[ { "id": "https://openalex.org/W7154498059", "doi": "10.1007/s10100-026-01019-9", "title": "Comparative study of MILP and MINLP formulations for mothership–drones routing problems", "year": 2026, "publication_date": "2026-04-15", "type": "article", "language": "en", "authors": [ "Astrid Wahyu Adventri Wibowo", "Boglárka G.-Tóth", "József Békési" ], "first_author": "Astrid Wahyu Adventri Wibowo", "venue": "Central European Journal of Operations Research", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Heuristics", "Convergence (economics)", "Integer programming", "Grid", "Linear programming", "Benchmark (surveying)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10100-026-01019-9" }, { "id": "https://openalex.org/W7112227504", "doi": null, "title": "Dynamic truck-and-drone routing problem with multiple tasks after disasters", "year": 2025, "publication_date": "2025-01-08", "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 study addresses the dynamic routing problem for a truck-and-drone collaboration system after disasters. Specifically, the truck-and-drone system consists of multiple trucks and drones with flexible collaboration rules to serve all rescue tasks. Two critical tasks after disasters are considered in this paper, i.e., delivery and surveillance tasks. Delivery tasks involve providing essential supplies such as food, water, and medicine to affected individuals. The surveillance tasks focus on assessing the network states. Given the highly unstable post-disaster environment, where rescue tasks may arise stochastically, it is essential to dynamically update the routes of trucks and drones as new demands emerge. The objective of the proposed problem is to minimize the priority cost, which is related to the rescue arrival time and the task priority. As the survival probability decreases with time, minimizing the priority cost can maximize the number of rescued people. In this paper, we model this problem as a Markov Decision Process (MDP) and solve it using a multi-agent reinforcement learning (MARL) method, where each truck and drone acts as an agent. Leveraging the attention mechanism among nodes and agents, trucks and drones collaboratively make decisions to serve all demands in a dynamic environment. After training, our proposed method can get a near-optimal solution in deterministic scenarios. For dynamic scenarios, it can outperform other methods in terms of priority cost and quality of service. Meanwhile, the proposed method demonstrates a short solution time, highlighting its potential for application in disaster response operations.", "keywords": [ "Truck", "Markov decision process", "Routing (electronic design automation)", "Drone", "Task (project management)", "Process (computing)", "Reinforcement learning", "Dynamic programming", "Focus (optics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hub.hku.hk/handle/10722/353609" }, { "id": "https://openalex.org/W4416258931", "doi": "10.2139/ssrn.5600420", "title": "Two-echelon stochastic vehicle routing problem with delivery options", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Amin Karimi", "Junchang Zhang", "Mark Fackrell" ], "first_author": "Amin Karimi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Plan (archaeology)", "Benders' decomposition", "Stochastic programming", "Hierarchy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5600420" }, { "id": "https://openalex.org/W4400644526", "doi": "10.1109/iv55156.2024.10588689", "title": "The Parallel Scheduling Vehicle Routing Problem for Multimodal Package Delivery*", "year": 2024, "publication_date": "2024-06-02", "type": "conference-paper", "language": "en", "authors": [ "Marcella Kaplan", "Kevin P. Heaslip" ], "first_author": "Marcella Kaplan", "venue": null, "publisher": null, "abstract": "This study introduces a novel model known as the Parallel Scheduling Vehicle Routing Problem (PSVRP) in an endeavor to revolutionize package delivery by enhancing its efficiency, accessibility, and cost-effectiveness. The PSVRP represents a state-of-the-art approach to vehicle routing problems, incorporating a diversified fleet of innovative delivery modes. The multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems works in unison to minimize operational costs in various settings. The model can also be adapted to different scenarios, such as variations in customer numbers and package weights, by strategically deploying the most suitable mode of transport.", "keywords": [ "Computer science", "Vehicle routing problem", "Scheduling (production processes)", "Routing (electronic design automation)", "Distributed computing", "Parallel computing", "Computer network", "Mathematical optimization", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/iv55156.2024.10588689" }, { "id": "https://openalex.org/W2894127305", "doi": "10.3390/s18113756", "title": "A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption", "year": 2018, "publication_date": "2018-11-03", "type": "article", "language": "en", "authors": [ "Saravanan Venkatachalam", "Kaarthik Sundar", "Sivakumar Rathinam" ], "first_author": "Saravanan Venkatachalam", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.", "keywords": [ "Fuel efficiency", "Vehicle routing problem", "Context (archaeology)", "Tabu search", "Mathematical optimization", "Routing (electronic design automation)", "Computer science", "Heuristic", "Constraint (computer-aided design)", "Exploit", "Operations research", "Engineering" ], "cited_by_count": 43, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/18/11/3756/pdf" }, { "id": "https://openalex.org/W3134391858", "doi": "10.1080/19427867.2021.1896063", "title": "Transporting COVID-19 testing specimens by routing unmanned aerial vehicles with range and payload constraints: the case of Istanbul", "year": 2021, "publication_date": "2021-03-02", "type": "article", "language": "en", "authors": [ "Ömer Özkan", "Ömer Atli" ], "first_author": "Ömer Özkan", "venue": "Transportation Letters", "publisher": "Taylor & Francis", "abstract": "This paper aims to emphasize the impacts of the COVID-19 pandemic at the healthcare logistics and supply chain networks and to transport polymerase chain reaction testing samples between hospitals and laboratories by using unmanned aerial vehicles (UAVs). To achieve the mission, range and payload capacitated UAV routing problem is defined and a mixed-integer linear programming model (MILP) is developed. The MILP model aims to decide the requisite quantity of the UAVs and to minimize the total transportation distance while satisfying the predetermined constraints. As a real-life instance, the healthcare network of Istanbul is selected and the network includes 219 hospitals and 23-unit basic testing laboratories for the COVID-19 pandemic. The results indicated that the proposed optimization model can find the needed number of UAVs with the minimum tour distances in reasonable run times and achieves to transport 25,000 testing specimens between hospitals and laboratories via UAVs.", "keywords": [ "Payload (computing)", "Routing (electronic design automation)", "Range (aeronautics)", "Integer programming", "Coronavirus disease 2019 (COVID-19)", "Computer science", "Drone", "Linear programming", "Pandemic", "Operations research", "Vehicle routing problem", "Integer (computer science)" ], "cited_by_count": 33, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/19427867.2021.1896063" }, { "id": "https://openalex.org/W7171079403", "doi": "10.5281/zenodo.21545770", "title": "A Multi-Stage Heuristic for a Bi-Objective Collaborative Truck–Drone Routing Problem", "year": 2026, "publication_date": "2026-07-25", "type": "article", "language": null, "authors": [ "Reza Ahmadi Malakoot" ], "first_author": "Reza Ahmadi Malakoot", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": null, "keywords": [ "Heuristic", "Routing (electronic design automation)", "Heuristics", "Process (computing)", "Class (philosophy)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.21545770" }, { "id": "https://openalex.org/W7160405000", "doi": "10.1109/dasa68193.2025.11499104", "title": "Model Development of Drone-Based Humanitarian Logistics Using the Location Routing Problem", "year": 2025, "publication_date": "2025-12-01", "type": "conference-paper", "language": null, "authors": [ "Titi Iswari", "Samuel Chiko Surya Kundjojo", "Hilton Tnunay" ], "first_author": "Titi Iswari", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Humanitarian Logistics", "Development (topology)", "Routing (electronic design automation)", "Key (lock)", "Vehicle routing problem", "Process (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dasa68193.2025.11499104" }, { "id": "https://openalex.org/W3174428122", "doi": "10.15760/honors.1148", "title": "Maximum Coverage Facility Location Drone Routing Problem with Multiple Trip Stops", "year": 2021, "publication_date": "2021-06-07", "type": "dissertation", "language": "en", "authors": [ "Marie Roza" ], "first_author": "Marie Roza", "venue": null, "publisher": null, "abstract": "Integer linear optimization can be used to formulate routing problems as models to find optimal solutions given a set of inputs and constraints. In this research, Chauhan et al.’s publication, “Maximum coverage capacitated facility location problem with range constrained drones,” is expanded upon by modeling a facility location problem with two-to-one drone deliveries. The model analyzes a set of potential facilities that are available to meet the demands of a set of demand points. The drone delivery routes are modeled with the assumption that each opened facility is assigned one drone that can fulfill up to two demand points’ demands per trip. The objective of this model is to locate the open facilities and serviced demand points while meeting drone battery constraints and maximizing demand coverage. This research explores the results of this model for a set of facilities and demand points in the Portland Metropolitan Area based on different facility and battery constraint scenarios.", "keywords": [ "Drone", "Facility location problem", "Routing (electronic design automation)", "Computer science", "Metropolitan area", "Set (abstract data type)", "Constraint (computer-aided design)", "Operations research", "Mathematical optimization", "Integer programming", "Range (aeronautics)", "Integer (computer science)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1016&context=cengin_honorstheses" }, { "id": "https://openalex.org/W7113408087", "doi": null, "title": "Dynamic truck-drone routing problem for multiple tasks in post-disaster scenarios", "year": 2024, "publication_date": "2024-04-27", "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 study investigates a dynamic routing problem of a truck-and-drone system for delivery and surveillance tasks in post-disaster scenarios. To maximize the number of rescued people under uncertainties, the route of trucks and drones need to keep updating. A reinforcement learning method is developed to solve the proposed problem.", "keywords": [ "Routing (electronic design automation)", "Reinforcement learning", "Truck", "Dynamic programming", "Adaptive routing", "Task (project management)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hub.hku.hk/handle/10722/353546" }, { "id": "https://openalex.org/W4414050416", "doi": "10.15607/rss.2025.xxi.101", "title": "How to Coordinate UAVs and UGVs for Efficient Mission Planning? Optimizing Energy-Constrained Cooperative Routing with a DRL Framework", "year": 2025, "publication_date": "2025-06-21", "type": "conference-paper", "language": "en", "authors": [ "Mohammad Mondal", "R. Subramanian", "Pranav A. Bhounsule" ], "first_author": "Mohammad Mondal", "venue": null, "publisher": null, "abstract": "Efficient mission planning for cooperative systems involving Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) requires addressing energy constraints, scalability, and coordination challenges between agents.UAVs excel in rapidly covering large areas but are constrained by limited battery life, while UGVs, with their extended operational range and capability to serve as mobile recharging stations, are hindered by slower speeds.This heterogeneity makes coordination between UAVs and UGVs critical for achieving optimal mission outcomes.In this work, we propose a scalable deep reinforcement learning (DRL) framework to address the energyconstrained cooperative routing problem for multi-agent UAV-UGV teams, aiming to visit a set of task points in minimal time with UAVs relying on UGVs for recharging during the mission.The framework incorporates sortie-wise agent switching to efficiently manage multiple agents, by allocating task points and coordinating actions.Using an encoder-decoder transformer architecture, it optimizes routes and recharging rendezvous for the UAV-UGV team in the task scenario.Extensive computational experiments demonstrate the framework's superior performance over heuristic methods and a DRL baseline, delivering significant improvements in solution quality and runtime efficiency across diverse scenarios.Generalization studies validate its robustness, while dynamic scenario highlights its adaptability to real-time changes with a case study.This work advances UAV-UGV cooperative routing by providing a scalable, efficient, and robust solution for multi-agent mission planning.More details are available on the website: https://sites.google.com/view/muavugvdrl.", "keywords": [ "Routing (electronic design automation)", "Component (thermodynamics)", "Key (lock)", "Process (computing)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.15607/rss.2025.xxi.101" }, { "id": "https://openalex.org/W7169864211", "doi": "10.5281/zenodo.21479183", "title": "A Multi-Stage Heuristic for a Bi-Objective Collaborative Truck–Drone Routing Problem", "year": 2026, "publication_date": "2026-07-21", "type": "article", "language": null, "authors": [ "Reza Ahmadi Malakoot", "Rashed Sahraeian" ], "first_author": "Reza Ahmadi Malakoot", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": null, "keywords": [ "Heuristic", "Routing (electronic design automation)", "Heuristics", "Process (computing)", "Class (philosophy)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.21479183" }, { "id": "https://openalex.org/W7170066904", "doi": "10.5281/zenodo.21486531", "title": "A Multi-Stage Heuristic for a Bi-Objective Collaborative Truck–Drone Routing Problem", "year": 2026, "publication_date": "2026-07-22", "type": "article", "language": null, "authors": [ "Reza Ahmadi Malakoot", "Rashed Sahraeian" ], "first_author": "Reza Ahmadi Malakoot", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": null, "keywords": [ "Heuristic", "Routing (electronic design automation)", "Heuristics", "Process (computing)", "Class (philosophy)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.21486531" }, { "id": "https://openalex.org/W7165794382", "doi": "10.2139/ssrn.6989047", "title": "A Stochastic and Dynamic Drone Routing Problem for On-demand Delivery during Peak Hours", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Shuxuan Li", "Ponnuthurai Nagaratnam Suganthan", "Dezhi Zhang", "Teng Ren", "Ponnuthurai Nagaratnam Suganthan" ], "first_author": "Shuxuan Li", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Scheduling (production processes)", "Cluster analysis", "Convergence (economics)", "Dynamic programming", "Job shop scheduling", "Dynamic priority scheduling", "Lagrangian relaxation", "Operator (biology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6989047" }, { "id": "https://openalex.org/W7170064139", "doi": "10.5281/zenodo.21486437", "title": "A Multi-Stage Heuristic for a Bi-Objective Collaborative Truck–Drone Routing Problem", "year": 2026, "publication_date": "2026-07-22", "type": "article", "language": null, "authors": [ "Reza Ahmadi Malakoot", "Rashed Sahraeian" ], "first_author": "Reza Ahmadi Malakoot", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": null, "keywords": [ "Heuristic", "Routing (electronic design automation)", "Heuristics", "Process (computing)", "Class (philosophy)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.21486437" }, { "id": "https://openalex.org/W4405632194", "doi": "10.1117/12.3054681", "title": "Optimization design of truck-mounted drone vehicle route considering consumer replenishment demand", "year": 2024, "publication_date": "2024-12-20", "type": "conference-paper", "language": "en", "authors": [ "Yuhua Jia", "Haozhen Kang", "Fengjie Xie" ], "first_author": "Yuhua Jia", "venue": null, "publisher": null, "abstract": "Aiming at the problems of freshness and shortage that often occur in fresh distribution, this study proposed an innovative distribution mode, in which trucks carry drones for delivery, supplemented by independent drones to respond to replenishment needs within and outside the limited range, and proposed two replenishment strategies. An optimization model aiming at cost minimization and customer satisfaction maximization was constructed. Simulated annealing algorithm combined with three improved neighborhood search algorithms was used to optimize the path, and an adaptive strategy was designed for dynamic adjustment. At the same time, considering the suddenness and uncertainty of replenishment demand, a reasonable replenishment time threshold acceptable to enterprises is proposed to cope with the sudden demand. Through comparative analysis of numerical examples, it is proved that the proposed joint delivery mode of truck and drone can not only effectively solve the problem of replenishment demand, improve customer satisfaction, but also significantly reduce the cost compared with the traditional distribution mode. This distribution model not only brings new research perspectives and methods to the field of fresh commodity distribution, but also can significantly reduce urban road traffic congestion and improve the efficiency and sustainability of the traffic environment.", "keywords": [ "Drone", "Truck", "Automotive engineering", "Computer science", "On demand", "Engineering", "Multimedia" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3054681" }, { "id": "https://openalex.org/W2967887251", "doi": "10.1109/icuas.2019.8797839", "title": "A Multi-UAS Trajectory optimization Methodology for Complex Enclosed Environments", "year": 2019, "publication_date": "2019-06-01", "type": "conference-paper", "language": "en", "authors": [ "Sarah E. Barlow", "Youngjun Choi", "Simon I. Briceño", "Dimitri N. Mavris" ], "first_author": "Sarah E. Barlow", "venue": null, "publisher": null, "abstract": "This paper explores a multi-UAV trajectory optimization methodology for confined environments. One potential application of this technology is performing warehouse inventory audits; this application is used to evaluate the methodologie's impact on minimizing total mission times. This paper investigates existing algorithms and improves upon them to better address the constraints of warehouse-like environments. An existing inventory scanning algorithm generates sub-optimal, collision free paths for multi-UAV operations, which has two sequential processes: solving a vehicle routing problem, and determining optimal deployment time without any collision. To improve the sub-optimal results, this paper introduces three possible improvements on the multi-UAV inventory tracking scenario. First, a new algorithm logic which seeks to minimize the total mission time once collision avoidance has been ensured rather than having separate processes. Next, an objective function that seeks to minimize the maximum UAV mission time rather than minimizing the total of all UAV mission times. Last, an operational setup consisting of multiple deployment locations instead of only one. These algorithms are evaluated individually and in combination with one another to assess their impact on the overall mission time using a representative inventory environment. The best combination will be further analyzed through a design of experiments by varying several inputs and examining the resulting fleet size, computation time, and overall mission time.", "keywords": [ "Software deployment", "Computer science", "Trajectory", "Collision avoidance", "Real-time computing", "Computation", "Routing (electronic design automation)", "Trajectory optimization", "Operations research", "Collision", "Mathematical optimization", "Algorithm" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "http://repository.gatech.edu/bitstreams/bc552d4b-9f2a-4d73-be4d-9ae4bea0873f/download" }, { "id": "https://openalex.org/W7166843401", "doi": "10.1007/978-3-032-30494-0_34", "title": "Evaluating Alternative Continuous Metaheuristic Solution Encodings for Routing Problems with Multiple Drones with Interceptions", "year": 2026, "publication_date": "2026-07-01", "type": "conference-paper", "language": "en", "authors": [ "Sarah Dillon", "Jacomine Grobler" ], "first_author": "Sarah Dillon", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Metaheuristic", "Routing (electronic design automation)", "Network routing", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-30494-0_34" }, { "id": "https://openalex.org/W7170287307", "doi": "10.2139/ssrn.7171337", "title": "A Multimodal Parallel Scheduling Vehicle Routing Problem for Rural Package Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Marcella Kaplan", "Kevin P. Heaslip", "John Squires" ], "first_author": "Marcella Kaplan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Scheduling (production processes)", "Service delivery framework", "Customer satisfaction", "Vehicle routing problem", "Investment (military)", "Drone", "Service (business)", "Rural area" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7171337" }, { "id": "https://openalex.org/W4295959754", "doi": "10.1016/j.cja.2022.09.009", "title": "Aerial-ground collaborative routing with time constraints", "year": 2022, "publication_date": "2022-09-15", "type": "article", "language": "en", "authors": [ "Jiahao Xing", "Lichen Su", "Wenjing HONG", "Lu Carol Tong", "Renli LYU", "Wenbo Du" ], "first_author": "Jiahao Xing", "venue": "Chinese Journal of Aeronautics", "publisher": "Elsevier BV", "abstract": "The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage. To reach a balance between green transportation and competitive edge, the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery, where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services. In this paper, we propose the Vehicle Routing Problem with Drones under Time constraints (VRPD-T) and focus on the time constraints involved in realistic scenarios during the delivery. A mixed-integer linear programming model has been developed to minimize the total delivery completion time. Furthermore, to overcome the limitations of standard solvers in handling large-scale complex issues, a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution. The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.", "keywords": [ "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Integer programming", "Computer science", "Truck", "Heuristic", "Operations research", "Linear programming", "Enhanced Data Rates for GSM Evolution", "Last mile (transportation)", "Mathematical optimization" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S1000936122002175/pdf" }, { "id": "https://openalex.org/W7167639026", "doi": "10.1007/978-3-032-30795-8_19", "title": "A Unmanned Aerial Vehicle Routing Problem with Full Coverage Constraints", "year": 2026, "publication_date": "2026-07-08", "type": "conference-paper", "language": "en", "authors": [ "Yantong Li", "Qi Yang", "Shanshan Zhou", "Shuai Zhang" ], "first_author": "Yantong Li", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Vehicle routing problem", "Remotely operated underwater vehicle", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-30795-8_19" }, { "id": "https://openalex.org/W7164551829", "doi": "10.5281/zenodo.20667509", "title": "HED-CVRP: A Hybrid Evolutionary Diffusion Framework for 4D Asymmetric Capacitated Vehicle Routing in Drone Logistics", "year": 2026, "publication_date": "2026-06-12", "type": "preprint", "language": null, "authors": [ "Yi Min Lin" ], "first_author": "Yi Min Lin", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The Capacitated Vehicle Routing Problem (CVRP) is one of the most extensively studied problems in combinatorial optimization and logistics. In recent years, the emergence of Unmanned Aerial Vehicles (UAVs) as a viable last-mile delivery platform has introduced new dimensions of complexity to routing problems. Unlike ground-based vehicles that operate on a two-dimensional road network, drones navigate through three-dimensional airspace where altitude changes and atmospheric wind vectors create highly asymmetric energy costs. Traditional Operations Research (OR) solvers, most notably the Lin-Kernighan Heuristic (LKH-3), achieve near-optimal solutions for symmetric CVRP instances. However, their reliance on symmetric edge-swap operations causes severe performance degradation when confronted with asymmetric cost matrices arising from real-world 4D drone routing environments. In this paper, we propose HED-CVRP (Hybrid Evolutionary Diffusion for Capacitated Vehicle Routing Problem), a novel hybrid architecture that bridges the gap between Neural Combinatorial Optimization (NCO) and evolutionary heuristic search. The proposed framework consists of two tightly integrated components. First, we design a Graph Neural Network (GNN) trained via a Denoising Diffusion Probabilistic Model (DDPM) on ground-truth solutions generated by exact or near-exact solvers. This neural component is compressed into a single-step inference via Denoising Diffusion Implicit Models (DDIM) consistency distillation, generating a global edge-probability heatmap that effectively prunes over 90% of the combinatorial search space in approximately 0.48 seconds. Second, we introduce an Asynchronous Shared-Memory Consensus Mechanism implemented within a multi-threaded C++ backend, where multiple Simulated Annealing agents exchange elite genetic material in real-time to rapidly converge on globally optimal solutions. We evaluate HED-CVRP on simulated 4D asymmetric CVRP instances ranging from N=50 to N=1000 nodes. Experimental results demonstrate that HED-CVRP achieves double-dominance over LKH-3: on N=50 instances, our method reduces execution time from 1.70 seconds to 0.88 seconds (a 48% reduction) while simultaneously reducing total energy consumption from 3714.77 to 2340.84 (a 37% improvement). For large-scale instances up to N=1000, we employ a zone-parallel clustering strategy that maintains near-linear time scaling, solving city-wide logistics networks in under 4 seconds compared to over 20 minutes for LKH-3. Comprehensive ablation studies confirm that both the neural prior and the evolutionary backend are essential for achieving state-of-the-art performance. Keywords: Capacitated Vehicle Routing Problem, Unmanned Aerial Vehicles, Diffusion Models, Graph Neural Networks, Simulated Annealing, Evolutionary Algorithms, Combinatorial Optimization", "keywords": [ "Vehicle routing problem", "Drone", "Simulated annealing", "Routing (electronic design automation)", "Combinatorial optimization", "Heuristic", "Swarm behaviour", "Evolutionary algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20667509" }, { "id": "https://openalex.org/W7164572731", "doi": "10.5281/zenodo.20665208", "title": "HED-CVRP: A Hybrid Evolutionary Diffusion Framework for 4D Asymmetric Capacitated Vehicle Routing in Drone Logistics", "year": 2026, "publication_date": "2026-06-12", "type": "preprint", "language": null, "authors": [ "Yi Min Lin" ], "first_author": "Yi Min Lin", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The Capacitated Vehicle Routing Problem (CVRP) is one of the most extensively studied problems in combinatorial optimization and logistics. In recent years, the emergence of Unmanned Aerial Vehicles (UAVs) as a viable last-mile delivery platform has introduced new dimensions of complexity to routing problems. Unlike ground-based vehicles that operate on a two-dimensional road network, drones navigate through three-dimensional airspace where altitude changes and atmospheric wind vectors create highly asymmetric energy costs. Traditional Operations Research (OR) solvers, most notably the Lin-Kernighan Heuristic (LKH-3), achieve near-optimal solutions for symmetric CVRP instances. However, their reliance on symmetric edge-swap operations causes severe performance degradation when confronted with asymmetric cost matrices arising from real-world 4D drone routing environments. In this paper, we propose HED-CVRP (Hybrid Evolutionary Diffusion for Capacitated Vehicle Routing Problem), a novel hybrid architecture that bridges the gap between Neural Combinatorial Optimization (NCO) and evolutionary heuristic search. The proposed framework consists of two tightly integrated components. First, we design a Graph Neural Network (GNN) trained via a Denoising Diffusion Probabilistic Model (DDPM) on ground-truth solutions generated by exact or near-exact solvers. This neural component is compressed into a single-step inference via Denoising Diffusion Implicit Models (DDIM) consistency distillation, generating a global edge-probability heatmap that effectively prunes over 90% of the combinatorial search space in approximately 0.48 seconds. Second, we introduce an Asynchronous Shared-Memory Consensus Mechanism implemented within a multi-threaded C++ backend, where multiple Simulated Annealing agents exchange elite genetic material in real-time to rapidly converge on globally optimal solutions. We evaluate HED-CVRP on simulated 4D asymmetric CVRP instances ranging from N=50 to N=1000 nodes. Experimental results demonstrate that HED-CVRP achieves double-dominance over LKH-3: on N=50 instances, our method reduces execution time from 1.70 seconds to 0.88 seconds (a 48% reduction) while simultaneously reducing total energy consumption from 3714.77 to 2340.84 (a 37% improvement). For large-scale instances up to N=1000, we employ a zone-parallel clustering strategy that maintains near-linear time scaling, solving city-wide logistics networks in under 4 seconds compared to over 20 minutes for LKH-3. Comprehensive ablation studies confirm that both the neural prior and the evolutionary backend are essential for achieving state-of-the-art performance. Keywords: Capacitated Vehicle Routing Problem, Unmanned Aerial Vehicles, Diffusion Models, Graph Neural Networks, Simulated Annealing, Evolutionary Algorithms, Combinatorial Optimization", "keywords": [ "Vehicle routing problem", "Drone", "Simulated annealing", "Routing (electronic design automation)", "Combinatorial optimization", "Heuristic", "Swarm behaviour", "Evolutionary algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20665208" }, { "id": "https://openalex.org/W7111767756", "doi": null, "title": "The parallel drone scheduling problem with multiple drones and vehicles", "year": null, "publication_date": null, "type": "article", "language": null, "authors": [ "Mbiadou Saleu, Raïssa G.", "Deroussi, Laurent", "Feillet, Dominique", "Grangeon, Nathalie", "Quilliot, Alain" ], "first_author": "Mbiadou Saleu, Raïssa G.", "venue": "RePEc: Research Papers in Economics", "publisher": "Federal Reserve Bank of St. Louis", "abstract": "Delivery of goods into urban areas constitutes an important issue for logistics service providers. One of the most talked-about developments in recent years has been the potential use of unmanned aerial vehicles, or drones, for transporting packages, food, medicine, and other goods. Delivery by drones offers new possibilities, but also induces new challenging routing problems. In this paper, we address and extend the so-called Parallel Drone Scheduling Traveling Salesman Problem. Basically, in this problem, deliveries are split between a vehicle and one or several drones. The vehicle performs a classical delivery tour from the depot, while the drones are constrained to perform back and forth trips. The objective is to minimize the completion time. We extend the problem by considering several vehicles. We call it Parallel Drone Scheduling Multiple Traveling Salesman Problem. We propose a hybrid metaheuristic for its solution. The procedure starts by building a giant tour visiting all customers. Then, the giant tour is split in order to determine a set of vehicles tours (each vehicle tour following the order defined by the giant tour) and a set of customers assigned to drones. Thirdly, an improvement step move customers between vehicles or between vehicles and drones. We also propose a Mixed Integer Linear Programming formulation and a simple branch-and-cut approach. The proposed approach is validated via an experimental campaign on instances taken from the CVRPLIB11Capacited Vehicle Routing Problem LIBrary library. Computational experiments comparing several variants of the hybrid metaheuristic give some insights on this drone delivery system.", "keywords": [ "Drone", "Travelling salesman problem", "Scheduling (production processes)", "Vehicle routing problem", "Metaheuristic", "Integer programming", "Job shop scheduling" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.sciencedirect.com/science/article/pii/S0377221721006822" }, { "id": "https://openalex.org/W7164526619", "doi": "10.5281/zenodo.20665209", "title": "HED-CVRP: A Hybrid Evolutionary Diffusion Framework for 4D Asymmetric Capacitated Vehicle Routing in Drone Logistics", "year": 2026, "publication_date": "2026-06-12", "type": "preprint", "language": null, "authors": [ "Yi Min Lin" ], "first_author": "Yi Min Lin", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The Capacitated Vehicle Routing Problem (CVRP) is one of the most extensively studied problems in combinatorial optimization and logistics. In recent years, the emergence of Unmanned Aerial Vehicles (UAVs) as a viable last-mile delivery platform has introduced new dimensions of complexity to routing problems. Unlike ground-based vehicles that operate on a two-dimensional road network, drones navigate through three-dimensional airspace where altitude changes and atmospheric wind vectors create highly asymmetric energy costs. Traditional Operations Research (OR) solvers, most notably the Lin-Kernighan Heuristic (LKH-3), achieve near-optimal solutions for symmetric CVRP instances. However, their reliance on symmetric edge-swap operations causes severe performance degradation when confronted with asymmetric cost matrices arising from real-world 4D drone routing environments. In this paper, we propose HED-CVRP (Hybrid Evolutionary Diffusion for Capacitated Vehicle Routing Problem), a novel hybrid architecture that bridges the gap between Neural Combinatorial Optimization (NCO) and evolutionary heuristic search. The proposed framework consists of two tightly integrated components. First, we design a Graph Neural Network (GNN) trained via a Denoising Diffusion Probabilistic Model (DDPM) on ground-truth solutions generated by exact or near-exact solvers. This neural component is compressed into a single-step inference via Denoising Diffusion Implicit Models (DDIM) consistency distillation, generating a global edge-probability heatmap that effectively prunes over 90% of the combinatorial search space in approximately 0.48 seconds. Second, we introduce an Asynchronous Shared-Memory Consensus Mechanism implemented within a multi-threaded C++ backend, where multiple Simulated Annealing agents exchange elite genetic material in real-time to rapidly converge on globally optimal solutions. We evaluate HED-CVRP on simulated 4D asymmetric CVRP instances ranging from N=50 to N=1000 nodes. Experimental results demonstrate that HED-CVRP achieves double-dominance over LKH-3: on N=50 instances, our method reduces execution time from 1.70 seconds to 0.88 seconds (a 48% reduction) while simultaneously reducing total energy consumption from 3714.77 to 2340.84 (a 37% improvement). For large-scale instances up to N=1000, we employ a zone-parallel clustering strategy that maintains near-linear time scaling, solving city-wide logistics networks in under 4 seconds compared to over 20 minutes for