverse operational constraints tailored to particular delivery requirements. In existing work, a slight change in the structural framework or operational characteristics could generate a totally different problem variant, which often requires the design of specialized algorithms. Consequently, under the requirement of maintaining structural validation and adapting to multiple operational features, this paper presents a unified three-phase solution framework based on the Lin-Kernighan-Helsgaun algorithm to solve a wide family of truck-and-drone routing problems. To validate its flexibility and effectiveness, we carry out numerical experiments on three problem variants: the Flying Sidekick Traveling Salesman Problem (FSTSP), the Traveling Salesman Problem with Multiple Drones (TSP-mD), and the Vehicle Routing Problem with Drones (VRP-D), benchmarking each against an effective algorithm. Computational results show that the framework can closely match optimal solutions on small-size instances and even improve the best-known solutions for several medium-size instances. Moreover, additional extensions are discussed to further highlight its versatility.", "keywords": [ "Routing (electronic design automation)", "Drone", "Flexibility (engineering)", "Travelling salesman problem", "Range (aeronautics)", "Synchronization (alternating current)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2602.20310" }, { "id": "https://openalex.org/W4410545907", "doi": "10.1007/s10846-025-02266-9", "title": "A Team Orienteering Problem with Zoning for Safe and Flexible Operations of UAVs in a Dynamic Environment", "year": 2025, "publication_date": "2025-05-21", "type": "article", "language": "en", "authors": [ "Casper Bak Pedersen", "Inkyung Sung", "Peter Nielsen" ], "first_author": "Casper Bak Pedersen", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": "Abstract A Team Orienteering Problem (TOP) that seeks the most profitable routes for multiple players given limited travel time or length is one of the most relevant routing problems for service delivery by Unmanned Aerial Vehicles (UAVs) with limited battery or energy capacity. In this study, we propose a variant of the TOP, where an operational area for multiple UAVs is divided into non-overlapping zones and routes for UAVs are derived within the zones. Importantly, by allocating designated zones to each UAV, any update on a UAV’s route within its zone does not interfere with other UAVs’ routes. This aspect is critical for a service or a mission to be executed under a highly dynamic environment especially with limited communication between UAVs. The operational and practical benefits of applying the proposed solution approach are demonstrated by comparing it to a classic TOP solution in a simulation environment where a profit of visiting a location is uncertain.", "keywords": [ "Orienteering", "Zoning", "Computer science", "Engineering", "Geography", "Civil engineering", "Cartography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10846-025-02266-9.pdf" }, { "id": "https://openalex.org/W2118322872", "doi": null, "title": "UAV Scheduling via the Vehicle Routing Problem with Time Windows (Preprint)", "year": 2007, "publication_date": "2007-01-01", "type": "article", "language": "en", "authors": [ "Amanda Weinstein", "Corey J. Schumacher" ], "first_author": "Amanda Weinstein", "venue": "Defense Technical Information Center (DTIC)", "publisher": null, "abstract": "In an urban environment, multiple small unmanned aerial vehicles (UAVs) may be utilized to locate, surveil, or attack various targets. Whatever the task, the air vehicles must cooperate by efficiently communicating with each other and optimally assigning each UAV to the appropriate task at the appropriate target and at the appropriate time. In this paper, a vehicle assignment algorithm is developed using a mixed integer linear program (MILP) to find the global optimal scheduling solution. The MILP can accommodate both binary and continuous decision variables as well as a variety of constraints and objective functions; however, the NP-hard nature of the problem implies a dramatic increase in the computing complexity as the number of variables and constraints increase. This formulation accounts for an assortment of scenarios focused on the military necessity for precise intelligence, surveillance, and reconnaissance (ISR) by modifying the vehicle routing problem with time windows (VRPTW) formulation. The VRPTW is a type of capacitated vehicle routing problem which optimally assigns a designated number of delivery vehicles originating at a single depot to a known number of customers. Specifically, the VRPTW and network flow techniques account for various scenarios as well as operator imposed timing constraints such as precedence constraints. For example, certain targets may take precedence or require simultaneous arrival times where the targets are first hierarchically clustered according to their proximity to each other. Thus, this paper also focuses on methods of clustering targets and implements this information into the MILP to optimally assign UAVs to targets. Clustering targets that are near enough to alert each other of an attack will allow UAVs to recognize this potential and hence surveil these targets simultaneously to avoid early detection. This techn", "keywords": [ "Vehicle routing problem", "Scheduling (production processes)", "Computer science", "Mathematical optimization", "Cluster analysis", "Routing (electronic design automation)", "Task (project management)", "Job shop scheduling", "Integer programming", "Artificial intelligence", "Engineering", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA466627" }, { "id": "https://openalex.org/W4298070881", "doi": null, "title": "Vehicle Routing Problem Instances: Application to Multi-UAV Mission Planning", "year": 2010, "publication_date": "2010-01-01", "type": "article", "language": "en", "authors": [ "Mariam Adbelhafiz", "Mostafa, Ahmed", "Anouck Girard" ], "first_author": "Mariam Adbelhafiz", "venue": "Deep Blue (University of Michigan)", "publisher": "University of Michigan", "abstract": "Peer Reviewed", "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Operations research", "Computer network", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/2027.42/83659" }, { "id": "https://openalex.org/W7168249413", "doi": "10.1111/exsy.70368", "title": "An Intelligent DRL ‐Based Framework for Reliable UAV Swarm Communications in Dynamic Environments", "year": 2026, "publication_date": "2026-07-14", "type": "article", "language": "en", "authors": [ "Vi Hoai Nam", "Abdellah Chehri", "Weiwei Jiang", "Chu Thi Minh Hue", "Tan N. Nguyen", "Quy Vu Khanh" ], "first_author": "Vi Hoai Nam", "venue": "Expert Systems", "publisher": "Wiley", "abstract": "ABSTRACT Unmanned Aerial Vehicle (UAV) networks are increasingly deployed in dynamic environments where reliable and low‐latency communication is critical. However, high mobility, intermittent connectivity, spectrum limitations, and energy constraints make conventional static communication protocols inadequate for maintaining stable, dependable links. To address these challenges, this paper proposes TRC‐MAPPO, a topology‐aware reliability‐constrained multi‐agent deep reinforcement learning framework for adaptive UAV swarm communication. The routing problem is formulated as a constrained decision‐making task that jointly considers packet delivery reliability, end‐to‐end delay, link stability, bandwidth usage, and energy consumption. Unlike single‐agent DRL baselines, TRC‐MAPPO represents the swarm as a dynamic communication graph and combines local relay selection with centralized training, enabling cooperative routing decisions under time‐varying network conditions. Simulations are conducted in a controlled, dynamic UAV environment, with the same mobility and traffic settings used for all methods. The proposed framework is compared with DQN and PPO over different