[ { "id": "https://openalex.org/W7160127656", "doi": "10.5281/zenodo.20031291", "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.20031291" }, { "id": "https://openalex.org/W7171326958", "doi": "10.3724/sp.j.1249.2026.04377", "title": "Research progress in truck-drone collaborative route planning: a systematic review", "year": 2026, "publication_date": "2026-07-01", "type": "article", "language": "en", "authors": [ "Cui Haipeng", "Li Huan", "Zhang Xuxin", "Chen Xiangsheng", "Hu Mingwei" ], "first_author": "Cui Haipeng", "venue": "JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING", "publisher": "Science Press", "abstract": "To systematically review the research progress in truck-drone collaborative routing, 386 relevant publications retrieved from the Web of Science Core Collection were analyzed using VOSviewer for bibliometric and scientific knowledge mapping, complemented by a qualitative review of representative studies. The development trajectory and knowledge base of this research field were examined from five perspectives: annual publication trends, country and regional distributions, source journals, document co-citation networks, and keyword co-occurrence. Based on the keyword co-occurrence analysis, a research framework encompassing problem types, model development, solution algorithms, and application scenarios was established. The results show that truck-drone collaborative route planning has evolved from the traveling salesman problem (TSP) and the vehicle routing problem (VRP), with research settings gradually expanding from early single-truck-single-drone collaborative delivery to more complex scenarios involving multiple trucks and drones, flexible launch and recovery, multiple customer visits, integrated pickup and delivery, and dynamic operations. Model development have formed a comprehensive constraint framework centered on objective functions, network constraints, resource constraints, service constraints, truck-drone coordination and synchronization constraints, and uncertainty constraints. Solution approaches have evolved through the parallel development of exact algorithms, heuristic and metaheuristic methods, and learning-based methods. In terms of applications, relevant studies have extended from urban last-mile delivery to remote-area logistics, medical logistics, humanitarian logistics, agricultural and forestry operations, and infrastructure inspection. With the rapid advancement of air-ground collaborative transportation technologies, this systematic review not only clarifies the technical evolution of truck-drone collaborative route planning but also provides references for future model development and algorithm design in real-world applications, thereby facilitating the translation of theoretical research into engineering practice.", "keywords": [ "Vehicle routing problem", "Field (mathematics)", "Heuristic", "Resource (disambiguation)", "Memetics", "Knowledge base", "Service (business)", "Constraint (computer-aided design)", "Synchronization (alternating current)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3724/sp.j.1249.2026.04377" }, { "id": "https://openalex.org/W7129345246", "doi": "10.1007/s00291-026-00849-y", "title": "Very large-scale neighborhood search for drone routing with energy replenishment", "year": 2026, "publication_date": "2026-02-17", "type": "article", "language": "en", "authors": [ "Catherine Lorenz", "Nicola Mimmo", "Alena Otto", "Daniele Vigo" ], "first_author": "Catherine Lorenz", "venue": "OR Spectrum", "publisher": "Springer Science+Business Media", "abstract": "Abstract The drone routing problem with energy replenishment (DRP-E) describes a general class of routing problems with intermediate stops and synchronization constraints. In DRP-E, the drone has to visit a set of nodes and routinely requires battery swaps, energy- or payload replenishment from mobile or stationary replenishment stations. Thereby, the drone may visit several destinations between two replenishments, and mutual waiting at the rendezvous locations may occur. In this paper, we propose a very large-scale neighborhood that synergistically leverages two large-sized polynomially solvable DRP-E subproblems (SP1 and SP2). The number of feasible solutions in the resulting neighborhood is a multiple of those in SP1 and SP2, and, thus, exponential in the input size of the problem. We develop a non-trivial search procedure, VLNS, which examines this neighborhood entirely , in a computational time that remains polynomial in the problem size. The desired trade-off between accuracy and runtime of the proposed two-stage dynamic programming approach can be flexibly adjusted with just a single parameter. For large parameter values, it converts to an exact approach. VLNS is a flexible improvement procedure , which is easy to implement. It can be straightforwardly adapted to many DRP-E variants and may be embedded in any algorithmic scheme, meta- or math-heuristic. In computational tests, we demonstrate that a lean local-search-based implementation of VLNS already outperforms state-of-the-art heuristics for several DRP-E variants by a significant margin. We furthermore propose a well-performing exact approach for DRP-E.", "keywords": [ "Drone", "Heuristics", "Routing (electronic design automation)", "Set (abstract data type)", "Class (philosophy)", "Payload (computing)", "Energy (signal processing)", "Synchronization (alternating current)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s00291-026-00849-y.pdf" }, { "id": "https://openalex.org/W7171493245", "doi": "10.1007/978-981-92-2879-9_24", "title": "Reinforcement Learning-Based Route Optimization for Truck–Drone Collaborative Delivery", "year": 2026, "publication_date": "2026-07-28", "type": "conference-paper", "language": "en", "authors": [ "Zhitao Xu", "Pengcheng Yuan", "Ruiyin Du", "Wei Ye", "Xianyu Zhang" ], "first_author": "Zhitao Xu", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Truck", "Reinforcement learning", "Drone", "Hybrid algorithm (constraint satisfaction)", "Popularity", "Routing (electronic design automation)", "Linear programming", "Nonlinear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-92-2879-9_24" }, { "id": "https://openalex.org/W4287996934", "doi": "10.48550/arxiv.1912.04353", "title": "A Branch-and-Price Algorithm for a Team Orienteering Problem with Fixed-Wing Drones", "year": 2019, "publication_date": "2019-12-09", "type": "preprint", "language": "en", "authors": [ "Kaarthik Sundar", "Sujeevraja Sanjeevi", "Christopher Montez" ], "first_author": "Kaarthik Sundar", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This paper formulates a team orienteering problem with multiple fixed-wing drones and develops a branch-and-price algorithm to solve the problem to optimality. Fixed-wing drones, unlike rotary drones, have kinematic constraints associated with them, thereby preventing them to make on-the-spot turns and restricting them to a minimum turn radius. This paper presents the implications of these constraints on the drone routing problem formulation and proposes a systematic technique to address them in the context of the team orienteering problem. Furthermore, a novel branch-and-price algorithm with branching techniques specific to the constraints imposed due to fixed-wing drones are proposed. Extensive computational experiments on benchmark instances corroborating the effectiveness of the algorithms are also presented.", "keywords": [ "Orienteering", "Drone", "Computer science", "Context (archaeology)", "Mathematical optimization", "Benchmark (surveying)", "Turning radius", "Mathematics", "Engineering", "Geography", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1912.04353" }, { "id": "https://openalex.org/W7204241465", "doi": "10.1016/j.tre.2026.105186", "title": "Truck-drone collaborative routing with flexible cross-truck operations and slot constraints", "year": 2026, "publication_date": "2026-08-27", "type": "article", "language": "en", "authors": [ "Hang Fan", "Zhuxi Zhang", "Tong Guo", "Wenbo Du", "Xuechen Gu", "Tao Song", "Defu Lin" ], "first_author": "Hang Fan", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Routing (electronic design automation)", "Vehicle routing problem", "Key (lock)", "Minification" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.105186" }, { "id": "https://openalex.org/W7126348154", "doi": null, "title": "Hybrid matheuristics for mobile parcel locker delivery with robots and drone resupply [Abstract]", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Cheng Chen", "Emrah Demir" ], "first_author": "Cheng Chen", "venue": "ORCA Online Research @Cardiff (Cardiff University)", "publisher": "Cardiff University", "abstract": "This research introduces a novel vehicle routing problem with time windows in an autonomous last mile delivery system featuring mobile parcel lockers (MPLs), delivery robots, and drone resupply. Customers can choose between home delivery via robots or self-pickup from MPLs stationed at designated parking areas. Delivery robots operate from MPLs, while drones replenish these lockers as needed. We define this problem as the Mobile Parcel Locker Problem with Delivery Robot and Drone Resupply (MPLPDR-DR). To address this problem, we propose a mixed-integer linear programming (MILP) model and develop a hybrid matheuristic approach. Our method integrates a metaheuristic for optimising MPL and robot routing, while a MILP model determines optimal drone resupply operations. The metaheuristic is based on the artificial bee colony framework, incorporating large neighbourhood search, variable neighbourhood descent, and mutation mechanisms to enhance solution quality. Extensive computational experiments demonstrate the efficiency of the proposed approach in solving large-scale MPLPDR-DR instances. The findings provide key managerial insights into optimising autonomous delivery operations, improving efficiency, and reducing last mile logistics costs.", "keywords": [ "Drone", "Robot", "Metaheuristic", "Mobile robot", "Key (lock)", "Vehicle routing problem", "Linear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://orca.cardiff.ac.uk/view/cardiffauthors/A2271339I.html>" }, { "id": "https://openalex.org/W4390590565", "doi": "10.2139/ssrn.4684209", "title": "A Deep Reinforcement Learning Approach for Solving the Pickup and Delivery Problem with Drones and Time Windows", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Fuqiang Lu", "Nan Chen", "Bihua Ling" ], "first_author": "Fuqiang Lu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Pickup", "Reinforcement learning", "Computer science", "Reinforcement", "Artificial intelligence", "Psychology", "Biology", "Social psychology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4684209" }, { "id": "https://openalex.org/W4287241106", "doi": null, "title": "The optimization of UAV routing problem with a genetic algorithm to observe the damages of possible Istanbul earthquake", "year": 2019, "publication_date": "2019-07-26", "type": "article", "language": "en", "authors": [ "Muhammed HALAT", "Ömer Özkan" ], "first_author": "Muhammed HALAT", "venue": "DergiPark (Istanbul University)", "publisher": "Istanbul University", "abstract": "Bu çalışmada, olası İstanbul depremi sonrası oluşabilecek hasarları havadan gözlemlemek için ilk 24 saat içerisinde İstanbul’dan kaldırılan bir İHA’nın hangi rotada uçması gerektiği problemi ele alınmıştır. Problemde, İstanbul üzerinde İHA’nın ziyaret edebileceği 230 aday grid nokta belirlenmiş ve her aday nokta için noktanın deprem riski ağırlığı ile nüfus yoğunluğunu birleştiren ağırlık değerleri belirlenmiştir. Problemde en fazla sayıda aday noktanın ağırlığını toplayacak şekilde İHA’nın menzil kısıtı altında rotalanması amaçlanmıştır. Tanımlanan bu problem, literatürdeki Oryantring problemine uyarlanmıştır. Oryantring problemi NP-zor bir problem olduğundan dolayı, problemin çözümü için probleme özgü bir genetik algoritma ve bir tavlama benzetimi algoritması geliştirilmiştir. Algoritmaların parametreleri deneyler ile ayarlanmıştır. Gerçek hayata uygun olarak deprem sonrası İHA’nın kalktığı havalimanı ile günlük ziyaret (veya görüntü sayısı) durumlarını kapsayan 15 farklı senaryo oluşturulmuş ve senaryolar ILOG ile kesin ve geliştirilen metasezgisel algoritmalar ile yaklaşık olarak çözülmeye çalışılmıştır. 