LKH-3. Comprehensive ablation studies confirm that both the neural prior and the evolutionary backend are essential for achieving state-of-the-art performance. Keywords: Capacitated Vehicle Routing Problem, Unmanned Aerial Vehicles, Diffusion Models, Graph Neural Networks, Simulated Annealing, Evolutionary Algorithms, Combinatorial Optimization", "keywords": [ "Vehicle routing problem", "Drone", "Simulated annealing", "Routing (electronic design automation)", "Combinatorial optimization", "Heuristic", "Swarm behaviour", "Evolutionary algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20665209" }, { "id": "https://openalex.org/W4415146502", "doi": "10.1007/978-981-96-7292-9_8", "title": "Solving the Multi-truck Single-Drone Routing Problem Using Ant Colony Optimization", "year": 2025, "publication_date": "2025-10-14", "type": "conference-paper", "language": "en", "authors": [ "Sulabh Bansal", "M. K. Tandon" ], "first_author": "Sulabh Bansal", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Ant colony optimization algorithms", "Path (computing)", "Ant colony", "Routing (electronic design automation)", "Vehicle routing problem", "Drone", "Metaheuristic", "Optimization problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-96-7292-9_8" }, { "id": "https://openalex.org/W7160893223", "doi": "10.3390/math14101635", "title": "Goal-Induced Pareto Fronts for a Bi-Criterion Truck–Multiple-Drone Routing Problem", "year": 2026, "publication_date": "2026-05-12", "type": "article", "language": "en", "authors": [ "Pedro Luis González Rodríguez", "David Sánchez-Wells", "Jose Miguel Leon-Blanco", "Marcos Calle Suárez", "José Luis Andrade-Pineda" ], "first_author": "Pedro Luis González Rodríguez", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Truck–multiple-drone routing problems involve conflicting operational criteria and are therefore naturally suited to multiobjective analysis. In practical settings, however, decision makers may also specify aspiration levels for the considered criteria, which call for a target-oriented perspective. This paper studies a bi-criterion truck–multiple-drone routing problem through a goal-induced deviation framework in which the original objectives are transformed to normalized positive deviations with respect to prescribed targets. First, a general mathematical framework is introduced, and several structural properties are established, including dominance preservation, invariance under positive weighting, equivalence with the original Pareto structure when all the targets are violated, and the loss of discrimination when the targets are attainable. To address this latter effect, an enhanced goal-programming scalarization is proposed and shown to preserve consistency with the Pareto efficiency. The framework is then specialized to a truck–multiple-drone routing problem with truck time and makespan as criteria and evaluated on representative benchmark instances together with a broader attainable-target benchmark battery, using a common agent-based metaheuristic search framework adapted from literature. This search framework is employed both to estimate a reference Pareto frontier and to solve the GP and EGP scalarizations under the same computational scheme. The computational results illustrate two target regimes: When the targets are unattainable, both formulations are mainly driven by the minimization of positive deviations; when they are attainable, classical goal programming may return satisfactory but dominated solutions, whereas the enhanced formulation preserves discrimination and selects Pareto-efficient alternatives.", "keywords": [ "Pareto principle", "Benchmark (surveying)", "Equivalence (formal languages)", "Minification", "Multi-objective optimization", "Vehicle routing problem", "Routing (electronic design automation)", "Job shop scheduling" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2227-7390/14/10/1635/pdf?version=1778572525" }, { "id": "https://openalex.org/W7108455862", "doi": "10.2139/ssrn.5860543", "title": "Logic-Based Benders Decomposition approach to solve an Autonomous Drone-Truck Routing Problem", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": null, "authors": [ "Jahir Desaily Llagas Ortega", "Marcel Turkensteen", "Sanne Wøhlk" ], "first_author": "Jahir Desaily Llagas Ortega", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Swap (finance)", "Drone", "Benders' decomposition", "Routing (electronic design automation)", "Flexibility (engineering)", "Decomposition", "Scheduling (production processes)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5860543" }, { "id": "https://openalex.org/W7172417873", "doi": "10.5281/zenodo.21796260", "title": "A Machine-Learning-Assisted Heuristic for a Bi-Objective Collaborative Truck–Drone Routing Problem", "year": 2026, "publication_date": "2026-08-04", "type": "article", "language": null, "authors": [ "Reza Ahmadi Malakoot" ], "first_author": "Reza Ahmadi Malakoot", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": null, "keywords": [ "Heuristic", "Routing (electronic design automation)", "Heuristics", "Process (computing)", "Class (philosophy)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.21796260" }, { "id": "https://openalex.org/W7136350548", "doi": "10.1109/itsc60802.2025.11423254", "title": "Real-World Impacts of the Parallel Scheduling Vehicle Routing Problem in Rural Environments", "year": 2025, "publication_date": "2025-11-18", "type": "conference-paper", "language": null, "authors": [ "John Squires", "Marcella Kaplan", "Kevin P. Heaslip" ], "first_author": "John Squires", "venue": null, "publisher": null, "abstract": "The last-mile delivery problem in rural areas faces higher costs and environmental impacts than in urban areas. In this study, the performance of the Parallel Scheduling Vehicle Routing Problem (PSVRP)—an advanced delivery routing system utilizing electric vans, autonomous delivery vehicles, drones, and truck-drone combinations—is studied in rural settings. To analyze real-world potential, the PSVRP is modeled in real-world rural scenarios, comparing cost and emissions with traditional diesel fleets. Additionally, synthetic customer-location models (uniform and clustered) are assessed on how well they approximate real-world instances. Using minimal fleet sizes, the PSVRP was found to reduce real-world costs by$50 - 58 \\%$and emissions by$39 - 46\\%$compared to diesel fleets. The uniform distribution adequately approximated modeled real-world costs (within$6-8 \\%$), whereas clustered distributions consistently underestimated costs and emissions. This study demonstrates the PSVRP's economic and environmental benefits in rural settings and highlights the ability of uniformly distributed theoretical scenarios to approximate real-world conditions.", "keywords": [ "Scheduling (production processes)", "Vehicle routing problem", "Job shop scheduling", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itsc60802.2025.11423254" }, { "id": "https://openalex.org/W4386590672", "doi": "10.1109/mits.2023.3306969", "title": "Intelligent Distribution Transportation Route Planning Considering Traffic Congestion", "year": 2023, "publication_date": "2023-09-11", "type": "article", "language": "en", "authors": [ "Jiajing Gao", "Lu Zhen" ], "first_author": "Jiajing Gao", "venue": "IEEE Intelligent Transportation Systems Magazine", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The vehicle routing problem is the main content of intelligent transportation and plays a very important role in the logistics industry. Trucks and unmanned aerial vehicles (UAVs) collaborative delivery is an emerging intelligent delivery mode that can deliver packages in a more timely and efficient manner than traditional delivery models. A multitrucks’-and-UAVs’ cooperative delivery problem considering congestion is studied, and a mathematical model is established, minimizing total costs. A heuristic algorithm for column generation is proposed. In addition, we design a dynamic programming algorithm that solves the pricing problem. Finally, numerical experiments are carried out, and a sensitivity analysis is also conducted to derive implications for management.", "keywords": [ "Truck", "Column generation", "Heuristic", "Routing (electronic design automation)", "Vehicle routing problem", "Computer science", "Intelligent transportation system", "Traffic congestion", "Operations research", "Transport engineering", "Engineering", "Computer network" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/mits.2023.3306969" }, { "id": "https://openalex.org/W7201857415", "doi": "10.1109/tits.2026.3716617", "title": "A Learning-Enhanced Method for the Multi-Visit Vehicle-Drone Routing Problem With Continuous Locations", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Zongcheng Zhang", "Yanru Chen", "Shuang Liang", "Mujin Gao", "Bo Du", "Mohamed Wahab Mohamed Ismail" ], "first_author": "Zongcheng Zhang", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Network routing", "Equal-cost multi-path routing", "Algorithm design" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2026.3716617" }, { "id": "https://openalex.org/W7213301600", "doi": "10.1016/j.tre.2026.105235", "title": "Multi-trip truck-drone routing problem with simultaneous pickup and delivery and temporal constraints", "year": 2026, "publication_date": "2026-09-15", "type": "article", "language": "en", "authors": [ "Yongqi Luo", "Yanru Chen", "M.I.M. Wahab" ], "first_author": "Yongqi Luo", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pickup", "Routing (electronic design automation)", "Vehicle routing problem", "Constraint (computer-aided design)", "Key (lock)", "Minification" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105235" }, { "id": "https://openalex.org/W4283808542", "doi": null, "title": "Searching victims in post-disaster areas: A decentralized approach for the Drone Swarms Routing Problem", "year": 2022, "publication_date": "2022-07-03", "type": "conference-paper", "language": "en", "authors": [ "Matheus Nohra Haddad", "Andréa Cynthia Santos", "Christophe Didier Duhamel", "Damien Olivier" ], "first_author": "Matheus Nohra Haddad", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "International audience", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Search and rescue", "Computer security", "Computer network", "Artificial intelligence", "Robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-03713981" }, { "id": "https://openalex.org/W4414988478", "doi": null, "title": "A Branch-Cut-and-Price algorithm for the Truck-based Drone Delivery Routing Problem with Time Windows", "year": 2025, "publication_date": "2025-10-08", "type": "preprint", "language": "en", "authors": [ "Sylvain Lichau", "Daniel de Araujo", "Rémy Dupas", "Julien François", "Artur Alves Pessoa", "Marcos Costa Roboredo", "Eduardo Uchoa" ], "first_author": "Sylvain Lichau", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "We address the Truck-based Drone Delivery Routing Problem with Time Windows, a generalization of the Vehicle Routing Problem with Drones that allows drones to serve multiple customers per flight under customer time windows. We develop an exact branch-cut-and-price algorithm in which a labeling algorithm solves the pricing subproblem. We compare two alternative pricing models defined on distinct graphs and show that they are non-dominated: the most effective choice depends on instance characteristics. We further introduce an efficient drone flight enumeration algorithm and tailored preprocessing methods. Computational experiments demonstrate the efficiency of the proposed approach, and a sensitivity analysis quantifies the significant positive impact of preprocessing methods on runtime. We also propose new benchmark instances that permit drones to carry more packages and allow drone loops, enabling a broader and more challenging testbed for future research.", "keywords": [ "Drone", "Preprocessor", "Testbed", "Benchmark (surveying)", "Routing (electronic design automation)", "Generalization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-05303723v2/file/HAL___VRP_1D_Article.pdf" }, { "id": "https://openalex.org/W2788004130", "doi": null, "title": "Vehicle routing problem for persistent uav service = 지속적 무인 항공기 서비스를 위한 차량 경로 문제", "year": 2014, "publication_date": "2014-01-01", "type": "dissertation", "language": "ko", "authors": [ "Jong-Hoe Kim", "김종회" ], "first_author": "Jong-Hoe Kim", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Service (business)", "Computer science", "Computer network", "Operations research", "Aeronautics", "Distributed computing", "Engineering", "Business", "Marketing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://koasas.kaist.ac.kr/handle/10203/196981" }, { "id": "https://openalex.org/W4408107651", "doi": "10.2139/ssrn.5163912", "title": "The Energy Minimizing Single-Drone Routing Problem with Pickups and Deliveries and Battery Replacement Stations", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Themistoklis Stamadianos", "Nikolaos A. Kyriakakis", "Emmanouil Giannoulakis", "Magdalene Marinaki", "Ioannis Marinakis" ], "first_author": "Themistoklis Stamadianos", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Battery (electricity)", "Routing (electronic design automation)", "Computer science", "Energy (signal processing)", "Battery capacity", "Computer network", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5163912" }, { "id": "https://openalex.org/W7160852048", "doi": "10.3390/math14101589", "title": "A Multi-Objective Drone Routing Problem for On-Demand Delivery Considering Hybrid Delivery Modes", "year": 2026, "publication_date": "2026-05-08", "type": "article", "language": "en", "authors": [ "Shuxuan Li", "Teng Ren", "Guohua Wu" ], "first_author": "Shuxuan Li", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This paper investigates the multi-objective drone routing problem for on-demand delivery considering hybrid delivery modes. Unlike previous studies that assume a single delivery strategy or statically bind modes to heterogeneous drone types, real-world operations require a hybrid framework where homogeneous drones dynamically switch between exclusive and sharing modes to accommodate orders with distinct logics. We formulate a multi-objective mixed-integer programming model that minimizes operational costs while maximizing order revenue, explicitly accounting for dynamic order arrivals, drone battery swapping, and resource conflicts at shared lockers. To solve this problem under dynamic conditions, we propose an online optimization framework, the reinforcement learning knowledge-driven multi-objective evolutionary algorithm based on decomposition (RL-KMD-MOEA/D), which integrates Q-learning for adaptive operator selection within a rolling-horizon scheme. Comprehensive experiments demonstrate that RL-KMD-MOEA/D achieves competitive performance on small-scale instances and exhibits superior scalability and robustness on large-scale, highly constrained dynamic scenarios, outperforming other compared algorithms.", "keywords": [ "Drone", "Scalability", "Robustness (evolution)", "Reinforcement learning", "Routing (electronic design automation)", "Optimization problem", "Dynamic programming", "Decomposition" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/math14101589" }, { "id": "https://openalex.org/W7168389405", "doi": "10.1007/s12293-026-00523-4", "title": "Genetic programming-assisted automatic heuristics design for dynamic vehicle routing with drones considering time window", "year": 2026, "publication_date": "2026-07-15", "type": "article", "language": "en", "authors": [ "Tran Ho Khanh Ly", "Bui Trong Duc", "Nguyen Thi Tam", "Huỳnh Thị Thanh Bình" ], "first_author": "Tran Ho Khanh Ly", "venue": "Memetic Computing", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Heuristics", "Drone", "Crossover", "Truck", "Vehicle routing problem", "Routing (electronic design automation)", "Genetic algorithm", "Convergence (economics)", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s12293-026-00523-4" }, { "id": "https://openalex.org/W4306916431", "doi": "10.3390/electronics11203399", "title": "A Greedy Heuristic Based on Optimizing Battery Consumption and Routing Distance for Transporting Blood Using Unmanned Aerial Vehicles", "year": 2022, "publication_date": "2022-10-20", "type": "article", "language": "en", "authors": [ "Sumayah Al-Rabiaah", "Manar Hosny", "Sarab AlMuhaideb" ], "first_author": "Sumayah Al-Rabiaah", "venue": "Electronics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned Aerial Vehicles (UAVs) play crucial roles in numerous applications, such as healthcare services. For example, UAVs can help in disaster relief and rescue missions, such as by delivering blood samples and medical supplies. In this work, we studied a problem related to the routing of UAVs in a healthcare approach known as the UAV-based Capacitated Vehicle Routing Problem (UCVRP). This is classified as an NP-hard problem. The problem deals with utilizing UAVs to deliver blood to patients in emergency situations while minimizing the number of UAVs and the total routing distance. The UCVRP is a variant of the well-known capacitated vehicle routing problem, with additional constraints that fit the shipment type and the characteristics of the UAV. To solve this problem, we developed a heuristic known as the Greedy Battery—Distance Optimizing Heuristic (GBDOH). The idea was to assign patients to a UAV in such a way as to minimize the battery consumption and the number of UAVs. Then, we rearranged the patients of each UAV in order to minimize the total routing distance. We performed extensive experiments on the proposed GBDOH using instances tested by other methods in the literature. The results reveal that GBDOH demonstrates a more efficient performance with lower computational complexity and provides a better objective value by approximately 27% compared to the best methods used in the literature.", "keywords": [ "Routing (electronic design automation)", "Greedy algorithm", "Heuristic", "Computer science", "Vehicle routing problem", "Mathematical optimization", "Battery (electricity)", "Real-time computing", "Distributed computing", "Computer network", "Artificial intelligence", "Algorithm" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-9292/11/20/3399/pdf?version=1666670885" }, { "id": "https://openalex.org/W4293576275", "doi": "10.2139/ssrn.4203689", "title": "Optimization of Trucks and Drones in Tandem Delivery Network with Drone Trajectory Planning", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "C.S. Shui", "James C. Chu", "K.H. Lin" ], "first_author": "C.S. Shui", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Tandem", "Trajectory", "Computer science", "Aeronautics", "Business", "Engineering", "Aerospace engineering", "Biology", "Physics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4203689" }, { "id": "https://openalex.org/W4323322841", "doi": "10.48550/arxiv.2303.01543", "title": "Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem", "year": 2023, "publication_date": "2023-03-02", "type": "preprint", "language": "en", "authors": [ "Guangyao Shi", "Pratap Tokekar" ], "first_author": "Guangyao Shi", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output). Our motivating case study is a specific type of vehicle routing problem, in which a team of Unmanned Ground Vehicles (UGVs) can serve as mobile charging stations to recharge a team of Unmanned Ground Vehicles (UAVs) that execute persistent monitoring tasks. {We want to learn the mapping from observations of UAV task routes and wind field to the parameters of a submodular objective function, which describes the distribution of landing positions of the UAVs .} Traditionally, such a learning problem is solved independently as a prediction phase without considering the downstream task optimization phase. However, the loss function used in prediction may be misaligned with our final goal, i.e., a good routing decision. Good performance in the isolated prediction phase does not necessarily lead to good decisions in the downstream routing task. In this paper, we propose a framework that incorporates task optimization as a differentiable layer in the prediction phase. Our framework allows end-to-end training of the prediction model without using engineered intermediate loss that is targeted only at the prediction performance. In the proposed framework, task optimization (submodular maximization) is made differentiable by introducing stochastic perturbations into deterministic algorithms (i.e., stochastic smoothing). We demonstrate the efficacy of the proposed framework using synthetic data. Experimental results of the mobile charging station routing problem show that the proposed framework can result in better routing decisions, e.g. the average number of UAVs recharged increases, compared to the prediction-optimization separate approach.", "keywords": [ "Submodular set function", "Computer science", "Routing (electronic design automation)", "Smoothing", "Mathematical optimization", "Task (project management)", "Maximization", "Vehicle routing problem", "Function (biology)", "Context (archaeology)", "Optimization problem", "Differentiable function" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2303.01543" }, { "id": "https://openalex.org/W4221056733", "doi": "10.2139/ssrn.4066380", "title": "Multi-Depot Electric Vehicle and Drones Synchronized Delivery Routing Problem Under Time-Dependent Networks", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Hao Fan", "Yueguang Zhang", "Houming Fan" ], "first_author": "Hao Fan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Depot", "Drone", "Computer science", "Electric vehicle", "Vehicle routing problem", "Routing (electronic design automation)", "Computer network", "Engineering", "Geography", "Physics", "Power (physics)", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4066380" }, { "id": "https://openalex.org/W3048189541", "doi": null, "title": "Optimal routing problems for a ground vehicle and a carried energy constrained drone in search and rescue problems", "year": 2019, "publication_date": "2019-10-03", "type": "article", "language": "en", "authors": [ "Federico Di Stefano" ], "first_author": "Federico Di Stefano", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Search and rescue", "Routing (electronic design automation)", "Vehicle routing problem", "Computer science", "Artificial intelligence", "Robot", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.politesi.polimi.it/handle/10589/149499" }, { "id": "https://openalex.org/W7213532776", "doi": "10.1109/tccn.2026.3734993", "title": "Cost-Efficient Computation Offloading and Vehicle Routing for MEC-Assisted Drone-Vehicle Collaborative Inspection Systems", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Xumin Huang", "Xin Chen", "Weifeng Zhong", "Jiawen Kang", "Xiaohuan Li", "Yuan Wu" ], "first_author": "Xumin Huang", "venue": "IEEE Transactions on Cognitive Communications and Networking", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": null, "keywords": [ "Computation offloading", "Routing (electronic design automation)", "Computation", "Vehicle routing problem", "Server", "Routing protocol", "Vehicle safety" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tccn.2026.3734993" }, { "id": "https://openalex.org/W7213461917", "doi": "10.52436/1.jutif.2026.7.4.5914", "title": "Optimization of Drone Routing Problem with Energy Constraints and Charging Station Integration using Improved ParthenoGenetic Algorithm", "year": 2026, "publication_date": "2026-08-27", "type": "article", "language": null, "authors": [ "Sugiarto Cokrowibowo", "A. Amirul Asnan Cirua", "Nuralamsah Zulkarnaim", "Mahmuddin Mahmuddin" ], "first_author": "Sugiarto Cokrowibowo", "venue": "Jurnal Teknik Informatika (Jutif)", "publisher": null, "abstract": "The development of Unmanned Aerial Vehicles (UAVs) has introduced new challenges in route optimization, particularly in the Drone Routing Problem (DRP), where limited battery capacity directly affects operational feasibility. This study aims to optimize DRP by considering energy constraints and charging station integration using the Improved ParthenoGenetic Algorithm (IPGA). The proposed method employs a sequence-based chromosome representation, an energy consumption model, and adaptive charging station insertion during fitness evaluation to ensure that the generated routes remain feasible under battery limitations. The performance of IPGA was evaluated using three datasets consisting of 10, 30, and 50 customers and compared with PGA, GA, and PSO based on best fitness, minimum total energy consumption, computation time, and convergence behavior. The results show that IPGA consistently achieved the highest best fitness and the lowest minimum total energy across all dataset scenarios. In the 50-customer dataset, IPGA reduced total energy consumption by 42.89%, 36.19%, and 57.32% compared with PGA, GA, and PSO, respectively. The convergence analysis also indicates that IPGA provides more stable fitness improvement, particularly in medium and large problem instances. These findings show that IPGA is effective for solving energy-constrained DRP and contributes to the development of adaptive metaheuristic optimization methods for intelligent UAV-based logistics and autonomous distribution systems.", "keywords": [ "Energy consumption", "Convergence (economics)", "Drone", "Routing (electronic design automation)", "Energy (signal processing)", "Particle swarm optimization", "Battery (electricity)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://jutif.if.unsoed.ac.id/index.php/jurnal/article/download/5914/1380" }, { "id": "https://openalex.org/W7203610926", "doi": "10.1016/j.cie.2026.112302", "title": "Multi-objective green location-routing problem for drone-assisted delivery under uncertain demand", "year": 2026, "publication_date": "2026-08-17", "type": "article", "language": "en", "authors": [ "Yuanrui Li", "Qiuhong Zhao", "Qi Lin" ], "first_author": "Yuanrui Li", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Truck", "Decomposition", "Heuristic", "Realization (probability)", "Routing (electronic design automation)", "Vehicle routing problem", "Stochastic programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2026.112302" }, { "id": "https://openalex.org/W338054739", "doi": null, "title": "Routing Unmanned Aerial Vehicles While Considering General Restricted Operating Zones", "year": 2012, "publication_date": "2012-12-04", "type": "article", "language": "en", "authors": [ "Darin T. Brown" ], "first_author": "Darin T. Brown", "venue": "Defense Technical Information Center (DTIC)", "publisher": null, "abstract": "U.S. military forces employ unmanned aerial vehicles (UAVs) to conduct intelligence-gathering missions worldwide. For a typical mission, commanders may task UAV operators to gather imagery on 100 or more sites or targets. UAV operators must quickly prepare mission plans that meet the needs of their commanders while dealing with real-world constraints such as time windows, site priorities, imagery requirements, UAVs with different capabilities (i.e. imagery equipment, speed, and range), and UAVs departing from different bases. Previous AFIT research provided the UAV Battlelab with a tool, AFIT Router, for generating high-quality routes to aid mission planning. This research enhances the AFIT Router by providing the ability to define general restricted operating zones and to build routes that consider these zones. This research also examines and compares a probabilistic tabu search heuristic and two reactive tabu search heuristics for solving vehicle routing problems.", "keywords": [ "Heuristics", "Tabu search", "Routing (electronic design automation)", "Router", "Search and rescue", "Heuristic", "Drone", "Task (project management)", "Computer science", "Probabilistic logic", "Systems engineering", "Operations research" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA391265" }, { "id": "https://openalex.org/W3211328972", "doi": "10.21528/cbic2021-107", "title": "Biased Random-key Genetic Algorithm for theHybrid Vehicle-drone Routing Problem for Pick-upand Delivery", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Anderson Zudio", "Igor Machado Coelho", "Luiz Satoru Ochi" ], "first_author": "Anderson Zudio", "venue": null, "publisher": null, "abstract": "The Hybrid Vehicle drone Routing Problem (HVDRP) was recently introduced as an extension of the classic Vehicle Routing Problem (VRP). In this version, one vehicle is equipped with multiple drones to serve customers with demands for pick-up and delivery. The vehicle travels between stations that serve as parking locations to dispatch drones to attend clients. The drones have limitations in their maximum flight range and carrying capacity. We propose a BRKGA algorithm to solve HVDRP with a decoder component specially tailored to find feasible solutions. The proposed method is empirically analyzed in solution quality through a test set that a mixed-integer programming (MIP) model implementation can optimally solve in reasonable computation time. The computational result shows that the best solution found by BRKGA for each instance of the test set matches the solution quality devised by the MIP implementation. The data also show that the proposed algorithm achieves the best solution consistently through many independent executions. The instance set used and its respective best solutions attained for this work are publicly available.", "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Key (lock)", "Routing (electronic design automation)", "Integer programming", "Set (abstract data type)", "Mathematical optimization", "Genetic algorithm", "Component (thermodynamics)", "Computation", "Integer (computer science)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://sbic.org.br/wp-content/uploads/2021/09/pdf/CBIC_2021_paper_107.pdf" }, { "id": "https://openalex.org/W7166685226", "doi": "10.5281/zenodo.21077220", "title": "Dataset and Code for 'Fight Drones with Drones: Detecting Aerial Perimeter Intrusion using Drone-mounted Microphones'", "year": 2026, "publication_date": "2026-06-30", "type": "dataset", "language": "en", "authors": [ "Vincenzo Sammartino", "Nathanaël Denis", "Omar Adel Ibrahim", "Roberto Di Pietro" ], "first_author": "Vincenzo Sammartino", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Description This repository contains the dataset and source code associated with the research paper: \"Fight Drones with Drones: Detecting Aerial Perimeter Intrusion using Drone-mounted Microphones\"Authors: Vincenzo Sammartino, Nathanaël Denis, Omar Ibrahim, and Roberto Di Pietro. Published in the Proceedings of the 31st European Symposium on Research in Computer Security (ESORICS 2026). Overview This project presents an empirically grounded framework for deploying a cooperative drone swarm as an adaptive acoustic barrier to protect critical infrastructure perimeters (e.g., airports, oil refineries, and national borders). Starting from an acoustic sensing and propagation model calibrated on real DJI flight data (Mavic Air 2 and Mini 3 Pro), we analyze the speed–detection trade-off (the Defender's Dilemma) and optimize patrol speeds to minimize the required fleet size while maintaining high detection probability. Contents of this Repository This archive includes: Empirical Calibration Dataset: Telemetry Data: Synchronized 10 Hz flight telemetry (including GPS coordinates, altitudes, 3-axis velocity vectors, and throttle percentage) for the DJI Mavic Air 2 (intruder) and DJI Mini 3 Pro (defenders). Acoustic Recordings: Onboard audio data recorded via RODE Wireless GO II microphones mounted on the patrolling defender drones, covering various flight regimes and pass-by patterns of the intruder. Simulation & Analysis Source Code: Ego-Noise Cancellation Pipeline: Spectral subtraction scripts used to filter out the defender's rotor noise, yielding a significant increase in effective detection range. Monte Carlo Engine: A highly vectorized simulation framework designed to evaluate intruder detection rates across different defender configurations, speeds, and diverse attacker flight profiles (Linear, Zigzag, Evasive, and Altitude-change). Operational Sizing Algorithms: Numerical tools to analyze energy budgets, battery-swap logistics, and collision-avoidance spacing under realistic deployment constraints. Repository & Code Access The active development repository is maintained at:https://github.com/ilsamaritano/BoundaryDefense How to Cite If you find this dataset or code useful in your research, please cite our paper: @inproceedings{sammartino2026drones2D, title={Fight Drones with Drones: Detecting Aerial Perimeter Intrusion using Drone-mounted Microphones}, author={Sammartino, Vincenzo and Denis, Nathana{\\\"e}l and Ibrahim, Omar and Di Pietro, Roberto}, booktitle={Proceedings of the 31st European Symposium on Research in Computer Security (ESORICS 2026)}, year={2026} }", "keywords": [ "Drone", "Patrolling", "Calibration", "Intrusion detection system", "Cockpit", "Global Positioning System", "Transponder (aeronautics)", "Source code", "Code (set theory)", "Filter (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.21077220" }, { "id": "https://openalex.org/W7127138749", "doi": "10.1109/ieem63636.2025.11357781", "title": "Whether to Implement and How to Improve Drone Delivery Services: A Strategic Two-Echelon Location-Allocation-Fleet-Sizing Perspective", "year": 2025, "publication_date": "2025-12-07", "type": "conference-paper", "language": null, "authors": [ "Yating Feng", "Zhiyu Xiang", "Kexin Cao", "Maohua Cai", "Xinglu Liu" ], "first_author": "Yating Feng", "venue": null, "publisher": null, "abstract": "This study evaluates the financial viability of drone-enabled delivery services through a strategic optimization framework addressing critical investment challenges. We define the studied problem as a two-echelon system incorporating three interdependent decisions: joint location optimization of automated kiosks and drone launchpads, two-step allocation (kiosk-launchpad and launchpad-demand area), and launchpad-specific drone fleet sizing. The problem is initially formulated as a profit-maximizing mixed-integer nonlinear program, which is then equivalently reformulated into a mixed-integer linear program to efficiently optimize the relevant decisions while ensuring computational traceability. Extensive numerical experiments based on Shenzhen data reveal that optimal deployment strategies yield stable profit margins of approximately 14–15%, providing quantitative validation of the economic potential of drone-based delivery systems. We also derive several valuable strategic insights based on the results. These insights reveal that in early-stage operations, enhancing infrastructure density is an effective strategy, while in more mature phases, stimulating demand becomes the key to further profitability growth. Additionally, profit volume is mainly driven by launchpad deployments (enabling service coverage), whereas profit margin is governed by the spatial efficiency (i.e., the deployments) of kiosk layouts (influencing delivery costs). Together, these findings offer practical and valuable guidance on whether and how to implement drone delivery systems, contributing to the ongoing development of the low-altitude logistics economy.", "keywords": [ "Drone", "Software deployment", "Profitability index", "Interdependence", "Strategic planning", "Interactive kiosk", "Profit margin", "Profit (economics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ieem63636.2025.11357781" }, { "id": "https://openalex.org/W7166654738", "doi": "10.5281/zenodo.21077219", "title": "Dataset and Code for 'Fight Drones with Drones: Detecting Aerial Perimeter Intrusion using Drone-mounted Microphones'", "year": 2026, "publication_date": "2026-06-30", "type": "dataset", "language": "en", "authors": [ "Vincenzo Sammartino", "Nathanaël Denis", "Omar Adel Ibrahim", "Roberto Di Pietro" ], "first_author": "Vincenzo Sammartino", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Description This repository contains the dataset and