swarm densities. Results show that TRC‐MAPPO achieves a higher packet delivery ratio, lower end‐to‐end delay, and more efficient energy behaviour, particularly in dense deployments. These findings indicate that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks.", "keywords": [ "Reinforcement learning", "Swarm behaviour", "Scalability", "Adaptive routing", "Relay", "Network packet", "Routing (electronic design automation)", "Bandwidth (computing)", "Routing protocol" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1111/exsy.70368" }, { "id": "https://openalex.org/W7167481222", "doi": "10.57760/sciencedb.41369", "title": "Truck-drone collaborative routing problem with cross-route drone recovery and soft time windows", "year": 2026, "publication_date": "2026-06-26", "type": "dataset", "language": "zh", "authors": [ "xuyongxian" ], "first_author": "xuyongxian", "venue": "ScienceDB", "publisher": null, "abstract": "Rules for Generating Test Case Requirement Points", "keywords": [ "Drone", "Routing (electronic design automation)", "Key (lock)", "Test (biology)", "Component (thermodynamics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.57760/sciencedb.41369" }, { "id": "https://openalex.org/W7109196071", "doi": "10.17632/3yyt8wtkhx.4", "title": "Computational Study Instances of the Electric Vehicle Routing Problem with Multi-visit Drones Considering Time Windows and Battery Swap Stations", "year": 2025, "publication_date": "2025-12-05", "type": "dataset", "language": null, "authors": [ "Meng, Shanshan" ], "first_author": "Meng, Shanshan", "venue": "Mendeley Data", "publisher": null, "abstract": "The set includes all instances used in the electric vehicle routing problem with multi-visit drones considering time windows and battery swap stations: 1. Small instances 2. Large instances 3. Instances for sensitivity analysis", "keywords": [ "Swap (finance)", "Drone", "Electric vehicle", "Vehicle routing problem", "Set (abstract data type)", "Sensitivity (control systems)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/3yyt8wtkhx.4" }, { "id": "https://openalex.org/W7162405772", "doi": "10.57760/sciencedb.38086", "title": "Truck-drone collaborative routing problem with cross-route drone recovery and soft time windows", "year": 2026, "publication_date": "2026-05-25", "type": "dataset", "language": "zh", "authors": [ "xuyongxian" ], "first_author": "xuyongxian", "venue": "ScienceDB", "publisher": null, "abstract": "Data related to the problem of cross recovery of drones and the coordinated path between vehicles and drones in soft time windows", "keywords": [ "Drone", "Routing (electronic design automation)", "Path (computing)", "Window (computing)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.57760/sciencedb.38086" }, { "id": "https://openalex.org/W7204051979", "doi": "10.17632/4cmjn7dzby.2", "title": "The Multivisit Parallel Drone Scheduling Electric Vehicle Routing Considering Soft Time Windows and Drone Heterogeneity", "year": 2026, "publication_date": "2026-08-23", "type": "dataset", "language": null, "authors": [ "Shanshan Meng" ], "first_author": "Shanshan Meng", "venue": "Mendeley Data", "publisher": null, "abstract": "The set includes all instances used in the multi-visit parallel drone scheduling electric vehicle routing considering soft time windows and drone heterogeneity: 1. Small instances 2. Large instances 3. Instances for sensitivity analysis", "keywords": [ "Drone", "Scheduling (production processes)", "Vehicle routing problem", "Electric vehicle", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/4cmjn7dzby.2" }, { "id": "https://openalex.org/W4416009219", "doi": "10.1117/12.3089510", "title": "A greedy heuristic for truck-and-drone arc routing problem", "year": 2025, "publication_date": "2025-11-07", "type": "conference-paper", "language": null, "authors": [ "Yuxin Liu", "Ziwei Zeng" ], "first_author": "Yuxin Liu", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Greedy algorithm", "Heuristic", "Routing (electronic design automation)", "Arc (geometry)", "Arc routing", "Greedy randomized adaptive search procedure" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3089510" }, { "id": "https://openalex.org/W4312915297", "doi": "10.2139/ssrn.4240825", "title": "Sustainable Two-Echelon Truck and Drone Routing Problem", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Ebrahim Teimoury", "Reza Rashid" ], "first_author": "Ebrahim Teimoury", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Vehicle routing problem", "Aeronautics", "Business", "Computer science", "Operations research", "Engineering", "Computer network", "Aerospace engineering", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4240825" }, { "id": "https://openalex.org/W7161139235", "doi": "10.1109/temscon-aspac65329.2025.11512185", "title": "Solving a Bi-Objective Two-Echelon Vehicle Routing Problem with Drones Using a Hybrid K-Means and NSGA-II Algorithm", "year": 2025, "publication_date": "2025-09-14", "type": "conference-paper", "language": null, "authors": [ "Santoso Santoso", "Nurhadi Siswanto", "Ir. Budi Santosa", "Budisantoso Wirjodirdjo" ], "first_author": "Santoso Santoso", "venue": null, "publisher": null, "abstract": "Recent technological advances and COVID-19 pandemic have shifted consumer behavior from direct shopping to online shopping. This shift has led to a significant increase in package delivery volumes, resulting in negative impacts such as higher CO2emissions in urban areas. The advancement of drone technology has introduced a promising alternative for last-mile delivery through a joint method involving both drones and trucks. This study addresses the two-echelon vehicle routing problem with drones (2E VRP-D), adopting a bi-objective approach: minimizing both the total arrival time of trucks at the depot and the CO2emissions produced. A metaheuristic approach is employed to handle large problem instances. Initial solutions are generated by clustering customers based on their locations using the k-means algorithm. Subsequently, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is applied to solve the bi-objective optimization problem, as no single solution can simultaneously optimize all objectives in such cases. The innovative use of truck-drone collaboration has the potential to significantly enhance the efficiency of last-mile delivery operations.", "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Hybrid algorithm (constraint satisfaction)", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/temscon-aspac65329.2025.11512185" }, { "id": "https://openalex.org/W7133412880", "doi": null, "title": "Solution to the multi-echelon vehicle routing problem considering time windows and drones in the last mile delivery for the management of humanitarian operations", "year": 2023, "publication_date": "2023-02-28", "type": "dissertation", "language": "es", "authors": [ "Jennifer María Cataño Albarracín" ], "first_author": "Jennifer María Cataño Albarracín", "venue": "Universidad Industrial de Santander", "publisher": null, "abstract": "Este documento presenta un modelo matemático basado en el problema de ruteo de vehículos con drones, considerando el estudio de dos escalones y ventanas de tiempo, conocido en la literatura como el 2E-VRPD+TW (Two Echelon Vehicle Routing Problem with Drones + Time Windows), como herramienta para facilitar la toma de decisiones durante las etapas de preparación y respuesta en la gestión de desastres, específicamente en operaciones de entrega de suministros en áreas afectadas por desastres naturales; teniendo como objetivo la minimización del costo total de operación. Para representar el problema abordado, se hace uso de la programación lineal, utilizando como técnica de solución al algoritmo genético (Genetic Algorithm, GA) en su versión general y paralela. El algoritmo genético implementa la heurística del vecino más cercano en el ruteo de los drones y un método aleatorio en el de los vehículos, al ser