15 senaryonun 2’sinde optimal çözüm bulunmuş olup diğer senaryolar için genetik algoritma daha iyi sonuçlar elde etmiş ve kabul edilebilir CPU süreleri içinde problemi çözebilmiştir.", "keywords": [ "Damages", "Genetic algorithm", "Routing (electronic design automation)", "Computer science", "Algorithm", "Mathematical optimization", "Machine learning", "Mathematics", "Computer network", "Political science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/tr/pub/pajes/issue/61143/908717" }, { "id": "https://openalex.org/W7126032662", "doi": "10.5281/zenodo.18417916", "title": "A Novel Framework for Optimizing the Routes of Drones During the Last Mile Delivery Process", "year": 2023, "publication_date": "2023-06-01", "type": "article", "language": null, "authors": [ "Archit Agarwal", "Ayush Goyal", "Dinesh Kumar Vishwakarma" ], "first_author": "Archit Agarwal", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The growing e-commerce industry and demand for fast deliveries have led to the exploration of alternative lastmile delivery methods, including drone delivery systems. Optimising the routes for drone delivery presents a formidable challenge due to the intricate nature of the problem. This research paper introduces a novel solution that combines the Vehicle Routing Problem (VRP) and the Travelling Salesman Problem (TSP) to tackle the last-mile drone delivery issue. A mathematical model and a heuristic algorithm are devised, incorporating clustering of delivery locations, TSP routing for the truck, and VRP routing for the drones. Two approaches are employed to determine the most efficient truck-drone route. The first approach is the flying sidekick Travelling Salesman Problem (FSTSP), which employs a single drone alongside the truck. The second approach is the parallel drone scheduling Travelling Salesman Problem (PDSTSP), which involves two or more drones. Each drone can fly between its designated delivery location and the depot. In both variations, the objective is to deliver packages from a warehouse to a group of customer locations using a truck and a cluster of drones. To enhance the PDSTSP, we propose a modified approach where the truck serves as a dynamic drone depot, releasing drones at each cluster of delivery locations. The proposed solution is evaluated using real-world data, and the results demonstrate a significant optimization in terms of distance travelled and delivery time compared to traditional delivery methods.", "keywords": [ "Drone", "Travelling salesman problem", "Vehicle routing problem", "Cluster analysis", "Routing (electronic design automation)", "Scheduling (production processes)", "Truck", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18417916" }, { "id": "https://openalex.org/W4415277686", "doi": "10.5505/pajes.2025.22378", "title": "Two-stage Unmanned Aerial Vehicle (UAV) Assisted Pickup and Delivery Routing Problem", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": null, "authors": [ "Tolga Gedikli", "İsmail Karaoğlan" ], "first_author": "Tolga Gedikli", "venue": "Pamukkale University Journal of Engineering Sciences", "publisher": "Pamukkale University", "abstract": "Son dönemlerde insansız hava araçları (İHA) teknolojisinin lojistik süreçlerde kullanılma potansiyeli ve bu alanda yaşanan hızlı gelişmeler, İHA destekli rotalama problemlerini akademik ve endüstriyel açıdan önemli bir araştırma konusu haline getirmiştir.Bu makalede, iki aşamalı İHA destekli rotalama problemi ele alınmıştır.Bu problemde, toplam çalışma süresini en küçükleyecek şekilde, kendisine yüklenen ürünler ve İHA'lar ile depodan hareket eden bir kara aracının hangi mobil depolara uğrayacağı ve bu mobil depolardan serbest bırakılan İHA'lar aracılığıyla hangi müşterilere hizmet verileceği belirlenmektedir.Bu çalışmada iki farklı durum ele alınmıştır.Birinci durumda, müşterilerin sadece dağıtım müşterisi olduğu ve İHA'ların mobil depolarda bir veya birkaç kez kullanılabildiği iki varyasyon ele alınmıştır.Bu problemler için karma tamsayılı doğrusal programlama modelleri geliştirilmiş ve geliştirilen modellerin literatürde var olan modellerden daha kısa sürede optimum çözüme ulaştığı deneysel olarak kanıtlanmıştır.İkinci durumda ise müşterilerin ya dağıtım ya da toplama müşterisi olabildiği İki Aşamalı İnsansız Hava Aracı Destekli Topla-Dağıt Rotalama Problemi (2A-İTDRP) isimli yeni bir problem tanıtılmıştır.2A-İTDRP için bir İHA'nın bir mobil depodan bir veya birkaç kez kullanılabildiği iki varyasyon ele alınmış ve çözümü için karma tamsayılı doğrusal programlama modelleri önerilmiştir.Modeller, GAMS/CPLEX çözücüsü kullanılarak çözülmüş, önerilen matematiksel modellerin deneysel çalışmaları gerçekleştirilmiş ve elde edilen sonuçlar ayrıntılı olarak raporlanmıştır.Recently, the potential of using unmanned aerial vehicles (UAVs) technology in logistics processes and the rapid developments in this field have made UAV-assisted routing problems an important focus.In this paper, a two-stage UAV-assisted routing problem is considered.In this problem, to minimize the completion time, a truck moving from a depot with loaded products and UAVs will determine which mobile depots to visit and which customers will be served by the UAV released from these mobile depots.In this study, two different cases are considered.In the first case, two variations are considered, where the customers are only delivery customers, and the UAVs can be used once or several times at the mobile depots.Mixed integer linear programming models are developed for these problems, and it is experimentally proven that the developed models reach the optimum solution in less time than the existing models in the literature.In the second case, a new problem called the \"Two-Stage Unmanned Aerial Vehicle Assisted Pickup and Delivery Routing Problem\" (2S-UAV-PDRP) is introduced where customers can be either pickup or delivery customers.For the 2A-ITDRP, two variations where a UAV can be used once or several times from a mobile depot are considered, and mixed integer linear programming models are proposed for its solution.The models are solved using the GAMS/CPLEX solver, experimental studies of the proposed mathematical models are performed, and the results are reported in detail.", "keywords": [ "Pickup", "Routing (electronic design automation)", "Vehicle routing problem", "Remotely operated underwater vehicle" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://jag.journalagent.com/z4/download_fulltext.asp?pdir=pajes&plng=eng&un=PAJES-22378" }, { "id": "https://openalex.org/W4407255454", "doi": "10.2139/ssrn.5127607", "title": "Constraint-Aware Deep Reinforcement Learning for Generalizable Drone Routing Solutions", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Chuankai Xiang", "Zhibin Wu", "Lishuai Li" ], "first_author": "Chuankai Xiang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Reinforcement learning", "Drone", "Computer science", "Artificial intelligence", "Routing (electronic design automation)", "Constraint (computer-aided design)", "Machine learning", "Mathematical optimization", "Computer network", "Engineering", "Mathematics", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5127607" }, { "id": "https://openalex.org/W4414620708", "doi": "10.1007/s12065-025-01092-0", "title": "Adaptive ant colony optimization for solving dynamic vehicle and drone routing with time window constraints", "year": 2025, "publication_date": "2025-09-30", "type": "article", "language": "en", "authors": [ "Phan Duc Hung", "Nguyen Thi Tam", "Huỳnh Thị Thanh Bình" ], "first_author": "Phan Duc Hung", "venue": "Evolutionary Intelligence", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Heuristics", "Ant colony optimization algorithms", "Routing (electronic design automation)", "Ant colony", "Exploit", "Benchmark (surveying)", "Dynamic programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s12065-025-01092-0" }, { "id": "https://openalex.org/W7126081051", "doi": "10.5281/zenodo.18417915", "title": "A Novel Framework for Optimizing the Routes of Drones During the Last Mile Delivery Process", "year": 2023, "publication_date": "2023-06-01", "type": "article", "language": null, "authors": [ "Archit Agarwal", "Ayush Goyal", "Dinesh Kumar Vishwakarma" ], "first_author": "Archit Agarwal", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "The growing e-commerce industry and demand for fast deliveries have led to the exploration of alternative lastmile delivery methods, including drone delivery systems. Optimising the routes for drone delivery presents a formidable challenge due to the intricate nature of the problem. This research paper introduces a novel solution that combines the Vehicle Routing Problem (VRP) and the Travelling Salesman Problem (TSP) to tackle the last-mile drone delivery issue. A mathematical model and a heuristic algorithm are devised, incorporating clustering of delivery locations, TSP routing for the truck, and VRP routing for the drones. Two approaches are employed to determine the most efficient truck-drone route. The first approach is the flying sidekick Travelling Salesman Problem (FSTSP), which employs a single drone alongside the truck. The second approach is the parallel drone scheduling Travelling Salesman Problem (PDSTSP), which involves two or more drones. Each drone can fly between its designated delivery location and the depot. In both variations, the objective is to deliver packages from a warehouse to a group of customer locations using a truck and a cluster of drones. To enhance the PDSTSP, we propose a modified approach where the truck serves as a dynamic drone depot, releasing drones at each cluster of delivery locations. The proposed solution is evaluated using real-world data, and the results demonstrate a significant optimization in terms of distance travelled and delivery time compared to traditional delivery methods.", "keywords": [ "Drone", "Travelling salesman problem", "Vehicle routing