source code associated with the research paper: \"Fight Drones with Drones: Detecting Aerial Perimeter Intrusion using Drone-mounted Microphones\"Authors: Vincenzo Sammartino, Nathanaël Denis, Omar Ibrahim, and Roberto Di Pietro. Published in the Proceedings of the 31st European Symposium on Research in Computer Security (ESORICS 2026). Overview This project presents an empirically grounded framework for deploying a cooperative drone swarm as an adaptive acoustic barrier to protect critical infrastructure perimeters (e.g., airports, oil refineries, and national borders). Starting from an acoustic sensing and propagation model calibrated on real DJI flight data (Mavic Air 2 and Mini 3 Pro), we analyze the speed–detection trade-off (the Defender's Dilemma) and optimize patrol speeds to minimize the required fleet size while maintaining high detection probability. Contents of this Repository This archive includes: Empirical Calibration Dataset: Telemetry Data: Synchronized 10 Hz flight telemetry (including GPS coordinates, altitudes, 3-axis velocity vectors, and throttle percentage) for the DJI Mavic Air 2 (intruder) and DJI Mini 3 Pro (defenders). Acoustic Recordings: Onboard audio data recorded via RODE Wireless GO II microphones mounted on the patrolling defender drones, covering various flight regimes and pass-by patterns of the intruder. Simulation & Analysis Source Code: Ego-Noise Cancellation Pipeline: Spectral subtraction scripts used to filter out the defender's rotor noise, yielding a significant increase in effective detection range. Monte Carlo Engine: A highly vectorized simulation framework designed to evaluate intruder detection rates across different defender configurations, speeds, and diverse attacker flight profiles (Linear, Zigzag, Evasive, and Altitude-change). Operational Sizing Algorithms: Numerical tools to analyze energy budgets, battery-swap logistics, and collision-avoidance spacing under realistic deployment constraints. Repository & Code Access The active development repository is maintained at:https://github.com/ilsamaritano/BoundaryDefense How to Cite If you find this dataset or code useful in your research, please cite our paper: @inproceedings{sammartino2026drones2D, title={Fight Drones with Drones: Detecting Aerial Perimeter Intrusion using Drone-mounted Microphones}, author={Sammartino, Vincenzo and Denis, Nathana{\\\"e}l and Ibrahim, Omar and Di Pietro, Roberto}, booktitle={Proceedings of the 31st European Symposium on Research in Computer Security (ESORICS 2026)}, year={2026} }", "keywords": [ "Drone", "Patrolling", "Calibration", "Intrusion detection system", "Cockpit", "Global Positioning System", "Transponder (aeronautics)", "Source code", "Code (set theory)", "Filter (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.21077219" }, { "id": "https://openalex.org/W4401415144", "doi": "10.1109/icra57147.2024.10611264", "title": "ColAG: A Collaborative Air-Ground Framework for Perception-Limited UGVs’ Navigation", "year": 2024, "publication_date": "2024-05-13", "type": "conference-paper", "language": "en", "authors": [ "Zhehan Li", "Rui Mao", "Nanhe Chen", "Chao Xu", "Fei Gao", "Yanjun Cao" ], "first_author": "Zhehan Li", "venue": null, "publisher": null, "abstract": "Perception is necessary for autonomous navigation in an unknown area crowded with obstacles. It’s challenging for a robot to navigate safely without any sensors that can sense the environment, resulting in a blind robot, and becomes more difficult when comes to a group of robots. However, it could be costly to equip all robots with expensive perception or SLAM systems. In this paper, we propose a novel system named ColAG, to solve the problem of autonomous navigation for a group of blind UGVs by introducing cooperation with one UAV, which is the only robot that has full perception capabilities in the group. The UAV uses SLAM for its odometry and mapping while sharing this information with UGVs via limited relative pose estimation. The UGVs plan their trajectories in the received map and predict possible failures caused by the uncertainty of its wheel odometry and unknown risky areas. The UAV dynamically schedules waypoints to prevent UGVs from collisions, formulated as a Vehicle Routing Problem with Time Windows to optimize the UAV’s trajectories and minimize time when UGVs have to wait to guarantee safety. We validate our system through extensive simulation with up to 7 UGVs and real-world experiments with 3 UGVs.", "keywords": [ "Computer science", "Perception", "Human–computer interaction", "Psychology" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icra57147.2024.10611264" }, { "id": "https://openalex.org/W3113360774", "doi": "10.1061/9780784483053.408", "title": "Research on Vehicle Routing Problem in Urban Distribution Using Unmanned Aerial Vehicles", "year": 2020, "publication_date": "2020-12-09", "type": "conference-paper", "language": "en", "authors": [ "Tao Li", "Wen-Yin Yang", "Meng-Qing Shen", "Hang Liu" ], "first_author": "Tao Li", "venue": "CICTP 2020", "publisher": null, "abstract": "In recent years, unmanned technology for logistics has gradually developed from the concept of laboratory to application. Unmanned transport equipment, represented by unmanned aerial vehicles (UAV), has entered the public vision with the development and test by many well-known logistics enterprises such as UPS and DHL. Compared with traditional vehicle distribution, UAV distribution is flexible and low-cost, while avoiding the last kilometer delivery problem caused by road traffic congestion or vehicle restrictions. In this paper, UAV is used as an auxiliary tool of the traditional distribution vehicle. The artificial vehicles and unmanned aerial vehicles routing problem (AVUAVRP) is proposed, and a two-phase hybrid heuristic algorithm is applied to solve the problem. The effectiveness of the model and the algorithm is verified by a simulation example. The results show that UAV can effectively reduce transportation costs when it is used as an auxiliary tool in urban distribution under certain conditions.", "keywords": [ "Vehicle routing problem", "Heuristic", "Computer science", "Routing (electronic design automation)", "Traffic congestion", "Distribution (mathematics)", "City logistics", "Drone", "Transport engineering", "Real-time computing", "Automotive engineering", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1061/9780784483053.408" }, { "id": "https://openalex.org/W7172416416", "doi": "10.1016/j.trc.2026.105901", "title": "The vessel-unmanned aerial vehicle routing problem with time windows and speed optimization", "year": 2026, "publication_date": "2026-08-04", "type": "article", "language": "en", "authors": [ "Yantong Li", "Yantong Li", "Xingqi Wang", "Zheng Wang" ], "first_author": "Yantong Li", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Linearization", "Fuel efficiency", "Reduction (mathematics)", "Sensitivity (control systems)", "Routing (electronic design automation)", "Vehicle routing problem", "Nonlinear programming", "Optimization problem", "Nonlinear system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2026.105901" }, { "id": "https://openalex.org/W7118092336", "doi": "10.6084/m9.figshare.30987067.v1", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model", "year": 2026, "publication_date": "2026-01-01", "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 of the manuscript entitled \"Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model\".", "keywords": [ "Energy consumption", "Electric energy", "Routing (electronic design automation)", "Consumption (sociology)", "Energy (signal processing)", "Electric energy consumption", "Electricity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30987067.v1" }, { "id": "https://openalex.org/W7155612538", "doi": "10.6084/m9.figshare.32099392.v1", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model", "year": 2026, "publication_date": "2026-04-25", "type": "dataset", "language": null, "authors": [ "Yue Wu" ], "first_author": "Yue Wu", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model\".", "keywords": [ "Energy consumption", "Electric energy", "Routing (electronic design automation)", "Consumption (sociology)", "Energy (signal processing)", "Electric energy consumption", "Electricity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.32099392.v1" }, { "id": "https://openalex.org/W7116730053", "doi": "10.6084/m9.figshare.30282598.v2", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "Yue Wu" ], "first_author": "Yue Wu", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model\".", "keywords": [ "Energy consumption", "Electric energy", "Routing (electronic design automation)", "Consumption (sociology)", "Energy (signal processing)", "Electric energy consumption", "Electricity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30282598.v2" }, { "id": "https://openalex.org/W3112422894", "doi": "10.1061/9780784483053.406", "title": "Research on Routing Problem Based on Vehicle-Drone Joint Distribution—Taking Emergency Drugs Delivery as an Example", "year": 2020, "publication_date": "2020-12-09", "type": "conference-paper", "language": "en", "authors": [ "Yiwei Liang", "Dawei Hu", "Xiangqun Du" ], "first_author": "Yiwei Liang", "venue": "CICTP 2020", "publisher": null, "abstract": "In recent years, disasters such as global earthquakes and floods have occurred frequently. How to quickly improve rescue efficiency is the focus of emergency rescue work. In order to reduce the death rate of people in disasters, alleviate the impact on the lives and safety of the victims, and improve the timeliness of medical assistance, emergency medicine distribution has become essential. Due to the limited carrying capacity of drones, considering the road damage in the affected area and the urgency of rescue time, this paper will use vehicles and drones to distribute medicines for the disaster area. Therefore, this paper establishes a two-level vehicle-drone route optimization model based on the constraint of the soft time window of the disaster point. The model aims at minimizing vehicle operating costs, drone operating costs, and time penalty costs at the disaster site. It considers the flying range and carrying capacity of the drone, and the cooperation of the vehicle and the drone to establish the corresponding constraints. An optimization solver is used to solve small-scale examples to verify the feasibility and effectiveness of the model.", "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Constraint (computer-aided design)", "Solver", "Work (physics)", "Emergency management", "Routing (electronic design automation)", "Operations research", "Transport engineering", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1061/9780784483053.406" }, { "id": "https://openalex.org/W3214379842", "doi": "10.1007/s10846-021-01502-2", "title": "Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions", "year": 2021, "publication_date": "2021-11-10", "type": "article", "language": "en", "authors": [ "Grzegorz Radzki", "Paulina Golińska-Dawson", "Grzegorz Bocewicz", "Zbigniew Antoni Banaszak" ], "first_author": "Grzegorz Radzki", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": "Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.", "keywords": [ "Plan (archaeology)", "Computer science", "Emergency management", "Operations research", "Aeronautics", "Engineering", "Geography" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10846-021-01502-2.pdf" }, { "id": "https://openalex.org/W7146140152", "doi": null, "title": "Iterative Local Methods Based on Second-Order Cone Programming for Ship Routing Problem with Two Drones", "year": 2023, "publication_date": "2023-05-01", "type": "conference-paper", "language": "en", "authors": [ "Ning-Wen Yang", "Ning-Wen Yang", "真 山下", "Makoto Yamashita" ], "first_author": "Ning-Wen Yang", "venue": "Institutional Repositories DataBase (IRDB)", "publisher": "National Institute of Informatics", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Iterative method", "Cone (formal languages)", "Stability (learning theory)", "Control theory (sociology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100891945" }, { "id": "https://openalex.org/W7116875457", "doi": "10.6084/m9.figshare.30929732.v1", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "Yue Wu" ], "first_author": "Yue Wu", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model\".", "keywords": [ "Energy consumption", "Electric energy", "Routing (electronic design automation)", "Consumption (sociology)", "Energy (signal processing)", "Electric energy consumption", "Electricity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30929732.v1" }, { "id": "https://openalex.org/W7118088769", "doi": "10.6084/m9.figshare.30987067", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model", "year": 2026, "publication_date": "2026-01-01", "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 of the manuscript entitled \"Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model\".", "keywords": [ "Energy consumption", "Electric energy", "Routing (electronic design automation)", "Consumption (sociology)", "Energy (signal processing)", "Electric energy consumption", "Electricity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30987067" }, { "id": "https://openalex.org/W7116990151", "doi": "10.6084/m9.figshare.30929732", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "Yue Wu" ], "first_author": "Yue Wu", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"Multi-objective electric vehicle-drone routing problem: Incorporating a multi-factor energy consumption model\".", "keywords": [ "Energy consumption", "Electric energy", "Routing (electronic design automation)", "Consumption (sociology)", "Energy (signal processing)", "Electric energy consumption", "Electricity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30929732" }, { "id": "https://openalex.org/W7154342478", "doi": "10.2139/ssrn.6574359", "title": "Location-Routing Problem for Takeout Delivery Under Drone-Rider Joint Delivery Mode with Dynamic Orders", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Fuqiang Lu", "Jialong Liu", "Ziteng Wang", "Bihua Ling" ], "first_author": "Fuqiang Lu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Customer satisfaction", "Benchmark (surveying)", "Heuristic", "Delivery Performance", "Cluster analysis", "Drone", "Reduction (mathematics)", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6574359" }, { "id": "https://openalex.org/W7172508952", "doi": "10.1007/s40860-026-00274-6", "title": "Drones-dt: dynamic management of drone fleets represented by Digital Twins", "year": 2026, "publication_date": "2026-08-05", "type": "article", "language": "en", "authors": [ "Miqueias Araújo", "Lucas Silva", "Iure Fé", "Jonas Nunes", "Francinaldo Barbosa", "Iago Almeida", "Luiz Bittencourt", "Francisco Airton Silva" ], "first_author": "Miqueias Araújo", "venue": "Journal of Reliable Intelligent Environments", "publisher": "Springer Science+Business Media", "abstract": "Abstract Drone-based delivery systems have emerged as a promising solution for urban logistics, offering fast, flexible last-mile delivery. However, fluctuations in demand for requests over time make fleet sizing challenging. Overprovisioning leads to resource underutilization and higher operational costs, while underprovisioning may lead to delivery delays and violations of Service Level Agreements (SLAs). To address this challenge, this work proposes Drones-DT, a Digital Twin architecture designed for dynamic drone fleet management. The approach relies on a Stochastic Petri Net (SPN) analytical model synchronized with a delivery simulator to maintain a continuously updated Digital Shadow of the system. Based on real-time metrics, the Digital Twin performs predictive what-if analyses to estimate performance indicators, such as Mean Mission Time (MMT), system throughput, fleet utilization, and energy consumption, across different fleet configurations. A SLA-oriented decision mechanism, implemented through a binary search strategy, determines the minimum number of drones required to satisfy the target delivery time. The SPN model was statistically validated against a discrete-event simulator, yielding results equivalent to those of the simulator for MMT and throughput at the 95% confidence level. A case study demonstrates that Drones-DT maintains SLA compliance while operating with a significantly smaller average fleet than static provisioning strategies, achieving up to a 16% reduction in energy consumption and associated CO 2 emissions.", "keywords": [ "Provisioning", "Drone", "Sizing", "Throughput", "Energy consumption", "Service (business)", "Fleet management", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s40860-026-00274-6.pdf" }, { "id": "https://openalex.org/W4414812447", "doi": "10.1007/s13369-025-10688-8", "title": "A Parallel Drone-Courier Cooperative System for Dynamic Meal Delivery Routing Problem with Time-Sensitive Customers", "year": 2025, "publication_date": "2025-10-04", "type": "article", "language": "en", "authors": [ "Wenjie Wang", "Shen Gao", "Yulu Yin" ], "first_author": "Wenjie Wang", "venue": "Arabian Journal for Science and Engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Dynamic programming", "Vehicle routing problem", "Customer satisfaction", "Routing (electronic design automation)", "Time horizon", "Hybrid algorithm (constraint satisfaction)", "Drone", "Local search (optimization)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s13369-025-10688-8" }, { "id": "https://openalex.org/W4318003403", "doi": "10.1007/978-3-031-15412-6_15", "title": "Two Echelon Vehicle Routing Problem for Disaster Mapping Process Using Cooperated Vehicles Assignment", "year": 2023, "publication_date": "2023-01-01", "type": "book-chapter", "language": "en", "authors": [ "Anak Agung Ngurah Perwira Redi", "Nandini N. Sekaringtyas", "Handina B. Astiana", "Rahmad Inca Liperda", "Anna Maria Sri Asih", "Bertha Maya Sopha" ], "first_author": "Anak Agung Ngurah Perwira Redi", "venue": "Ecoproduction", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Solver", "Routing (electronic design automation)", "Schedule", "Computer science", "Vehicle routing problem", "Process (computing)", "Operations research", "Mathematical optimization", "Engineering", "Computer network", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-15412-6_15" }, { "id": "https://openalex.org/W4390124987", "doi": "10.59254/sbpo-2019-106716", "title": "DRONE ROUTING AND TRAJECTORY OPTIMISATION", "year": 2019, "publication_date": "2019-01-01", "type": "conference-paper", "language": "pt", "authors": [ "Walton Pereira Coutinho", "Jörg Fliege", "Maria Battarra", "Anand Subramanian" ], "first_author": "Walton Pereira Coutinho", "venue": "Anais do Simpósio Brasileiro de Pesquisa Operacional", "publisher": null, "abstract": "In recent years, employing Unmanned Aerial Vehicles (UAVs), a.k.a.drones, to collect data has gained popularity. Often, the use of UAVs allows for a reduction in costs for many operations.However, constraints due to the flight dynamics have often been neglected.In this paper we introduce the Unmanned Aerial Vehicle Routing and Trajectory Optimisation Problem (UAVRTOP), the problem of optimising the routes and trajectories of a fleet of UAVs.Motivated by a disaster assessment application, we propose a variant in which a fleet of autonomous aerial gliders is due to photographing a set of waypoints.We propose a Mixed-Integer Non-linear Programming (MINLP) formulation for this variant.In addition, we present a heuristic method that is composed of a novel trajectory optimisation heuristic and a classical routing matheuristic.We perform computational experiments with real-life instances based on flood risk maps of cities in the UK.", "keywords": [ "Trajectory", "Drone", "Computer science", "Routing (electronic design automation)", "Aeronautics", "Computer network", "Engineering", "Physics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://proceedings.science/proceedings/100090/_papers/106716/download/abstract_file2?lang=pt-br" }, { "id": "https://openalex.org/W4391557250", "doi": null, "title": "Droning on to Delivery: Examining the Energy Impacts of Using Drones for Moving Goods", "year": 2024, "publication_date": "2024-02-05", "type": "article", "language": "en", "authors": [ "Victor Walker" ], "first_author": "Victor Walker", "venue": "OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)", "publisher": "Office of Scientific and Technical Information", "abstract": "The demand for fast, localized delivery has grown significantly in recent years. Whether for cheesy snacks, prepared food, medical solutions, or business deliveries, fast and efficient delivery is increasingly a demand and differentiator. Drone delivery in the freight sector offers to revolutionize last-mile logistics and improve services. This research analyzes the impacts of drone energy for delivery operations, aiming to compare various types of drones (large and small, rotary and VTOL) and various types of business methods. The project executes novel open-air and laboratory-based testing to look at the impacts of weights, operations, temperatures and weather conditions. The study combines this real-world experimental data with fleet optimization mathematical models to assess energy consumption of different scenarios as well as examine the minimum fleet size and additional battery requirements. These fleet optimization models compare energy between types of deployments with existing delivery methods. The model was also extended to look at mixed fleets of aerial drones and ground vehicles to accommodate different restrictions on drones or when weather prohibits their use. The analysis provided insights into factors such as drone design, payload weight, flight distance, weather conditions and operational parameters and impacts of each. It showed how to combine different types of vehicles to reduce energy and improve services. And it showed that drone speed, routing restrictions, and unfavorable weather significantly influence energy consumption. Our open experiment data and optimization models can assist stakeholders and industry in understanding drone package delivery and offers keys to improving deployment.", "keywords": [ "Drone", "Energy (signal processing)", "Computer science", "Business", "Environmental economics", "Economics", "Physics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.osti.gov/servlets/purl/2283555" }, { "id": "https://openalex.org/W4200379974", "doi": "10.3390/app112412092", "title": "Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time", "year": 2021, "publication_date": "2021-12-19", "type": "article", "language": "en", "authors": [ "Javier Panadero", "Majsa Ammouriova", "Angel Alejandro Juan", "Alba Agustín", "María Nogal", "Carles Serrat" ], "first_author": "Javier Panadero", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an ‘agile’ optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.", "keywords": [ "Orienteering", "Agile software development", "Computer science", "Route planning", "Drone", "Flight plan", "Real-time computing", "Plan (archaeology)", "Heuristic", "Simulation", "Operations research", "Transport engineering" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/app112412092" }, { "id": "https://openalex.org/W4409394213", "doi": "10.30560/jems.v8n2p145", "title": "A Vehicle Routing Problem for Multi-depot Collaborative Delivery Considering Common and Neighboring Customers", "year": 2025, "publication_date": "2025-04-13", "type": "article", "language": "en", "authors": [ "Yanbing Gao" ], "first_author": "Yanbing Gao", "venue": "Journal of Economics and Management Sciences", "publisher": null, "abstract": "Facing the challenges of delivery cost, efficiency, and security in modern logistics systems, this study investigates a realistic scenario where multiple logistics companies jointly serve a common customer base, and customers' parcels can be picked up by their neighboring customers. By integrating mobile lockers and drones into the delivery process, we propose a multi-depot collaborative delivery vehicle routing problem with common and neighboring customers, and formulate a corresponding mixed-integer programming model. To solve the problem efficiently, a hybrid heuristic algorithm combining Adaptive Large Neighborhood Search and Tabu Search is developed. Numerical experiments demonstrate that collaboration among depots based on common and neighboring customers offers significant advantages: It not only reduces delivery costs but also improves the utilization efficiency of mobile lockers and reduces the required number of lockers. Moreover, the greater the number of depots participating in collaborative delivery, the more pronounced the benefits.", "keywords": [ "Depot", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Transport engineering", "Computer network", "Operations research", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://j.ideasspread.org/index.php/jems/article/download/917/1410" }, { "id": "https://openalex.org/W7202372477", "doi": "10.1016/j.tre.2026.105149", "title": "Electric truck–drone routing problem with flight-sensitive zones and partial truck recharging: A multi-objective optimization framework", "year": 2026, "publication_date": "2026-08-14", "type": "article", "language": "en", "authors": [ "Negar Jahanbakhsh Javid", "Sharan Srinivas" ], "first_author": "Negar Jahanbakhsh Javid", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Routing (electronic design automation)", "Vehicle routing problem", "Optimization problem", "Multi-objective optimization", "Mathematical model" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105149" }, { "id": "https://openalex.org/W7110639054", "doi": null, "title": "Solving Real-World Optimization Problems using Near-Term Quantum Computing with Applications in Vehicle Routing and Drone Delivery", "year": 2025, "publication_date": "2025-05-01", "type": "article", "language": null, "authors": [ "Holliday, James Bradley" ], "first_author": "Holliday, James Bradley", "venue": "Journal of the Arkansas Academy of Science", "publisher": "Arkansas Academy of Science", "abstract": "Quantum computing (QC) stands at the cusp of revolutionizing computation, yet its near-term potential, constrained by Noisy Intermediate-Scale Quantum (NISQ) devices, remains underexplored. This dissertation investigates how hybrid quantum-classical algorithms can address combinatorial optimization challenges in logistics, focusing on vehicle routing and drone delivery—NP-hard problems with exponential solution spaces that defy classical exhaustive methods. Amidst NISQ limitations like limited qubits and high noise, we confront key challenges: encoding complex constraints, e.g., time windows, battery capacity, into quantum models, balancing quantum and classical components for scalability, and accessing scarce quantum resources. By integrating quantum annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) with classical heuristics, this work demonstrates QC’s practical utility, advancing both optimization theory and real-world logistics applications. The dissertation unfolds across four core contributions. First, the Hybrid Quantum Tabu Search (HQTS) optimally solves the 50-location Capacitated Vehicle Routing Problem (CVRP), achieving a 4.72% optimality gap. Secondly, we refine HQTS to reach a 2.15% gap with enhanced QA utilization, outperforming prior hybrids on standard datasets. Third, adapting HQTS for the CVRPTWwith D-Wave’s Constrained Quadratic Model solver yields a 3.85% gap on 100-location instances, leveraging a novel heuristic to ensure time-window feasibility where quantum outputs falter. Lastly, QUADRO, a pioneering framework for drone delivery, employs QAOA-driven routing and hybrid scheduling, minimizing transit time and makespan for fleets of 2–11 drones needing fewer than 100 qubits, rivaling classical benchmarks on adapted Augerat datasets. These advances, tested via D-Wave’s Advantage system and Qiskit simulations, showcase hybrid strategies that mitigate NISQ constraints, delivering scalable, feasible solutions to logistics challenges. This work establishes near-term QC’s viability for combinatorial optimization, bridging theoretical promise with practical impact. Synthesizing quantum exploration with classical refinement achieves competitive performance, e.g., matching or surpassing Google ORTools in select CVRPTW cases, while illuminating NISQ limits, such as feasibility degradation beyond small scales. These hybrid strategies deliver scalable, feasible solutions, from a 2.15% optimality gap for CVRP to effective drone fleet management with fewer than 100 qubits, illuminating quantum computing’s potential despite its current constraints. Ultimately, this dissertation positions hybrid QC as a transformative tool for optimization challenges, offering a robust foundation for scaling quantum optimization into operational reality.", "keywords": [ "Quantum computer", "Solver", "Drone", "Quantum annealing", "Qubit", "Quantum", "Routing (electronic design automation)", "Vehicle routing problem", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://scholarworks.uark.edu/etd/5691" }, { "id": "https://openalex.org/W4416613035", "doi": "10.48550/arxiv.2505.23584", "title": "Collaborative Last-Mile Delivery: A Multi-Platform Vehicle Routing Problem With En-route Charging", "year": 2025, "publication_date": "2025-05-29", "type": "preprint", "language": "en", "authors": [ "Sumbal Malik", "Majid Khonji", "Khaled Elbassioni", "Jorge M.M. Dias" ], "first_author": "Sumbal Malik", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The rapid growth of e-commerce and the increasing demand for timely, cost-effective last-mile delivery have increased interest in collaborative logistics. This research introduces a novel collaborative synchronized multi-platform vehicle routing problem with drones and robots (VRP-DR), where a fleet of $\\mathcal{M}$ trucks, $\\mathcal{N}$ drones and $\\mathcal{K}$ robots, cooperatively delivers parcels. Trucks serve as mobile platforms, enabling the launching, retrieving, and en-route charging of drones and robots, thereby addressing critical limitations such as restricted payload capacities, limited range, and battery constraints. The VRP-DR incorporates five realistic features: (1) multi-visit service per trip, (2) multi-trip operations, (3) flexible docking, allowing returns to the same or different trucks (4) cyclic and acyclic operations, enabling return to the same or different nodes; and (5) en-route charging, enabling drones and robots to recharge while being transported on the truck, maximizing operational efficiency by utilizing idle transit time. The VRP-DR is formulated as a mixed-integer linear program (MILP) to minimize both operational costs and makespan. To overcome the computational challenges of solving large-scale instances, a scalable heuristic algorithm, FINDER (Flexible INtegrated Delivery with Energy Recharge), is developed, to provide efficient, near-optimal solutions. Numerical experiments across various instance sizes evaluate the performance of the MILP and heuristic approaches in terms of solution quality and computation time. The results demonstrate significant time savings of the combined delivery mode over the truck-only mode and substantial cost reductions from enabling multi-visits. The study also provides insights into the effects of en-route charging, docking flexibility, drone count, speed, and payload capacity on system performance.", "keywords": [ "Drone", "Payload (computing)", "Scalability", "Heuristic", "Truck", "Vehicle routing problem", "Routing (electronic design automation)", "Robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2505.23584" }, { "id": "https://openalex.org/W7155490756", "doi": "10.3390/drones10050322", "title": "Path Optimization for Multi-Vehicle and Multi-UAV Collaborative Delivery in Flood Rescue Under Road Disruptions: A Case Study of the 2024 Guangdong Flood Disaster", "year": 2026, "publication_date": "2026-04-24", "type": "article", "language": "en", "authors": [ "Xiya Dong", "Benhe Gao", "Runjia Liu" ], "first_author": "Xiya Dong", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Flood disasters often disrupt road networks and severely reduce ground accessibility, hindering the timely delivery of emergency supplies. To address this challenge, this study investigates a collaborative routing problem involving multiple vehicles and multiple UAVs under road disruptions and formulates a mixed-integer linear programming model that jointly minimizes mission makespan and priority-weighted response time for critical nodes. The model explicitly captures road feasibility, vehicle speeds affected by flood depth, multi-point UAV sorties, payload-dependent energy consumption, and vehicle–UAV spatiotemporal synchronization. To balance solution quality and scalability, a dual-track solution framework is developed: exact optimization is used for small instances, while a adaptive large neighborhood search algorithm with embedded dynamic programming is designed for larger instances. A case study based on the 2024 Guangdong flood with 135 demand points shows that the heuristic can obtain high-quality solutions efficiently and outperforms time-limited MILP solutions on large instances. Comparative experiments further demonstrate that multi-point sorties, integrated coordination, and embedded sortie refinement are all crucial to performance improvement. Sensitivity analysis indicates that setting the trade-off coefficient α within 0.2–0.8 provides a robust balance between overall mission efficiency and timely response to critical nodes.", "keywords": [ "Flood myth", "Heuristic", "Routing (electronic design automation)", "Linear programming", "Path (computing)", "Sensitivity (control systems)", "Job shop scheduling", "Duration (music)", "Optimization problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/5/322/pdf?version=1777030844" }, { "id": "https://openalex.org/W7204459502", "doi": "10.2139/ssrn.7358631", "title": "An Exact Method to Solve the Multi-Trip 2-Echelon Vehicle Routing Problem with Porters", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Suheyb Erdemci", "Antonio Martínez-Sykora", "Carlos Lamas Fernandez", "David Lai" ], "first_author": "Suheyb Erdemci", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Benchmark (surveying)", "Truck", "Vehicle routing problem", "Routing (electronic design automation)", "Range (aeronautics)", "Scalability", "Key (lock)", "Linear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7358631" }, { "id": "https://openalex.org/W4400943508", "doi": "10.1007/978-3-031-58919-5_9", "title": "A Greedy and Variable Neighborhood Search Metaheuristic Approach for the Cumulative Unmanned Aerial Vehicle Routing Problem", "year": 2024, "publication_date": "2024-01-01", "type": "book-chapter", "language": "en", "authors": [ "Nikolaos A. Kyriakakis", "Themistoklis Stamadianos", "Magdalene Marinaki", "Yannis Marinakis" ], "first_author": "Nikolaos A. Kyriakakis", "venue": "Springer optimization and its applications", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Vehicle routing problem", "Metaheuristic", "Variable neighborhood search", "Mathematical optimization", "Variable (mathematics)", "Computer science", "Greedy algorithm", "Routing (electronic design automation)", "Mathematics", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-58919-5_9" }, { "id": "https://openalex.org/W2531914063", "doi": "10.13016/m27f7z", "title": "Vehicle Routing Problems that Minimize the Completion Time: Heuristics, Worst-Case Analyses, and Computational Results", "year": 2016, "publication_date": "2016-01-01", "type": "dissertation", "language": "en", "authors": [ "Xingyin Wang" ], "first_author": "Xingyin Wang", "venue": "University Libraries (University of Maryland)", "publisher": "University of Maryland, College Park", "abstract": "In the standard Vehicle Routing Problem (VRP), we route a fleet of vehicles to deliver the demands of all customers such that the total distance traveled by the fleet is minimized. In