validado demuestra adaptarse bien al problema, ofreciendo resultados lógicos y coherentes. El análisis estadístico de los resultados obtenidos muestra que el factor con mayor influencia sobre el costo total y el tiempo computacional es el número de generaciones y que la implementación del paralelismo mejora los resultados obtenidos. Finalmente se hacen recomendaciones para futuras investigaciones.", "keywords": [ "Drone", "Routing (electronic design automation)", "Internet of Things", "Work (physics)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://noesis.uis.edu.co/bitstreams/a26635f5-743d-4e78-b311-329496f9854a/download" }, { "id": "https://openalex.org/W2384113454", "doi": null, "title": "Multiple UAVs routing in reconnaissance mission based on hybrid multi-objective evolutionary algorithm", "year": 2010, "publication_date": "2010-01-01", "type": "article", "language": "en", "authors": [ "Xiaofeng Wei" ], "first_author": "Xiaofeng Wei", "venue": "Systems engineering and electronics", "publisher": null, "abstract": "The observation time on the target is usually uncertain due to the complexity and uncertainty of reconnaissance missions.The multiple unmanned aerial vehicles(UAVs) reconnaissance problem with a stochastic observation time(MURSOT) is modeled as a multi-objective optimal routing problem including minimizing mission duration,total time and fleet size.For solving this problem,a multi-objective local search is incorporated to a steady-state multi-objective evolutionary algorithm(MOEA) with e-dominance conception(epsMOEA).Besides,several heuristic genetic operations using the insert-to-nearest method(INM) are proposed.Experimental results show that the proposed method is effective on MURSOT and its superiority is more remarkable with the growth of the size of missions.", "keywords": [ "Evolutionary algorithm", "Vehicle routing problem", "Computer science", "Genetic algorithm", "Heuristic", "Mathematical optimization", "Routing (electronic design automation)", "Algorithm", "Artificial intelligence", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-XTYD201002027.htm" }, { "id": "https://openalex.org/W7165947054", "doi": "10.17632/4cmjn7dzby", "title": "The Multivisit Parallel Drone Scheduling Electric Vehicle Routing Considering Soft Time Windows and Drone Heterogeneity", "year": 2026, "publication_date": "2026-08-24", "type": "dataset", "language": null, "authors": [ "Shanshan Meng" ], "first_author": "Shanshan Meng", "venue": "Mendeley Data", "publisher": null, "abstract": "The set includes all instances used in the multi-visit parallel drone scheduling electric vehicle routing considering soft time windows and drone heterogeneity: 1. Small instances 2. Large instances 3. Instances for sensitivity analysis", "keywords": [ "Drone", "Scheduling (production processes)", "Vehicle routing problem", "Electric vehicle", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/4cmjn7dzby" }, { "id": "https://openalex.org/W4410035551", "doi": "10.1007/978-3-031-83611-4_14", "title": "Modified Simulated Annealing Algorithm for Drone and Truck Routing Problem", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "İlayda Aral", "Ömer Özkan", "Damla Kızılay" ], "first_author": "İlayda Aral", "venue": "Lecture notes in mechanical engineering", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Simulated annealing", "Drone", "Truck", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Algorithm", "Environmental science", "Mathematical optimization", "Engineering", "Automotive engineering", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-83611-4_14" }, { "id": "https://openalex.org/W3162119353", "doi": "10.1016/j.ifacol.2022.10.234", "title": "Human Performance in Solving Multi-UAV Over-Constrained Dynamic Vehicle Routing Problems", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Ankit Gupta", "Clark Borst", "Max Mulder" ], "first_author": "Ankit Gupta", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "For many logistics applications, such as drone delivery missions, finding an optimized network of routes yields a Vehicle Routing Problem (VRP). Such optimizations are mostly conducted offline prior to actual operations for reasons of computational complexity. In case disturbances arise during operations, for example a sudden loss of a vehicle, the VRP needs to be re-optimized in real-time and this raises concerns regarding obtaining a solution within time. In a previous study, it was demonstrated that humans, when supported through a human-machine interface, can quickly deal with these routing problems through satisficing, providing workable solutions. This paper extends our previous research by exposing human operators to an over-constrained VRP with different mission priorities and vehicle capabilities. Experiment results (n = 16) indicate that the mission type had the largest impact on how participants used the interface and what constraints were relaxed. In particular, during a search-and-rescue context the mission emphasis was put on delivering (medical) payload (close) to as many customers as possible, even if this would involve sacrificing vehicles and relaxing the depot constraint. Ethical aspects of the VRP are taken into account which algorithms do not by themselves, underlining the importance of involving humans in automation. Human operators complement algorithms with their context awareness, yielding more safe, resilient and responsible systems.", "keywords": [ "Vehicle routing problem", "Context (archaeology)", "Payload (computing)", "Computer science", "Routing (electronic design automation)", "Complement (music)", "Heuristics", "Automation", "Satisficing", "Operations research", "Constraint (computer-aided design)", "Interface (matter)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2022.10.234" }, { "id": "https://openalex.org/W7165866784", "doi": "10.17632/4cmjn7dzby.1", "title": "The Multi-visit Parallel Drone Scheduling Electric Vehicle Routing Considering Soft Time Windows and Heterogeneous Drones", "year": 2026, "publication_date": "2026-06-25", "type": "dataset", "language": null, "authors": [ "Shanshan Meng" ], "first_author": "Shanshan Meng", "venue": "Mendeley Data", "publisher": null, "abstract": "The set includes all instances used in the multi-visit parallel drone scheduling electric vehicle routing considering soft time windows and heterogeneous drones: 1. Small instances 2. Large instances 3. Instances for sensitivity analysis", "keywords": [ "Drone", "Scheduling (production processes)", "Vehicle routing problem", "Electric vehicle", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/4cmjn7dzby.1" }, { "id": "https://openalex.org/W4236924340", "doi": "10.5194/nhess-2017-192-rc2", "title": "Brief communication: Vehicle routing problem and UAV application in the post-earthquake scenario", "year": 2017, "publication_date": "2017-09-08", "type": "peer-review", "language": "en", "authors": [], "first_author": null, "venue": null, "publisher": null, "abstract": "The discussion paper addresses a relevant research topic which is of high importance to appropriately survey post-disaster environments.UAVs have proven to be a suitable tool to support search and rescue operations after natural hazards.However, the ad-hoc search of an efficient flight plan remains a challenge.The proposed method adequately addresses this task and reveals promising results.The study aims to find an optimal flight path that includes all relevant points of interest.For this, a method that follows the approach of \"simulated annealing\" is introduced.The method searches for the global optimization of a function and the authors adapted the algorithm to solve a vehicle routing problem in a real world scenario.The origins of the method as well as variances to the equation are well-described.The results show the", "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5194/nhess-2017-192-rc2" }, { "id": "https://openalex.org/W7204784743", "doi": "10.3390/drones10090658", "title": "Joint