problem", "Cluster analysis", "Routing (electronic design automation)", "Scheduling (production processes)", "Truck", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18417915" }, { "id": "https://openalex.org/W7161064280", "doi": "10.1080/23249935.2026.2669862", "title": "Optimising the efficiency of logistics using hybrid truck-drone collaborative delivery", "year": 2026, "publication_date": "2026-05-13", "type": "article", "language": "en", "authors": [ "Shih-Hao Lu", "CHIA-CHEN CHANG", "Rohit Raj", "Yun-Hsin Lin" ], "first_author": "Shih-Hao Lu", "venue": "Transportmetrica A Transport Science", "publisher": "Taylor & Francis", "abstract": "Due to an increasing in online package orders, delivery providers need to determine how to reduce driver overtime while maintaining on-time delivery. To address this, our research introduces the Hybrid Helper Dispatching Problem with Drone (HHDPD), which aims to reduce the truck's return time to the depot and ensure the simultaneous return of both the driver and the drone. This study utilises the OR-Tools package and a greedy algorithm to address the routing problem and selection of drone service areas. Computational experiments are carried out to evaluate the best possible solution among four cases: TSP (Travelling Salesman Problem) without a driver helper, standard helper (including one driver, one independent helper, and one truck), HHDP (Hybrid-Helper Dispatching Problem), and HHDPD. Results demonstrate that the HHDPD model can significantly reduce the overall time spent, minimise idle resource wastage of driver helper and improve package delivery efficiency.", "keywords": [ "Component (thermodynamics)", "Key (lock)", "Process (computing)", "Production (economics)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/23249935.2026.2669862" }, { "id": "https://openalex.org/W7165134197", "doi": "10.1007/978-981-95-9342-2_9", "title": "Route Optimization for Autonomous Drone-based Blood Delivery Using a Hybrid Intelligent Algorithm", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Jun Hou", "X Chen", "L Chen", "Fan Liu" ], "first_author": "Jun Hou", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Genetic algorithm", "Key (lock)", "Software deployment", "Baseline (sea)", "Drone", "Vehicle routing problem", "Hybrid algorithm (constraint satisfaction)", "Parametric statistics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-95-9342-2_9" }, { "id": "https://openalex.org/W4402799224", "doi": "10.2139/ssrn.4966127", "title": "Optimizing the Efficiency of Logistics Using Hybrid Truck-Drone Collaborative Delivery", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Shih‐Hao Lu", "R. Thundil Karuppa Raj", "Yun-Hsin Lin" ], "first_author": "Shih‐Hao Lu", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Computer science", "Transport engineering", "Business", "Aeronautics", "Engineering", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.2139/ssrn.4966127" }, { "id": "https://openalex.org/W4389230388", "doi": "10.1109/ccdc58219.2023.10327279", "title": "Research of Drone-Truck Collaborative Delivery Path Optimization Based on Two-stage Heuristic Algorithm", "year": 2023, "publication_date": "2023-05-20", "type": "conference-paper", "language": "en", "authors": [ "Yan Li", "Lin Pan" ], "first_author": "Yan Li", "venue": null, "publisher": null, "abstract": "With the rapid development of e-commerce, the logistics industry is confronting the important challenge of catering for the rapidly growing customer demand. As an emerging vehicle, delivery using truck and drones have played an increasing role in satisfying customers demand for fast, flexible and reliable delivery services. Drones and trucks work together to deliver packages cooperatively, a new delivery model that has been proven to reduce overall costs, CO2emissions and delivery times. This paper studies the problem of drone-truck cooperative distribution route optimization, establishes a mixed integer programming model, and proposes a two-stage heuristic algorithm including the nearest neighbor algorithm and K-means clustering, as well as the hybridization of variable neighborhood simulated annealing algorithm to solve the drone-vehicle routing problem (Vehicle Routing Problem with Drones, VRP-D). Through the analysis and discussion of the experimental results, the effectiveness of the proposed algorithm is verified.", "keywords": [ "Drone", "Vehicle routing problem", "Truck", "Computer science", "Simulated annealing", "Variable neighborhood search", "Cluster analysis", "Heuristic", "Routing (electronic design automation)", "Integer programming", "Algorithm", "Operations research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/ccdc58219.2023.10327279" }, { "id": "https://openalex.org/W7166804027", "doi": "10.48550/arxiv.2606.30680", "title": "Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones", "year": 2026, "publication_date": "2026-06-26", "type": "preprint", "language": null, "authors": [ "Xuanyu Liu", "Hui Hu", "Jiao Zhao", "Ziliang Wang", "Zhengbing He" ], "first_author": "Xuanyu Liu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Truck-drone delivery is an emerging last-mile logistics mode combining the long-haul capacity of trucks with the flexible service capability of drones. In locker-based operations, smart lockers serve not only as temporary parcel storage facilities but also as automated drone docking and service nodes. These automated nodes support drone takeoff, landing, parcel handover, and battery replacement, thereby significantly extending the service range and operational flexibility of drone-assisted delivery networks. However, practical locker-based delivery systems face complex real-world challenges, requiring the integrated coordination of not only parcel delivery, return pickup, battery-constrained and load-dependent drone flights, but also necessary detours around restricted airspace. To address this practical and multifaceted challenge, this paper introduces a locker-based truck-drone routing problem with integrated considerations of pickups, deliveries, and no-fly zones (LTDRP-PDNF), with the objective of minimizing the total operational cost of a fleet of drone-equipped trucks. We formulate the route construction process as a Markov Decision Process and develop a two-stage deep reinforcement learning-based neural heuristic. The first stage utilizes an attention-based encoder and a Bidirectional Gated Recurrent Unit decoder to solve the truck-only routing problem, formulated as a capacitated vehicle routing problem. The second stage combines a policy-transfer strategy with a hybrid dispatch assignment heuristic to construct fully coordinated truck and drone routes for LTDRP-PDNF. Experiments on instances of different scales demonstrate that the proposed method outperforms metaheuristic and neural heuristic baselines in most cases while maintaining exceptionally short computation times, offering an effective, scalable solution framework under practical operational constraints.", "keywords": [ "Drone", "Markov decision process", "Routing (electronic design automation)", "Flexibility (engineering)", "Scalability", "Truck", "Heuristic", "Service (business)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2606.30680" }, { "id": "https://openalex.org/W4400662866", "doi": "10.2514/1.d0423", "title": "Strategic Planning of Aerial Assets for Disaster Response", "year": 2024, "publication_date": "2024-07-15", "type": "article", "language": "en", "authors": [ "Christopher Chin", "Akila Saravanan", "Hamsa Balakrishnan", "Adriana Andreeva-Mori" ], "first_author": "Christopher Chin", "venue": "Journal of Air Transportation", "publisher": "American Institute of Aeronautics and Astronautics", "abstract": "The rapid deployment of fleets of small, uncrewed aircraft (drones) in the immediate aftermath of a natural disaster to search impacted regions for people in need of rescue is one of the most vital applications of advanced air mobility. Effective drone-based search operations require that the drone fleets operate out of bases that are appropriately located in advance of the disaster. Using a case study based in the Iwate prefecture of Japan, we develop optimization formulations to strategically locate drone bases. It is important to be capable of responding quickly to the locations most likely to require a search, while covering as large an area as possible. We evaluate the disparities in the level of access afforded to different areas. We extend our optimization formulation to account for the probability of the base locations themselves being impacted by the disaster and the possibility of base relocation. Finally, we illustrate how a vehicle routing component can be used to address the tactical portion of drone-based search operations.", "keywords": [ "Disaster response", "Business", "Strategic planning", "Disaster planning", "Emergency response", "Emergency management", "Operations management", "Medical emergency", "Engineering", "Poison control", "Suicide prevention", "Economics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/1.d0423" }, { "id": "https://openalex.org/W4280647876", "doi": "10.21541/apjess.1113053", "title": "Calculation Of The Optimum Number Of Unmanned Air Vehicles Required For Surveillance Missions", "year": 2022, "publication_date": "2022-05-01", "type": "article", "language": "en", "authors": [ "İkbal Özdemir", "Serkan Çaşka" ], "first_author": "İkbal Özdemir", "venue": "Academic Platform Journal of Engineering and Smart Systems", "publisher": null, "abstract": "The aim of this study is to determine minimum quantity of unmanned aerial vehicles (UAVs) that should be used in an area where an aerial reconnaissance/observation activity will be carried out. In order to make this kind of calculation, firstly, the energy consumption of a UAV while flying with a constant speed was examined and then % energy level consumed by the UAV while passing each meter was obtained approximately. In this study, by considering the length of the trajectory which a UAV will navigate, required % energy level of a UAV to complete a single tour is calculated. If it is determined that one UAV can not complete its assigned trajectory, the number of UAVs are increased until each UAV complete its trajectory. In this study, the vehicle routing problem approach was used to calculate the UAV trajectories. Genetic algorithm method that is one of the metaheuristic optimization methods, was used obtain the solution of the vehicle routing problem (VRP). The developed algorithm has been run in Matlab environment. By changing the parameters of crossing rate and the population number in the genetic algorithm (GA) method, the lowest number of UAVs that is enough to carry out aerial observation over a target area and the shortest UAV trajectories were obtained. Results were presented in table form.", "keywords": [ "Genetic algorithm", "Trajectory", "Computer science", "Population", "Energy consumption", "MATLAB", "Energy (signal processing)", "Routing (electronic design automation)", "Table (database)", "Real-time