this dissertation, we study variants of the VRP that minimize the completion time, i.e., we minimize the distance of the longest route. We call it the min-max objective function. In applications such as disaster relief efforts and military operations, the objective is often to finish the delivery or the task as soon as possible, not to plan routes with the minimum total distance. Even in commercial package delivery nowadays, companies are investing in new technologies to speed up delivery instead of focusing merely on the min-sum objective. In this dissertation, we compare the min-max and the standard (min-sum) objective functions in a worst-case analysis to show that the optimal solution with respect to one objective function can be very poor with respect to the other. The results motivate the design of algorithms specifically for the min-max objective. We study variants of min-max VRPs including one problem from the literature (the min-max Multi-Depot VRP) and two new problems (the min-max Split Delivery Multi-Depot VRP with Minimum Service Requirement and the min-max Close-Enough VRP). We develop heuristics to solve these three problems. We compare the results produced by our heuristics to the best-known solutions in the literature and find that our algorithms are effective. In the case where benchmark instances are not available, we generate instances whose near-optimal solutions can be estimated based on geometry. We formulate the Vehicle Routing Problem with Drones and carry out a theoretical analysis to show the maximum benefit from using drones in addition to trucks to reduce delivery time. The speed-up ratio depends on the number of drones loaded onto one truck and the speed of the drone relative to the speed of the truck.", "keywords": [ "Heuristics", "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Mathematical optimization", "Operations research", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.13016/m27f7z" }, { "id": "https://openalex.org/W4399145584", "doi": "10.2139/ssrn.4846772", "title": "The Truck-Drone Routing Problem Under Uncertainty in Rural Last-Mile Delivery: An Improved Variable Neighborhood Search Metaheuristic", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Jianhua Xiao", "Jiuhong Xiao", "Ying Li", "Yunyun Niu", "Xiaofan Wu" ], "first_author": "Jianhua Xiao", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Mile", "Metaheuristic", "Variable neighborhood search", "Last mile (transportation)", "Vehicle routing problem", "Routing (electronic design automation)", "Variable (mathematics)", "Computer science", "Transport engineering", "Operations research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4846772" }, { "id": "https://openalex.org/W4413521034", "doi": "10.1145/3745533.3745543", "title": "A branch-and-price algorithm for robust truck-drone collaborative routing problem with road restoration uncertainty in disaster relief", "year": 2025, "publication_date": "2025-03-21", "type": "conference-paper", "language": "en", "authors": [ "Qianru Li", "Xuting Sun" ], "first_author": "Qianru Li", "venue": null, "publisher": null, "abstract": "With the frequent occurrence of natural disasters and extreme weather, the issue of relief transportation in humanitarian logistics has become increasingly critical. In disaster-affected areas, roads are often blocked by debris, requiring trucks to clear obstacles and restore the accessibility of roads before they can pass. Moreover, the uncertainty of road damage further complicates rescue efforts. Drones, unaffected by road conditions, serve as excellent auxiliary tools for trucks. This study addresses a robust truck-drone collaborative routing problem integrating road restoration and relief delivery. A truck and a drone are deployed to deliver supplies, aiming to maximize the fulfillment of prioritized demands within the limited rescue time. We have constructed a model for this problem and proposed a Branch-and-Price algorithm with customized labeling algorithm for task allocation in road restoration and rescue material delivery. The computational results demonstrate its superior performance, making the method well-suited for route planning in emergency rescue scenarios.", "keywords": [ "Truck", "Drone", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Operations research", "Transport engineering", "Mathematical optimization", "Computer network", "Engineering", "Mathematics", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dl.acm.org/doi/pdf/10.1145/3745533.3745543" }, { "id": "https://openalex.org/W4412351362", "doi": "10.2139/ssrn.5348525", "title": "A Variable Neighborhood Search Algorithm for the Truck-Drone Routing Problem Under Uncertainty in Rural Last-Mile Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Jianhua Xiao", "Hang Yu", "Jiuhong Xiao", "Yunyun Niu", "Xiaofan Wu" ], "first_author": "Jianhua Xiao", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Mile", "Truck", "Vehicle routing problem", "Variable (mathematics)", "Last mile (transportation)", "Routing (electronic design automation)", "Computer science", "Transport engineering", "Operations research", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5348525" }, { "id": "https://openalex.org/W4413205496", "doi": "10.1109/ic2em63689.2025.11101219", "title": "Multi Objective Vehicle Routing Problem Using Hybrid Approach For Gas Distribution Map", "year": 2025, "publication_date": "2025-05-06", "type": "conference-paper", "language": "en", "authors": [ "Cherifi Youcef", "Yasser Bouzid", "Guiatni Mohammed", "Bouras Abdelwahab" ], "first_author": "Cherifi Youcef", "venue": null, "publisher": null, "abstract": "As the threat of Nuclear, Biological, and Chemical (NBC) weapons rises, efficient gas distribution mapping and rapid detection are crucial for public safety. This study focuses on building a Gas Distribution Map (GDM) with a multi-UAV system equipped with gas sensors, aiming to minimize system performance factors like mission time, total time, and the number of UAVs. The problem is modeled as a Multi-Objective Capacitated Vehicle Routing Problem (MO-CVRP), and a hybrid approach combining Genetic Algorithms (GA) and Simulated Annealing (SA) is proposed to optimize this task. Simulations show that this method improves significantly the system performance while maintaining high-quality of GDMs.", "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Distribution (mathematics)", "Mathematical optimization", "Computer network", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ic2em63689.2025.11101219" }, { "id": "https://openalex.org/W7165032048", "doi": "10.2139/ssrn.6960405", "title": "A Bi-Population Multi-Agent-Assisted Multi-Objective Optimization Approach for Periodic Vehicle-Drones Cooperative Routing Problem", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Mingyuan Yang", "Wei Wang", "Fuwang Dong" ], "first_author": "Mingyuan Yang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Reinforcement learning", "Evolutionary algorithm", "Convergence (economics)", "Benchmark (surveying)", "Solver", "Stability (learning theory)", "Graph", "Memetic algorithm", "Embedding", "Initialization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6960405" }, { "id": "https://openalex.org/W7139952696", "doi": "10.2139/ssrn.6446547", "title": "Drone-Truck Rural Postman Problem: A Single-Drone and Single-Truck Case", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Saif H. AL-Nimer", "Sung Hoon Chung" ], "first_author": "Saif H. AL-Nimer", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Heuristic", "Set (abstract data type)", "Footprint", "Service (business)", "Line (geometry)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6446547" }, { "id": "https://openalex.org/W7203722447", "doi": "10.1016/j.tre.2026.105167", "title": "Truck-drone routing problem under time-dependent traffic congestion: A novel variable neighborhood search metaheuristic", "year": 2026, "publication_date": "2026-08-18", "type": "article", "language": "en", "authors": [ "Shaorui Zhou", "Xiaorui Liu", "Jihong Chen", "Lingxiao Wu", "Zhou Xu", "Min Zhao" ], "first_author": "Shaorui Zhou", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Metaheuristic", "Heuristics", "Routing (electronic design automation)", "Benchmark (surveying)", "Truck", "Variable neighborhood search", "Variable (mathematics)", "Traffic congestion" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105167" }, { "id": "https://openalex.org/W7160963297", "doi": "10.6084/m9.figshare.32252883", "title": "A Drone Routing Problem Considering Charging and Fuzzy Time Windows Using an Adaptive Large Neighborhood Search Algorithm", "year": 2026, "publication_date": "2026-05-12", "type": "dataset", "language": null, "authors": [ "Yuan Fang" ], "first_author": "Yuan Fang", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.32252883" }, { "id": "https://openalex.org/W4385588096", "doi": "10.1007/978-3-030-97940-9_126", "title": "Energy-Smart Transportation Systems", "year": 2023, "publication_date": "2023-01-01", "type": "book-chapter", "language": "en", "authors": [ "Saba Sabet", "Bilal Farooq" ], "first_author": "Saba Sabet", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Electrification", "Transport engineering", "Sustainable transport", "Greenhouse gas", "Environmental economics", "Engineering", "Business", "Sustainability", "Electricity", "Economics" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-97940-9_126" }, { "id": "https://openalex.org/W7160943702", "doi": "10.6084/m9.figshare.32252883.v1", "title": "A Drone Routing Problem Considering Charging and Fuzzy Time Windows Using an Adaptive Large Neighborhood Search Algorithm", "year": 2026, "publication_date": "2026-05-12", "type": "dataset", "language": null, "authors": [ "Yuan Fang" ], "first_author": "Yuan Fang", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.32252883.v1" }, { "id": "https://openalex.org/W7161580791", "doi": "10.5281/zenodo.20272750", "title": "kallebkcs/quantum_drone_routing: Quantum Drone Routing", "year": 2026, "publication_date": "2026-05-18", "type": "software", "language": null, "authors": [ "Khare" ], "first_author": "Khare", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This repository contains the implementation and analysis of a hybrid quantum-classical framework designed to solve the Vehicle Routing Problem with spatio-temporal collision constraints. The project specifically compares the performance of the standard Quantum Approximate Optimization Algorithm (QAOA) against a specialized QAOA Ansatz utilizing the XY-mixer to navigate a $21 x 21$ discrete grid.", "keywords": [ "Routing (electronic design automation)", "Drone", "Ansatz", "Quantum", "Collision", "Quantum computer" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20272750" }, { "id": "https://openalex.org/W4316670145", "doi": "10.3389/fenrg.2022.1054859", "title": "Large-scale power inspection: A deep reinforcement learning approach", "year": 2023, "publication_date": "2023-01-17", "type": "article", "language": "en", "authors": [ "Qingshu Guan", "Xiangquan Zhang", "Minghui Xie", "Jianglong Nie", "Hui Cao", "Chen Yang Zhao", "Zhouqiang He" ], "first_author": "Qingshu Guan", "venue": "Frontiers in Energy Research", "publisher": "Frontiers Media", "abstract": "Power inspection plays an important role in ensuring the normal operation of the power grid. However, inspection of transmission lines in an unoccupied area is time-consuming and labor-intensive. Recently, unmanned aerial vehicle (UAV) inspection has attracted remarkable attention in the space-ground collaborative smart grid, where UAVs are able to provide full converge of patrol points on transmission lines without the limitation of communication and manpower. Nevertheless, how to schedule UAVs to traverse numerous, dispersed target nodes in a vast area with the least cost (e.g., time consumption and total distance) has rarely been studied. In this paper, we focus on this challenging and practical issue which can be considered as a family of vehicle routing problems (VRPs) with regard to different constraints, and propose a Diverse Trajectory-driven Deep Reinforcement Learning (DT-DRL) approach with encoder-decoder scheme to tackle it. First, we bring in a threshold unit in our encoder for better state representation. Secondly, we realize that the already visited nodes have no impact on future decisions, and then devise a dynamic-aware context embedding which removes irrelevant nodes to trace the current graph. Finally, we introduce multiply decoders with identical structure but unshared parameters, and design a Kullback-Leibler divergence based regular term to enforce decoders to output diverse trajectories, which expands the search space and enhances the routing performance. Comprehensive experiments on five types of routing problems show that our approach consistently outperforms both DRL and heuristic methods by a clear margin.", "keywords": [ "Computer science", "Reinforcement learning", "Context (archaeology)", "Margin (machine learning)", "Schedule", "Grid", "Real-time computing", "Embedding", "Encoder", "Artificial intelligence", "Machine learning", "Mathematics" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3389/fenrg.2022.1054859" }, { "id": "https://openalex.org/W2570548894", "doi": "10.1109/apeie.2016.7806419", "title": "Modeling of bio-inspired algorithms AntHocNet and BeeAdHoc for Flying Ad Hoc Networks (FANETs)", "year": 2016, "publication_date": "2016-10-01", "type": "conference-paper", "language": "en", "authors": [ "Alexey V. Leonov" ], "first_author": "Alexey V. Leonov", "venue": "2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE)", "publisher": null, "abstract": "Due to versatility, flexibility and fairly small operating costs, the use of small unmanned aerial vehicles (UAVs) groups provides new opportunities for civil application. FANET (Flying Ad Hoc Network), similar to mobile peer-to-peer networks MANET and vehicular peer-to-peer networks VANET represents a special type of peer-to-peer ad hoc network based on UAVs. FANETs are characterized by high nodes mobility, dynamically-changing topology and movement in 3D-space. To organize multi-UAV network one needs to use special routing algorithms developed due to their specific features. The article gives a short overview of the existing FANET algorithms, as well as of the algorithms based on the swarm intelligence (of the ant and bee colonies). The experimental analysis was conducted, that proved the possibility of efficient application of bio-inspired algorithms. The analysis was performed on protocols AntHocNet and BeeAdHoc, simulating the behavior of bees and ants in wildlife, to solve the routing problems in FANETs.", "keywords": [ "Computer science", "Mobile ad hoc network", "Wireless ad hoc network", "Vehicular ad hoc network", "Network topology", "Computer network", "Flexibility (engineering)", "Distributed computing", "Optimized Link State Routing Protocol", "Swarm behaviour", "Routing protocol", "Swarm intelligence" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/apeie.2016.7806419" }, { "id": "https://openalex.org/W7120874217", "doi": null, "title": "Autonomous drones routing: an algorithmic study", "year": 2020, "publication_date": "2020-02-28", "type": "dissertation", "language": "pt", "authors": [ "Lettiery D’Lamare Portela Procópio" ], "first_author": "Lettiery D’Lamare Portela Procópio", "venue": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "publisher": "LA Referencia", "abstract": "This work formulates the Assicron’s version of the Close-Enough Vehicle Routing Problem, used for aerial reconnaissance route planning. We formulate the problem with the second-order programming model and apply heuristic optimization techniques based on a geometric property of the problem to solve it. We present the results of extensive computational experiments with adapted instances of the literature, the tests show that our method produces high quality solutions quickly when compared to the solver.", "keywords": [ "Drone", "Property (philosophy)", "Heuristic", "Routing (electronic design automation)", "Work (physics)", "Quality (philosophy)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://repositorio.ufrn.br/jspui/handle/123456789/29656" }, { "id": "https://openalex.org/W7161572948", "doi": "10.5281/zenodo.20272749", "title": "kallebkcs/quantum_drone_routing: Quantum Drone Routing", "year": 2026, "publication_date": "2026-05-18", "type": "software", "language": null, "authors": [ "Khare" ], "first_author": "Khare", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This repository contains the implementation and analysis of a hybrid quantum-classical framework designed to solve the Vehicle Routing Problem with spatio-temporal collision constraints. The project specifically compares the performance of the standard Quantum Approximate Optimization Algorithm (QAOA) against a specialized QAOA Ansatz utilizing the XY-mixer to navigate a $21 x 21$ discrete grid.", "keywords": [ "Routing (electronic design automation)", "Drone", "Ansatz", "Quantum", "Collision", "Quantum computer" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20272749" }, { "id": "https://openalex.org/W2099515255", "doi": "10.1109/cec.2008.4630897", "title": "A two-step evolutionary and ACO approach for solving the multi-agent patrolling problem", "year": 2008, "publication_date": "2008-06-01", "type": "conference-paper", "language": "en", "authors": [ "Fabrice Lauri", "Abderrafìâa Koukam" ], "first_author": "Fabrice Lauri", "venue": null, "publisher": null, "abstract": "Patrolling an environment involves a team of agents whose goal usually consists in continuously visiting its most relevant areas as frequently as possible. For such a task, agents have to coordinate their actions in order to achieve optimal performance. Current research that tackles this complex multi-agent problem usually defines the environment as a graph, so that a wide range of applications can be dealt with, from computer network management to computer games and vehicle routing. In this paper, we consider only the instances of the multi-agent patrolling problem where all the agents are located on the same starting node. These instances are often encountered in robotics applications, where e.g. drones start from the same area, disperse over it and finally patrol around distant locations. We introduce a new ant colony optimization (ACO) algorithm that is combined with an evolutionary algorithm (EA) technique. The novel ACO algorithm uses several ant colonies that are engaged in a competition for finding out the best multi-agent patrolling strategy. The goal of the EA is to find the best set of distant nodes enabling each agent to disperse efficiently over the graph. Experimental results show that, irrespective of the number of the involved patrolling agents and for all the graphs evaluated, our two-step EA and ACO algorithm outperforms significantly and with efficiency the best techniques proposed in the literature since now.", "keywords": [ "Patrolling", "Computer science", "Ant colony optimization algorithms", "Ant colony", "Evolutionary algorithm", "Artificial intelligence", "Node (physics)", "Mathematical optimization", "Graph", "Task (project management)", "Set (abstract data type)", "Theoretical computer science" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cec.2008.4630897" }, { "id": "https://openalex.org/W4289597315", "doi": "10.48550/arxiv.2208.00931", "title": "Networked Drones for Industrial Emergency Events", "year": 2022, "publication_date": "2022-08-01", "type": "preprint", "language": "en", "authors": [ "Maryam Khalid", "Edward W. Knightly" ], "first_author": "Maryam Khalid", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Uncontrolled emissions of gases from industrial accidents and disasters result in huge loss of life and property. Such extreme events require a quick and reliable survey of the site for effective rescue strategy planning. To achieve these goals, a network of unmanned aerial vehicles can be deployed that survey the affected region and identify safe and danger zones. Although single UAV-based systems for gas sensing applications are well-studied in literature, research on the deployment of a UAV network for such applications, which is more robust and fault tolerant, is still in infancy. The objective of this project is to design a system that can be deployed in emergency situations to provide a quick survey and identification of safe and dangerous zones in a given region that contains a toxic plume without making any assumptions about plume location. We focus on an end-to-end solution and formulate a two-phase strategy that can not only guarantee detection/acquisition of plume but also its characterization with high spatial resolution. To guarantee coverage of the region with a certain spatial resolution, we set up a vehicle routing problem. To overcome the limitations imposed by limited range of sensors and drone resources, we estimate the concentration map by using Gaussian kernel extrapolation. Finally, we evaluate the suggested framework in simulations. Our results suggest that this two-phase strategy not only gives better error performance but is also more efficient in terms of mission time. Moreover, the comparison between 2-phase random search and 2-phase uniform coverage suggest that the latter is better for single drone systems whereas for multiple drones the former gives reasonable performance at low computational cost.", "keywords": [ "Drone", "Computer science", "Software deployment", "Identification (biology)", "Event (particle physics)", "Real-time computing", "Focus (optics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2208.00931" }, { "id": "https://openalex.org/W4312432094", "doi": "10.1109/tits.2022.3225269", "title": "Optimal Routing of Unmanned Aerial Vehicle for Joint Goods Delivery and in-Situ Sensing", "year": 2022, "publication_date": "2022-12-05", "type": "article", "language": "en", "authors": [ "Bin Liu", "Wei Ni", "Ren Ping Liu", "Y. Jay Guo", "Hongbo Zhu" ], "first_author": "Bin Liu", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper puts forth a new application of an unmanned aerial vehicle (UAV) to joint goods delivery and in-situ sensing, and proposes a new algorithm that jointly optimizes the route and sensing task selection to minimize the UAV’s energy consumption, maximize its sensing reward, and ensure timely goods delivery. This problem is new and non-trivial due to its nature of mixed integer programming. The key idea behind the new algorithm is that we interpret the possible waypoints of the UAV as location-dependent tasks to incorporate routing and sensing in one task selection process. Another critical aspect is that we construct a new task-time graph to describe the process, where each vertex corresponds to a task associated with its location, time and reward, and each edge indicates the propulsion energy required for the UAV to travel between two tasks. By redistributing the weight of a vertex to its incoming edges, the new UAV routing and sensing task selection problem can be converted to a weighted routing problem in the new task-time graph and solved optimally using the Bellman-Ford algorithm. Validated by a real-world case study, our approach can outperform its alternatives by over 18% in task reward.", "keywords": [ "Computer science", "Task (project management)", "Routing (electronic design automation)", "Energy consumption", "Key (lock)", "Integer programming", "Graph", "Vehicle routing problem", "Real-time computing", "Distributed computing", "Process (computing)", "Engineering" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2022.3225269" }, { "id": "https://openalex.org/W4396886677", "doi": "10.1109/ieeeconf58110.2023.10520580", "title": "UAV Fleet Size Optimization in Disaster Management: A Comparative Analysis using HFFPSO", "year": 2023, "publication_date": "2023-11-22", "type": "conference-paper", "language": "en", "authors": [ "Indu Chandran", "Kizheppatt Vipin" ], "first_author": "Indu Chandran", "venue": null, "publisher": null, "abstract": "Disasters, whether they are of natural origin or caused by human activities, can have devastating consequences on both human lives and infrastructure. In many cases, these disasters can render certain areas inaccessible to first responders due to safety concerns or challenging terrain. To address this challenge, researchers are exploring the potential of unmanned aerial vehicles, commonly known as drones, as a promising solution for disaster monitoring, assessment, and recovery efforts. Current relief operations depend on intelligent robotic agents to evaluate the damage, expedite timely rescues, and generate maps to assist the affected individuals. To effectively carry out these time-sensitive missions, it is crucial to deploy an appropriate number of reliable UAVs in the affected area. Furthermore, the adoption of efficient path planning techniques is essential to ensure swift coverage of the area. The goal of this research is to establish a well-balanced method for calculating the ideal quantity of unmanned aerial vehicles (UAVs) necessary to effectively survey a specific area while maximizing coverage within a predefined time frame. This involves developing a coverage trajectory through the processes of area decomposition and task assignment, utilizing a Hybrid Firefly-Particle Swarm Optimization (HFFPSO) approach. The study considers two distinct architectures with a homogeneous set of UAVs, and their performance is evaluated and compared in terms of mission latency. The study makes use of PX4-SITL flight stack in ROS framework, and the vehicle dynamics are visualized in the Gazebo physical environment. The research compares the effectiveness of the proposed approach to benchmark optimization strategies using comprehensive theoretical analysis and experimental evaluations.", "keywords": [ "Computer science", "Drone", "Search and rescue", "Benchmark (surveying)", "Terrain", "Task (project management)", "Trajectory", "Resilience (materials science)", "Particle swarm optimization", "Real-time computing", "Operations research", "Systems engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/ieeeconf58110.2023.10520580" }, { "id": "https://openalex.org/W7170640647", "doi": "10.1016/j.tre.2026.105111", "title": "Robust heterogeneous bus-assisted drone delivery under transit uncertainty and drone failures", "year": 2026, "publication_date": "2026-07-24", "type": "article", "language": "en", "authors": [ "Xuefeng Jin", "Mingyang Pei", "Lingshu Zhong" ], "first_author": "Xuefeng Jin", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Benchmark (surveying)", "Transit (satellite)", "Nonlinear programming", "Robust optimization", "Software deployment", "Public transport", "Elevator" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105111" }, { "id": "https://openalex.org/W4321063214", "doi": "10.1109/indicon56171.2022.10039853", "title": "Multi-Agent Coverage Path Planning using a Swarm of Unmanned Aerial Vehicles", "year": 2022, "publication_date": "2022-11-24", "type": "conference-paper", "language": "en", "authors": [ "Ragala Chethan", "Indrani Kar" ], "first_author": "Ragala Chethan", "venue": "2022 IEEE 19th India Council International Conference (INDICON)", "publisher": null, "abstract": "In recent years, rapid technological advancements in unmanned aerial vehicles (UAVs) have propelled their applications to a wide range of areas such as agriculture, mapping & surveying, surveillance, and many more. A swarm of UAVs can efficiently accomplish the goals rather than a single UAV due to its ability to cover a larger area. Multi-agent coverage path planning (CPP) is the process of determining efficient coverage paths for the swarm of UAVs to completely cover an area of interest. A cellular grid is formed using hexagonal decomposition to ensure complete coverage, with the centroid of each cell acting as a waypoint. The k-means clustering algorithm is implemented to divide the set of waypoints such that the workload is uniformly distributed among the UAVs. Multi-agent CPP is transformed into multiple single-agent CPP, thereby alleviating the exploratory complexity of multi-agent CPP. A mixed-integer linear programming based vehicle routing problem is formed to optimize the path of each UAV considering various constraints.", "keywords": [ "Computer science", "Swarm behaviour", "Motion planning", "Set cover problem", "Waypoint", "Distributed computing", "Cluster analysis", "Real-time computing", "Path (computing)", "Cover (algebra)", "Integer programming", "Decomposition" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/indicon56171.2022.10039853" }, { "id": "https://openalex.org/W7154949844", "doi": "10.48550/arxiv.2604.15890", "title": "Robust Fleet Sizing for Multi-UAV Inspection Missions under Synchronized Replacement Demand", "year": 2026, "publication_date": "2026-04-17", "type": "preprint", "language": null, "authors": [ "Vishal Ramesh", "Antony Thomas" ], "first_author": "Vishal Ramesh", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Multi-UAV inspection missions require spare drones to replace active drones during recharging cycles. Existing fleet-sizing approaches often assume steady-state operating conditions that do not apply to finite-horizon missions, or they treat replacement requests as statistically independent events. The latter provides per-request blocking guarantees that fail to translate to mission-level reliability when demands cluster. This paper identifies a structural failure mode where efficient routing assigns similar workloads to each UAV, leading to synchronized battery depletion and replacement bursts that exhaust the spare pool even when average capacity is sufficient. We derive a closed-form sufficient fleet-sizing rule, k = m(ceil(R) + 1), where m is the number of active UAVs and R is the recovery-to-active time ratio. This additive buffer of m spares absorbs worst-case synchronized demand at recovery-cycle boundaries and ensures mission-level reliability even when all UAVs deplete simultaneously. Monte Carlo validation across five scenarios (m in [2, 10], R in [0.87, 3.39], 1000 trials each) shows that Erlang-B sizing with a per-request blocking target epsilon = 0.01 drops to 69.9% mission success at R = 3.39, with 95% of spare exhaustion events concentrated in the top-decile 5-minute demand windows. In contrast, the proposed rule maintains 99.8% success (Wilson 95% lower bound 99.3%) across all tested conditions, including wind variability up to CV = 0.30, while requiring only four additional drones in the most demanding scenario.", "keywords": [ "Spare part", "Sizing", "Reliability (semiconductor)", "Blocking (statistics)", "Drone", "Failure mode, effects, and criticality analysis", "Upper and lower bounds", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2604.15890" }, { "id": "https://openalex.org/W4393624630", "doi": "10.2139/ssrn.4781375", "title": "Bi-Objective Optimization Models for Truck-Drone-Boat Routing Problem with an Equity-Based Perspective in Disaster Relief Distribution", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Fadillah Ramadhan", "Chandra Ade Irawan", "Antony Paulraj", "Cai Zhao" ], "first_author": "Fadillah Ramadhan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Equity (law)", "Perspective (graphical)", "Vehicle routing problem", "Distribution (mathematics)", "Operations research", "Computer science", "Routing (electronic design automation)", "Transport engineering", "Business", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4781375" }, { "id": "https://openalex.org/W197844793", "doi": null, "title": "Ant Colony Optimization applied to the Multi-Agent Patrolling Problem", "year": 2006, "publication_date": "2006-05-12", "type": "article", "language": "en", "authors": [ "Fabrice Lauri", "François Charpillet" ], "first_author": "Fabrice Lauri", "venue": null, "publisher": null, "abstract": "Abstract- Patrolling an environment involves a team of agents whose goal usually consists of continuously visiting its most relevant areas as frequently as possi-ble. For such a task, agents have to coordinate their ac-tions in order to achieve optimal performance. Current research that tackles this complex multi-agent prob-lem usually defines the environment as a graph, so that a wide range of applications can be dealt with, from computer network management to computer games and vehicle routing. In this paper, we consider only in-stances of the multi-agent patrolling problem where all agents are placed on the same starting node. These in-stances are often encountered in robotics applications, where e.g. drones start to patrol from the same area. The Ant Colony Optimization is adopted as the solu-tion approach to these problem instances. Two novel ACO algorithms are proposed here, in which several ants ' colonies are engaged in a competition for finding out the best multi-agent patrolling strategy. Experi-mental results show that, for three graph topologies out of the six which were evaluated, one of our ACO techniques, GU/AA, significantly outperforms the re-inforcement learning technique proposed by Santana et al. (2004) (i.e. GBLA), irrespective of the number of the involved patrolling agents. For the other graph topologies, GU/AA approaches the results obtained with GBLA. 1", "keywords": [ "Patrolling", "Ant colony optimization algorithms", "Computer science", "Reinforcement learning", "Network topology", "Ant colony", "Artificial intelligence", "Graph", "Mathematical optimization", "Optimization problem", "Node (physics)", "Theoretical computer science" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.726.6662" }, { "id": "https://openalex.org/W3200556744", "doi": "10.1007/978-3-030-86433-0_9", "title": "A Feature Based Solution Approach for the Flying Sidekick Traveling Salesman Problem", "year": 2021, "publication_date": "2021-01-01", "type": "conference-paper", "language": "en", "authors": [ "Maurizio Boccia", "Andrea Mancuso", "Adriano Masone", "Claudio Sterle" ], "first_author": "Maurizio Boccia", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Solver", "Computer science", "Drone", "Travelling salesman problem", "Vehicle routing problem", "A priori and a posteriori", "Truck", "Operations research", "Routing (electronic design automation)", "Feature (linguistics)", "Problem solver", "Computer network" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-86433-0_9" }, { "id": "https://openalex.org/W4402299041", "doi": "10.17725/j.rensit.2024.16.629", "title": "Enhancing Drone Stability in Adverse Weather Conditions: A Novel MySQL-Based Approach for Balancing UAVs in Rain", "year": 2024, "publication_date": "2024-09-06", "type": "article", "language": "en", "authors": [ "Mohammed Hussein", "Abdullah Qays Taher", "Mohammed Jasim Mohammed", "Alaan Ghazi", "Abdullah Mohammed Awad", "Sharmeen Izzat Hassan", "Haider Mahmood Jawad", "Marwa Saad Mahdi Hussin", "Bektur Abdyrahmanovich Azimov" ], "first_author": "Mohammed Hussein", "venue": "Radioelectronics Nanosystems Information Technologies", "publisher": null, "abstract": "Background: Unmanned Aerial Vehicles (UAVs) can potentially transform several sectors, notably last-mile delivery. Despite its potential, their implementation in real-world situations, particularly in severe weather like rain, must be improved with route optimisation and stability issues. Objective: This study aims to provide a fresh approach to the issues that drones confront in wet weather using unique MySQL code designed to improve drone performance in such environments. Methods: A customised algorithm based on the Vehicle Routing Problem (VRP) was specially adapted for drone delivery. It considers battery life, payload weight, and estimated delivery timeframes. This algorithm aims to alter the drone's path under wet circumstances to maintain balance, preserve energy, and reduce the possibility of delivery failure. Results: Using MySQL custom code for drones may bring several advantages. It may not only increase battery life by guaranteeing appropriate routing, but it may also shorten delivery