Fleet Sizing and Routing for Multi-Truck–Multi-Drone Collaborative Delivery", "year": 2026, "publication_date": "2026-08-28", "type": "article", "language": "en", "authors": [ "Fengjie Xie", "Guojin Zhang", "Yuhua Jia" ], "first_author": "Fengjie Xie", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Truck–multi-drone collaborative delivery can reduce last-mile costs, but fleet sizing and routing are often optimized separately, making it difficult to match resources with demand under a delivery-period constraint. This study addresses the scenario of collaborative delivery involving multiple trucks and multiple drones by constructing a two-stage optimization framework that integrates fleet sizing and route planning. In the first stage, queueing models and continuous approximation are employed to determine the initial configuration of trucks and drones based on demand intensity and delivery cycle constraints. The second stage introduces continuous drone delivery and cross-vehicle retrieval to enhance the flexibility of truck–drone collaboration; while optimizing collaborative routes, the framework adjusts the allocation of trucks and drones—adding or reducing resources based on route feasibility and equipment utilization—thereby achieving the joint optimization of transport capacity and collaborative routes with the objective of minimizing total system costs. A node–resource–flow-separated three-chain encoding and an adaptive large neighborhood search–simulated annealing algorithm are designed to solve the model. Multi-scale numerical experiments show that, compared with four simplified fleet-sizing strategies, the proposed framework achieves average cost savings of 15.4–15.5% for medium- and large-scale instances. The results reveal an economic saturation point of the delivery period that shifts with node scale and a non-monotonic relationship between fleet size and coordination efficiency. The framework supports demand-driven fleet configuration and provides operational guidance for cost-effective truck–drone last-mile delivery.", "keywords": [ "Sizing", "Truck", "Flexibility (engineering)", "Simulated annealing", "Queueing theory", "Routing (electronic design automation)", "Drone", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10090658" }, { "id": "https://openalex.org/W7111266854", "doi": "10.6084/m9.figshare.29928152.v1", "title": "Solving electric vehicle routing problem with heterogeneous drone and stochastic no-fly zones using enhanced adaptive large neighborhood search algorithm", "year": 2025, "publication_date": "2025-08-18", "type": "dataset", "language": null, "authors": [ "Wanting Chen (18853951)" ], "first_author": "Wanting Chen (18853951)", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"Solving electric vehicle routing problem with heterogeneous drone and stochastic no-fly zones using enhanced adaptive large neighborhood search algorithm\".", "keywords": [ "Drone", "Vehicle routing problem", "Electric vehicle", "Routing (electronic design automation)", "Local search (optimization)", "Search algorithm", "Stochastic programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/dataset/Solving_electric_vehicle_routing_problem_with_heterogeneous_drone_and_stochastic_no-fly_zones_using_enhanced_adaptive_large_neighborhood_search_algorithm/29928152" }, { "id": "https://openalex.org/W7128641325", "doi": "10.1109/icraic67376.2025.11375992", "title": "The River Water Sampling Routing Problem with Cooperated Truck and Drone", "year": 2025, "publication_date": "2025-10-31", "type": "conference-paper", "language": null, "authors": [ "Shuo Dang", "Yao Liu", "Zhihao Luo", "Zheng Liu", "Jianmai Shi" ], "first_author": "Shuo Dang", "venue": null, "publisher": null, "abstract": "Monitoring and protecting urban river water quality stands as a pivotal concern in environmental protection. Traditional approaches utilizing vessels or manned vehicles frequently entail considerable labor and resource allocations. The integration of unmanned aerial vehicles (UAVs, or drones) and ground vehicles (hereinafter referred to as trucks) presents an innovative solution for efficient water sampling. This study introduces a river water sampling method that combines a drone and a truck. We develop a comprehensive task planning framework to address multi-vehicle coordination challenges, and a two-stage route algorithm is introduced alongside the Adaptive Large Neighborhood Search algorithm enhanced by Simulated Annealing and Tabu Search (ALNS_SA_TS). Experimental results demonstrate that the proposed algorithm achieves superior efficiency and solution quality compared to classic heuristics, with significant improvements in cost optimization. The practical significance extends to environmental monitoring agencies through reduced operational costs, enhanced sampling coverage, and improved resource utilization. Compared with traditional sampling methods, the truck-drone approach shows marked advantages in technical feasibility and operational effectiveness.", "keywords": [ "Drone", "Sampling (signal processing)", "Truck", "Routing (electronic design automation)", "Resource (disambiguation)", "Simulated annealing", "Tabu search", "Task (project management)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icraic67376.2025.11375992" }, { "id": "https://openalex.org/W4385267485", "doi": "10.1007/978-3-031-36121-0_28", "title": "Metaheuristic Approach for the Location and Routing Problem with Truck and Drones: A Sustainable Perspective", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Carlos S. Rodríguez-Salcedo", "María F. Giraldo-Quintero", "Laura S. Ortega-García", "María Alejandra Albarracin-Vanegas", "Carlos Alberto Vega-Mejía", "Elyn Lizeth Solano Charris", "Carlos L. Quintero-Araújo" ], "first_author": "Carlos S. Rodríguez-Salcedo", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Metaheuristic", "Drone", "Vehicle routing problem", "Cluster analysis", "Truck", "Computer science", "Routing (electronic design automation)", "Heuristic", "Mathematical optimization", "Last mile (transportation)", "Facility location problem", "Operations research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-36121-0_28" }, { "id": "https://openalex.org/W4408062338", "doi": "10.5220/0013217600003893", "title": "Synchronized Drone and Truck Routing Problem: A Multi-Stakeholder Perspective", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti", "Giuseppe Muratore", "Vincenzo Gentile" ], "first_author": "Maria Elena Bruni", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Perspective (graphical)", "Routing (electronic design automation)", "Computer science", "Truck", "Operations research", "Computer network", "Engineering", "Artificial intelligence", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5220/0013217600003893" }, { "id": "https://openalex.org/W4297626324", "doi": "10.1061/9780784484265.290", "title": "An Exact Solution Method for Truck-Assisted Drone Routing Problem", "year": 2022, "publication_date": "2022-09-08", "type": "conference-paper", "language": "en", "authors": [ "Shuang Wang", "Sebastian Wandelt", "Xiaoqian Sun" ], "first_author": "Shuang Wang", "venue": "CICTP 2022", "publisher": null, "abstract": "Drone-truck combined delivery has huge potential for becoming an innovative last-mile solution for logistics. This paper presents a variation of drone-truck operation with the goal to minimize the completion time, where the truck provides a mobile recharging station for drones and drones serve all the customers. This problem can be decomposed into two elementary phases: (1) truck with a drone arriving at rendezvous nodes and (2) drone flying to the customer points. Our exact method is based on branch-and-bound, in which a tree node represents a customer subsequence and the lower bound is the objective value with optimal launch and retrieve locations for the fixed customer sequence. The best departing and