computing", "Simulation", "Mathematics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/en/download/article-file/2413115" }, { "id": "https://openalex.org/W7171796407", "doi": "10.48550/arxiv.2607.26423", "title": "FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets", "year": 2026, "publication_date": "2026-07-29", "type": "preprint", "language": null, "authors": [ "Peisen Xu", "Jérémie Garcia", "Peter Cleveland", "Ooi Wei Tsang", "Christophe Jouffrais" ], "first_author": "Peisen Xu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "As autonomous drone deployments scale from individual units to coordinated swarms, the human operator's role shifts from direct piloting to high-level supervision. Current interfaces often treat multi-drone control as a scaled-up version of single-drone operation. We instead investigate how reframing fleet supervision as spatial interaction can better support the spatial, temporal, and safety demands of complex missions. We present FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data while enabling fluid transitions between manual intervention and autonomous supervision. We developed a high-fidelity building inspection simulation that generates and streams synchronized multi-drone and environmental data for MR visualizations. We used this prototype to conduct a user study with six experienced drone pilots managing fleets of up to 15 drones. Our findings show that FleetScape supports situational awareness through layered spatial representations and clarifies control mode transitions. However, a limit to situational awareness was observed as fleet size increases, leading to different supervisory strategies. Finally, we derive design implications for supporting scalable drone fleet supervision.", "keywords": [ "Drone", "Situation awareness", "Cognitive reframing", "Scalability", "Control (management)", "Mixed reality", "Scale (ratio)", "Situational ethics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.48550/arxiv.2607.26423" }, { "id": "https://openalex.org/W7112469100", "doi": null, "title": "Joint optimisation of drone routing and battery wear for sustainable supply chain development", "year": null, "publication_date": null, "type": "preprint", "language": null, "authors": [ "Yang Xia", "Wenjia Zeng", "Xinjie Xing", "Yuanzhu Zhan", "Kim Hua Tan", "Ajay Kumar" ], "first_author": "Yang Xia", "venue": "RePEc: Research Papers in Economics", "publisher": "Federal Reserve Bank of St. Louis", "abstract": "Alongside the rise of ‘last-mile' delivery in contemporary urban logistics, drones have demonstrate commercial potential, given their outstanding triple-bottom-line performance. However, as a lithium-ion battery-powered device, drones' social and environmental merits can be overturned by battery recycling and disposal. To maintain economic performance, yet minimise environmental negatives, fleet sharing is widely applied in the transportation field, with the aim of creating synergies within industry and increasing overall fleet use. However, if a sharing platform's transparency is doubted, the sharing ability of the platform will be discounted. Known for its transparent and secure merits, blockchain technology provides new opportunities to improve existing sharing solutions. In particular, the decentralised structure and data encryption algorithm offered by blockchain allow every participant equal access to shared resources without undermining security issues. Therefore, this study explores the implementation of a blockchain-enabled fleet sharing solution to optimise drone operations, with consideration of battery wear and disposal effects. Unlike classical vehicle routing with fleet sharing problems, this research is more challenging, with multiple objectives (i.e., shortest path and fewest charging times), and considers different levels of sharing abilities. In this study, we propose a mixed-integer programming model to formulate the intended problem and solve the problem with a tailored branch-and-price algorithm. Through extensive experiments, the computational performance of our proposed solution is first articulated, and then the effectiveness of using blockchain to improve overall optimisation is reflected, and a series of critical influential factors with managerial significance are demonstrated.", "keywords": [ "Drone", "Supply chain", "Transparency (behavior)", "Sustainable development", "Routing (electronic design automation)", "Joint (building)", "Battery (electricity)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hal.science/hal-04381308v1/document" }, { "id": "https://openalex.org/W7165631533", "doi": "10.3390/drones10070476", "title": "Data-Driven Optimization of Truck–Drone Collaborative Delivery with Shared Fleet Allocation", "year": 2026, "publication_date": "2026-06-23", "type": "article", "language": "en", "authors": [ "Didem Cicek", "Murat Şimşek", "Burak Kantarcı" ], "first_author": "Didem Cicek", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Truck–drone collaborative delivery (TDCD) refers to a coordinated logistics paradigm in which drones are deployed from delivery trucks to serve nearby customers, enabling parallelized last-mile operations. Much of the existing TDCD literature relies on synthetic datasets and manufacturer-declared drone specifications, which may overestimate performance in real-world operations. This study develops an empirically informed, route-based Mixed-Integer Linear Programming (MILP) framework that integrates empirically derived drone performance models with constrained fleet allocation decisions. Using delivery routes from the Amazon Last Mile Routing Dataset (2021), we consider three electric trucks departing from a common depot, each equipped with drones drawn from a shared fleet of 10 units. Drone flight time and energy consumption are modeled using regression functions calibrated with real flight test data from a DJI Matrice 100 platform, capturing observed variations due to payload and operational conditions. The optimization jointly determines truck stop selection, customer assignments, and drone allocation while minimizing a weighted combination of route makespan, total energy consumption, and fleet size under operational and energy constraints. The results indicate that coordinated truck–drone delivery can achieve substantial reductions in both delivery completion time and energy consumption relative to conventional truck-only delivery. These findings demonstrate the effectiveness of coordinated truck–drone operations under realistic constraints and highlight the importance of data-driven modeling and fleet-level resource allocation in improving last-mile delivery performance.", "keywords": [ "Drone", "Truck", "Payload (computing)", "Resource allocation", "Energy consumption", "Routing (electronic design automation)", "Linear programming", "Last mile (transportation)", "Fuel efficiency" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10070476" }, { "id": "https://openalex.org/W7171998036", "doi": "10.1145/3830398.3830597", "title": "FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets", "year": 2026, "publication_date": "2026-07-29", "type": "preprint", "language": null, "authors": [ "Peisen Xu", "Jérémie Garcia", "Peter Cleveland", "Ooi Wei Tsang", "Christophe Jouffrais" ], "first_author": "Peisen Xu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "As autonomous drone deployments scale from individual units to coordinated swarms, the human operator's role shifts from direct piloting to high-level supervision. Current interfaces often treat multi-drone control as a scaled-up version of single-drone operation. We instead investigate how reframing fleet supervision as spatial interaction can better support the spatial, temporal, and safety demands of complex missions. We present FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data while enabling fluid transitions between manual intervention and autonomous supervision. We developed a high-fidelity building inspection simulation that generates and streams synchronized multi-drone and environmental data for MR visualizations. We used this prototype to conduct a user study with six experienced drone pilots managing fleets of up to 15 drones. Our findings show that FleetScape supports situational awareness through layered spatial representations and clarifies control mode transitions. However, a limit to situational awareness was observed as fleet size increases, leading to different supervisory strategies. Finally, we derive design implications for supporting scalable drone fleet supervision.", "keywords": [ "Drone", "Situation awareness", "Cognitive reframing", "Scalability", "Control (management)", "Mixed reality", "Scale (ratio)", "Situational ethics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2607.26423" }, { "id": "https://openalex.org/W4292719508", "doi": "10.48550/arxiv.2208.09067", "title": "Dynamic Order Fulfillment in Last-Mile Drone Delivery under Demand Uncertainty", "year": 2022, "publication_date": "2022-08-18", "type": "preprint", "language": "en", "authors": [ "Linxuan Shi", "Zhengtian Xu", "Lejeune, Miguel", "Luo, Qi" ], "first_author": "Linxuan Shi", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Drones have attracted growing interest in last-mile delivery due to their potential to significantly reduce costs and enhance operational flexibility, particularly in areas of sparse and uncertain demand where traditional truck delivery proves inefficient. This paper addresses the dynamic order fulfillment problem faced by a retailer operating a fleet of drones to service delivery requests that arrive stochastically. These delivery requests may vary in package profiles, delivery locations, and urgency. We adopt a rolling-horizon framework for order fulfillment and devise a two-stage stochastic program aimed at strategically managing existing orders while considering incoming requests that are subject to various uncertainties. A significant challenge in deploying the envisioned two-stage model lies in its incorporation of vehicle routing constraints, on which exact or brute-force methods are computationally inefficient and unsuitable for real-time operational decisions. To address this, we propose an accelerated L-shaped algorithm that (i) reduces the branching tree size, (ii) replaces exact second-stage solutions with heuristic estimates, and (iii) adapts an alternating strategy for adding optimality cuts. The proposed heuristic demonstrates remarkable performance superiority over the exact method, achieving a 20-fold reduction in average runtime while maintaining an average optimality gap of less than 1\\%. We apply the algorithm to a wide range of instances to evaluate the benefits of postponing orders for batch service using the stochastic model. Our results show potential long-term cost savings of up to 20\\% when demand uncertainty is explicitly considered in order fulfillment decisions. Meanwhile, the derived savings tend to diminish as the uncertainty increases in order arrivals.", "keywords": [ "Computer science", "Mathematical optimization", "Stochastic programming", "Integer programming", "Context (archaeology)", "Last mile (transportation)", "Order (exchange)", "Dynamic programming", "Integer (computer science)", "Mile", "Real-time computing", "Operations research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2208.09067" }, { "id": "https://openalex.org/W7164138785", "doi": "10.2139/ssrn.6910773", "title": "Simulating City-Scale Drone Delivery: Noise Pollution and Population Exposure in London", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Volodymyr Shevchenko", "Cheriesse Bema", "Leslie‐Anne Duvic‐Paoli", "Martim Brandão" ], "first_author": "Volodymyr Shevchenko", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Noise pollution", "Population", "Routing (electronic design automation)", "Noise (video)", "Global Positioning System", "Motion planning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6910773" }, { "id": "https://openalex.org/W4410139175", "doi": "10.1007/s11831-025-10281-0", "title": "A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications", "year": 2025, "publication_date": "2025-05-07", "type": "article", "language": "en", "authors": [ "Sylia Mekhmoukh Taleb", "Elham Tahsin Yasin", "Amylia Ait Saadi", "Musa Doğan", "Selma Yahia", "Yassine Meraihi", "Murat Köklü", "Seyedali Mirjalili", "Amar Ramdane-Chérif" ], "first_author": "Sylia Mekhmoukh Taleb", "venue": "Archives of Computational Methods in Engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s11831-025-10281-0" }, { "id": "https://openalex.org/W7122643066", "doi": "10.1016/j.ejor.2026.01.013", "title": "Improving last-mile delivery efficiency using cargo bikes and autonomous robots", "year": 2026, "publication_date": "2026-01-12", "type": "article", "language": "en", "authors": [ "Biao Yuan", "Bingjie Yang", "Na Geng", "Roberto Baldacci" ], "first_author": "Biao Yuan", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Lagrangian relaxation", "Robot", "Heuristic", "Drone", "Routing (electronic design automation)", "Bounded function", "Focus (optics)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S0377221726000329" }, { "id": "https://openalex.org/W7131367364", "doi": "10.2139/ssrn.6300941", "title": "The Iterative Chainlet Partitioning Algorithm for the Traveling Salesman Problem with Drone and Neural Acceleration", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Jae Hyeok Lee", "Minwoo Kim", "Minjun Kim", "Jinkyoo Park", "Changhyun Kwon" ], "first_author": "Jae Hyeok Lee", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Subroutine", "Travelling salesman problem", "Iterative method", "Acceleration", "Benchmark (surveying)", "Bottleneck", "Artificial neural network", "Dynamic programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6300941" }, { "id": "https://openalex.org/W7163697115", "doi": "10.1016/j.trpro.2026.04.020", "title": "A Novel Adaptive Large Neighborhood Search Heuristic for the UAV Routing Problem with Multiple Time Windows", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Omayma Sedik", "Slim Belhaiza" ], "first_author": "Omayma Sedik", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "Unmanned Aerial Vehicles (UAVs) have emerged as a promising alternative for last-mile delivery, offering cost-effective, rapid, and flexible logistics solutions; however, their limited battery life, payload capacity, and operational range impose significant routing challenges. This paper introduces a novel hybrid Adaptive Large Neighborhood Search (ALNS) framework for the UAV Routing Problem with Multiple Time Windows (UAV-RPMTW), a problem variant that explicitly allows flexible service scheduling. The proposed method integrates adaptive destroy-and-repair operator selection for optimal time-window assignment, enabling an effective balance between routing efficiency and service feasibility. The algorithm further enhances search performance through diversified neighborhood operators and tabu-based restart strategies to balance exploration and exploitation. Comprehensive experiments on new UAV-RPMTW benchmark datasets indicate that the method surpasses baseline approaches in both solution quality and runtime performance, yielding an average improvement of approximately 40 cost units between average and best solutions and achieving best solutions within an average of 39 seconds per instance.", "keywords": [ "Benchmark (surveying)", "Payload (computing)", "Routing (electronic design automation)", "Heuristic", "Vehicle routing problem", "Range (aeronautics)", "Baseline (sea)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2026.04.020" }, { "id": "https://openalex.org/W4285209151", "doi": "10.2139/ssrn.4102592", "title": "Integrated Optimal Repair Crew Routing and Drone Scheduling after a Natural Disaster", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Motahhareh Safdari Shadlou", "Mohammad Reza Ranjbar", "Majid Salari" ], "first_author": "Motahhareh Safdari Shadlou", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Crew", "Drone", "Natural disaster", "Scheduling (production processes)", "Aeronautics", "Computer science", "Crew scheduling", "Routing (electronic design automation)", "Operations research", "Engineering", "Operations management", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4102592" }, { "id": "https://openalex.org/W4417057004", "doi": "10.1109/ojits.2025.3638659", "title": "Multi-Modal Last-Mile Delivery Optimization With Smart Micro-Hubs: A Branch-and-Price Approach With Valid Inequalities", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Kassem Danach", "Ali Raad", "Abbass Nasser" ], "first_author": "Kassem Danach", "venue": "IEEE Open Journal of Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The rapid growth of e-commerce and urban logistics has intensified the challenges associated with last-mile delivery, necessitating the integration of autonomous delivery vehicles (ADVs), drones, and smart micro-hubs for cost-effective and sustainable operations. This study introduces a novel Multi-Depot Capacitated Vehicle Routing Problem with Time Windows and Multi-Modal Transportation (MD-CVRPTW-MM), where deliveries are dynamically assigned to either ADVs or drones while optimizing hub locations and vehicle routing. The objective is to minimize total operational costs, including transportation, hub activation, and delivery mode selection, while ensuring service-level constraints such as time windows and capacity limits. To efficiently solve this complex combinatorial problem, we propose a mathematical formulation enhanced with valid inequalities for computational efficiency. Furthermore, a Branch-and-Price algorithm is developed, where column generation is employed to iteratively refine delivery routes while enforcing depot assignments and mode selection constraints. The solution approach integrates dual stabilization techniques and path-based heuristics to accelerate convergence. Experimental evaluations on benchmark datasets and real-world logistics scenarios demonstrate the effectiveness of the proposed method in reducing last-mile delivery costs, optimizing fleet utilization, and enhancing service reliability. The results highlight the significance of multi-modal last-mile delivery optimization in improving urban logistics and sustainability. This research contributes a new mathematical model, efficient optimization techniques, and practical insights for intelligent last-mile delivery planning in smart cities.", "keywords": [ "Vehicle routing problem", "Heuristics", "Benchmark (surveying)", "Column generation", "Optimization problem", "Routing (electronic design automation)", "Dual (grammatical number)", "Service (business)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ojits.2025.3638659" }, { "id": "https://openalex.org/W7133978656", "doi": "10.32469/10355/111051", "title": "Optimization frameworks for collaborative truck-drone logistics under environmental and operational considerations", "year": 2025, "publication_date": "2025-12-01", "type": "dissertation", "language": "en", "authors": [ "Arash Alizadeh" ], "first_author": "Arash Alizadeh", "venue": null, "publisher": null, "abstract": "The integration of unmanned vehicles (UVs) into last-mile logistics enables costefficient, rapid, and environmentally sustainable deliveries. Yet, most prior work treats operational and environmental factors separately and simplifies key dependencies such as speed regulation, roadway grade, and coordination among heterogeneous vehicles. This dissertation develops a unified suite of optimization frameworks that address these gaps through three progressively enriched variants, each supported by mixed-integer programming (MIP) and advanced metaheuristics. The first variant introduces the Truck Multi-Drone Pollution Routing Problem with Pickup and Delivery under Variable Truck Speeds (TMD-PRP-PDVS). The formulation integrates multi-drone coordination, segment-specific truck speed control, and weight-dependent fuel and energy models. A MIP minimizes truck fuel, drone energy, and labor costs, while an adaptive large neighborhood search (ALNS) efficiently solves larger instances. Experiments show that collaboration with drones reduces total cost by about 40 percent relative to truck-only delivery, and speed optimization yields an additional 7–13 percent reduction across contrasting cost regimes. The second variant extends the framework to the Gradient-Aware Truck–Drone Pollution Routing Problem with Adaptive Drone Deployment Sites and Soft Time Windows (GA-TDPRP-AS-STW), explicitly modeling roadway slopes, variable truck speeds, and flexible drone deployment sites under penalty-based time windows. Both a MIP and an enhanced ALNS are developed. On small benchmarks, the MIP proves optimality and the ALNS attains near-optimal solutions; on larger sets (up to 100 customers), the ALNS scales effectively. Collaboration delivers 33–40 percent savings over truck-only, and speed optimization contributes a further 6–12 percent, reaching up to 62 percent total savings. Elevation data and adaptive sites expand synchronization options and lower fuel consumption even when route length increases modestly. Lastly, the third variant, A Synergistic Truck-Drone-Robot System for Last-Mile Delivery with Hand-Off Points and Microstations for Dynamic Repositioning, unifies tri-vehicle operations with a network of micro-/docking stations that enable parallel service, recovery without truck waiting, and dynamic drone and ADR repositioning. For this variant, an ALNS developed to coordinate facility usage, routing, and allocation decisions. Computational studies indicate 20–38 percent reductions in completion time versus truck-only baselines, with additional gains when hybrid trucks carry UVs between stations. Across variants, extensive experiments demonstrate that integrating environmental characteristics, speed selection, adaptive deployment sites, and facility-aided repositioning materially improves both cost and completion time performance. The resulting models and algorithms provide actionable guidance for designing sustainable, multi-agent last-mile networks.", "keywords": [ "Truck", "Software deployment", "Drone", "Variable (mathematics)", "Routing (electronic design automation)", "Key (lock)", "Vehicle