times and prevent cases of lost or damaged items due to weather. Delivery efficiency may be significantly improved. Conclusion: Incorporating bespoke MySQL code provides a viable option for addressing the inherent issues that drones confront in damp situations. This technology allows for more dependable and effective drone deliveries, even in less-than-ideal weather circumstances, by optimising routes and assuring stability.", "keywords": [ "Drone", "Adverse weather", "Stability (learning theory)", "Weather modification", "Meteorology", "Computer science", "Environmental science", "Aeronautics", "Geography", "Engineering", "Machine learning", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://en.rensit.ru/vypuski/article/594/16(5)629-642e.pdf" }, { "id": "https://openalex.org/W4388508461", "doi": "10.1016/j.trc.2023.104406", "title": "Unmanned aerial vehicle service network design for urban monitoring", "year": 2023, "publication_date": "2023-11-08", "type": "article", "language": "en", "authors": [ "Bolong Zhou", "Wei Liu", "Hai Long Yang" ], "first_author": "Bolong Zhou", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Heuristic", "Flow network", "Service (business)", "Routing (electronic design automation)", "Mathematical optimization", "Plan (archaeology)", "Network planning and design", "Real-time computing", "Operations research", "Engineering", "Computer network" ], "cited_by_count": 35, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10397/108914" }, { "id": "https://openalex.org/W7153544331", "doi": "10.15878/j.instr.202500300", "title": "A Memetic Algorithm for Solving UAV Routing Problems with Profits", "year": 2025, "publication_date": "2025-12-15", "type": "article", "language": "en", "authors": [ "S Hua", "J Xu", "H Zhang", "Q Zhang", "L Qin", "L Hua" ], "first_author": "S Hua", "venue": "Liverpool John Moores University", "publisher": "Liverpool John Moores University", "abstract": "This study addresses the Unmanned Aerial Vehicle routing problems with profits, which requires balancing mission profit, path efficiency, and battery health under complex constraints, particularly the nonlinear degradation of batteries. This paper proposes an enhanced memetic algorithm by integrating adaptive local search and a dynamic population management mechanism. The algorithm employs a hybrid initialization strategy to generate high-quality initial solutions. It incorporates an improved linear crossover operator to preserve beneficial path characteristics and introduces dynamically probability-controlled local search to optimize solution quality. To enhance global exploration capability, a population screening mechanism based on solution similarity and a population restart strategy simulating biological mass extinction are designed. Extensive experiments conducted on standard Tsiligirides's and Chao's datasets demonstrate the algorithm's robust performance across scenarios ranging from 21 to 66 nodes and time constraints spanning 5 to 130 minutes. The algorithm attains 95% accuracy relative to maximum total score within 30 iterations, surpassing 99% accuracy after 100 iterations. Its comprehensive performance significantly surpasses that of traditional heuristic methods. The proposed method provides an efficient and robust solution for Unmanned Aerial Vehicle routing planning under intricate constraints.", "keywords": [ "Memetic algorithm", "Initialization", "Crossover", "Population", "Heuristic", "Local search (optimization)", "Vehicle routing problem", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://researchonline.ljmu.ac.uk/id/eprint/28356/1/005_202500300_Siliang%2BHua_L.pdf" }, { "id": "https://openalex.org/W7165544990", "doi": "10.1016/j.cie.2026.112210", "title": "A novel NSGA-II method integrated with Hopfield Neural Network for solving the multi-objective drone routing problem: pharmaceutical distribution application", "year": 2026, "publication_date": "2026-06-22", "type": "article", "language": "en", "authors": [ "Serap Ercan Cömert", "Sena Kır" ], "first_author": "Serap Ercan Cömert", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Metric (unit)", "Drone", "Sorting", "Benchmark (surveying)", "Routing (electronic design automation)", "Artificial neural network", "Genetic algorithm", "Population" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2026.112210" }, { "id": "https://openalex.org/W4310820021", "doi": "10.48550/arxiv.2212.01445", "title": "Drones-aided Asset Maintenance in Hospitals", "year": 2022, "publication_date": "2022-12-02", "type": "preprint", "language": "en", "authors": [ "Muhammad Asif Khan", "Hamid Menouar", "Ridha Hamila" ], "first_author": "Muhammad Asif Khan", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets.", "keywords": [ "Drone", "Service (business)", "Asset (computer security)", "Heuristic", "Vehicle routing problem", "Pandemic", "Computer science", "Operations research", "Routing (electronic design automation)", "Coronavirus disease 2019 (COVID-19)", "Integer programming", "Business" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2212.01445" }, { "id": "https://openalex.org/W4386410340", "doi": "10.3390/vehicles5030058", "title": "Planning Integrated Unmanned Aerial Vehicle and Conventional Vehicle Delivery Operations under Restricted Airspace: A Mixed Nested Genetic Algorithm and Geographic Information System-Assisted Optimization Approach", "year": 2023, "publication_date": "2023-09-02", "type": "article", "language": "en", "authors": [ "Κωνσταντίνος Κουρέτας", "Konstantinos L. Kepaptsoglou" ], "first_author": "Κωνσταντίνος Κουρέτας", "venue": "Vehicles", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Using Unmanned Aerial Vehicles (UAVs), commonly referred to as “drones”, as a supplementary mode for last-mile deliveries has been a research focus for some years now. Motivation lies in the reduced dependency on Conventional Vehicles (CVs) and fossil fuels and in serving remote areas and underprivileged populations. We are building a flexible, modular framework for integrated CV-UAV parcel delivery operations planning that is responsive to infrastructure and demand and offers an open and practical tool for future adaptations. The entire model and solution methodology are practical tools for decision making and strategic planning, with novelties such as the variable Launch Site types for Launch and Recovery Operations (LAROs), the tailored Assignment and Routing Optimization nested GA, the consideration of airspace restrictions of any shape and size, the inclusion of GIS tools in the process, the modularity of the platform, and most importantly, the inclusion of all the above in a single, comprehensive, and holistic approach. Because of the need for safe UAV deployment sites and the high presence of restricted airspace zones in urban environments, the intended field of application is assumed to be the delivery of small packages in rural and under-connected areas, the execution of inter-city deliveries, and the expansion of a city’s original service range. A single CV is equipped onboard with UAVs, while special locations, such as Remote Depots (RDs) with UAVs and Virtual Hubs (VHs) for UAV deployment facilitation, are introduced. The framework considers the presence of Restricted Zones (RZs) for UAV flights. Part of the methodology is implemented in a GIS environment, taking advantage of modern tools for spatial analysis and optimal path planning. We have designed a tailored nested GA method for solving the occurring mode assignment and vehicle routing optimization problems and have implemented our workflow on a devised case study with benchmark characteristics. Our model responds well to unfavorable network types and demand locations, while the presence of RZs notably affects the expected solution and should be considered in the decision-making process.", "keywords": [ "Software deployment", "Computer science", "Modular design", "Process (computing)", "Vehicle routing problem", "Drone", "Systems engineering", "Operations research", "Engineering", "Routing (electronic design automation)", "Computer network", "Software engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2624-8921/5/3/58/pdf?version=1693880370" }, { "id": "https://openalex.org/W2889042213", "doi": "10.1007/978-3-319-99972-2_6", "title": "Deployment of Battery Swapping Stations for Unmanned Aerial Vehicles Subject to Cyclic Production Flow Constraints", "year": 2018, "publication_date": "2018-01-01", "type": "conference-paper", "language": "en", "authors": [ "Grzegorz Bocewicz", "Peter Nielsen", "Zbigniew Antoni Banaszak", "Amila Thibbotuwawa" ], "first_author": "Grzegorz Bocewicz", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Software deployment", "Workstation", "Computer science", "Pickup", "Vehicle routing problem", "Battery (electricity)", "Real-time computing", "Production (economics)", "Routing (electronic design automation)", "Simulation", "Automotive engineering", "Power (physics)" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-319-99972-2_6" }, { "id": "https://openalex.org/W2002037549", "doi": "10.1109/icuas.2014.6842249", "title": "Routing of two Unmanned Aerial Vehicles with communication constraints", "year": 2014, "publication_date": "2014-05-01", "type": "conference-paper", "language": "en", "authors": [ "Satyanarayana G. Manyam", "Sivakumar Rathinam", "Swaroop Darbha", "David W. Casbeer", "Phil R. Chandler" ], "first_author": "Satyanarayana G. Manyam", "venue": null, "publisher": null, "abstract": "A novel GPS denied routing problem for UAVs is described, where two UAVs cooperatively navigate through an array of non-communicating Unattended Ground Sensors (UGS). Contact with UGS is strictly maintained, which allows the UGS act as beacons for relative navigation eliminating the need for dead reckoning. This problem is referred to as the Communication Constrained UAV Routing Problem (CCURP). To solve the CCURP, shortest paths between targets are computed by means of a graph transformation. Given the shortest paths between targets, two solution methods are presented. The first is a 15 over 2-approximation algorithm. The second method poses the CCURP as an one-in-a-set Traveling Salesman Problem (TSP), which can then be solved using known methods by transforming the problem into a regular asymmetric TSP. Computational results corroborating the performance bounds in this article are also presented.", "keywords": [ "Travelling salesman problem", "Computer science", "Beacon", "Routing (electronic design automation)", "Shortest path problem", "Transformation (genetics)", "Mathematical optimization", "Graph", "Real-time computing", "Computer network", "Algorithm", "Mathematics" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas.2014.6842249" }, { "id": "https://openalex.org/W7130932331", "doi": "10.2514/1.d0554", "title": "Privacy-Aware Routing for Drone Delivery as a Service", "year": 2026, "publication_date": "2026-02-22", "type": "article", "language": "en", "authors": [ "Geoffrey Ding", "Alex Berke", "Christopher Chin", "Kwassi Holali Degue", "Hamsa Balakrishnan", "Karthik Gopalakrishnan", "Max Z. Li" ], "first_author": "Geoffrey Ding", "venue": "Journal of Air Transportation", "publisher": "American Institute of Aeronautics and Astronautics", "abstract": "Uncrewed aerial vehicles (UAVs), or drones, are increasingly being used to deliver goods from vendors to customers. In an emerging business model, a drone operator partners with multiple businesses to offer drone delivery as a service. However, this business model poses a privacy risk due to regulations requiring drones to broadcast location information. Third-party observers may leverage broadcast trajectories to link customers to vendors, resulting in potential privacy issues. A probabilistic definition of privacy risk is proposed based on the likelihood that a third-party observer can correctly infer which vendor a customer ordered from. Next, these risks are quantified, and the impacts of order count, drone capacity, decoy stops, and delivery time requirements on privacy are evaluated. Privacy risk mitigation is then formulated as an optimization problem and integrated into the vehicle routing problem. Doing so allows optimization of efficiency while satisfying a privacy constraint. A comparison of the tradeoffs between privacy and efficiency based on the geographical arrangement of vendors and customers is presented. Finally, limitations, including complexity, assumptions, and areas for future work, are discussed.", "keywords": [ "Drone", "Leverage (statistics)", "Probabilistic logic", "Information privacy", "Routing (electronic design automation)", "Service (business)", "Order (exchange)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/1.d0554" }, { "id": "https://openalex.org/W2612960064", "doi": null, "title": "Drones reveal efficiency savings in delivery services", "year": 2015, "publication_date": "2015-07-12", "type": "conference-paper", "language": "en", "authors": [ "Wolfgang Garn" ], "first_author": "Wolfgang Garn", "venue": "Surrey Research Insight Open Access (The University of Surrey)", "publisher": "University of Surrey", "abstract": "Drone delivery services (DDS) are an upcoming reality. Companies such as Amazon, DHL and Google are investing in developments in this area. German’s parcel delivery company DHL has begun applying them commercially. This emphasises the importance to have insights in the economic benefits of drone delivery services. This study compares drone delivery services with traditional delivery services. It looks at the cost effectiveness of integrating drones into a delivery services’ operations. The study identifies and categorise factors that control the delivery operations. It develops a mathematical model that compares 3D flight paths of a fleet of drones with a fleet of 2D van routes. The developed method can be seen as an extension of the Vehicle Routing Problem (VRP). Efficiency savings of drone services of real world rural postcode sectors are analysed. The study limits itself to the use of small unmanned aerial vehicles (UAVs) with a low payload, which are GPS controlled and autonomous. The case study shows time, distance and cost savings when using drones rather than delivery vans. The model reveals efficiency factors to operate DDS. The study shows the economic necessity for delivering low weight goods via DDS. The primary methodological novelty of this study is a model that integrates factors relevant to drones into the VRP.", "keywords": [ "Drone", "Payload (computing)", "Operations research", "Global Positioning System", "Computer science", "Transport engineering", "Engineering", "Computer security", "Telecommunications" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://epubs.surrey.ac.uk/808168/1/SRI_deposit_agreement.pdf" }, { "id": "https://openalex.org/W7155632713", "doi": "10.6084/m9.figshare.32099392", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating multi-factor energy-consumption inputs and a partial recharging strategy", "year": 2026, "publication_date": "2026-04-25", "type": "dataset", "language": null, "authors": [ "Yue Wu" ], "first_author": "Yue Wu", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"Multi-objective electric vehicle-drone routing problem: Incorporating multi-factor energy-consumption inputs and a partial recharging strategy\".", "keywords": [ "Energy consumption", "Electric energy", "Routing (electronic design automation)", "Consumption (sociology)", "Energy (signal processing)", "Electric energy consumption", "Electricity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.32099392" }, { "id": "https://openalex.org/W7169767478", "doi": "10.6084/m9.figshare.32099392.v2", "title": "Multi-objective electric vehicle-drone routing problem: Incorporating multi-factor energy-consumption inputs and a partial recharging strategy", "year": 2026, "publication_date": "2026-04-25", "type": "dataset", "language": null, "authors": [ "yue wu" ], "first_author": "yue wu", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"Multi-objective electric vehicle-drone routing problem: Incorporating multi-factor energy-consumption inputs and a partial recharging strategy\".", "keywords": [ "Electric energy", "Routing (electronic design automation)", "Energy consumption", "Energy (signal processing)", "Consumption (sociology)", "Electric utility" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.32099392.v2" }, { "id": "https://openalex.org/W7161115351", "doi": "10.1109/metroagrifor66923.2025.11512593", "title": "Ant Colony Optimization for Drone Formation Deployment in Agriculture", "year": 2025, "publication_date": "2025-10-28", "type": "conference-paper", "language": null, "authors": [ "Salvatore Rosario Bassolillo", "Egidio D’Amato", "Immacolata Notaro" ], "first_author": "Salvatore Rosario Bassolillo", "venue": null, "publisher": null, "abstract": "In the context of precision agriculture, the integration of Unmanned Aerial Vehicles (UAV) offers a significant potential to improve sustainability and operating efficiency. This article proposes an approach through a centralized mission planner based on an Ant Colony Optimization (ACO) procedure to optimize drones logistics and manage drone formations in the agricultural context. The proposed procedure solves a Vehicle Routing Problem (VRP), to efficiently assign monitoring targets to UAVs, considering constraints about drones capacity and autonomy. To prove the effectiveness of the proposed approach, preliminary results are presented in a simulated environment.", "keywords": [ "Software deployment", "Drone", "Agriculture", "Ant colony optimization algorithms", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/metroagrifor66923.2025.11512593" }, { "id": "https://openalex.org/W7141944184", "doi": "10.2139/ssrn.6487608", "title": "UAV-based search and rescue routing problem with environmental risk assessment", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Shaolong Hu", "Yuqing Li", "Guangdong Wu" ], "first_author": "Shaolong Hu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Search and rescue", "Bridging (networking)", "Flood myth", "Vehicle routing problem", "Heuristic", "Orienteering", "Profit (economics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6487608" }, { "id": "https://openalex.org/W2189195931", "doi": null, "title": "Heterogeneous, Multiple Depot, Multiple UAV Routing Problem", "year": 2010, "publication_date": "2010-01-01", "type": "article", "language": "en", "authors": [ "Paul Oberlin", "Sivakumar Rathinam", "Swaroop Darbha" ], "first_author": "Paul Oberlin", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Depot", "Computer science", "Routing (electronic design automation)", "Computer network", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://ieeexplore.ieee.org/iel5/100/5663470/05663700.pdf" }, { "id": "https://openalex.org/W7131133273", "doi": "10.6084/m9.figshare.31387540", "title": "A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm", "year": 2026, "publication_date": "2026-01-01", "type": "dataset", "language": null, "authors": [ "Yuan Fang" ], "first_author": "Yuan Fang", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31387540" }, { "id": "https://openalex.org/W7103879706", "doi": "10.6084/m9.figshare.30518582.v1", "title": "A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "Fang, Yuan" ], "first_author": "Fang, Yuan", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/dataset/A_Novel_Drone_Routing_Problem_Considering_Hybrid_Charging_and_Fuzzy_Time_Windows_Using_an_Enhanced_Adaptive_Large_Neighborhood_Search_Algorithm/30518582" }, { "id": "https://openalex.org/W7154940543", "doi": "10.3390/drones10040305", "title": "Knowledge-Driven Two-Stage Hybrid Algorithm for Collaborative Reconnaissance Routing Problem of Ground Vehicle and Drones Considering Multi-Type Targets", "year": 2026, "publication_date": "2026-04-19", "type": "article", "language": "en", "authors": [ "Xiao Liu", "Qizhang Luo", "Tianjun Liao", "Guohua Wu" ], "first_author": "Xiao Liu", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The collaboration of ground vehicles (GVs) and drones offers a powerful approach for enhancing drone capabilities. Current research focuses on drone-only or single-type target reconnaissance, failing to adequately account for practical scenarios. This paper introduces a GV–drone collaboration routing problem with multi-type target reconnaissance (GVD-MTR), which explicitly integrates GV–drone collaboration with simultaneous reconnoitering of point, line, and area targets. To address this problem, we propose a knowledge-driven two-stage hybrid algorithm (KDHA). In the first stage, K-means clustering combined with heuristic operators is applied to generate and refine routes for the GV. In the second stage, an improved Adaptive Large Neighborhood Search (IALNS) method is implemented to produce optimized drone routes. KDHA leverages hybrid search strategies, such as a population-based initialization strategy and a multi-level acceptance strategy, to efficiently generate high-quality solutions. Regarding recognizing the impacts of different target types on the total travel distance, we incorporate the related domain knowledge to design problem-specific search operators. Extensive simulation experiments demonstrate that KDHA consistently outperforms several state-of-the-art heuristics in terms of solution quality and runtime. Sensitivity analyses further confirm the robustness of the proposed approach across a range of parameter settings and problem instances.", "keywords": [ "Drone", "Robustness (evolution)", "Heuristics", "Initialization", "Cluster analysis", "Heuristic", "Vehicle routing problem", "Range (aeronautics)", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10040305" }, { "id": "https://openalex.org/W7103901221", "doi": "10.6084/m9.figshare.30518588.v1", "title": "A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "purpose), Authors (for anonymous review" ], "first_author": "purpose), Authors (for anonymous review", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/dataset/A_Novel_Drone_Routing_Problem_Considering_Hybrid_Charging_and_Fuzzy_Time_Windows_Using_an_Enhanced_Adaptive_Large_Neighborhood_Search_Algorithm/30518588" }, { "id": "https://openalex.org/W4416009089", "doi": "10.1117/12.3089263", "title": "An adaptive large neighborhood search algorithm based on multitree genetic programming for solving vehicle routing problems with unmanned aerial vehicles", "year": 2025, "publication_date": "2025-11-07", "type": "conference-paper", "language": null, "authors": [ "Yuxin Liu", "Huipeng Ji" ], "first_author": "Yuxin Liu", "venue": null, "publisher": null, "abstract": "Over the past decade, the use of Unmanned Aerial Vehicles (UAVs), also known as drones, for last-mile delivery has gained increasing popularity in both research and industry. This paper proposes a three-stage hybrid optimization strategy. First, a Multi-tree Genetic Programming (MGP) algorithm is used for dynamic clustering and tree-structure encoding to generate high-quality initial solutions. Then, an improved Adaptive Large Neighborhood Search (ALNS) algorithm, combined with a new string removal operator and a greedy repair strategy, is applied for path optimization. Finally, a Tabu Search (TS) algorithm is employed to focus on optimizing UAV sub-paths, comprehensively handling constraints such as endurance, load capacity, and collaborative waiting. Experimental results show that the hybrid algorithm (MGP-ALNS-TS) outperforms similar algorithms in terms of both optimal objective values and algorithm efficiency.", "keywords": [ "Tabu search", "Greedy algorithm", "Cluster analysis", "Genetic algorithm", "Vehicle routing problem", "Dynamic programming", "Focus (optics)", "Bees algorithm", "Encoding (memory)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3089263" }, { "id": "https://openalex.org/W7131096303", "doi": "10.6084/m9.figshare.31387540.v1", "title": "A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm", "year": 2026, "publication_date": "2026-01-01", "type": "dataset", "language": null, "authors": [ "Yuan Fang" ], "first_author": "Yuan Fang", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.31387540.v1" }, { "id": "https://openalex.org/W7103882911", "doi": "10.6084/m9.figshare.30514928.v1", "title": "A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "purpose), Authors (for anonymous review" ], "first_author": "purpose), Authors (for anonymous review", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30514928.v1" }, { "id": "https://openalex.org/W7103897289", "doi": "10.6084/m9.figshare.30514928.v2", "title": "A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "purpose), Authors (for anonymous review" ], "first_author": "purpose), Authors (for anonymous review", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30514928.v2" }, { "id": "https://openalex.org/W2026589647", "doi": "10.1137/1006005", "title": "Heuristic Methods for Location-Allocation Problems", "year": 1964, "publication_date": "1964-01-01", "type": "article", "language": "en", "authors": [ "Leon N. Cooper" ], "first_author": "Leon N. Cooper", "venue": "SIAM Review", "publisher": "Society for Industrial and Applied Mathematics", "abstract": "Previous article Next article Heuristic Methods for Location-Allocation ProblemsLeon CooperLeon Cooperhttps://doi.org/10.1137/1006005PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] Leon Cooper, Location-allocation problems, Operations Res., 11 (1963), 331–343, No. 3 MR0152365 0113.14201 CrossrefISIGoogle Scholar[2] Google Scholar[3] D. H. Lehmer, Teaching combinatorial tricks to a computerProc. Sympos. Appl. Math., Vol. 10, American Mathematical Society, Providence, R.I., 1960, 179–193, Combinatorial Analysis MR0113289 0096.00504 CrossrefGoogle Scholar[4] J. Heller, Some numerical experiments for an $M\\times J$ flow shop and its decision-theoretical aspects, Operations Res., 8 (1960), 178–184, No. 2 MR0111626 0092.27910 CrossrefISIGoogle Scholar[5] J. Moshman, The Application of Sequential Estimation To Computer Simulation and Monte Carlo Procedures, J. Association for Computing Machinery, 5 (1958), 0086.11603 CrossrefISIGoogle Scholar[6] A. Wald, Sequential Analysis, John Wiley and Sons, New York, 1952 Google Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails An elliptical cover problem in drone delivery network design and its solution algorithmsEuropean Journal of Operational Research, Vol. 304, No. 3 | 1 Feb 2023 Cross Ref Two lower-bounding algorithms for the p-center problem in an areaComputational Urban Science, Vol. 2, No. 1 | 24 January 2022 Cross Ref Optimal planar facility location with dense demands along a curveJournal of the Operational Research Society, Vol. 73, No. 8 | 8 May 2021 Cross Ref A continuous location and maintenance routing problem for offshore wind farms: Mathematical models and hybrid methodsComputers & Operations Research, Vol. 144 | 1 Aug 2022 Cross Ref Spatial autocorrelation informed approaches to solving location–allocation problemsSpatial Statistics, Vol. 50 | 1 Aug 2022 Cross Ref Electric vehicle charging stations emplacement using genetic algorithms and 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Environment and Urban Systems, Vol. 88 | 1 Jul 2021 Cross Ref Self-Configuring (1 + 1)-Evolutionary Algorithm for the Continuous p-Median Problem with Agglomerative MutationAlgorithms, Vol. 14, No. 5 | 22 April 2021 Cross Ref Complex Energy Networks Optimization: Part I—Development and Validation of a Software for Optimal Load AllocationJournal of Engineering for Gas Turbines and Power, Vol. 143, No. 4 | 22 February 2021 Cross Ref Fiber to the Home (FTTH) Automation Planning, Its Impact on Customer Satisfaction & Cost-EffectivenessWireless Personal Communications, Vol. 117, No. 2 | 24 November 2020 Cross Ref Heuristic-Based Location Allocation of Single Frequency Network StationsIEEE Transactions on Broadcasting, Vol. 67, No. 1 | 1 Mar 2021 Cross Ref Multi-objective solid transportation-location problem with variable carbon emission in inventory management: a hybrid approachAnnals of Operations Research, Vol. 41 | 5 February 2021 Cross Ref Optimized Supply Chain Management of Rice in 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gradual cover: th", "keywords": [ "Computer science", "Heuristic", "Location-allocation", "Mathematical optimization", "Operations research", "Mathematics", "Artificial intelligence" ], "cited_by_count": 537, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1137/1006005" }, { "id": "https://openalex.org/W7103883969", "doi": "10.6084/m9.figshare.30518588", "title": "A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "purpose), Authors (for anonymous review" ], "first_author": "purpose), Authors (for anonymous review", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30518588" }, { "id": "https://openalex.org/W7103877570", "doi": "10.6084/m9.figshare.30518582", "title": "A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "Fang, Yuan" ], "first_author": "Fang, Yuan", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Drone", "Fuzzy logic", "Routing (electronic design automation)", "Vehicle routing problem", "Search algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30518582" }, { "id": "https://openalex.org/W4323706406", "doi": "10.48550/arxiv.2303.04280", "title": "Solving Vehicle Routing Problem for unmanned heterogeneous vehicle systems using Asynchronous Multi-Agent Architecture (A-teams)", "year": 2023, "publication_date": "2023-03-07", "type": "preprint", "language": "en", "authors": [ "Ramasamy, Subramanian", "Md Safwan Mondal", "Pranav A. Bhounsule" ], "first_author": "Ramasamy, Subramanian", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to survey large areas in an effective and efficient manner. In order to solve this computationally challenging problem in a reasonable time, we created a two-level optimization heuristics. At the outer level, the UGV route is parameterized by few free parameters and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judiciously in order to create high quality solutions. We explore two methods for tuning the free UGV parameters: (1) a genetic algorithm, and (2) Asynchronous Multi-agent architecture (Ateams). The A-teams uses multiple agents to create, improve, and destroy solutions. The parallel asynchronous architecture enables A-teams to quickly optimize the parameters. Our results on test cases show that the A-teams produces similar solutions as genetic algorithm but with a speed up of 2-3 times.", "keywords": [ "Asynchronous communication", "Computer science", "Heuristics", "Routing (electronic design automation)", "Parameterized complexity", "Genetic algorithm", "Unmanned ground vehicle", "Vehicle routing problem", "Architecture", "Distributed computing", "Real-time computing", "Simulation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2303.04280" }, { "id": "https://openalex.org/W2994982398", "doi": "10.1109/ismsit.2019.8932830", "title": "Evaluation of The Applicability of Industry 4.0 Processes in Businesses and Supply Chain Applications", "year": 2019, "publication_date": "2019-10-01", "type": "conference-paper", "language": "en", "authors": [ "Deniz Merdin", "Filiz Ersöz" ], "first_author": "Deniz Merdin", "venue": "2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)", "publisher": null, "abstract": "Increasing the flexibility and effeciency of enterprises from procurement to sales provides a great competitive advantage for meeting consumer demands. Providing competitive advantage is possible through the effective implementation of innovative technologies of fourth industrial revolution in the all stages of supply chain process. In this context, research has been carried out for industry 4.0 applications at every stage of the supply chain process and the role of digitalization in the supply chain has been mentioned. In addition, views and in-depth data from 32 companies based in Turkey were collected by questionnaire and subsequently analysed in a detailed format with data mining methods. In this context, using the augmented reality glasses in supply chain operations, the robots used in production can adapt more quickly to the works, especially in the logistics sector. Also, the use of drone has an important place in the external logistics processes. It was also observed that the use of autonomous electric vehicles can reduce harmful environmental impacts, thereby providing optimum vehicle routing. In summary, increase in digital data and the end of the life of the robots used in the development of robot resulted in huge data dumps; the small size and volumes of the firms' warehouses made it difficult to properly use robots; managers had troubles adapting their digital visions to the changing times and processes; employees experienced difficulties during transitions from one system to another; and finally, it is unclear whether the country's current energy capacity is sufficient for the growth of smart factories.", "keywords": [ "Supply chain", "Flexibility (engineering)", "Context (archaeology)", "Procurement", "Process (computing)", "Business", "Competitive advantage", "Robot", "Computer science", "Process management", "Manufacturing engineering", "Industrial organization" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ismsit.2019.8932830" }, { "id": "https://openalex.org/W4321381455", "doi": "10.7232/jkiie.2023.49.1.063", "title": "Route Optimization in Nested Three Transportation Modes with Drones", "year": 2023, "publication_date": "2023-02-15", "type": "article", "language": "en", "authors": [ "Donghyun Kim", "Jihye Jung", "Inchan Choi" ], "first_author": "Donghyun Kim", "venue": "Journal of Korean Institute of Industrial Engineers", "publisher": null, "abstract": "This study addresses the vehicle routing optimization problem with three nested transportation modes, consisting of ships, trucks, and drones. A typical nested-three-mode vehicle routing problem appears in delivery systems that include areas of sparsely scattered, varying-size islands. In the problem, drones play an important role in achieving high efficiency in the entire delivery system, of their high-speed accessibility. In this paper, we present a mathematical model for the problem and explore its potential usability as a heuristic. For the usability check-up, a geographic data set from the island area of Tongyeong in Korea is used in an illustrative scenario for disaster relief goods distribution. Our limited computational experiment shows promise in the proposed optimization model as a heuristic with a preset computation time limit when compared with greedy heuristics.", "keywords": [ "Heuristics", "Usability", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Heuristic", "Greedy algorithm", "Drone", "Truck", "Set (abstract data type)", "Mode (computer interface)", "Mathematical optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.7232/jkiie.2023.49.1.063" }, { "id": "https://openalex.org/W4416309409", "doi": "10.1007/978-3-032-09120-8_4", "title": "Optimizing the Number of Drones for Aerial Power-Line Maintenance", "year": 2025, "publication_date": "2025-11-18", "type": "conference-paper", "language": "en", "authors": [ "Francesco Betti Sorbelli", "Sajjad Ghobadi", "Lorenzo Palazzetti", "Cristina Maria Pinotti" ], "first_author": "Francesco Betti Sorbelli", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Software deployment", "Scalability", "Key (lock)", "Integer programming", "Linear programming", "Unitary state", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-09120-8_4" }, { "id": "https://openalex.org/W4239113754", "doi": "10.20944/preprints201809.0487.v2", "title": "Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance and Reconnaissance", "year": 2019, "publication_date": "2019-01-16", "type": "preprint", "language": "en", "authors": [ "Yao Liu", "Zhong Liu", "Jianmai Shi", "Guohua Wu", "Chao Chen" ], "first_author": "Yao Liu", "venue": "Preprints.org", "publisher": null, "abstract": "The location routing problem of unmanned aerial vehicles (UAV) in border patrol for intelligence, surveillance and reconnaissance is