collection sites for each tree node are obtained through custom cuts, which consider a customer-based pruning strategy and the interaction-based pruning between customers. Our experimental results show that the novel exact algorithm outperforms CPLEX in terms of computational time, while obtaining results on par regarding quality.", "keywords": [ "Drone", "Pruning", "Computer science", "Truck", "Subsequence", "Routing (electronic design automation)", "Tree (set theory)", "Node (physics)", "Mathematical optimization", "Operations research", "Real-time computing", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1061/9780784484265.290" }, { "id": "https://openalex.org/W7134198103", "doi": "10.1016/j.cstp.2026.101776", "title": "Optimizing rural logistics: A bi-layer heuristic for the truck-drone location-routing problem with multiple drone stations", "year": 2026, "publication_date": "2026-03-07", "type": "article", "language": "en", "authors": [ "Lirong Wu", "Hang Zhang", "Hang Zhang", "Zehao Wang", "Yuankai Zhang", "Suile Wang" ], "first_author": "Lirong Wu", "venue": "Case Studies on Transport Policy", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Heuristic", "Solver", "Truck", "Flexibility (engineering)", "Heuristics", "Ant colony optimization algorithms", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cstp.2026.101776" }, { "id": "https://openalex.org/W4413095424", "doi": "10.1016/j.adhoc.2025.103974", "title": "A multi-depot provisioned UAV swarm trajectory optimization scheme for collaborative data acquisition in a large-scale IoT environment", "year": 2025, "publication_date": "2025-07-17", "type": "article", "language": "en", "authors": [ "Saugata Roy", "Nabajyoti Mazumdar", "Rajendra Pamula" ], "first_author": "Saugata Roy", "venue": "Ad Hoc Networks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Scheme (mathematics)", "Trajectory", "Scale (ratio)", "Computer science", "Depot", "Swarm behaviour", "Provisioning", "Real-time computing", "Distributed computing", "Aerospace engineering", "Computer network", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.adhoc.2025.103974" }, { "id": "https://openalex.org/W4283776704", "doi": null, "title": "The probabilistic drone routing problem applied to large-scale disasters", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Amadeu Almeida Coco", "Christophe Didier Duhamel", "Andréa Cynthia Santos" ], "first_author": "Amadeu Almeida Coco", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "International audience", "keywords": [ "Drone", "Probabilistic logic", "Computer science", "Routing (electronic design automation)", "Scale (ratio)", "Computer security", "Computer network", "Artificial intelligence", "Geography", "Cartography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-03596238" }, { "id": "https://openalex.org/W7170165600", "doi": "10.1016/j.tre.2026.105114", "title": "Heterogeneous drone-truck routing problem with En-route operations", "year": 2026, "publication_date": "2026-07-24", "type": "article", "language": "en", "authors": [ "Shuhong Liu", "Shaoxuan Zhu", "Canrong Zhang" ], "first_author": "Shuhong Liu", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Vehicle routing problem", "Minification", "Flow network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105114" }, { "id": "https://openalex.org/W4410975944", "doi": "10.1093/imaman/dpaf022", "title": "New saving-based formulae for the truck–drone routing problem", "year": 2025, "publication_date": "2025-06-03", "type": "article", "language": "en", "authors": [ "Batool Madani", "Malick Ndiaye", "Saı̈d Salhi" ], "first_author": "Batool Madani", "venue": "IMA Journal of Management Mathematics", "publisher": "Oxford University Press", "abstract": "Abstract Accepted by: M. Zied Babai This study investigates the hybrid truck–drone routing problem with an emphasis on last-mile delivery. To address this challenge, novel savings rules are developed by extending the classical Clarke and Wright savings formula, and these are incorporated into an adapted savings-based heuristic. In contrast to the traditional Clarke and Wright algorithm, the drone launch and retrieval points are not predetermined but instead depend on the truck’s route, resulting in greater complexity and flexibility. Consequently, nine distinct configurations are identified, each associated with a specific savings formulation. These contribute to the development of an enhanced Clarke and Wright-based heuristic, further augmented with a computational acceleration mechanism. For comparative evaluation, a sweep-based algorithm is also implemented to handle larger problem instances. The proposed savings-based heuristic (MSH) demonstrates superior performance over the sweep-based method, effectively capturing the operational structure of a moving depot. While the new savings equations remain relatively simple, they offer analytically rigorous and computationally efficient solutions that can serve as a strong foundation for future hybrid or metaheuristic approaches.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Operations research", "Aeronautics", "Automotive engineering", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1093/imaman/dpaf022" }, { "id": "https://openalex.org/W4386487775", "doi": "10.1007/978-3-031-43612-3_20", "title": "A Variable Neighborhood Search Algorithm for the Truck-Drone Routing Problem", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Batool Madani", "Malick Ndiaye" ], "first_author": "Batool Madani", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Truck", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Variable (mathematics)", "Last mile (transportation)", "Operations research", "Computer network", "Mile", "Engineering", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-43612-3_20" }, { "id": "https://openalex.org/W3129019409", "doi": "10.1049/cmu2.12106", "title": "An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling", "year": 2021, "publication_date": "2021-02-01", "type": "article", "language": "en", "authors": [ "Xiaoxiang Yuan", "Jie Zhu", "Yixuan Li", "Haiping Huang", "Min Hua Wu" ], "first_author": "Xiaoxiang Yuan", "venue": "IET Communications", "publisher": "Institution of Engineering and Technology", "abstract": "Abstract This paper examines a scheduling problem with heterogeneous logistics unmanned aerial vehicles (UAVs) in urban environment. Different from traditional vehicle routing problem (VRP), it introduces some new characteristics such as the loading capacity, the maximum flight time and the flight speed. As a variant of VRP, the considered scheduling problem is known to be an non‐deterministic Polynomial (NP)‐hard problem. The UAV scheduling problem model with the heterogeneous UAV settings is formulated first. Secondly, a genetic‐based algorithm framework is presented for solving the scheduling problem, in which the encoding/decoding method, the initial population generation method and genetic operations are delicately designed. In order to reduce the search space and faster the execution of this algorithm, a weight‐based loading method is adopted. For the purpose of performance evaluation and statistical analysis, the proposed algorithm is compared with the other two existing algorithms. The experimental results show that the presented algorithm can solve this problem efficiently.", "keywords": [ "Computer science", "Scheduling (production processes)", "Vehicle routing problem", "Job shop scheduling", "Mathematical optimization", "Genetic algorithm", "Population", "Real-time computing", "Routing (electronic design automation)", "Mathematics", "Computer network", "Machine learning" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cmu2.12106" }, { "id": "https://openalex.org/W4416874775", "doi": "10.1109/qce65121.2025.10478", "title": "Quantum Optimization for Solving Coordinated Truck–Drone Routing Problem", "year": 2025, "publication_date": "2025-08-30", "type": "conference-paper", "language": "en", "authors": [ "Ahmad Bassaleh", "Syed Farhan Ahmad", "Ramkumar Harikrishnakumar", "Gregory T. Byrd", "Bayram Yildirim" ], "first_author": "Ahmad Bassaleh", "venue": null, "publisher": null, "abstract": "The integration of drones into last-mile delivery operations has the potential to significantly enhance responsiveness and reduce delivery costs. In this paper, we study the Coordinated Truck-Drone Routing Problem (CTDRP), where a truck serves as mobile base station that launches and retrieves a drone to deliver to geographically dispersed customers. Some customers are served by the truck, while others are served by the drone. The objective is to optimize the truck route and drone assignments in order to minimize the total delivery distance, including both ground and aerial travel. We model the CTDRP as a combinatorial optimization problem and solve it using the Quantum Approximate Optimization Algorithm (QAOA), a variational hybrid quantum-classical algorithm that offers promising capabilities for discrete optimization. The proposed method is benchmarked against classical solutions obtained using the Gurobi optimizer, and the results show that QAOA delivers competitive performance. These findings demonstrate the applicability of QAOA in solving integrated aerial-ground logistics problems and support its potential for advancing quantum-enhanced transportation systems.", "keywords": [ "Drone", "Routing (electronic design automation)", "Truck", "Optimization problem", "Order (exchange)", "Vehicle routing problem", "Quantum" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/qce65121.2025.10478" }, { "id": "https://openalex.org/W4285821908", "doi": "10.2139/ssrn.4165926", "title": "Time-Dependent Two-Echelon Truck and Drone Routing Problem", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Ebrahim Teimoury", "Reza Rashid" ], "first_author": "Ebrahim Teimoury", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Vehicle routing problem", "Computer science", "Operations research", "Aeronautics", "Business", "Engineering", "Computer network", "Automotive engineering", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4165926" }, { "id": "https://openalex.org/W4415778591", "doi": "10.59254/sbpo-2025-212493", "title": "Algorithms for solving routing problems with drones: a systematic literature review", "year": 2025, "publication_date": "2025-01-01", "type": "review", "language": null, "authors": [ "Israel Pereira de Souza", "Igor Machado Coelho", "Maria Claudia Silva Bóeres" ], "first_author": "Israel Pereira de Souza", "venue": "Anais do Simpósio Brasileiro de Pesquisa Operacional", "publisher": null, "abstract": null, "keywords": [ "Systematic review", "Routing (electronic design automation)", "Key (lock)", "Algorithm design" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.59254/sbpo-2025-212493" }, { "id": "https://openalex.org/W7124461526", "doi": "10.52202/083767-0005", "title": "Autonomous Vehicle and Drone Cooperative Delivery and Pickup Routing Problem", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": null, "authors": [ "Jili Kong", "JIANG Xuefeng", "WEI Litong" ], "first_author": "Jili Kong", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Pickup", "Routing (electronic design automation)", "Vehicle routing problem", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.52202/083767-0005" }, { "id": "https://openalex.org/W2963575480", "doi": null, "title": "Multi Objective Vehicle and Drone Routing Problem with Time Window", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "Taejoon Park", "Yerim Chung" ], "first_author": "Taejoon Park", "venue": "Journal of the Korea Society of Computer and Information", "publisher": "Korean Society of Computer Information", "abstract": null, "keywords": [ "Drone", "Window (computing)", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Window of opportunity", "Real-time computing", "Aeronautics", "Computer network", "Engineering", "Biology", "World Wide Web" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://journal.kci.go.kr/jksci/archive/articleView?artiId=ART002434806" }, { "id": "https://openalex.org/W4210760388", "doi": "10.23919/jcc.2022.01.014", "title": "Routing protocol for heterogeneous FANETs with mobility prediction", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Qihui Wu", "Min Zhang", "Chao Dong", "Yong Feng", "Yanli Yuan", "Simeng Feng", "Tony Q. S. Quek" ], "first_author": "Qihui Wu", "venue": "China Communications", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In recent years, with the growth in Unmanned Aerial Vehicles (UAVs), UAV-based systems have become popular in both military and civil applications. In these scenarios, the lack of reliable communication infrastructure has motivated UAVs to establish a network as flying nodes, also known as Flying Ad Hoc Networks (FANETs). However, in FANETs, the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology, making end-to-end connections in FANETs challenging. Mobility estimation and prediction of UAVs can address the challenge mentioned above since it can provide better routing planning and improve overall FANET performance in terms of continuous service availability. We thus develop a Software Defined Network (SDN)-based heterogeneous architecture for reliable communication in FANETs. In this architecture, we apply an Extended Kalman Filter (EKF) for accurate mobility estimation and prediction of UAVs. In particular, we formulate the routing problem in SDN-based Heterogeneous FANETs as a graph decision problem. As the problem is NP-hard, we further propose a Directional Particle Swarming Optimization (DPSO) approach to solve it. The extensive simulation results demonstrate that the proposed DPSO routing can exhibit superior performance in improving the goodput, packet delivery ratio, and delay.", "keywords": [ "Computer science", "Computer network", "Mobility model", "Goodput", "Routing protocol", "Network packet", "Wireless ad hoc network", "Distributed computing", "Wireless", "Throughput", "Telecommunications" ], "cited_by_count": 50, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/jcc.2022.01.014" }, { "id": "https://openalex.org/W7171155317", "doi": "10.1007/978-3-032-30228-1_11", "title": "A Multi-granularity Collaborative Genetic Algorithm for Truck-Drone Routing Problem", "year": 2026, "publication_date": "2026-07-25", "type": "conference-paper", "language": "en", "authors": [ "Li HU", "G Wang", "Q F Liu" ], "first_author": "Li HU", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Crossover", "Scalability", "Routing (electronic design automation)", "Flexibility (engineering)", "Genetic algorithm", "Truck", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-30228-1_11" }, { "id": "https://openalex.org/W7160941385", "doi": "10.1109/tvt.2026.3692486", "title": "Joint Multi-Vehicle Routing and Multi-Drone Scheduling for a Hybrid Drone-Assisted Last-Mile Delivery System in Smart Cities", "year": 2026, "publication_date": "2026-05-12", "type": "article", "language": null, "authors": [ "Yuanhang Qi", "Xumin Huang", "Gewen Huang", "Junhao Wu", "Shengli Xie" ], "first_author": "Yuanhang Qi", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Drone-assisted last-mile delivery is adopted in smart cities to enhance the logistics service performance in terms of reducing the delivery time and cost while improving the user satisfaction. General vehicles without on-board drones and advanced vehicles equipped with multiple drones are simultaneously scheduled to perform the delivery tasks. There exists a hybrid multi-vehicle routing problem with multi-drones (HMVRPMD) that has not been well investigated in the previous works. Particularly, we take into account sufficient real-world considerations on the vehicle, drone and customer sides to make our problem more applicable. We design the optimal routing for two kinds of the vehicles, the optimal launch locations where the drones depart