routing problem", "Throughput", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.32469/10355/111051" }, { "id": "https://openalex.org/W7117110014", "doi": "10.2139/ssrn.5964221", "title": "Physics-aware Truck and Drone Delivery Planning Using Optimization & Machine Learning", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": null, "authors": [ "Yineng Sun", "Armin R. Fügenschuh", "Vikrant Vaze" ], "first_author": "Yineng Sun", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Trajectory", "Truck", "Key (lock)", "Plan (archaeology)", "Motion planning", "Train", "Kinematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5964221" }, { "id": "https://openalex.org/W7147471444", "doi": "10.1007/978-3-032-21628-1_35", "title": "Balancing Sustainability and Service in Drone-Assisted Logistics via a Hybrid NSGA-II-VNS Approach", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Nguyen Thi My Binh", "Do Thi Ngoc Huyen", "Ho Viet Duc Luong", "Nguyen Thi Kim Son", "Trinh Van Chien", "Ban Ha Bang", "Nguyen Van Truong", "Dang Trong Hop", "Nguyen Quang Dai", "Tran Hung Cuong", "Duong Thi Hien Thanh" ], "first_author": "Nguyen Thi My Binh", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Sustainability", "Service (business)", "Thesaurus", "Supply chain", "Chain (unit)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-21628-1_35" }, { "id": "https://openalex.org/W4409273357", "doi": "10.2139/ssrn.5209336", "title": "Dynamic Order Fulfillment in Last-Mile Drone Delivery Under Demand Uncertainty", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Linxuan Shi", "Zhengtian Xu", "Miguel A. Lejeune", "Qi Luo" ], "first_author": "Linxuan Shi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Order (exchange)", "Computer science", "Business", "Geography", "Geodesy", "Finance", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5209336" }, { "id": "https://openalex.org/W7138155779", "doi": "10.1080/23249935.2026.2643437", "title": "Route optimisation of truck-and-drone systems for multi-type rescue tasks: a heuristic approach", "year": 2026, "publication_date": "2026-03-16", "type": "article", "language": "en", "authors": [ "W. D. Sun", "Fangni Zhang", "Lingxiao Wu", "Yong‐Hong Kuo" ], "first_author": "W. D. Sun", "venue": "Transportmetrica A Transport Science", "publisher": "Taylor & Francis", "abstract": "This study investigates a Vehicle Routing Problem with Drones (VRPD) for multi-type rescue tasks in post-disaster response. Specifically, we consider a truck-and-drone collaborative system that can simultaneously deliver essential relief supplies and assess network conditions with drones in disaster scenarios. To enhance the rescue efficiency, drones can collaborate with different trucks and perform multi-type tasks (delivery and surveillance), making the proposed problem challenging to solve. We first provide a Mixed Integer Linear Programming (MILP) model for the proposed problem. The objective is to minimise the priority cost, which is calculated as the weighted delay cost of serving all rescue tasks and is different from most existing studies on VRPD for parcel deliveries. Then, we develop an Adaptive Simulated Annealing (ASA) algorithm. Since the priority cost depends on the intertwined influence of truck-and-drone routes, the ASA algorithm is integrated with a dynamic programming method to calculate the priority cost. The ASA algorithm can always get the (near) optimal solution on instances with fewer than 30 nodes, and solves larger instances efficiently. Numerical experiments demonstrate that enabling drones to perform multi-type tasks and allowing flexible truck-and-drone collaboration reduces the priority cost by an average of 15.7% across all tested instances, compared with the single-type task and dedicated truck-and-drone collaboration. Sensitivity analysis shows that introducing more drones can reduce the number of trucks involved in the disaster response while achieving similar performance. Moreover, this study reveals a trade-off between minimising priority cost and other objectives, such as minimising travel cost or makespan.", "keywords": [ "Heuristic", "Work (physics)", "Key (lock)", "Process (computing)", "Minification" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.tandfonline.com/doi/pdf/10.1080/23249935.2026.2643437?needAccess=true" }, { "id": "https://openalex.org/W4226216705", "doi": "10.2139/ssrn.4093008", "title": "Drone-Based Persistent Surveillance System on Large Circular Perimeters: Design and Performance Assessment", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "José Luis Andrade-Pineda", "David Canca", "Marcos Calle" ], "first_author": "José Luis Andrade-Pineda", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Computer security", "Computer science", "Aeronautics", "Engineering", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4093008" }, { "id": "https://openalex.org/W4297659275", "doi": "10.48550/arxiv.2107.10382", "title": "Capacitated Vehicle Routing with Target Geometric Constraints", "year": 2021, "publication_date": "2021-07-21", "type": "preprint", "language": "en", "authors": [ "Gao, Kai", "Jingjin Yu" ], "first_author": "Gao, Kai", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We investigate the capacitated vehicle routing problem (CVRP) under a robotics context, where a vehicle with limited payload must complete delivery (or pickup) tasks to serve a set of geographically distributed customers with varying demands. In classical CVRP, a customer location is modeled as a point. In many robotics applications, however, it is more appropriate to model such \"customer locations\" as 2D regions. For example, in aerial delivery, a drone may drop a package anywhere in a customer's lot. This yields the problem of CVRG (Capacitated Vehicle Routing with Target Geometric Constraints). Computationally, CVRP is already strongly NP-hard; CVRG is therefore more challenging. Nevertheless, we develop fast algorithms for CVRG, capable of computing high quality solutions for hundreds of regions. Our algorithmic solution is guaranteed to be optimal when customer regions are convex. Numerical evaluations show that our proposed methods significantly outperform greedy best-first approaches. Comprehensive simulation studies confirm the effectiveness of our methods.", "keywords": [ "Vehicle routing problem", "Robotics", "Computer science", "Pickup", "Heuristic", "Context (archaeology)", "Routing (electronic design automation)", "Point (geometry)", "Mathematical optimization", "Payload (computing)", "Regular polygon", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2107.10382" }, { "id": "https://openalex.org/W4399545478", "doi": "10.2139/ssrn.4861173", "title": "Path Pool Based Transformer Model in Reinforcement Framework for Dynamic Urban Drone Delivery Problem", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Chuankai Xiang", "Yanfang Mo", "Wei Liu", "Zhibin Wu", "Lishuai Li" ], "first_author": "Chuankai Xiang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Reinforcement learning", "Transformer", "Reinforcement", "Computer science", "Path (computing)", "Simulation", "Operations research", "Engineering", "Artificial intelligence", "Electrical engineering", "Structural engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.2139/ssrn.4861173" }, { "id": "https://openalex.org/W4405918489", "doi": "10.2139/ssrn.5077746", "title": "Path Pool Based Transformer Model in Reinforcement Framework for Dynamic Urban Drone Delivery Problem", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Chuankai Xiang", "Yanfang Mo", "Wei Liu", "Zhibin Wu", "Lishuai Li" ], "first_author": "Chuankai Xiang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Reinforcement learning", "Transformer", "Reinforcement", "Computer science", "Path (computing)", "Mathematical optimization", "Engineering", "Artificial intelligence", "Mathematics", "Electrical engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5077746" }, { "id": "https://openalex.org/W4308500171", "doi": "10.1007/s10288-022-00526-0", "title": "A 2-phase approach for planning of hazardous waste collection using an unmanned aerial vehicle", "year": 2022, "publication_date": "2022-11-04", "type": "article", "language": "en", "authors": [ "Jihene Kaabi", "Youssef Harrath", "Amine Mahjoub", "Nabil M. Hewahi", "Khadija Abdulsattar" ], "first_author": "Jihene Kaabi", "venue": "4OR", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Hazardous waste", "Scale (ratio)", "Phase (matter)", "Computer science", "Set (abstract data type)", "Process (computing)", "Routing (electronic design automation)", "TRIPS architecture", "Linear programming", "Transfer (computing)", "Mathematical optimization", "Mathematics" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10288-022-00526-0" }, { "id": "https://openalex.org/W4416701524", "doi": "10.48550/arxiv.2509.01886", "title": "Deep Reinforcement Learning for Drone Route Optimization in Post-Disaster Road Assessment", "year": 2025, "publication_date": "2025-09-02", "type": "preprint", "language": "en", "authors": [ "Gong, Huatian", "Jiuh‐Biing Sheu", "Zheng Wang", "Xiaoguang Yang", "Yan, Ran" ], "first_author": "Gong, Huatian", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Rapid post-disaster road damage assessment is critical for effective emergency response, yet traditional optimization methods suffer from excessive computational time and require domain knowledge for algorithm design, making them unsuitable for time-sensitive disaster scenarios. This study proposes an attention-based encoder-decoder model (AEDM) for rapid drone routing decision in post-disaster road damage assessment. The method employs deep reinforcement learning to determine high-quality drone assessment routes without requiring algorithmic design knowledge. A network transformation method is developed to convert link-based routing problems into equivalent node-based formulations, while a synthetic road network generation technique addresses the scarcity of large-scale training datasets. The model is trained using policy optimization with multiple optima (POMO) with multi-task learning capabilities to handle diverse parameter combinations. Experimental results demonstrate two key strengths of AEDM: it outperforms commercial solvers by 20--71\\% and traditional heuristics by 23--35\\% in solution quality, while achieving rapid inference (1--2 seconds) versus 100--2,000 seconds for traditional methods. The model exhibits strong generalization across varying problem scales, drone numbers, and time constraints, consistently outperforming baseline methods on unseen parameter distributions and real-world road networks. The proposed method effectively balances computational efficiency with solution quality, making it particularly suitable for time-critical disaster response applications where rapid decision-making is essential for saving lives. The source code for AEDM is publicly available at https://github.com/PJ-HTU/AEDM-for-Post-disaster-road-assessment.", "keywords": [ "Drone", "Reinforcement learning", "Heuristics", "Baseline (sea)", "Generalization", "Key (lock)", "Benchmark (surveying)", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2509.01886" }, { "id": "https://openalex.org/W7110781028", "doi": "10.1016/j.ifacol.2025.11.895", "title": "Design and Optimization of a Geofenced Pesticide Spraying System with Energy-Efficient Drone Route Planning Using CVRP", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Thavarasa Hareeshanan", "Amila Thibbotuwawa", "W. Madushan Fernando", "Peter Nielsen" ], "first_author": "Thavarasa Hareeshanan", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "Agriculture plays an important role in supporting national economies and community livelihoods, with growing demand for food posing considerable challenge to agricultural productivity. Traditional approaches often fall short in satisfying these requirements. The use of pesticides is a key part of agricultural operations; nevertheless, traditional application methodologies have poor efficiency and expose agricultural workers to danger and therefore become impractical for widespread use. Unmanned Aerial Vehicles (UAVs), also known as drones, have been utilized for pesticide application, but still less accuracy, causing healthy crops to become contaminated by spraying. To prevent this problem, geofencing technology can be utilized to restrict pesticide sprays to target regions. In addition, Google OR-Tools utilizes the Capacitated Vehicle Routing Problem (CVRP) for improving efficiency in routing drones. In this work, both methodologies have been incorporated together to maximize efficiency in pesticide application with a view to minimizing energy consumption.", "keywords": [ "Drone", "Agriculture", "Pesticide", "Key (lock)", "Routing (electronic design automation)", "Vehicle routing problem", "Route planning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2025.11.895" }, { "id": "https://openalex.org/W7127226167", "doi": null, "title": "Developing efficient order and split heuristics for coordinated covering tour problems with drones", "year": 2022, "publication_date": "2022-05-01", "type": "dissertation", "language": "en", "authors": [ "Abhishake Kundu" ], "first_author": "Abhishake Kundu", "venue": "ThinkTech (Texas Tech University)", "publisher": "Texas Tech University", "abstract": "Coordinated Covering Tour Problems (CCTP) represents a class of routing problems that involve a primary vessel that is equipped with one or more secondary vessels who undergo a launch and recovery process from the primary vessel. The secondary vessels are constrained to meet up with the primary vessel at various nodes in the network and possibly many times, presumably due to the coverage limitations of the secondary vessels. The problem is thus one of simultaneously determining which nodes should be visited by which vessel, in what order each vessel must visit its assigned nodes, and at which nodes the secondary vessels should be launched and recovered by the primary vessel. Last-mile delivery of parcels using trucks (as the primary vessel) and drone (as the secondary vessel) is an example of Coordinated Covering Tour Problems with application in logistics. This problem has received a lot of attention in the last seven years since its seminal definition in 2015. Various authors have extended this problem by including multiple primary/ secondary vehicles, defining new network configurations for the vehicles, and by incorporating environmental and drone dynamics within the model. However, it being an NP-hard problem, there is no way we can solve a problem of modest size in polynomial time. The key lies in developing heuristics that are both efficient and fast, and it has been observed that there is space for considerable improvement in this research area. I propose developing a novel ‘order and split’ heuristic which is an improvement to the best heuristic to date. Furthermore, the algorithm is extended to several variants of the CTTP, and likewise, its accuracy and efficiency are evaluated.", "keywords": [ "Heuristics", "Drone", "Heuristic", "Routing (electronic design automation)", "Truck", "Class (philosophy)", "Process (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/2346/89264" }, { "id": "https://openalex.org/W4224253774", "doi": "10.1117/12.2618783", "title": "Path planning algorithms for robotic aquaculture monitoring", "year": 2022, "publication_date": "2022-05-31", "type": "conference-paper", "language": "en", "authors": [ "Anthony Davis", "Srijita Mukherjee", "Paul S. Wills", "Bing Ouyang" ], "first_author": "Anthony Davis", "venue": null, "publisher": null, "abstract": "Aerial drones have great potential to monitor large areas quickly and efficiently. Aquaculture is an industry that requires continuous water quality data to successfully grow and harvest fish. The Hybrid Aerial Underwater Robotic System (HAUCS) is designed to collect water quality data of aquaculture ponds to reduce labor costs for farmers. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem. A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its performance is compared with the Google Linear Optimization Package (GLOP) and a Graph Attention Model (GAM) for routing problems. GLOP is the most efficient solver for 50 to 200 ponds at the expense of long run times, while HPP outperforms the other methods in solution quality and run time for instances larger than 200 ponds.", "keywords": [ "Aquaculture", "Drone", "Motion planning", "Solver", "Routing (electronic design automation)", "Underwater", "Computer science", "Environmental science", "Real-time computing", "Fishery", "Fish ", "Robot" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2618783" }, { "id": "https://openalex.org/W7214031425", "doi": "10.2139/ssrn.7507562", "title": "Hybrid Truck–Drone Delivery Systems: Optimization, Economic Viability, and Sustainability Challenges", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Farhood Farshadkhah", "Priyadarshi Nepal", "Abdur Rahman", "Mohammad Marufuzzaman", "Golam Kabir" ], "first_author": "Farhood Farshadkhah", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Sustainability", "Drone", "Scalability", "Framing (construction)", "Cost–benefit analysis", "Synchronization (alternating current)", "Energy system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7507562" }, { "id": "https://openalex.org/W7135330827", "doi": "10.82366/16", "title": "Routing of Hybrid Truck-Drone Delivery Systems: Mathematical Models and Solution Approaches", "year": 2023, "publication_date": "2023-03-01", "type": "dissertation", "language": "en", "authors": [ "Batool Madani" ], "first_author": "Batool Madani", "venue": "DSpace at AUS (American University of Sharjah)", "publisher": "American University of Sharjah", "abstract": "With the continuous growth of e-commerce, the responsibility of logistics providers for solving last mile delivery challenges is surging. The last mile delivery is the most polluting, most expensive, and least efficient part of the e-commerce supply chain. Therefore, logistics providers seek new solutions to improve delivery operations continuously. Drone advancements have paved the way for several applications in the field of logistics. However, restrictions such as battery and payload capacity have restricted the effective operational use of drones in many practical applications. To assist toward alleviating these operational limitations, several hybrid delivery models incorporating one or more drones launched from a larger vehicle have emerged. A recent research avenue is to integrate drones with traditional delivery methods such as trucks to form a hybrid truck-drone delivery system. The hybrid truck-drone delivery system (HTDDS) has received significant attention in the literature, due to its new operational challenges and the interdependency between the vehicles and their roles. Most of the literature considers a single customer visit per drone dispatch while launching and collecting the drone from customer nodes visited by the truck. In this research, three HTDDS models with drone multi-visits are introduced where the first model investigates the routing considerations of a truck acting as a moving depot for launching and collecting a drone at non-customer nodes. The second model permits flexibility in the drone’s launching and collecting operations by utilizing customer and non-customer nodes. The final model studies the extended version of the second model by employing several trucks in the delivery system as well as considering time-related constraints. For handling large instances of the proposed models, efficient solution approaches with novel strategies are introduced. In specific, a modified saving algorithm with neighborhood reduction schemes and variable neighborhood search frameworks with different neighborhood selection strategies are developed. In addition, a generalized nomenclature for defining the neighborhood structures is established, which can be applied to a variety of routing problems. Extensive computational analysis is performed to validate and test the performance of the proposed solution methods. Numerical findings indicate that the proposed solution methods outperform the basic approaches.", "keywords": [ "Mathematical model", "Routing (electronic design automation)", "Work (physics)", "Automation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.82366/16" }, { "id": "https://openalex.org/W2323399166", "doi": "10.2514/6.2012-2561", "title": "VRP with Minimum Delivery Latency Using Linear Programming", "year": 2012, "publication_date": "2012-06-19", "type": "conference-paper", "language": "en", "authors": [ "Chelsea M. Sabo", "Manish Kumar", "Kelly De Oliveira Cohen", "Derek B. Kingston" ], "first_author": "Chelsea M. Sabo", "venue": "Infotech@Aerospace 2012", "publisher": null, "abstract": "Vehicle Routing Problems (VRPs) concern allocating multiple vehicles to service requests with routes that start and end at a depot, visit all requests, and minimize some operational cost. Many UAV situations can be modeled in a manner that represents tasks to be accomplished by service requests and UAVs as agents that fulfill these requests. Often, communications are restricted by available power, data rate, noise margins, and cost. These constraints cause information gathered at request locations to be delivered only when the UAV returns to the “depot.” In other words, UAVs must be allocated to services requests and then deliver information gathered at the request location to a communication range to be able to transmit information back to a command and control HQ. Because of limited communication ranges which result from limited power onboard small UAVs, information is not delivered until the UAV returns to the depot. Research on similar problems show some related work on minimizing the latency of the pickup of requests but not the delivery. This work attempts to study the VRP with minimum delivery latency and formulate a Linear Programming (LP) solution. The objective function is shown to be nonlinear, but it can be linearized at a cost: many new variables are added to linearize it. This in turn, makes the problem intractable, and only solutions to small problems