investigated, where the location 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 Sino-Vietnamese border is presented to illustrate the problem and the solution approach. The performance of the two algorithms are analyzed and compared through randomly generated instances.", "keywords": [ "Computer science", "Base station", "Heuristic", "Integer programming", "Drone", "Base (topology)", "Military Base", "Real-time computing", "Artificial intelligence", "Operations research", "Computer network", "Engineering" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/manuscript/201809.0487/v2/download" }, { "id": "https://openalex.org/W7164514277", "doi": "10.1109/iccsse67426.2025.11552529", "title": "A Cooperative Transport System of Vehicle and Drones in Obstacles Areas", "year": 2025, "publication_date": "2025-10-17", "type": "conference-paper", "language": null, "authors": [ "Xinghan Sun", "Yi Zhou", "D H Zhang" ], "first_author": "Xinghan Sun", "venue": null, "publisher": null, "abstract": "Researchers have been concentrating on the cooperative system of multi-rotor drones and ground vehicles for transport and logistics in recent years, which could overcome the short range and insufficient load capacity of drones. These problems are known as Flying Sidekick Traveling Salesman Problem (FSTSP) or Vehicle Routing Problem with Drones (VRPD). However, existing research mainly considers the operations in a barrier-free or road environment. Complex obstacle areas such as disaster terrains can really play to the advantage of drones. Therefore, this paper proposes a solution to the Vehicle Routing Problem with Drones in Obstacle Areas (VRPD-O), which introduces environmental obstacles the vehicle can’t pass through. To solve the problem, firstly, the mathematical model of VRPD-O is established in the form of mixed integer linear programming equations, considering the special case of target nodes locating within obstacle areas. Second, the alternative paths of ground vehicle in obstacle areas are obtained using path planning algorithm. In addition, a heuristic optimization algorithm based on cluster analysis is proposed. Last, numerical experiments under different obstacle scenarios are carried out to verify the effectiveness of the proposed solution. The optimization algorithm is proved to reduce the computation time, and its reasonable parameter analysis is also studied.", "keywords": [ "Drone", "Transport system", "Obstacle", "Key (lock)", "Automatic vehicle location", "Measure (data warehouse)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iccsse67426.2025.11552529" }, { "id": "https://openalex.org/W7120838250", "doi": null, "title": "O Problema do Roteamento de Veículos com Drones", "year": 2019, "publication_date": "2019-06-18", "type": "dissertation", "language": "en", "authors": [ "Joao Guilherme Cavalcanti Costa" ], "first_author": "Joao Guilherme Cavalcanti Costa", "venue": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "publisher": "LA Referencia", "abstract": "In this Dissertation, the Vehicle Routing Problem with Drones (VRPD), motivated by the growing interest on Unmanned Aerial Vehicles (UAVs, or Drones) by the industry and their applications in logistics is studied. A pioneer work by (MURRAY; CHU, 2015) shows a combination between UAV and a truck to deliver products, presenting an adaptation to the Traveling Salesman Problem (TSP). After a literature review, an extension of the model from Murray and Chu (2015) we present a model for the problem with multiple vehicles. This model is developed as a Mixed Integer Linear Programming (MILP) problem and solved with the solver CPLEX. A heuristic based on a Hybrid Genetic Algorithm (HGA) is also developed and presented. Our results show that the use of drones reduces the total mileage of the trucks by a significant percentage.", "keywords": [ "Travelling salesman problem", "Drone", "Solver", "Vehicle routing problem", "Truck", "Extension (predicate logic)", "Integer programming", "Heuristic", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.11606/D.55.2019.tde-29082019-091040" }, { "id": "https://openalex.org/W7160435431", "doi": "10.1109/tmc.2026.3690854", "title": "SLIM+: Jointly Optimizing EIV Placement and UAV Fleet Sizing for Deadline-Driven Tasks", "year": 2026, "publication_date": "2026-05-06", "type": "article", "language": null, "authors": [ "Jianping Huang", "Feng Shan", "Junzhou Luo" ], "first_author": "Jianping Huang", "venue": "IEEE Transactions on Mobile Computing", "publisher": "IEEE Computer Society", "abstract": "Unmanned Aerial Vehicles (UAVs) are crucial for deadline-driven tasks but face dual bottlenecks: limited flight endurance and constrained onboard processing for data-intensive tasks. Edge Intelligent Vehicles (EIVs) can effectively serve as mobile logistical and computational hubs to alleviate these issues. However, existing research on such air-ground collaboration often assumes predetermined EIV locations and a fixed UAV fleet. This inflexibility leads to costly resource over-provisioning or mission failures under tight deadlines. Motivated by this, this paper studies theSLIM+problem, which focuses on jointly optimizing proactive EIV placements and UAV fleet sizing to minimize total deployment cost. This problem is challenging due to the deep coupling between strategic EIV placement, which shapes the mission structure, and the resulting operational cost of the UAV fleet. Therefore, we propose a novel two-level algorithm, where the outer layer employs dynamic programming to strategically place EIVs to partition the mission into independent route segments, while the inner layer consists of two complementary algorithms to determine the minimum UAV fleet size and optimal speeds for each segment: an optimal DP-based method and an approximation algorithm with a theoretical guarantee. Extensive simulations show that our integrated solution reduces total deployment cost by an average of 21.9% compared to baseline solutions, highlighting the benefits of a fully optimized co-deployment strategy.", "keywords": [ "Sizing", "Key (lock)", "Algorithm design", "Data modeling", "Mobile telephony" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tmc.2026.3690854" }, { "id": "https://openalex.org/W4283375253", "doi": "10.7737/jkorms.2022.47.2.001", "title": "Group Search Optimization for Truck-Drone Scheduling", "year": 2022, "publication_date": "2022-05-31", "type": "article", "language": "en", "authors": [ "Sung-Soo Kim", "Min-Seop Shin" ], "first_author": "Sung-Soo Kim", "venue": "한국경영과학회지", "publisher": null, "abstract": "In parallel drone scheduling traveler salesman problem, the customer can be served by a single truck or by a fleet of one or more identical drones to minimize the completion time. The vehicle routes customer locations, while the drone fly back and forth between depot and customers. The objective of this paper is to propose novel Routing Group Search Optimization (RGSO) for truck-drone scheduling to find the best schedule and routing of a truck and a droneto better balance vehicle and drone delivery. The RGSO based on Group Search Optimization (GSO) is proposed for truck-drone system with scheduling solution representation. Our proposed RGSO is very effective in finding group which are generated by relative distances of customer locations for routing of truck. Our simulation results show that RGSO is very effective comparing to standard GSO based on the several experiments and analysis.", "keywords": [ "Drone", "Truck", "Vehicle routing problem", "Scheduling (production processes)", "Computer science", "Travelling salesman problem", "Schedule", "Job shop scheduling", "Operations research", "Routing (electronic design automation)", "Engineering", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7737/jkorms.2022.47.2.001" }, { "id": "https://openalex.org/W4309852231", "doi": "10.3390/su142315623", "title": "Correction: Bruni, M.E.; Khodaparasti, S. A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing. Sustainability 2022, 14, 9978", "year": 2022, "publication_date": "2022-11-24", "type": "erratum", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti" ], "first_author": "Maria Elena Bruni", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "There was an error in the original publication [...]", "keywords": [ "Sustainability", "Variable (mathematics)", "Descent (aeronautics)", "Drone", "Computer science", "Geography", "Mathematics", "Ecology", "Biology", "Meteorology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/14/23/15623/pdf?version=1669281490" }, { "id": "https://openalex.org/W7155032939", "doi": "10.57001/huih5804.2025.356", "title": "Reinforcement learning for the traveling salesman problem with drone", "year": 2025, "publication_date": "2025-09-30", "type": "article", "language": null, "authors": [], "first_author": null, "venue": "Journal of Science and Technology - HaUI", "publisher": null, "abstract": "Recently, the problem of transportation routing with integrated drones in IoT-based logistics has emerged as a prominent research area within the academic community.It is particularly important for IoT applications in the transportation and logistics sectors, especially in last-mile delivery.Drones take off from predefined stop points to serve customers and then land at hubs to connect with other vehicles for continued delivery, all while adhering to constraints such as flight range and payload capacity.Integrating drone and vehicle routing poses unique challenges due to the need to synchronize both drone and vehicle schedules to optimize overall delivery time or system costs.In this paper, we introduce the Traveling Salesman Problem with Drone (TSPD) to optimize delivery costs.We then develop two efficient algorithms to address the TSPD.Our Q-learning and Sarsa algorithms enable a drone, transported by a vehicle, to deliver multiple parcels to customers at different locations, with the goal of minimizing delivery costs and maximizing customer satisfaction upon parcel delivery.Through experiments on artificially generated realistic scenarios, the results demonstrate that the proposed algorithms achieve promising computational performance.", "keywords": [ "Drone", "Travelling salesman problem", "Payload (computing)", "Routing (electronic design automation)", "Vehicle routing problem", "Reinforcement learning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.57001/huih5804.2025.356" }, { "id": "https://openalex.org/W4405592010", "doi": "10.54254/2755-2721/2024.18741", "title": "Collaborative optimization of urban drone and public transit integration", "year": 2024, "publication_date": "2024-12-19", "type": "article", "language": "en", "authors": [ "Hairu Jin", "Dan Liu" ], "first_author": "Hairu Jin", "venue": "Applied and Computational Engineering", "publisher": null, "abstract": "The efficient and cost-effective delivery of goods ordered online poses a significant challenge in the field of logistics. Many companies are exploring the use of drone technology to streamline delivery times and expenses. In the context of last-mile deliveries, the deployment of drones represents a promising technology that offers environmental and economic advantages. This study focuses on last-mile delivery systems, where a fleet of drones operates in conjunction with public transportation to fulfill customer orders. To address this issue, the paper expands upon a mathematical model based on the Vehicle Routing Problem (VRP). Furthermore, it presents a real-life scenario inspired by Stanford’s transportation paradigm. The findings indicate that optimizing the sequence for visiting customers and public transportation stations has a substantial impact on both remaining power and time efficiency during drone deliveries. Moreover, leveraging public transport vehicles for recharging drones or approaching customers can reduce the required number of drones within service areas, thereby extending their operational lifespan. These results highlight the potential for using public transportation as a mobile charging station to save energy and improve package delivery efficiency in last-mile drone deliveries.", "keywords": [ "Drone", "Transit (satellite)", "Public transport", "Urban transit", "Transport engineering", "Computer science", "Environmental planning", "Regional science", "Geography", "Engineering", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.ewadirect.com/proceedings/ace/article/view/18741/pdf" }, { "id": "https://openalex.org/W7133297220", "doi": "10.48550/arxiv.2603.00328", "title": "Asymptotic Bounds for the Traveling Salesman Problem with Drone", "year": 2026, "publication_date": "2026-02-27", "type": "preprint", "language": null, "authors": [ "Jae Hyeok Lee", "Taekang Hwang", "Changhyun Kwon" ], "first_author": "Jae Hyeok Lee", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The asymptotic behavior of the optimal TSP tour length is well known from the classical Beardwood--Halton--Hammersley theorem. We extend this result to the Traveling Salesman Problem with Drone (TSPD), a cooperative routing problem in which a truck and a drone jointly serve customers. Using a nonmonotone subadditive Euclidean functional framework, we establish the existence of an almost sure limit for the optimal TSPD makespan scaled by the square root of the problem size. We derive explicit upper and lower bounds for the speed-scaled Euclidean TSPD model: upper bounds are obtained via structured ring-based tour constructions and Monte Carlo evaluation, while lower bounds are derived using nearest-neighbor distance distributions and the $k$-traveling salesman problem. Computational results illustrate how tight the bounds are. We also extend the analysis to the Rectilinear--Euclidean mixed TSPD model, in which truck travel is measured by the rectilinear distance and drone travel by the Euclidean distance.", "keywords": [ "Travelling salesman problem", "Drone", "Euclidean geometry", "Limit (mathematics)", "Upper and lower bounds", "Parametric statistics", "Euclidean distance", "Bottleneck traveling salesman problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2603.00328" }, { "id": "https://openalex.org/W4417056915", "doi": "10.48550/arxiv.2503.24301", "title": "QUADRO: A Hybrid Quantum Optimization Framework for Drone Delivery", "year": 2025, "publication_date": "2025-03-31", "type": "preprint", "language": "en", "authors": [ "James B. Holliday", "Darren Blount", "Hoang Quan Nguyen", "Samee U. Khan", "Ky Luu" ], "first_author": "James B. Holliday", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Quantum computing holds transformative potential for optimizing large-scale drone fleet operations, yet its near-term limitations necessitate hybrid approaches blending classical and quantum techniques. This work introduces Quantum Unmanned Aerial Delivery Routing Optimization (QUADRO), a novel hybrid framework addressing the Energy-Constrained Capacitated Unmanned Aerial Vehicle Routing Problem and the Unmanned Aerial Vehicle Scheduling Problem. By formulating these challenges as Quadratic Unconstrained Binary Optimization problems, QUADRO leverages the Quantum Approximate Optimization Algorithm for routing and scheduling, enhanced by classical heuristics and post-processing. We minimize total transit time in routing, considering payload and battery constraints, and optimize makespan scheduling across various drone fleets. Evaluated on adapted Augerat benchmarks (16-51 nodes), QUADRO competes against classical and prior hybrid methods, achieving scalable solutions with fewer than one hundred qubits. The proposed results underscore the viability of hybrid quantum-classical strategies for real-world drone logistics, paving the way for quantum-enhanced optimization in the Noisy Intermediate Scale Quantum era.", "keywords": [ "Drone", "Scheduling (production processes)", "Job shop scheduling", "Payload (computing)", "Heuristics", "Scalability", "Quantum" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2503.24301" }, { "id": "https://openalex.org/W3132325410", "doi": null, "title": "Facility and Routing Decisions in Truck-Drone Distribution", "year": 2020, "publication_date": "2020-08-06", "type": "other", "language": "en", "authors": [ "Vu Bich Na Doan", "Amr Mohammad Taiyeb" ], "first_author": "Vu Bich Na Doan", "venue": "DSpace@MIT (Massachusetts Institute of Technology)", "publisher": "Massachusetts Institute of Technology", "abstract": "Drones have the potential to be major players in the field of last-mile delivery, especially with the rise of e-commerce. However, they face several technological and regulatory restrictions for large-scale implementation. Combining drones with traditional ground delivery vehicles can bridge this gap while achieving significant improvements in distribution cost and speed over vehicle delivery alone. This research project focuses on modeling and solving the Location-Routing Problem with drones as an ancillary mode of delivery. The goal is to develop a model that identifies the optimal locations for the distribution facilities, as well as the set of combined routes that the trucks and drones will follow to deliver parcels to customers. To solve this problem, a two-steps metaheuristic approach is developed and implemented. The customer locations are first grouped into clusters with centroid positions where trucks would park, dispatch, and retrieve the onboard drones that perform the last step of the delivery. Once the optimal truck parking locations are identified, the selection of the optimal distribution facilities and the truck routes are determined simultaneously, by implementing the Multiple Ant Colony Optimization algorithm. The validation of the model revealed high reliability with a 1% average optimality gap from the exact solution. When applying the model to a real road network, with 200 customers and 5 candidate depot locations, the model confirmed a 24% saving in daily distribution costs from adding 3 drones to every delivery truck. The savings opportunity is less sensitive to the number of drones per truck and the drones’ speed, and more sensitive to the trucks’ speed and the drones’ traveling cost per mile. The analysis reveals that adding drones will generate savings as long as the traveling cost of drones is lower than that of trucks at around $4.00 on average for last-mile delivery.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Distribution (mathematics)", "Computer science", "Aeronautics", "Operations research", "Business", "Transport engineering", "Engineering", "Automotive engineering", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1721.1/126493" }, { "id": "https://openalex.org/W1537641709", "doi": "10.17134/sbd.04040", "title": "An Integer Programming Model for the Heterogeneous UAV Fleet Routing Problems", "year": 2013, "publication_date": "2013-01-01", "type": "article", "language": "en", "authors": [ "Cihan Ercan", "Cevriye Gencer" ], "first_author": "Cihan Ercan", "venue": "DergiPark (Istanbul University)", "publisher": "Istanbul University", "abstract": "Son zamanlarda, kablosuz haberleşme ve bilgisayar işlemcilerindeki gelişmeler sayesinde, operasyon sahasında daha çok İnsansız Hava Araçları (İHA) kullanılmaya başlanmıştır. Kullanılan İHA’ların ve çeşitlerinin hızla artması ile birlikte, heterojen İHA filolarının rota planlamaları için yeni yaklaşımlar, harekât araştırmacıları için ilgi çekici bir alan olagelmiştir. Bu çalışmada, değişik imkan ve kabiliyetlerdeki İHA‘lardan oluşan heterojen filoların statik araç rotalama problemleri (ARP) için tam sayılı programlama çözüm modeli önerilmiştir. İHA ve ARP’lerinin tanımları ile askeri operasyonlar için önemleri açıklanmış, ayrıca homojen ve heterojen İHA tanımları detaylandırılmıştır. Bir önceki çalışma genişletilmiş ve literatür ile karşılaştırmaları verilmiştir.", "keywords": [ "Integer programming", "Computer science", "Routing (electronic design automation)", "Integer (computer science)", "Vehicle routing problem", "Operations research", "Computer network", "Mathematical optimization", "Engineering", "Mathematics", "Algorithm", "Programming language" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/tr/pub/khosbd/issue/19222/204280" }, { "id": "https://openalex.org/W4407940143", "doi": "10.1007/s10922-025-09914-3", "title": "A Novel Mathematical Model with Fairness Constraints for Uninterrupted Network Coverage in Disasters", "year": 2025, "publication_date": "2025-02-25", "type": "article", "language": "en", "authors": [ "Özge Şatır Akpunar", "Şener Akpınar" ], "first_author": "Özge Şatır Akpunar", "venue": "Journal of Network and Systems Management", "publisher": "Springer Science+Business Media", "abstract": "Abstract Drones can be used as base stations to support search and rescue operations due to damage to telecommunications infrastructure after a disaster. This involves identifying a number of hotspots over the disaster area where the drones should hover for a certain amount of time, and moving the drones between these points along the pre-determined routes. Allocating the drones to appropriate locations and finding optimal routes is an optimization problem, and solving this problem to optimality is crucial to ensure sustainable operations with limited resources. This paper addresses this optimization problem and presents the drone base station allocation and routing problem (DBSARP) under disaster conditions. The problem aims at separately optimizing two objectives: spatial coverage by maximizing the coverage areas and temporal coverage by maximizing the total hover time as well as considering fairness. A mathematical model is proposed to determine the locations and routes of the drone base stations (DBSs) and their lengths of stay at each location. Furthermore, a real-life implementation of the proposed model is realized based on the earthquake that happened in the Bayrakli district of Izmir, Türkiye in 2020. Finally, a set of sensitivity analyses is performed by varying key parameters of the problem, such as battery capacities and number of the DBSs.", "keywords": [ "Computer science", "Computer security", "Computer network", "Operations research" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10922-025-09914-3" }, { "id": "https://openalex.org/W3123430446", "doi": "10.1115/dscc2020-3244", "title": "Optimal Path Planning for a Team of Heterogeneous Drones to Monitor Agricultural Fields", "year": 2020, "publication_date": "2020-10-05", "type": "conference-paper", "language": "en", "authors": [ "Saba Faryadi", "Mohammadreza Davoodi", "Javad Mohammadpour Velni" ], "first_author": "Saba Faryadi", "venue": null, "publisher": null, "abstract": "Abstract In this work, we investigate the problem of finding the minimum coverage time of an agricultural field using a team of heterogeneous unmanned aerial vehicles (UAVs). The aerial robotic system is assumed to be heterogeneous in terms of the equipped cameras’ field of view, flight speed, and battery capacity. The coverage problem is formulated as a vehicle routing problem (VRP) [1] with two significant extensions. First, the field is converted into a graph, including nodes and edges generated based on sweep direction and the minimum length of UAVs’ footprints. Second, the underlying optimization problem accounts for aerial vehicles having different sensor footprints. A series of simulation experiments are carried out to demonstrate that the proposed strategy can yield a satisfactory monitoring performance and offer promise to be used in practice.", "keywords": [ "Drone", "Motion planning", "Computer science", "Routing (electronic design automation)", "Route planning", "Real-time computing", "Battery capacity", "Vehicle routing problem", "Field (mathematics)", "Path (computing)", "Mathematical optimization", "Robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1115/dscc2020-3244" }, { "id": "https://openalex.org/W4407556849", "doi": "10.30574/ijsra.2025.14.2.0391", "title": "Solar powered coordinated drone for last mile delivery", "year": 2025, "publication_date": "2025-02-12", "type": "article", "language": "en", "authors": [ "Olakunle Abimbola Kumuyi" ], "first_author": "Olakunle Abimbola Kumuyi", "venue": "International Journal of Science and Research Archive", "publisher": "GSC Online Press", "abstract": "The rapid growth of e-commerce and urban logistics has intensified challenges in last-mile delivery, including traffic congestion, high operational costs, payload limitation for single drones, and environmental impacts. This study proposes a solar-powered coordinated drone system to address these issues by leveraging renewable energy and multi-drone collaboration. The research focuses on four objectives: (1) developing a coordinated drone system capable of transporting heavy payloads for greater payload capabilities, (2) optimizing energy efficiency through solar panel integration and advanced battery technologies, and (3) calculate environmental impact reduction achieved by using solar-powered, (4) hybrid methodology combining system design, energy consumption modeling, and environmental impact analysis was employed to evaluate technical feasibility and sustainability. (6) Mathematical models for solar energy harvesting, thrust requirements, and power management were developed to optimize payload capacity and flight range. Results indicate that when compared to using fossil-fueled delivery for payloads beyond the capabilities of a single small delivery drone, using coordinated drones instead can reduce greenhouse gas emissions by up to 80% compared to fossil-fueled delivery trucks, with operational costs decreasing by 30-40%. Key innovations include dynamic solar charging analysis, application of Green Vehicle Routing Problem (GVRP) Model for route optimization, and Environmental Impact Analysis (Sustainability Assessment). Challenges such as solar panel efficiency constraints (15-30%), temperature impacts on energy generation. Power and Battery Capacity Relationship. The study concludes that solar-powered coordinated drones offer a scalable, eco-friendly alternative to traditional logistics, aligning with global sustainability goals. Future research should explore advanced solar materials, regulatory frameworks, and AI-driven traffic management to enhance adoption. This work contributes to the evolution of sustainable urban logistics, emphasizing reduced carbon footprints for payloads beyond the capabilities of a single drone, cost efficiency, and improved customer satisfaction through rapid, reliable deliveries.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Solar powered", "Aeronautics", "Computer science", "Engineering", "Telecommunications", "Electrical engineering", "Solar energy", "Physics", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-0391.pdf" }, { "id": "https://openalex.org/W4411019380", "doi": "10.46254/an15.20250087", "title": "Optimization of Collaborative Truck-Drone-Subway Delivery for Urban Logistics", "year": 2025, "publication_date": "2025-02-18", "type": "conference-paper", "language": "en", "authors": [ "So Rim Han", "Byung Duk Song" ], "first_author": "So Rim Han", "venue": null, "publisher": null, "abstract": "The truck-drone hybrid delivery system has been proposed as an alternative for urban logistics due to its advantages in fast delivery, cost-efficiency, and environmental benefits. However, UAVs face limitations in battery capacity and payload, which make it challenging to meet the increasing demand for logistics services. Additionally, there are constraints in building new logistics facilities in urban areas. Underground logistics has emerged as an alternative solution to address these problems. By utilizing the subway network, the system can avoid urban traffic congestion and manage logistics flows efficiently. Moreover, it minimizes the cost of establishing new facilities by making the most of existing infrastructure. In this study, we propose a new urban delivery system that combines the existing truck-drone hybrid model with the subway network. The established subway network and stations are used to transport goods from suburban areas to urban areas, where they also function as urban logistics hubs. A truck and drones handle the last-mile delivery to customers from the subway stations. Both the truck and multiple drones operate independently; the truck does not act as a mothership for the drones, and they perform deliveries across multiple logistics depots. We developed a mathematical optimization model for a multi-depot, multi-trip vehicle routing problem with time windows. The developed model was validated through numerical experiments, demonstrating its practical applicability to urban delivery services.The truck-drone hybrid delivery system has been proposed as an alternative for urban logistics due to its advantages in fast delivery, cost-efficiency, and environmental benefits. However, UAVs face limitations in battery capacity and payload, which make it challenging to meet the increasing demand for logistics services. Additionally, there are constraints in building new logistics facilities in urban areas. Underground logistics has emerged as an alternative solution to address these problems. By utilizing the subway network, the system can avoid urban traffic congestion and manage logistics flows efficiently. Moreover, it minimizes the cost of establishing new facilities by making the most of existing infrastructure. In this study, we propose a new urban delivery system that combines the existing truck-drone hybrid model with the subway network. The established subway network and stations are used to transport goods from suburban areas to urban areas, where they also function as urban logistics hubs. A truck and drones handle the last-mile delivery to customers from the subway stations. Both the truck and multiple drones operate independently; the truck does not act as a mothership for the drones, and they perform deliveries across multiple logistics depots. We developed a mathematical optimization model for a multi-depot, multi-trip vehicle routing problem with time windows. The developed model was validated through numerical experiments, demonstrating its practical applicability to urban delivery services.", "keywords": [ "Truck", "Drone", "Transport engineering", "City logistics", "Computer science", "Aeronautics", "Engineering", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ieomsociety.org/proceedings/singapore2025/87.pdf" }, { "id": "https://openalex.org/W7167097135", "doi": "10.1109/raits68656.2026.11580185", "title": "Minimizing Costs in UAV Inspection Routing: Trade-offs Between Fleet Size and Recharging Strategies", "year": 2026, "publication_date": "2026-01-23", "type": "conference-paper", "language": null, "authors": [ "Yiwei 艺娓 SUN 孙", "Geqi Qi", "Wei Guan", "Rongge Guo" ], "first_author": "Yiwei 艺娓 SUN 孙", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Minification", "Automation", "Key (lock)", "Global Positioning System", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/raits68656.2026.11580185" }, { "id": "https://openalex.org/W7171544684", "doi": "10.1504/ijcse.2026.155103", "title": "Graph transformer decision model for efficiently resolving the UAV-UGV joint routing problem", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Ke Zhang", "Yuelong Su" ], "first_author": "Ke Zhang", "venue": "International Journal of Computational Science and Engineering", "publisher": "Inderscience Publishers", "abstract": "The integration of unmanned aerial vehicles with unmanned ground vehicles presents a promising solution for last-mile delivery in urban environments. However, current research often models vehicle movement as discrete nodes, rather than as the waypoint selection in continuous space. This paper proposes a novel framework named graph transformer decision model (GTDM), to address the challenging problem of joint routing problem for multiple drones and ground vehicles. The framework employs a graph transformer module for state encoding of the heterogeneous multi-agent system. Then, during the decision process, each agent computes task assignment probabilities for drones through an attention mechanism and generates optimal scheduling strategies using greedy decoding. Evaluated on the real-world road network of Xiong'an New Area, results demonstrate that the proposed method successfully balances solution quality with computational efficiency compared with heuristic algorithms, such as large neighbour search, showcasing its robustness and potential for practical deployment in future smart cities.", "keywords": [ "Transformer", "Drone", "Greedy algorithm", "Robustness (evolution)", "Scheduling (production processes)", "Robot", "Software deployment", "Graph" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijcse.2026.155103" }, { "id": "https://openalex.org/W4414746498", "doi": "10.62660/bcstu/1.2025.80", "title": "5G and artificial intelligence integration to improve drone routing", "year": 2025, "publication_date": "2025-02-28", "type": "article", "language": "en", "authors": [ "Roman Zaivyi", "Oleh Melnychok" ], "first_author": "Roman Zaivyi", "venue": "Вісник Черкаського державного технологічного університету", "publisher": "Cherkasy State Technological University", "abstract": "The development of 5G and artificial intelligence technologies creates new opportunities for improving the routing of unmanned aerial vehicles, which is particularly relevant for logistics, rescue operations, and monitoring of critical infrastructure. The purpose of the study was to analyse the prospects for implementing 5G and AI in drone routing, identify key challenges, and develop recommendations for their effective integration into Ukrainian airspace. The study used methods of theoretical analysis of scientific sources, comparative analysis of international experience, and systematisation of modern approaches to drone routing using 5G and AI. The architecture of 5G networks, route optimisation algorithms, and coordination mechanisms for swarms of drones was analysed. The main results of the study showed that the combination of 5G and AI provides a significant increase in the efficiency of autonomous unmanned systems, allowing helping to quickly adapt routes, optimise energy consumption, and improve the level of flight safety. Special attention was paid to comparing two popular route optimisation algorithms for UAVs: the particle swarm and the ant colony optimisation algorithms. The analysis showed that both algorithms effectively solve routing problems, but they have their advantages depending on the specifics of the application. The particle swarm algorithm proved to be more efficient for problems with a large number of variables, helping to optimise routes in real time under rapidly changing conditions. The ant colony optimisation algorithm, in turn, demonstrated an advantage in solving complex problems with a large number of obstacles. The practical significance of the study was to identify key technical and regulatory challenges associated with the integration of 5G and AI into drone routing, and to develop evidence-based approaches to solving them. The results obtained can be used to improve national regulations, promote the introduction of intelligent unmanned systems in logistics, infrastructure monitoring and rescue operations, and for further research in the field of autonomous aviation technologies", "keywords": [ "Drone", "Ant colony optimization algorithms", "Key (lock)", "Routing (electronic design automation)", "Ant colony", "Particle swarm optimization", "Swarm intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://bulletin-chstu.com.ua/web/uploads/pdf/Bulletin of Cherkasy State Technological University, Vol 30, No 1, 2025-80-90.pdf" }, { "id": "https://openalex.org/W7168411221", "doi": "10.1016/j.trpro.2026.04.108", "title": "A Comparative Study of Last-mile Truck Delivery with and without Drones", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Rémy Dupas", "Sylvain Lichau", "Julien François", "Tienté Hsu" ], "first_author": "Rémy Dupas", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "Parcels delivery in city centers is a topical issue in major cities