from the associated vehicles, and the optimal sequence of locations for the drones to visit multiple customers in each flight. We first evaluate the user satisfaction of a customer with respect to different package arrival time, according to the time window constraint, and then introduce a penalty cost function to calculate the accumulated loss in the total user satisfaction. We aim to minimize the weight sum of the penalty cost and total delivery cost of all employed vehicles and drones. To address the HMVRPMD, we present a bee-foraging learning particle swarm optimization algorithm with a comprehensive local search strategy. We particularly design the encoding and decoding strategies to efficiently cope with the route segmentation problem according to the vehicle types and the number of drones carried. Furthermore, we adopt the comprehensive local search strategy mixed with three kinds of neighbour search operations to enhance the search ability of the algorithm. Finally, extensive experimental results demonstrate that compared with the baseline algorithms, our algorithm has great advantages in both the convergence speed and algorithm accuracy.", "keywords": [ "Scheduling (production processes)", "Joint (building)", "Routing (electronic design automation)", "Job shop scheduling", "Routing protocol", "Processor scheduling" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2026.3692486" }, { "id": "https://openalex.org/W3114389069", "doi": "10.3390/rs13010027", "title": "Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions", "year": 2020, "publication_date": "2020-12-23", "type": "article", "language": "en", "authors": [ "Amjaad Alhaqbani", "Heba A. Kurdi", "Kamal Youcef‐Toumi" ], "first_author": "Amjaad Alhaqbani", "venue": "Remote Sensing", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.", "keywords": [ "Computer science", "Task (project management)", "Search and rescue", "Scalability", "Scheme (mathematics)", "Ant colony optimization algorithms", "Real-time computing", "Flexibility (engineering)", "Fish ", "Simulation", "Foraging", "Artificial intelligence" ], "cited_by_count": 40, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2072-4292/13/1/27/pdf?version=1608783486" }, { "id": "https://openalex.org/W7140330447", "doi": "10.1016/j.comnet.2026.112254", "title": "Sustainable autonomous multi-UAV edge computing: An energy-positive framework with hierarchical multi-timescale optimization", "year": 2026, "publication_date": "2026-03-26", "type": "article", "language": "en", "authors": [ "Ali A. AL-Bakhrani", "Mingchu Li", "Mohammad S. Obaidat", "Ramesh R. Manza", "Gehad Abdullah Amran", "Jiyu Tian" ], "first_author": "Ali A. AL-Bakhrani", "venue": "Computer Networks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Enhanced Data Rates for GSM Evolution", "Edge device", "Optimization problem", "Edge computing", "Optimization algorithm" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.comnet.2026.112254" }, { "id": "https://openalex.org/W2018960578", "doi": "10.1109/acc.2012.6315620", "title": "Route planning algorithms for unmanned aerial vehicles with refueling constraints", "year": 2012, "publication_date": "2012-06-01", "type": "conference-paper", "language": "en", "authors": [ "Kaarthik Sundar", "Sivakumar Rathinam" ], "first_author": "Kaarthik Sundar", "venue": null, "publisher": null, "abstract": "Small UAVs are currently used in several monitoring applications to monitor a set of targets and collect relevant data. One of the main constraints that characterize a small UAV is the maximum amount of fuel the vehicle carry. In this article, we consider a single UAV routing problem where there are multiple depots and the vehicle is allowed to refuel at any depot. The objective of the problem is to find a path for the UAV such that each target is visited at least once by the vehicle, the fuel constraint is never violated along the path for the UAV, and the total cost of the edges present in the path is a minimum. We first develop a mixed integer, linear programming formulation to solve the problem optimally. We also propose fast and efficient construction and improvement heuristics to solve the same. Computational results are also presented to corroborate the solution quality and the running times of all the algorithms.", "keywords": [ "Heuristics", "Computer science", "Integer programming", "Motion planning", "Path (computing)", "Mathematical optimization", "Set (abstract data type)", "Linear programming", "Vehicle routing problem", "Routing (electronic design automation)", "Constraint (computer-aided design)", "Algorithm" ], "cited_by_count": 35, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/acc.2012.6315620" }, { "id": "https://openalex.org/W7135238497", "doi": "10.17632/ygxgwrfmvp", "title": "MDRP-P Benchmark Dataset for Multi-Objective Drone Routing Problems", "year": 2026, "publication_date": "2026-03-13", "type": "dataset", "language": null, "authors": [ "Serap Ercan Cömert", "Sena Kır" ], "first_author": "Serap Ercan Cömert", "venue": "Mendeley Data", "publisher": null, "abstract": "This dataset contains the benchmark instances and operational parameters used in the study on the Multi-Objective Drone Routing Problem (MDRP). The dataset, referred to as the MDRP-P benchmark set, is derived from the classical P-set instances of the CVRPLIB database and extended with drone-specific operational features such as energy constraints, payload limits, and charging station locations. It is intended to support benchmarking and reproducibility in multi-objective drone routing research.", "keywords": [ "Benchmark (surveying)", "Benchmarking", "Drone", "Routing (electronic design automation)", "Payload (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/ygxgwrfmvp" }, { "id": "https://openalex.org/W4413839508", "doi": "10.2139/ssrn.5418654", "title": "Arc Routing Problems with Multiple Trucks and Drones: A Hybrid Genetic Algorithm", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Abhay Sobhanan", "Hadi Charkhgard", "Changhyun Kwon" ], "first_author": "Abhay Sobhanan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Arc routing", "Arc (geometry)", "Truck", "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Genetic algorithm", "Algorithm", "Engineering", "Computer network", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5418654" }, { "id": "https://openalex.org/W4401163720", "doi": "10.2139/ssrn.4912357", "title": "The Min Max Multi-Trip Drone Location Arc Routing Problem", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Teresa Corberán", "Isaac Plana", "José María Sanchis" ], "first_author": "Teresa Corberán", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Arc routing", "Drone", "Routing (electronic design automation)", "Arc (geometry)", "Computer science", "Computer network", "Engineering", "Mechanical engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.2139/ssrn.4912357" }, { "id": "https://openalex.org/W3183510582", "doi": "10.1109/icuas51884.2021.9476848", "title": "Cooperative route planning of multiple fuel-constrained Unmanned Aerial Vehicles with recharging on an Unmanned Ground Vehicle", "year": 2021, "publication_date": "2021-06-15", "type": "conference-paper", "language": "en", "authors": [ "Subramanian Ramasamy", "Jean-Paul Reddinger", "James M. Dotterweich", "Marshal Childers", "Pranav A. Bhounsule" ], "first_author": "Subramanian Ramasamy", "venue": null, "publisher": null, "abstract": "Multiple small, low cost, multi-rotor Unmanned Aerial Vehicles (UAVs) are ideal for aerial surveillance over large areas. However, their limited battery capacity restricts them to areas in proximity of stationary recharging depots. One solution is to use an Unmanned Ground Vehicle (UGV) to provide a moving recharging depot. The problem is then to find the time-or energy-optimal paths for the multiple fuel-constrained UAVs to visit a set of mission points while being recharged by stopping at the UGV, whose path also needs to be determined. This is a combinatorial optimization problem that is computationally challenging, but may be solved relatively fast using heuristics. In this paper, we present two-level optimization that involves, (1) finding a UGV path by fixing waypoints using K-means and then formulating and solving a traveling salesman problem (TSP), and (2) finding paths for the multiple UAVs using a vehicle routing problem (VRP) formulation with capacity constraints, time windows, and dropped visits. We used constraint programming to solve these problems in less than a minute on a standard desktop computer for up to 25 mission points and 4 UAVs. Our main observation is that increasing the number of UAVs decreases the mission time and refueling stops, but does not decrease the total distance covered or total time taken.", "keywords": [ "Travelling salesman problem", "Unmanned ground vehicle", "Heuristics", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Optimization problem", "Drone", "Motion planning", "Real-time computing", "Mathematical optimization", "Artificial intelligence" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas51884.2021.9476848" }, { "id": "https://openalex.org/W4214749388", "doi": "10.2139/ssrn.4040528", "title": "The Latency Location Drone Routing Problem Under Travel Time Uncertainty", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti" ], "first_author": "Maria Elena Bruni", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Latency (audio)", "Computer science", "Travel time", "Time travel", "Real-time computing", "Operations research", "Transport engineering", "Engineering", "Artificial intelligence", "Telecommunications" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4040528" }, { "id": "https://openalex.org/W4386449337", "doi": null, "title": "Solving the probabilistic drone routing problem for searching targets in case of disasters", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Amadeu Almeida Coco", "Christophe Didier Duhamel", "Andréa Cynthia Santos" ], "first_author": "Amadeu Almeida Coco", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "International audience", "keywords": [ "Drone", "Probabilistic logic", "Routing (electronic design automation)", "Computer science", "Computer security", "Artificial intelligence", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-04197087v1/document" }, { "id": "https://openalex.org/W7112074282", "doi": null, "title": "A reinforcement learning method to solve dynamic truck-drone routing problem", "year": 2024, "publication_date": "2024-12-10", "type": "conference-paper", "language": "en", "authors": [ "Sun, Wenbo", "Zhang, Fangni" ], "first_author": "Sun, Wenbo", "venue": "The HKU Scholars Hub (University of Hong Kong)", "publisher": "University of Hong Kong", "abstract": "This paper develops a reinforcement learning method to solve the dynamic routing problem of a truck-and-drone collaboration system with multiple types of tasks. Specifically, the collaboration system considers a post-disaster scenario. On the one hand, trucks and drones can deliver necessary relief logistics (medicine, food, and water) to the affected people. On the other hand, drones, with small cameras onboard, can perform surveillance tasks to assess the network and find new demand after disasters. Trucks and drones can collaborate on serving all demands. As new demands may arrive during the rescue process, the route of trucks and drones should be updated to rescue more people. In this paper, we develop a reinforcement learning method to solve the proposed problem in real time. Since the problem is an NP-hard problem with a large state and action space, the attention mechanism is adopted for the state representation. In numerical studies, we extensively compare the proposed reinforcement learning methods with other methods.", "keywords": [ "Reinforcement learning", "Drone", "Truck", "Routing (electronic design automation)", "State (computer science)", "Q-learning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hub.hku.hk/handle/10722/353608" }, { "id": "https://openalex.org/W7126018928", "doi": "10.48550/arxiv.2601.20693", "title": "Drone-Aided Blood Collection Routing Problem: A Column Generation Approach", "year": 2026, "publication_date": "2026-01-28", "type": "preprint", "language": null, "authors": [ "Amirhossein Abbaszadeh", "Hossein Hashemi Doulabi" ], "first_author": "Amirhossein Abbaszadeh", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Platelet extraction requires whole blood to be processed within six hours of donation. To meet this deadline, blood collection organizations must optimally route a fleet of vehicles to pick up blood units from donation sites and deliver them to a processing center. This paper introduces a drone-aided blood collection routing problem in which a fleet of trucks, each equipped with a drone, operates in a synchronized manner to collect blood units before their processing time limit expires. Each truck-drone tandem can perform multiple trips throughout the planning horizon, allowing donation sites to be visited repeatedly as new blood units become available over time. We formulate this problem as a mixed-integer linear program that jointly optimizes the routing of trucks and drones, pickup schedules, and timing decisions to maximize the total number of viable blood units collected. We also develop a column generation approach that decomposes the problem into a master problem to select the optimal set of truck-drone tours and a pricing subproblem, which is solved using a tailored memetic algorithm to generate promising new columns. Through a comprehensive computational study, we show the operational benefits of integrating drones into the blood collection system. In addition, we demonstrate the superior performance of the proposed algorithm over Gurobi and two metaheuristics from the literature, namely the hybrid genetic algorithm and the invasive weed optimization, in both the drone-aided and truck-only settings.", "keywords": [ "Column generation", "Routing (electronic design automation)", "Vehicle routing problem", "Set (abstract data type)", "Truck", "Genetic algorithm", "Blood collection", "Linear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2601.20693" }, { "id": "https://openalex.org/W4288772330", "doi": "10.2139/ssrn.4176344", "title": "The Drone Location-Routing Problem with Homogeneous Demand and Load-Dependent Energy Consumption", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti" ], "first_author": "Maria Elena Bruni", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Homogeneous", "Energy consumption", "Consumption (sociology)", "Routing (electronic design automation)", "Computer science", "Economics", "Computer network", "Engineering", "Electrical engineering", "Mathematics", "Sociology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4176344" }, { "id": "https://openalex.org/W7135167308", "doi": "10.17632/ygxgwrfmvp.1", "title": "MDRP-P Benchmark Dataset for Multi-Objective Drone Routing Problems", "year": 2026, "publication_date": "2026-03-13", "type": "dataset", "language": null, "authors": [ "Serap Ercan Cömert", "Sena Kır" ], "first_author": "Serap Ercan Cömert", "venue": "Mendeley Data", "publisher": null, "abstract": "This dataset contains the benchmark instances and operational parameters used in the study on the Multi-Objective Drone Routing Problem (MDRP). The dataset, referred to as the MDRP-P benchmark set, is derived from the classical P-set instances of the CVRPLIB database and extended with drone-specific operational features such as energy constraints, payload limits, and charging station locations. It is intended to support benchmarking and reproducibility in multi-objective drone routing research.", "keywords": [ "Benchmark (surveying)", "Benchmarking", "Drone", "Routing (electronic design automation)", "Payload (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17632/ygxgwrfmvp.1" } ]