and metropolis. To meet this problem, alternatives delivery modes have been proposed such as for instance drones, robots, bikes as well a combination of these with trucks. In this paper, we compare two delivery modes: trucks alone and drone-equipped trucks. Our comparison is based on an exact approach using an improved Branch-Cut-and-Price algorithm to solve the two-echelon vehicle routing problem with drones (VRPD) and without drones (VRP). Experiments are carried out on instances of literature and real data from the city of Bordeaux. The performance indicators used for comparison are the duration of the tour and the number of vehicles used; in addition, we carry out a preliminary analysis of tour costs and emissions.", "keywords": [ "Drone", "Truck", "Duration (music)", "Routing (electronic design automation)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2026.04.108" }, { "id": "https://openalex.org/W4394617363", "doi": "10.4271/2024-01-2882", "title": "Internet of Autonomous Vehicles for The Distribution System of Smart Cities", "year": 2024, "publication_date": "2024-04-08", "type": "conference-paper", "language": "en", "authors": [ "Shizi Ma", "Sheng Wang", "Zhitao Ma", "Zhiguo QI" ], "first_author": "Shizi Ma", "venue": "SAE technical papers on CD-ROM/SAE technical paper series", "publisher": null, "abstract": "With the development of internet technology and autonomous vehicles (AVs), the multimodal transportation and distribution model based on AVs will be a typical application paradigm in the smart city scenario. Before AVs carry out logistics distribution, it is necessary to plan a reasonable distribution path based on each customer point, and this is also known as Vehicle Routing Problem (VRP). Unlike traditional VRP, the urban logistics distribution process based on multimodal transportation mode will use a set of different types of AVs, mainly including autonomous ground vehicles and unmanned aerial vehicles (UAVs). It is worth pointing out that there is currently no research on combining the planning of AVs distribution paths with the trajectory planning of UAVs. To address this issue, this article establishes a bilevel programming model. The upper-level model aims to plan the optimal delivery plan for AVs, while the lower-level model aims to plan a driving trajectory for UAVs. Furthermore, this paper proposes an improved heuristic algorithm for the bilevel programming model. Due to the tendency of Group Search Optimizer (GSO) algorithm to fall into local optima during the process of solving large-scale complex problems, this paper designs an improved GSO algorithm based on improved strategies such as spiral search strategy. During the solving process, based on the upper-level model and using the IGSO algorithm, the distribution order of AVs can be directly solved. In the process of trajectory planning based on the lower-level model, the RRT algorithm is first used to plan an initial trajectory for the UAV. Furthermore, the IGSO algorithm is used to further optimize this trajectory, ultimately achieving the delivery task of the UAV. Finally, a simulation experiment was conducted to compare the effectiveness of the proposed scheme with other algorithms.", "keywords": [ "The Internet", "Computer science", "Distribution (mathematics)", "Internet of Things", "Telecommunications", "Computer security", "World Wide Web" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4271/2024-01-2882" }, { "id": "https://openalex.org/W4401508831", "doi": "10.1109/iscv60512.2024.10620093", "title": "Contribution to the optimization of the flight duration of autonomous drones: Flight path planning", "year": 2024, "publication_date": "2024-05-08", "type": "conference-paper", "language": "en", "authors": [ "Khaoula Khaled", "Ouadoudi Zytoune" ], "first_author": "Khaoula Khaled", "venue": null, "publisher": null, "abstract": "Unmanned aerial vehicle(UAV) are suitable tools to provide a fast wireless communication infrastructure. They can be deployed to support the capacity of conventional base stations(BS) when they are overloaded. This can happen in some cases as special events or abnormal congestion in a cell. However, UAVs entail multiple constraints such as the cost of service, lifetime of drones , and the need for fast and high-quality service. Although previous research papers propose a mixed programming model to address this challenging problem, given that drones can perform multiple supports for many BSs. This raises different questions as : how to manage the cost of service ? and how to maximize the quality of the services while minimizing the time service including capacity of the drone. for this purpose , we consider the question as a single, how the branch-and-bound approach can be tailored to solve the vehicle routing problem faster. Subsequently, a dual approach was used. The used method takes the different aspect into consideration and captures changes in total profit. the performance of our method is analyzed and compared with a different approach, by considering another aspect of optimization.", "keywords": [ "Drone", "Duration (music)", "Aeronautics", "Path (computing)", "Motion planning", "Computer science", "Aerospace engineering", "Engineering", "Artificial intelligence", "Robot", "Physics", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/iscv60512.2024.10620093" }, { "id": "https://openalex.org/W7161996549", "doi": "10.1007/978-3-032-26123-6_5", "title": "Bigraphical Model Checking for Coordinated Drone Landings on Vertiports", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Dominik Grzelak", "Tianxiong Zhang", "Uwe Aßmann" ], "first_author": "Dominik Grzelak", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": "Coordinating multiple drones for landing on vertiport-like infrastructures constitutes a general safety-critical swarm intelligence problem that combines discrete decision-making with domain-specific spatial occupancy constraints. Existing scheduling and control approaches often lack formal, executable correctness guarantees and remain loosely coupled to low-level control logic, making cyber-physical inconsistencies likely, especially when the number of drones increases. This work presents a formal and executable model for flight operation management of multiple tiny drones on multiple simplified vertiports based on bigraphical reactive systems. The proposed model captures both the spatial layout and the interactions among drones and the infrastructure, providing a unified understanding of a complex coordination problem. Compositionality enables the reconfiguration of takeoff and landing pad topologies, as well as fleet sizes, without altering the underlying rules. We employ model checking to generate the full state space from a set of initial states and verify safety invariants for 4 drones on a $$3\\times 7$$ grid, and 3 drones on a $$4\\times 5$$ grid. Landing success is formalized as a reachability condition. The compositional design of the specification facilitates reusability and modular verification, and the rule-based specification allows for execution of the traces of the proof.", "keywords": [ "Drone", "Model checking", "Formal verification", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-26123-6_5" }, { "id": "https://openalex.org/W2899307889", "doi": "10.13016/m2v698g3x", "title": "HYBRID ROUTING MODELS UTILIZING TRUCKS OR SHIPS TO LAUNCH DRONES", "year": 2018, "publication_date": "2018-01-01", "type": "dissertation", "language": "en", "authors": [ "Stefan Poikonen" ], "first_author": "Stefan Poikonen", "venue": "University Libraries (University of Maryland)", "publisher": "University of Maryland, College Park", "abstract": "Technological advances for unmanned aerial vehicles, commonly referred to as drones, have opened the door to a number of new and interesting applications in areas including military, healthcare, communications, cinematography, emergency response, and logistics. However, limitations due to battery capacity, maximum take-off weight, finite range of wireless communications, and legal regulations have restricted the effective operational range of drones in many practical applications. Several hybrid operational models involving one or more drones launching from a larger vehicle, which may be a ship, truck, or airplane, have emerged to help mitigate these range limitations. In particular, the drones utilize the larger vehicle as both a mobile depot and a recharging or refueling platform. In this dissertation, we describe routing models that leverage the tandem of one or more drones with a larger vehicle. In these models, there is generally a set of targets that should be visited in an efficient (usually time-minimizing) manner. By using multiple vehicles, these targets may be visited in parallel thereby reducing the total time to visit all targets. The vehicle routing problem with drones (VRPD) and traveling salesman problem with a drone (TSP-D) consider hybrid truck-and-drone models of delivery, where the goal is to minimize the time required to deliver a set of packages to their respective customers and return the truck(s) and drone(s) to the origin depot. In both problems, the drone can carry one homogeneous package at a time. Theoretical analysis, exact solution methods, heuristic solution methods, and computational results are presented. In the mothership and drone routing problem (MDRP), we consider the case where the larger launch vehicle is free to move in Euclidean space (the open seas) and launch a drone to visit one target location at a time, before returning to the ship to pick up new cargo or refuel. The mothership and high capacity drone routing problem (MDRP-HC) is a generalization of the mothership and drone routing problem, which allows the drone to visit multiple targets consecutively before returning to the ship. MDRP and MDRP-HC contain elements of both combinatorial optimization and continuous optimization. In the multi-visit drone routing problem (MVDRP), a drone can visit multiple targets consecutively before returning to the truck, subject to energy constraints that take into account the weight of packages carried by the drone.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Aeronautics", "Computer science", "Engineering", "Transport engineering", "Computer network", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.13016/m2v698g3x" }, { "id": "https://openalex.org/W7203715511", "doi": "10.48550/arxiv.2608.16435", "title": "Drive, Pack, Fly: The Travelling Thief Problem with Drone", "year": 2026, "publication_date": "2026-08-17", "type": "preprint", "language": null, "authors": [ "Kabir Murjani", "Abhay Sobhanan" ], "first_author": "Kabir Murjani", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency. An onboard drone can offset this penalty by retrieving outlying items, thereby shortening the makespan and increasing operational profit. However, travel time remains load-dependent, and each item collected by the ground vehicle shifts the arrival times that govern the drone's launch and rendezvous points. This paper introduces the Travelling Thief Problem with Drone (TTP-D), which maximises the collected profit, net of a time-based rental cost, by jointly optimising item selection, vehicle routing, and flight synchronisation. We formulate a mixed-integer linear program that solves small instances to optimality, and develop both metaheuristics and an attention-based Deep Reinforcement Learning (DRL) policy for larger instances. We further propose a learner-initialised hybrid solver, in which the DRL policy constructs an initial solution that a short annealing run subsequently refines. On two benchmark sets, this hybrid recovers most of the metaheuristic baseline's quality at a fraction of its computational budget, although the largest instances still require the baseline at its full budget. Finally, a sensitivity analysis reveals that the rental ratio is the primary driver of profitability, whereas the fleet parameters affect profit only at the margin.", "keywords": [ "Drone", "Metaheuristic", "Simulated annealing", "Offset (computer science)", "Job shop scheduling", "Scheduling (production processes)", "Linear programming", "Rendezvous" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2608.16435" }, { "id": "https://openalex.org/W7130414080", "doi": "10.3390/app16042016", "title": "A Three-Phase Heuristic for Optimizing Truck-Drone Collaborative Delivery", "year": 2026, "publication_date": "2026-02-18", "type": "article", "language": "en", "authors": [ "Ying Wang", "Jicong Duan", "Qin He Zhang", "Yu Ding" ], "first_author": "Ying Wang", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Truck-drone collaborative delivery has attracted increasing attention as an effective means to enhance flexibility and efficiency in complex distribution systems. However, the resulting Vehicle Routing Problem with Drones (VRPD) is NP-hard, and existing heuristics often struggle to balance solution quality and computational efficiency, especially in large-scale and multi-trip settings. To address these challenges, this paper proposes a Structure-Guided Adaptive Large Neighborhood Search (S-ALNS) framework for truck-drone collaborative routing. The proposed approach explicitly exploits problem-specific structural characteristics through a three-phase solution process. First, balanced initial truck routes are constructed using customer clustering. Second, a structured split-based heuristic reallocates suitable customers from truck routes to a drone service. Third, the solution is further refined within an improved ALNS framework, where a structure-guided repair mechanism based on dynamic programming is introduced to efficiently handle batch customer insertions under coupled capacity and feasibility constraints. Extensive computational experiments on instances of varying scales show that S-ALNS consistently produces near-optimal solutions for small-scale instances used for validation. For medium- and large-scale instances, S-ALNS significantly outperforms classical heuristics, including simulated annealing and a standard ALNS baseline, in terms of solution quality while maintaining competitive in computational efficiency. These results demonstrate the effectiveness of incorporating the problem structure into Adaptive Large Neighborhood Searches for complex truck-drone routing problems.", "keywords": [ "Vehicle routing problem", "Heuristics", "Simulated annealing", "Flexibility (engineering)", "Exploit", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/16/4/2016/pdf?version=1771407263" }, { "id": "https://openalex.org/W4402958310", "doi": "10.1007/978-981-97-3690-4_45", "title": "Optimizing Parcel Delivery Routes", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Sulabh Bansal", "Priyanshi Vyas" ], "first_author": "Sulabh Bansal", "venue": "Smart innovation, systems and technologies", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Computer science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-97-3690-4_45" }, { "id": "https://openalex.org/W7128293621", "doi": "10.1007/s00500-025-11027-7", "title": "A hybrid airspace model for utilizing drones in logistics operations", "year": 2026, "publication_date": "2026-02-07", "type": "article", "language": "en", "authors": [ "Bruno Avelino", "Mauro Wassilewsky Caetano", "Christopher Shneider Cerqueira", "Anderson Ribeiro Correia", "Evandro José da Silva", "Ronaldo Martins da Costa" ], "first_author": "Bruno Avelino", "venue": "Soft Computing", "publisher": "Springer Science+Business Media", "abstract": "Abstract This study evaluates the effectiveness of the Biased Random-Key Genetic Algorithm (BRKGA) in solving the Traveling Salesman Problem (TSP) and its extension with trucks and drones, the Flying Sidekick Traveling Salesman Problem (FSTSP). The algorithm was tested on benchmark instances and two real-world scenarios. BRKGA achieved an average gap of 1.4% for TSP and showed better runtime compared to Simulated Annealing (SA) for smaller FSTSP instances. Although it produced faster computational results than SA for medium and large instances, the solution quality was slightly lower. When compared to leading metaheuristics like HTGVNS and HGVNS, BRKGA had competitive runtimes but was less optimal in solutions. In practice, the algorithm reduced delivery times and enabled drones to reach difficult locations. These results suggest that BRKGA is a valuable and effective heuristic for routing problems involving hybrid truck-drone systems.", "keywords": [ "Travelling salesman problem", "Simulated annealing", "Drone", "Metaheuristic", "Benchmark (surveying)", "Vehicle routing problem", "Genetic algorithm", "Hybrid algorithm (constraint satisfaction)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s00500-025-11027-7.pdf" }, { "id": "https://openalex.org/W7160080220", "doi": "10.1016/j.apm.2026.117027", "title": "Monitoring network infrastructures with drones: Application to the railway commuter network of Madrid", "year": 2026, "publication_date": "2026-05-01", "type": "article", "language": "en", "authors": [ "David Canca", "Jose Miguel Leon-Blanco", "José Luis Andrade-Pineda", "Pedro Luis González-R", "Marcos Calle" ], "first_author": "David Canca", "venue": "Applied Mathematical Modelling", "publisher": "Elsevier BV", "abstract": "The reliable monitoring of civil network infrastructures is a critical component of modern maintenance and safety strategies, particularly in transportation systems such as commuter railways. Traditional inspection methods, which rely on human teams and specialized vehicles, are costly, infrequent, and prone to operational limitations. This research proposes a comprehensive methodological framework for optimizing drone-based monitoring of large-scale railway networks. The methodology divides the network into non-overlapping linear segments and determines the optimal flight paths and recharging locations to ensure full coverage within the defined operational parameters. From the set of non-overlapping segments, we propose a double-direction vehicle routing problem formulation to assign routes to drones, integrating constraints related to drone endurance, charging rates, and mission duration. The proposed approach is applied to the commuter railway network of Madrid, comprising 391 km of tracks and 92 stations. The computational results demonstrate that complete daily monitoring can be achieved using a fleet of 15 drones and 42 recharging bases, offering a substantial improvement in the inspection frequency compared to conventional methods at a slightly lower annual cost. A sensitivity analysis further highlights the influence of drone endurance and shift monitoring length on the fleet size and infrastructure requirements. The findings confirm the feasibility and cost-effectiveness of integrating optimization-based mission planning with unmanned aerial vehicle technologies, providing a scalable and efficient solution for the recurrent monitoring of complex network infrastructures.", "keywords": [ "Flow network", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.apm.2026.117027" }, { "id": "https://openalex.org/W4416340099", "doi": "10.2514/1.i011709", "title": "Adaptive Policy Optimization for Battery-Constrained Drone Delivery Routing", "year": 2025, "publication_date": "2025-11-19", "type": "article", "language": "en", "authors": [ "G.S. Kim", "Sang Hyun Kim" ], "first_author": "G.S. Kim", "venue": "Journal of Aerospace Information Systems", "publisher": "American Institute of Aeronautics and Astronautics", "abstract": "This study addresses a drone delivery routing optimization problem with battery constraints, a variant of a vehicle routing problem (VRP), where drones deliver goods to multiple customer nodes while adhering to operational limitations such as battery and payload capacities. Our study proposes Adaptive-Policy Optimization with Multiple Optima (A-POMO), an enhanced framework based on POMO, which is a reinforcement-learning-based method for solving combinatorial optimization problems like VRP without relying on labeled data. A-POMO for battery-constrained drone delivery routing improves solution quality and computational efficiency through a constraint-guided probability distribution and an adaptive elite reward mechanism. Especially, A-POMO adjusts reward weights adaptively and employs a refined baseline for elite solutions to strike a balance between exploration and exploitation. Numerical experiments with 20, 50, and 100 customer nodes demonstrate that A-POMO achieves near-optimal solutions compared to MILP, with significant time efficiency for small-scale instances. It also outperforms the original POMO in large-scale instances for constraint-guided route optimization.", "keywords": [ "Payload (computing)", "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Baseline (sea)", "Optimization problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/1.i011709" }, { "id": "https://openalex.org/W4402935412", "doi": "10.1007/978-3-031-71922-6_17", "title": "Overcoming Design Challenges in Coupled System Device Problems Using Agent-Based Models", "year": 2024, "publication_date": "2024-09-27", "type": "conference-paper", "language": "en", "authors": [ "Cole Jetton", "Christopher J. Hoyle", "Matthew I. Campbell" ], "first_author": "Cole Jetton", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Computer science", "Systems engineering", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-71922-6_17" }, { "id": "https://openalex.org/W4200100194", "doi": "10.1117/12.2619662", "title": "Drone patrol routing planning: application of new patrol mode", "year": 2021, "publication_date": "2021-12-23", "type": "conference-paper", "language": "en", "authors": [ "Qi S. Wang", "Yunhao Liang" ], "first_author": "Qi S. Wang", "venue": "Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021)", "publisher": null, "abstract": "Drone is known as a small and flexible green vehicle and is a potential contributor in improving efficiency in the field of inspection, however, due to the limited battery life of drone and the lack of relevant solutions, drone has not been widely used in the field of inspection. For this reason, we base our routing planning for drone patrols on existing base stations, to ease the difficulties of the limited battery life of drones, expand the scope of patrol, and vigorously promote the work mode of drone patrolling in the later stage. Unlike the simple vehicle routing problem, however, designing drone’s vehiclerouting is a challenging due to the problem of drone cannot go back to the initial base station. Therefore, we construct a mathematical model to solve the problem, and then a small calculation example is solved with CPLEX to verify the applicability of the model. Finally, based on our research results, it can have a certain reference value for the later promotion of the application of drones in the field of drone patrol.", "keywords": [ "Computer science", "Drone", "Routing (electronic design automation)", "Mode (computer interface)", "Computer network", "Human–computer interaction" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2619662" }, { "id": "https://openalex.org/W4405709919", "doi": "10.1109/robot61475.2024.10797345", "title": "Dynamics-Aware Fast Multi-Drone Exploration of Unknown Environments", "year": 2024, "publication_date": "2024-11-06", "type": "conference-paper", "language": "en", "authors": [ "Juan Zacarías Bardají", "David Morilla-Cabello", "Eduardo Montijano" ], "first_author": "Juan Zacarías Bardají", "venue": null, "publisher": null, "abstract": "Tasks such as surveillance, search and rescue operations, or disaster mapping require exploring unknown environments in the shortest possible time. Furthermore, when these tasks are performed by multiple robots, team coordination is crucial to achieve the desired efficiency. In this spirit, this paper proposes a method that introduces notions about each robot's dynamics into the planning process. We formulate novel costs into the Vehicle Routing Problem (VRP) to maintain each drone's inertia, required for efficiency. The costs of exploring regions are calculated based on the individual state of each robot, such as their speed and orientation, to favour distinct behaviours. The proposed solution is implemented on RACER, a state-of-the-art decentralized exploration system. The original implementation of RACER is modified to integrate our method, including a different optimization library to solve the VRP. To validate our approach, we conduct exhaustive experiments in four maps, with two and four agents and varying initial conditions, comparing our proposal with the state-of-the-art method. The results illustrate the advantages of incorporating knowledge about the team's dynamics into the planning process, demonstrating improvements in the efficiency and effectiveness of exploration in unknown environments.", "keywords": [ "Drone", "Computer science", "Dynamics (music)", "Physics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/robot61475.2024.10797345" }, { "id": "https://openalex.org/W7168344172", "doi": "10.21203/rs.3.rs-10273297/v1", "title": "Mothership and multi-drone routing for spatial marine management", "year": 2026, "publication_date": "2026-07-15", "type": "preprint", "language": null, "authors": [ "Ryu B. Lippmann", "Kate J. Helmstedt", "Paul Corry" ], "first_author": "Ryu B. Lippmann", "venue": "Research Square", "publisher": null, "abstract": null, "keywords": [ "Metaheuristic", "Routing (electronic design automation)", "Drone", "Software deployment", "Weighting", "Vehicle routing problem", "Computation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-10273297/latest.pdf" }, { "id": "https://openalex.org/W4415399745", "doi": "10.1109/aimv66517.2025.11203569", "title": "Optimizing Parcel Delivery: The Role of Drones in Modern Logistics", "year": 2025, "publication_date": "2025-08-16", "type": "conference-paper", "language": null, "authors": [ "Keni Tandel", "Niya Patel", "Samir B Patel" ], "first_author": "Keni Tandel", "venue": null, "publisher": null, "abstract": "Online shopping has been more popular in recent years, which has resulted in a rise in parcel deliveries. In the past, parcel distribution by truck was an efficient method. However, with developing technologies, deliveries can now be conducted using unmanned air vehicles such as drones. This study paper explains the use of drones in logistics and compares them to traditional delivery techniques in order to develop optimized delivery methods. Deliveries with drones require various critical components, such as determining the trajectory or path to be taken, weight limitations, battery life, and drone speed. Using these drone features and designing a path that includes the warehouse, charging station, and as many delivery destinations as feasible transforms the logistics industry into a smart transport firm. This can be accomplished using a variety of techniques, including the traveling salesman problem, routing problem, and combinatorial optimization. Additionally, deploying multiple drones for distribution is advantageous. This research study examines how parcel delivery might be enhanced to improve the logistics industry. To employ drones, companies must grasp networking and computer vision technology. Drone delivery relies on computer vision to perceive and analyze surrounding data in real time. In the final stage of delivery (last mile delivery), computer vision is used to authenticate or verify the delivery address. All of these aspects and components come together to make smart transportation a reality.", "keywords": [ "Drone", "Variety (cybernetics)", "Last mile (transportation)", "Travelling salesman problem", "Destinations", "Path (computing)", "Truck", "Delivery Performance" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/aimv66517.2025.11203569" }, { "id": "https://openalex.org/W4409865151", "doi": "10.1007/978-3-031-87345-4_10", "title": "A Simulation-Optimisation Tool for Routing Drones in Realistic Conditions", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Xabier A. Martin", "Peter Keenan", "Javier Panadero", "Seán McGarraghy", "Angel Alejandro Juan" ], "first_author": "Xabier A. Martin", "venue": "Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering", "publisher": null, "abstract": null, "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Simulation", "Aeronautics", "Engineering", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-87345-4_10" }, { "id": "https://openalex.org/W7120566098", "doi": null, "title": "Autonomous flight plan and architecture Drone-as-a-Service for IoT network coverage", "year": 2020, "publication_date": "2020-08-04", "type": "dissertation", "language": "pt", "authors": [ "Lucas Soares Rodrigues" ], "first_author": "Lucas Soares Rodrigues", "venue": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "publisher": "LA Referencia", "abstract": "This work is an approach to the vehicle routing problem focusing on drones as data collectors in a network of IoT sensors that generate useful information for an intelligent environment system. The dissertation presents a model for one aircraft as well as for multiple ones. The flight plan generated from the model focuses on preventing breakdowns due to lack of battery charge. Thus, in order to maximize the number of nodes that will be visited, various restrictions differ in this model from conventional vehicle routing models. In addition to the drone's flight autonomy, another limiting aspect is considered: data storage. The work also proposes an architecture with the purpose of sharing this data collection function between various external applications, the architecture is based on services and components in the cloud and applies the concept of Drone-as-a-Service. A simulation environment is also presented to visualize the flights generated by routing algorithms with the conversion of the flight plan into MAVlink commands, and a tool is also defined for the generation of those flight plans and management of sensors in a given area.", "keywords": [ "Plan (archaeology)", "Routing (electronic design automation)", "Drone", "Architecture", "Flight plan", "Cloud computing", "Function (biology)", "Work (physics)", "Limiting" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://repositorio.bc.ufg.br/tede/handle/tede/10659" }, { "id": "https://openalex.org/W7204757605", "doi": "10.1016/j.tre.2026.105191", "title": "Multi-objective optimization of truck-drone collaborative forestry monitoring", "year": 2026, "publication_date": "2026-09-01", "type": "article", "language": "en", "authors": [ "Jiantao Zhang", "Haosheng Lin", "Weitiao Wu", "Weihua Gu" ], "first_author": "Jiantao Zhang", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Data collection" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105191" }, { "id": "https://openalex.org/W2064511244", "doi": "10.1016/j.apm.2014.12.010", "title": "Unmanned Aerial Vehicle hub-location and routing for monitoring geographic borders", "year": 2014, "publication_date": "2014-12-20", "type": "article", "language": "en", "authors": [ "İnci Sarıçiçek", "Yasemin Akkuş" ], "first_author": "İnci Sarıçiçek", "venue": "Applied Mathematical Modelling", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Operations research", "Transport engineering", "Computer science", "Facility location problem", "Geography", "Computer security", "Engineering", "Computer network" ], "cited_by_count": 35, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.apm.2014.12.010" }, { "id": "https://openalex.org/W4417509780", "doi": "10.1109/leficlu65987.2025.11297254", "title": "Optimizing Drone Delivery Networks for Urban Air Mobility: A Comparative Analysis", "year": 2025, "publication_date": "2025-09-26", "type": "conference-paper", "language": "en", "authors": [ "Elham Fakhraian", "El-Houssaine Aghezzaf", "Ivana Semanjski" ], "first_author": "Elham Fakhraian", "venue": null, "publisher": null, "abstract": "This paper explores the application of the Capacitated Vehicle Routing Problem (CVRP) for drones in optimizing delivery networks, comparing three optimization methods: Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA), and Google OR-Tools. We assess the computational efficiency and effectiveness of these methods under different problem scales using selected locations from the Solomon dataset RC101. The results demonstrate that while exact routing methods provide optimal solutions for smaller scales, metaheuristic routing methods outperform in large-scale scenarios, offering a promising alternative for real-world drone logistics applications in Urban Air Mobility (UAM).", "keywords": [ "Drone", "Routing (electronic design automation)", "Metaheuristic", "Vehicle routing problem", "Linear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/leficlu65987.2025.11297254" }, { "id": "https://openalex.org/W4403842291", "doi": "10.1007/978-981-97-6710-6_27", "title": "Algorithm for Observation and Routing of Drones Based on Multi-detection and Mobilize", "year": 2024, "publication_date": "2024-10-28", "type": "conference-paper", "language": "en", "authors": [ "Pramod Kumar Patel", "Gurjeet Singh", "Neeraj Agrawal", "Dilshad Usmani", "Rajesh Kumar Nema", "Prachi Bhadhouriya" ], "first_author": "Pramod Kumar Patel", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "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/10.1007/978-981-97-6710-6_27" }, { "id": "https://openalex.org/W7165647828", "doi": "10.3390/drones10070477", "title": "Reinforcement-Learning-Based Hybrid Truck–Drone Delivery Optimization", "year": 2026, "publication_date": "2026-06-23", "type": "article", "language": "en", "authors": [ "Youyao Gao", "Tongchang Liu", "Huan Jin" ], "first_author": "Youyao Gao", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "This paper studies large-scale last-mile delivery using a heterogeneous fleet of trucks, onboard drones in a hybrid truck–drone mode, and independent drones. Orders are first screened by a feasibility check; feasible orders are then assigned to one of the three modes by a delivery mode selection policy and routed using mode-specific planning algorithms. The delivery mode selection policy is trained with Proximal Policy Optimization (PPO), warm-started by behaviour cloning from heuristic decisions. For route planning, we use a five-step procedure for the hybrid mode and simple depot round trips for independent drones. Experiments on Solomon VRPTW benchmarks and extended instances (100/200/400 customers; R/C/RC distributions) show lower total cost than representative heuristic baselines and metaheuristics, with practical runtime. Sensitivity analysis over fleet sizes further indicates competitive performance across a range of truck and drone configurations, especially for medium and large fleets.", "keywords": [ "Heuristic", "Truck", "Selection (genetic algorithm)", "Mode (computer interface)", "Range (aeronautics)", "Drone", "Sensitivity (control systems)", "TRIPS architecture" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10070477" }, { "id": "https://openalex.org/W4392943933", "doi": "10.1109/iceic61013.2024.10457090", "title": "Multi-Trip Routing of Delivery Drones with Load-Dependent Flight Speed", "year": 2024, "publication_date": "2024-01-28", "type": "conference-paper", "language": "en", "authors": [ "Mao Nishira", "Hiroki Nishikawa", "Xiangbo Kong", "Hiroyuki Tomiyama" ], "first_author": "Mao Nishira", "venue": null, "publisher": null, "abstract": "In recent years, there has been growing interest in companies using drones to deliver packages as a means of solving logistics challenges, but it is important that routes are optimized in advance. Delivery drone routing, considering variations in flight speed due to payload, has been proposed. This is referred to as Flight Speed-aware Vehicle Routing Problem (FSVRP), and approximate solution methods for the FSVRP have already been suggested. In this paper, we aim to increase the quantity of deliverable packages and reduce delivery time by proposing a delivery problem that considers multiple deliveries while accounting for variations in flight speed due to payload. Experiments demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/iceic61013.2024.10457090" }, { "id": "https://openalex.org/W7116829183", "doi": "10.1109/itnac66378.2025.11302564", "title": "Feasibility of Delay-Based Scheduling to Solve Flock Formation of Drones", "year": 2025, "publication_date": "2025-11-26", "type": "conference-paper", "language": "en", "authors": [ "Sujan Warnakulasooriya", "Andreas Willig", "Xiaobing Wu" ], "first_author": "Sujan Warnakulasooriya", "venue": null, "publisher": null, "abstract": "To optimise the use of drones, flocking behaviour enhances the efficiency of task execution. Inter-drone collision avoidance is a critical requirement in any flocking algorithm. Conventional approaches typically adjust individual trajectories to prevent collisions. This paper investigates the feasibility of using a start-delay scheduling approach, in which drones follow straight-line trajectories with optimised velocity profiles. Two major constraints that could result in circular dependencies are identified. The overall system relationships between drones are represented as an adjacency matrix, and the presence of cycles is detected using a topological sorting algorithm. Simulation results across fleet sizes from 25 to 5,000 drones identify the density ranges that support cycle-free scheduling, leading to the derivation of a density function that guarantees at least $95 \\%$ of configurations remain cycle-free. The proposed analysis provides a foundation for collision-free flock scheduling and establishes the basis for algorithms that assign individual time delays to drones while maintaining efficient operation.", "keywords": [ "Drone", "Flocking (texture)", "Scheduling (production processes)", "Adjacency list", "Job shop scheduling", "Dynamic priority scheduling" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itnac66378.2025.11302564" }, { "id": "https://openalex.org/W2888927531", "doi": "10.5072/zenodo.41308", "title": "A Decision Support System for Dynamic Heterogeneous Unmanned Aerial System Fleets", "year": 2018, "publication_date": "2018-09-01", "type": "article", "language": "en", "authors": [ "Cihan Ercan", "Cevriye Gencer" ], "first_author": "Cihan Ercan", "venue": "DergiPark (Istanbul University)", "publisher": "Istanbul University", "abstract": "The Dynamic Unmanned Aerial System Routing Problem (DUASRP) is a variant of the classic Vehicle Routing Problem (VRP) in which both planned and unplanned targets are observed by a fleet of Unmanned Aerial Systems (UASs). In the dynamic environment of UAS, the rapid response for the new important targets is a very critical process, especially for the military operations in battlespace conditions. This study describes a heuristic method for the solution of the dynamic heterogeneous UAS routing problems without causing the initial tour to be completely changed. For the dynamic routing of Unmanned Aerial Vehicles (UAV), it is necessary to determine a combination of the least additional costs of vehicle routes through a set of geographically scattered targets, and quick responses for immediate targets during the reconnaissance missions. The most frequent cases assumed in the existing literature of classical DUASs consider all UASs as identical (homogenous), all targets as having two geographical coordinates (x and y), and the thread of the targets are ignored. In this paper, a dynamic routing decision support system (DSS) based on both fuzzy clustering and leveraged cheapest insertion neighborhood method is studied for pop-up threat in the case where the UAV fleet is heterogeneous, and targets have both three-dimensional information and threads. Instead of selecting an a priori code, the proposed control methodology dynamically starts with the route based on observed behavior of the new target and the routes. It describes an efficient heuristic method capable of producing quick dynamic solutions on a series of empirical test problems.", "keywords": [ "Computer science", "Heuristic", "Routing (electronic design automation)", "Vehicle routing problem", "Operations research", "A priori and a posteriori", "Drone", "Thread (computing)", "Cluster analysis", "Real-time computing", "Engineering", "Artificial intelligence" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/en/download/article-file/527401" }, { "id": "https://openalex.org/W7170085165", "doi": "10.48550/arxiv.2607.18874", "title": "Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments", "year": 2026, "publication_date": "2026-07-21", "type": "preprint", "language": null, "authors": [ "Xin Ouyang", "Songxin Lei", "Xiang Guo", "Yutian Jiang", "Sijie Ruan", "Y Liang" ], "first_author": "Xin Ouyang", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.g., air quality and noise levels, through agile aerial crowdsourcing. Despite this potential, existing UAV-based sensing approaches overlook environmental disturbances like wind that drastically impact drone velocity and energy efficiency. Consequently, directly applying existing methods to this joint delivery and sensing paradigm in dynamic environments faces two severe challenges: (1) scalability bottlenecks as fleet sizes expand; and (2) multi-timescale decision heterogeneity between macro task dispatching and micro velocity control. To tackle these, we formalize the problem as SensUAV and propose a Two TimeScale Reinforcement Learning framework (TSRL). Specifically, TSRL separates decision-making into two cooperative layers. At the macro level, a task-embedding sensing dispatcher handles scalability by separately encoding distinct task features and sequentially evaluating UAV suitability before task selection. At the micro level, a wind-aware velocity controller learns fine-grained velocity scheduling to adapt to dynamic environmental variations. Extensive experiments on real-world datasets demonstrate that TSRL significantly outperforms baselines, achieving average system profit improvements of 20.1% in Hangzhou and 46.6% in Shanghai.", "keywords": [ "Reinforcement learning", "Scalability", "Drone", "Macro", "Agile software development", "Task (project management)", "Scheduling (production processes)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2607.18874" }, { "id": "https://openalex.org/W1556757284", "doi": null, "title": "Optimum Route Planning and Scheduling for Unmanned Aerial Vehicles", "year": 2008, "publication_date": "2008-12-01", "type": "article", "language": "en", "authors": [ "Erkan Sonmezocak", "Senol Kurt" ], "first_author": "Erkan Sonmezocak", "venue": "Defense Technical Information Center (DTIC)", "publisher": null, "abstract": "New threat perceptions have extended the sense of self-defense to include preemptive strikes if a threat is going to occur. For its part, the military should have high Intelligence, Surveillance, and Reconnaissance (ISR) capabilities to implement this strategy. UAVs play an important role as the most effective way of providing high quality ISR in today s modern wars. The route planning of UAVs is the most critical and challenging problem of wartime. This thesis will develop three algorithms to solve a model that produces executable routings in order to dispatch three Unmanned Aerial Vehicles (UAV) to complete 20 different missions in different locations. These algorithms seek to maximize the bonus points are paired with the targets, representing the priority of the missions. By this definition, the problem can be classified as a Multiple Tour Maximum Prize Collection Problem (MTMPC). MTMCP is closely related to the classical Traveling Salesman and Vehicle Routing Problems with the difference that not all nodes can be visited in the available time. Each node is assigned a bonus point value representing the priority of that mission, and the objective of the MTMCP is to determine the nodes to be visited to maximize the collected bonus points.", "keywords": [ "Travelling salesman problem", "Scheduling (production processes)", "Computer science", "Operations research", "Route planning", "Executable", "Real-time computing", "Engineering", "Operations management", "Transport engineering", "Algorithm" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA494126" }, { "id": "https://openalex.org/W7111930925", "doi": null, "title": "Dynamic routing for trucks and drones for multiple tasks after disasters", "year": 2024, "publication_date": "2024-09-28", "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 study addresses the dynamic routing problem for a truck-and-drone collaboration system with multiple tasks after disasters. The post-disaster scenarios are highly unstable due to uncertainties, for example, the rescue tasks may come stochastically during the rescue response. Therefore, it is critical to update the route of trucks and drones when the new demand comes. Considering different priorities for the rescue tasks, the objective of the proposed problem is to minimize the priority cost, which can be regarded as maximizing the number of rescued people. In this paper, we model the problem as a Markov decision process (MDP) and solve it using the multi-agent reinforcement learning (MARL) method.", "keywords": [ "Drone", "Markov decision process", "Routing (electronic design automation)", "Truck", "Process (computing)", "Reinforcement learning", "Dynamic programming", "Markov process" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hub.hku.hk/handle/10722/353607" }, { "id": "https://openalex.org/W4412920821", "doi": "10.3233/atde250455", "title": "An Enhanced ALNS Algorithm for Collaborative Truck and Drone Routing in Delivery Logistics", "year": 2025, "publication_date": "2025-07-17", "type": "book-chapter", "language": "en", "authors": [ "Yuqi Liu", "Hengyu Xue", "Xu Zheng", "Hongpeng Wang" ], "first_author": "Yuqi Liu", "venue": "Advances in transdisciplinary engineering", "publisher": "IOS Press", "abstract": "With the advancement of autonomous robotics, truck-drone collaborative delivery shows the potential to reduce economic costs and improve distribution efficiency in delivery logistics. However, it also leads to several new problems, such as Vehicle Routing Problem with Drones (VRP-D). This paper addresses the VRP-D and develops an enhanced Adaptive Large Neighborhood Search (E-ALNS) algorithm to optimize routing costs. The E-ALNS combines the Metropolis acceptance criterion of simulated annealing with various neighbourhood search strategies, employs K-means clustering to generate high-quality initial solutions, and enhances solution quality through local search and mechanisms to escape local optima. For evaluation, experiments are conducted by comparing the proposed E-ALNS with the original Adaptive Large Neighborhood Search (ALNS) algorithm on instances with different scales. Results show that the proposed algorithm achieves superior solutions for VRP-D, demonstrating significant performance advantages.", "keywords": [ "Drone", "Vehicle routing problem", "Truck", "Routing algorithm", "Routing (electronic design automation)", "Computer science", "Operations research", "Business", "Engineering", "Aerospace engineering", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3233/atde250455" }, { "id": "https://openalex.org/W2035373203", "doi": "10.1007/s10846-012-9729-y", "title": "Route Planning for Angle Constrained Terrain Mapping Using an Unmanned Aerial Vehicle", "year": 2012, "publication_date": "2012-08-09", "type": "article", "language": "en", "authors": [ "P. B. Sujit", "B. P. Hudzietz", "Srikanth Saripalli" ], "first_author": "P. B. Sujit", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Terrain", "Photogrammetry", "Computer science", "Point of interest", "Motion planning", "Travelling salesman problem", "Kinematics", "Computer vision", "Artificial intelligence", "Geography", "Algorithm", "Robot" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10846-012-9729-y" }, { "id": "https://openalex.org/W4411207803", "doi": "10.1109/ichora65333.2025.11017293", "title": "Scalable Optimization for Drone and Mobile Robot Routing Using Genetic Algorithms", "year": 2025, "publication_date": "2025-05-23", "type": "conference-paper", "language": "en", "authors": [ "Orxan Shahbazov", "İlqar Mamishov", "Nesli Elxansoy", "Zeynep Sahutoglu" ], "first_author": "Orxan Shahbazov", "venue": null, "publisher": null, "abstract": "This study addresses the Drone Vehicle Routing Problem (TRPD) in a scenario involving a single drone and mobile robots, aiming to identify the optimal solution. A machine learning-based genetic algorithm (GA) approach utilizing function approximation is proposed, preserving a two-stage structure to enhance computational efficiency. The algorithm's performance is evaluated through numerical experiments and compared to the simulated annealing (SA) method under identical assumptions. The results demonstrate that the machine learning-based GA outperforms SA in solving small- and medium-sized problems, providing superior solutions in less time. However, for large-scale problems, the method exhibits slower performance due to the computational overhead associated with machine learning operations. These findings underscore the potential of integrating machine learning into optimization techniques to effectively address TRPD scenarios.", "keywords": [ "Drone", "Computer science", "Scalability", "Mobile robot", "Genetic algorithm", "Robot", "Routing (electronic design automation)", "Artificial intelligence", "Computer network", "Machine learning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ichora65333.2025.11017293" }, { "id": "https://openalex.org/W4417522830", "doi": "10.1115/1.4070721", "title": "Optimized Multi-Unmanned Aerial Vehicle Coverage Path Planning for Efficient Solar Panel Cleaning: A Rectilinear and Vehicle Routing Problem-Based Approach", "year": 2025, "publication_date": "2025-12-20", "type": "article", "language": "en", "authors": [ "M. Mahinzad Ghaziani", "Ulaş Yaman", "Ali Emre Turgut" ], "first_author": "M. Mahinzad Ghaziani", "venue": "Journal of Dynamic Systems Measurement and Control", "publisher": "ASM International", "abstract": "Abstract This study proposes a novel methodology for efficient multi-unmanned aerial vehicle (UAV) coverage path planning (CPP) tailored to solar panel cleaning applications. The approach incorporates rectilinear path planning and vehicle routing problem (VRP) strategies, enabling optimized and coordinated multi-UAV operations for large-scale solar farms. By systematically dividing the target area into rows and assigning optimized routes to UAVs, the methodology minimizes mission time and maximizes cleaning efficiency. The proposed CPP approach integrates realistic constraints such as UAV battery life, flight time, and operator limitations, ensuring practicality and robustness. Comprehensive evaluations, including simulations using the Gazebo platform and real-world tests with UAVs, validate the effectiveness of the methodology. The results demonstrate significant improvements in path optimization, energy efficiency, and area coverage, confirming its applicability to solar panel maintenance and other large-scale coverage tasks.", "keywords": [ "Motion planning", "Path (computing)", "Routing (electronic design automation)", "Row", "Route planning", "Operator (biology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/doi/10.1115/1.4070721/7579633/ds-25-1223.pdf" }, { "id": "https://openalex.org/W7213762488", "doi": "10.1016/j.cor.2026.107686", "title": "Drone pharmaceutical delivery network design considering operational risks", "year": 2026, "publication_date": "2026-09-01", "type": "article", "language": "en", "authors": [ "Xuting Sun", "Zeyang Li", "Jiage Huo", "Yichen Qin" ], "first_author": "Xuting Sun", "venue": "Computers & Operations Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Network planning and design", "Flow network", "Systems design", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cor.2026.107686" }, { "id": "https://openalex.org/W7130823784", "doi": "10.1007/978-981-95-6754-6_16", "title": "Research on Truck-Drone Hybrid Delivery with Real-Time Charging Strategies", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Fang Chu", "Xiaowei Wu" ], "first_author": "Fang Chu", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Bottleneck", "Drone", "Bridge (graph theory)", "Battery (electricity)", "Truck", "Dual (grammatical number)", "Flexibility (engineering)", "Energy (signal processing)", "Sensitivity (control systems)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-95-6754-6_16" }, { "id": "https://openalex.org/W7212424081", "doi": "10.1007/978-981-92-3570-4_14", "title": "Dynamic Truck-train-drone Routing Under Communication Interference Uncertainty", "year": 2026, "publication_date": "2026-09-13", "type": "conference-paper", "language": "en", "authors": [ "Shuyang Zhu", "Xianbo Tan", "Zhiyuan Liu", "Yimeng Zhang", "Mi Gan", "Xiaobo Liu" ], "first_author": "Shuyang Zhu", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Interference (communication)", "Routing (electronic design automation)", "Noise (video)", "Process (computing)", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-92-3570-4_14" }, { "id": "https://openalex.org/W3206865252", "doi": "10.7275/24602740", "title": "Analytics-Based Optimization for the Integration of Drones into Last-Mile Logistics", "year": 2021, "publication_date": "2021-10-25", "type": "dissertation", "language": "en", "authors": [ "Amro M. El-Adle" ], "first_author": "Amro M. El-Adle", "venue": "ScholarWorks@UMassAmherst (University of Massachusetts Amherst)", "publisher": "University of Massachusetts Amherst", "abstract": "The growing volume and consistency of online ordering has renewed focus on the optimization of logistics and interest in technology-driven supply chain operations. Recent trials have demonstrated the viability of using unmanned aerial vehicles (UAVs), known colloquially as drones, in last-mile deliveries. In minimizing delivery times for customers, reducing the load of logistics workers, and reducing congestion and pollution per delivery, the integration of drones into traditional delivery networks presents transformative potential to benefit consumers, firms, and society at large. This dissertation investigates operational and tactical problems that inform routing and assignment decisions for vehicle-drone delivery systems. In three essays that follow, mathematical programming models are developed for these problems and their computational tractability enhanced via optimization constructs spanning preprocessing, valid inequalities, and reformulation techniques. Moreover, insights are provided into the network topographies that stand to benefit from the integration of drones into last-mile deliveries. The impact of these optimization techniques is assessed using benchmark instances from the growing literature. Chapter 1, based on the work of El-Adle et al. (2019), investigates the operational problem of assigning customers to be visited either by a delivery vehicle or by a portable companion drone launched from the vehicle, recently known in the literature as a Traveling Salesman Problem with Drone (TSP-D). A novel 0-1 mixed-integer program (MIP) is proposed for the TSP-D that synchronizes vehicle and drone operations with the objective of minimizing the total duration of the joint tour. Using a combination of valid inequalities, pre-processing, and other bound tightening strategies, the tractability of the proposed MIP formulation is enhanced to produce exact solutions to benchmark instances having up to 24 customers, the largest in the literature. Our work further enabled exact solutions for certain networks involving 32 customers, twice the size of instances solved in the extant literature at that point. Since commercial parcel delivery typically may involve 100 packages per route, Chapter 2 builds on the breakthrough in Chapter 1 and proposes an optimization-based variable neighborhood search heuristic. Starting with a relaxed problem structure, the heuristic uses two distinct MIP formulations to progressively integrate constraints relating to synchronous travel between the vehicle and drone as well as cyclic flights. By termination, the heuristic restores all problem assumptions to yield a high-quality feasible solution. On a set of benchmark instances from the literature, the heuristic improves upon the best-known results for 113/120 instances having up to 100 nodes, with comparable computational effort to approaches in the literature (Schermer et al., 2018). We also propose several pre-processing techniques that simplify decision-making associated with drone cycles, and analytically investigate the conditions under which they are optimal. Chapter 2 is based on the work of El-Adle et al. (2021). Building upon the previous work, Chapter 3 examines the idea of simplifying operational last-mile delivery problems that involve vehicles and portable drones. By optimizing a multi-period last-mile delivery problem, it is possible to identify, at a more tactical level, customers that should be served by drone and those who should receive their packages by vehicle in a given season. This tactical assignment is informed by geospatial and demand data analytics and an optimization model that optimizes the underlying operational vehicle routing and drone flights over the multi-period horizon under investigation. We propose a novel mixed-integer formulation to this challenging multi-period last-mile delivery problem with drone eligibility, which is also embedded in an optimization-based variable neighborhood search that effectively and consistently discovers near-optimal solutions (within 0.5% optimal) for networks involving 200 customers in manageable computational times.", "keywords": [ "Drone", "Mile", "Analytics", "Last mile (transportation)", "Computer science", "Data science", "Big data", "Geography", "Data mining" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://scholarworks.umass.edu/dissertations_2/2309" }, { "id": "https://openalex.org/W7110605046", "doi": null, "title": "Charging-Aware Routing Problem for Multi-UAV Wildfire Detection with Station Deployment", "year": 2025, "publication_date": "2025-01-01", "type": "dissertation", "language": "en", "authors": [ "Hou, Ke" ], "first_author": "Hou, Ke", "venue": "elib (German Aerospace Center)", "publisher": "Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)", "abstract": null, "keywords": [ "Software deployment", "Routing (electronic design automation)", "Key (lock)", "Field (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://elib.dlr.de/218850/1/2024-2025-HouKKe.pdf" }, { "id": "https://openalex.org/W7204160888", "doi": "10.2139/ssrn.7349897", "title": "Pickup and Delivery in Two-Echelon UGV-UAV Networks: A Location-Routing Problem", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Wenwei Zhang", "Jialu Shi", "Zhengyang Wei", "Hongmei Shan", "Lele Zhang", "Pengfei Li" ], "first_author": "Wenwei Zhang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Agile software development", "Leverage (statistics)", "Pickup", "Robustness (evolution)", "Mode (computer interface)", "Economies of scale", "Computation", "Outsourcing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7349897" }, { "id": "https://openalex.org/W7162078784", "doi": "10.1504/ijcse.2026.10078648", "title": "Graph transformer decision model for efficiently UAV-UGV joint routing problem", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Ke Zhang", "Yuelong Su" ], "first_author": "Ke Zhang", "venue": "International Journal of Computational Science and Engineering", "publisher": "Inderscience Publishers", "abstract": null, "keywords": [ "Decision model", "Transformer", "Graph", "Joint (building)", "Decision problem", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijcse.2026.10078648" }, { "id": "https://openalex.org/W2783682091", "doi": null, "title": "Optimization Modeling and Analysis of Customer-Centric Delivery Logistics", "year": 2017, "publication_date": "2017-01-01", "type": "dissertation", "language": "en", "authors": [ "Mohammad Moshref‐Javadi" ], "first_author": "Mohammad Moshref‐Javadi", "venue": "Purdue e-Pubs (Purdue University System)", "publisher": "Purdue University System", "abstract": "Distribution of products and services is an essential part of production and service systems. Customer-centric delivery systems that are considered in this research are a class of logistics systems that focuses on minimizing the delivery time by making decisions on questions such as vehicle routing, locations of distribution centers, and capacity planning. The minimum delivery time plays a crucial role in both disaster relief operations and commercial delivery systems. While disaster relief logistics seek to minimize loss and damage, commercial delivery systems aim at enhancing customer satisfaction and profit maximization. In this research, first a taxonomy of customer-centric routing problems is developed. Then, the first part of the research considers the customer-centric routing problems with large customer demands which result in split delivery among more than a vehicle. Assuming multiple commodities are distributed to customers, the goal is to find the optimal routes of the vehicles allowing the split delivery. This problem is mathematically formulated in two models and valid inequalities are provided to enhance the understandings of the problem and strengthen the computational performance of the mathematical models. The properties of the problem are used to develop an efficient algorithm which combines the concepts and operators of Simulated Annealing and Variable Neighborhood Search. The second part of the research studies the customer-centric routing problems in combination with location problem due to their interrelated nature. In this research, the goal is to decide distribution center locations and vehicle routes simultaneously to minimize delivery time. The problem is formulated and two efficient metaheuristic algorithms are designed to deal with large-scale problems: Memetic Algorithm (MA) and Recursive Granular Algorithm (RGA). In the third study, the goal is to study how we can benefit from the emerging “drone technology” in reducing delivery time. This research assumes a combined system of vehicles and drones in which a vehicle carries both supplies and drones and launches drones at some points in its route to deliver packages to recipients. The objective is to decide vehicle route, drone launching locations, and customers to serve by the vehicle and drones. The problem is then extended to the multi-trip case in which each drone can be relaunched to serve several customers at each stop. These problems are mathematically formulated and a bound analysis is conducted to investigate the maximum possible savings achievable by employing drones. The models are tested and analyzed in various hypothetical problems and in a case study developed upon Richmond, Virginia.", "keywords": [ "Computer science", "Business", "Process management" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://docs.lib.purdue.edu/dissertations/AAI10607427" }, { "id": "https://openalex.org/W7172106402", "doi": "10.1016/j.tre.2026.105113", "title": "The location-routing problem for UAV monitoring under time-varying noise constraints", "year": 2026, "publication_date": "2026-08-01", "type": "article", "language": "en", "authors": [ "Yuping Li", "Kewei Xiang", "Mingjie Li", "Xingyu Wang" ], "first_author": "Yuping Li", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Noise (video)", "Control theory (sociology)", "Aircraft noise", "Background noise", "Constraint (computer-aided design)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105113" }, { "id": "https://openalex.org/W4414511919", "doi": "10.2139/ssrn.5528646", "title": "The Location-Routing Problem for UAV Monitoring under Time-Varying Noise Constraints", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Kewei Xiang", "Yuping Li" ], "first_author": "Kewei Xiang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Bottleneck", "Solver", "Noise (video)", "Energy consumption", "Heuristic", "Tabu search", "Bridge (graph theory)", "Iterated local search" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5528646" }, { "id": "https://openalex.org/W7163543852", "doi": "10.2514/6.2026-4335", "title": "Optimal Control-Based Adaptive Fleet Sizing of UAV Communication Relays for Post-Disaster Network Connectivity Recovery", "year": 2026, "publication_date": "2026-06-04", "type": "conference-paper", "language": null, "authors": [ "Jimin Choi", "Max Z. Li" ], "first_author": "Jimin Choi", "venue": null, "publisher": null, "abstract": "Reliable communication is critical for effective post-disaster response, but major disasters often disrupt or disable ground communication networks. Uncrewed aerial vehicles (UAVs) can act as aerial communication relays to help restore connectivity across affected areas. After a disaster, communication demand and ground network capacity vary across locations and over time, causing the need for UAV relay support to change across zones throughout recovery. To capture this spatiotemporal heterogeneity, we partition the disaster area into zones with distinct recovery and demand patterns. We therefore propose a model predictive control (MPC)-based framework for adaptive UAV relay fleet sizing that couples three key components: (i) a zone-specific recovery model describing the restoration of ground communication capacity, (ii) a dynamic demand model representing residual communication traffic for UAV relays, and (iii) a system-level energy availability constraint capturing fleet-wide duty cycle limitations on simultaneous UAV operation. Two controller variants arise from the proposed framework, both formulated as convex quadratic programs and operated in a receding-horizon fashion. The first, a demand-only MPC, draws exclusively on demand forecasts, while the second, a recovery-aware MPC, further integrates predictions of ground network restoration. Both controllers are compared against a static allocation policy and a greedy reactive policy across 100 randomly generated scenarios. The recovery-aware MPC cuts cumulative service backlog by 86.6% versus static allocation and by 16.5% versus the demand-only MPC, while deploying on average 10 fewer UAVs per step. The benefit of recovery-aware forecasting depends on fleet size. Below a critical threshold of approximately 55 UAVs, this benefit is no longer observed because there is too little deployment slack to take advantage of recovery information. Furthermore, sensitivity analysis confirms robustness to key controller parameters and the computational scalability of proposed controllers.", "keywords": [ "Sizing", "Key (lock)", "Reliability (semiconductor)", "Flow network", "Relay" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2026-4335" }, { "id": "https://openalex.org/W7160122063", "doi": "10.5281/zenodo.20031292", "title": "Autonomous Drone Fleet Management and Pathfinding Optimization using $A^*$ and Dijkstra Algorithms", "year": 2026, "publication_date": "2026-05-05", "type": "report", "language": "en", "authors": [ "shaaban aya" ], "first_author": "shaaban aya", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This project introduces an integrated software platform designed for the autonomous management and coordination of a drone delivery fleet within controlled environments, such as university campuses or small urban areas. The system automates the entire delivery lifecycle—from order processing and intelligent drone assignment to real-time telemetry tracking and monitoring via a web-based dashboard. At its core, the platform leverages artificial intelligence pathfinding techniques, specifically the $A^*$ and Dijkstra algorithms, to compute optimal delivery routes while dynamically adapting to constraints like battery levels, no-fly zones, and simulated environmental conditions. Built on a scalable microservices architecture and utilizing a hybrid SQL/NoSQL database approach, the system demonstrates significant improvements in routing efficiency and operational reliability, providing a flexible framework for future real-world autonomous logistics deployment. This work was conducted at Arab International University (AIU), Syria. The official website of the university is https://www.aiu.edu.sy", "keywords": [ "Drone", "Dijkstra's algorithm", "Pathfinding", "Scalability", "Fleet management", "Software", "Architecture", "Routing (electronic design automation)", "Executable" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.20031292" }, { "id": "https://openalex.org/W4308080862", "doi": "10.1109/itsc55140.2022.9922062", "title": "Routing of Heterogeneous Fleets for Flash Deliveries via Vehicle Group Assignment", "year": 2022, "publication_date": "2022-10-08", "type": "conference-paper", "language": "en", "authors": [ "Maximilian Kronmueller", "Andrés Fielbaum", "Javier Alonso–Mora" ], "first_author": "Maximilian Kronmueller", "venue": "2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)", "publisher": null, "abstract": "This paper presents a novel approach to route heterogeneous fleets for flash delivery operations. Flash deliveries offer to serve customers' wishes in minutes. We investigate a scenario that allows to pick up orders at multiple depots with a heterogeneous vehicle fleet leveraging different modes of transportation. We propose the Heterogeneous Vehicle Group Assignment (HVGA) method, which, given a problem state, identifies potential pick-up locations, calculates potential trips for all modes of transportation and last chooses from the set of potential trips. Experiments to analyze the proposed method are executed using a fleet featuring two modes of transportation, trucks and drones. We compare to a state-of-the-art method. Results show that HVGA is able to serve more orders while requiring less total traveled distance. Further, the effects of the fleet size and fleet composition between drones and trucks are examined by simulating three hours of a flash delivery operation in the city center of Amsterdam.", "keywords": [ "Truck", "Computer science", "TRIPS architecture", "Flash (photography)", "Routing (electronic design automation)", "Fleet management", "Transport engineering", "Vehicle routing problem", "State (computer science)", "Drone", "Duration (music)", "Operations research" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://resolver.tudelft.nl/uuid:add17ec7-acf4-40c7-8823-765dc279d92b" }, { "id": "https://openalex.org/W3097857994", "doi": "10.1109/iros51168.2021.9636549", "title": "Planning for Aerial Robot Teams for Wide-Area Biometric and Phenotypic Data Collection", "year": 2021, "publication_date": "2021-09-27", "type": "conference-paper", "language": "en", "authors": [ "Shashwata Mandal", "Tianshuang Gao", "Sourabh Bhattacharya" ], "first_author": "Shashwata Mandal", "venue": "2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)", "publisher": null, "abstract": "This work presents an efficient and implementable solution to the problem of joint task allocation and path planning in a multi-UAV platform. The sensing requirement associated with the task gives rise to an uncanny variant of the traditional vehicle routing problem with coverage/sensing constraints. As is the case in several multi-robot path-planning problems, our problem reduces to an mTSP problem. In order to tame the computational challenges associated with the problem, we propose a hierarchical solution that decouples the vehicle routing problem from the target allocation problem. As a tangible solution to the allocation problem, we use a clustering-based technique that incorporates temporal uncertainty in the cardinality and position of the robots. Finally, we implement the proposed techniques on our multi-quadcopter platforms.", "keywords": [ "Computer science", "Motion planning", "Task (project management)", "Robot", "Routing (electronic design automation)", "Vehicle routing problem", "Quadcopter", "Cluster analysis", "Artificial intelligence", "Sonar", "Distributed computing", "Real-time computing" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iros51168.2021.9636549" } ]