are obtained.", "keywords": [ "Latency (audio)", "Computer science", "Linear programming", "Parallel computing", "Algorithm", "Telecommunications" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2012-2561" }, { "id": "https://openalex.org/W4415902834", "doi": "10.2139/ssrn.5705648", "title": "Slope-Aware Energy Optimization for Cooperative Truck–Drone Delivery and Pickup Routing with Time Windows", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": null, "authors": [ "Ying Wang", "Taofeng Ye", "Jiajun Sun", "Jicong Duan" ], "first_author": "Ying Wang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Pickup", "Benchmark (surveying)", "Terrain", "Energy consumption", "Routing (electronic design automation)", "Drone", "Vehicle routing problem", "Energy (signal processing)", "Sensitivity (control systems)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5705648" }, { "id": "https://openalex.org/W2800295115", "doi": "10.48550/arxiv.1805.04417", "title": "Cooperative Planning for Fuel-constrained Aerial Vehicles and Ground-based Refueling Vehicles for Large-Scale Coverage", "year": 2018, "publication_date": "2018-05-11", "type": "preprint", "language": "en", "authors": [ "Parikshit Maini", "Kaarthik Sundar", "Sivakumar Rathinam", "P. B. Sujit" ], "first_author": "Parikshit Maini", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Low cost Unmanned Aerial Vehicles (UAVs) need multiple refuels to accomplish large area coverage. The number of refueling stations and their placement plays a vital role in determining coverage efficiency. In this paper, we propose the use of a ground-based refueling vehicle (RV) to increase the operational range of a UAV in both spatial and temporal domains. Determining optimal routes for the UAV and RV, and selecting optimized locations for refueling to aid in minimizing coverage time is a challenging problem due to different vehicle speeds, coupling between refueling location placement, and the coverage area at each location. We develop a two-stage strategy for coupled route planning for UAV and RV to perform a coverage mission. The first stage computes a minimal set of refueling sites that permit a feasible UAV route. In the second stage, multiple Mixed-Integer Linear Programming (MILP) formulations are developed to plan optimal routes for the UAV and the refueling vehicle taking into account the feasible set of refueling sites generated in stage one. The performance of different formulations is compared empirically. In addition, computationally efficient heuristics are developed to solve the routing problem. Extensive simulations are conducted to corroborate the effectiveness of proposed approaches.", "keywords": [ "Heuristics", "Integer programming", "Range (aeronautics)", "Computer science", "Set (abstract data type)", "Linear programming", "Plan (archaeology)", "Scale (ratio)", "Routing (electronic design automation)", "Mathematical optimization", "Engineering", "Algorithm" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1805.04417" }, { "id": "https://openalex.org/W4415480015", "doi": "10.2139/ssrn.5648580", "title": "Slope-Aware Energy Optimization for Cooperative Truck–Drone Delivery and Pickup Routing with Time Windows", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": null, "authors": [ "Ying Wang", "Taofeng Ye", "Jiajun Sun", "Jicong Duan" ], "first_author": "Ying Wang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Pickup", "Benchmark (surveying)", "Terrain", "Energy consumption", "Routing (electronic design automation)", "Drone", "Vehicle routing problem", "Energy (signal processing)", "Sensitivity (control systems)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5648580" }, { "id": "https://openalex.org/W4408608888", "doi": "10.2139/ssrn.5185679", "title": "Research on Truck-Drone Collaborative Emergency Routing Optimization Considering Road Blockage in Large-Scale Disaster Scenarios", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Wang Bochen", "Guo Yuhan", "Bochen Wang" ], "first_author": "Wang Bochen", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Scale (ratio)", "Routing (electronic design automation)", "Vehicle routing problem", "Emergency response", "Transport engineering", "Computer science", "Aeronautics", "Operations research", "Engineering", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5185679" }, { "id": "https://openalex.org/W4409295615", "doi": "10.2139/ssrn.5210954", "title": "Truck-Drone Tandem Delivery with Time Window: Its Efficiency, Cost Structure, and Carbon Emission", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "NA WU", "J. Z.", "Yuandong Liu" ], "first_author": "NA WU", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Truck", "Window (computing)", "Tandem", "Drone", "Carbon fibers", "Environmental science", "Business", "Automotive engineering", "Computer science", "Engineering", "Aerospace engineering", "Operating system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5210954" }, { "id": "https://openalex.org/W7160040405", "doi": "10.1016/j.jnlssr.2026.100323", "title": "A resilience-oriented active suppression decision-making mechanism for WUI fires using drones", "year": 2026, "publication_date": "2026-04-01", "type": "article", "language": "en", "authors": [ "Aoran Cheng", "Yifei Ding", "Xinyan Huang", "Zuo‐Jun Max Shen" ], "first_author": "Aoran Cheng", "venue": "Journal of Safety Science and Resilience", "publisher": "Elsevier BV", "abstract": "Wildland–urban interface (WUI) fires are increasing in frequency and severity, posing growing risks to both urban and natural environments. While drones are often used in fire management, they typically serve observational roles rather than actively suppressing fires. This study presents an active suppression mechanism that leverages drones for real-time firefighting. We integrate a fire spread model based on cellular automata with a resource allocation layer and a lightweight task dispatcher that prioritizes actions based on benefit-to-energy efficiency, while considering temporal and exclusivity factors. We evaluate the framework across two representative WUI regions, three wind-driven fire spread scenarios, 16 base-station layouts, and drone fleets ranging from 4 to 64 units, with extensive Monte Carlo simulations for each setup. The results show that increasing the fleet size from 4 to 16 drones reduces the time needed to extinguish fires by 45% to 60%, while also significantly lowering the exposure-weighted burned area. However, the improvements become less significant with fleets beyond 32 drones, where further reductions in fire suppression time are only 10% to 15%. Wind intensity is a key external factor, increasing both the average losses and the variability of outcomes. In strong-wind conditions, the median fire suppression time increases significantly, with the highest losses reaching up to 200 units. The placement of base stations plays a critical role in operational efficiency. Bases placed in transition zones outperform urban-edge placements in over 93% of configurations. Additionally, a dual-base architecture, with bases symmetrically placed in the transition zone, reduces drone energy consumption by approximately 33% compared to urban-based layouts. Despite the NP-hardness of the underlying resource allocation problem, the dispatcher is able to generate high-quality, feasible schedules in real-time. These findings suggest that operating fleets of 16 to 32 drones, prioritizing dual transition-zone bases, and employing energy-efficient dispatch strategies can significantly reduce fire extinction times and minimize the damage caused by WUI fires.", "keywords": [ "Drone", "Mechanism (biology)", "Firefighting", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.jnlssr.2026.100323" }, { "id": "https://openalex.org/W4417198009", "doi": "10.1177/03611981251394973", "title": "Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics", "year": 2025, "publication_date": "2025-12-10", "type": "article", "language": "en", "authors": [ "Tanveer Hossain Bhuiyan", "Sayed Hamid Hosseini Dolatabadi", "Md Jalal Uddin", "Victor Walker" ], "first_author": "Tanveer Hossain Bhuiyan", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.", "keywords": [ "Drone", "Sizing", "Energy consumption", "Scheduling (production processes)", "Battery (electricity)", "Genetic algorithm", "Greedy algorithm", "Heuristic", "Flowchart" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981251394973" }, { "id": "https://openalex.org/W7159958927", "doi": "10.3389/frsc.2026.1798928", "title": "Hybrid metaheuristic optimization for multi-criteria truck–drone collaborative routing in sustainable supply chains", "year": 2026, "publication_date": "2026-05-01", "type": "article", "language": "en", "authors": [ "K. N. Mayuri", "M. Gowsalya" ], "first_author": "K. N. Mayuri", "venue": "Frontiers in Sustainable Cities", "publisher": "Frontiers Media", "abstract": "Background Last-mile logistics accounts for approximately 28% of total logistics costs and is a major contributor to urban carbon emissions. The integration of drones with electric trucks offers a path toward more efficient and sustainable delivery, but the resulting Truck–Drone Collaborative Routing Problem (TDCRP) is NP-hard, combining binary customer-to-mode assignment, route sequencing, and drone sortie scheduling into a single combinatorial challenge that exact methods cannot solve at realistic instance sizes. Objective This paper proposes a novel Hybrid ACO+BOA algorithm—the first to couple Ant Colony Optimisation (ACO) with the Butterfly Optimisation Algorithm (BOA) for any routing problem—to simultaneously minimize truck distance, delivery makespan, and carbon emissions in the TDCRP. Methods ACO constructs 20 route sets per iteration using pheromone-heuristic guided selection. BOA refines the entire population using fragrance-scaled guided Or-opt relocation—a discrete analog of the continuous BOA position update—with greedy acceptance. Pheromone deposits are drawn from BOA-refined solutions, creating a reinforcing feedback loop. Five benchmark CVRP instances were evaluated against Pure ACO, BOA, PSO, and ABC over 20 independent runs each. A 33-configuration sensitivity analysis identified the optimal parameter set. Full non-parametric statistical validation was performed (Friedman, Nemenyi, Wilcoxon with Cohen's d ). Results The Hybrid achieved the lowest or statistically equivalent normalized combined objective on all five instances, with significant superiority on P-series instances. On P-n101-k4, it recorded a mean normalized objective of 0.2267 ± 0.0046—reductions of 5.2%, 63.6%, 72.4%, and 69.3% relative to ACO, BOA, PSO, and ABC, respectively. On A-series instances, the Hybrid was statistically equivalent to Pure ACO ( p >0.19), reflecting structural characteristics where short per-vehicle routes leave limited scope for BOA refinement. The Hybrid was significantly superior to BOA, PSO, and ABC on all instances ( p < 0.001, | d | = 13.7–47.8). Drone sorties were 1.9–3.1 × higher than non-ACO baselines, directly reducing fleet-level emission. Conclusion Tightly coupling pheromone-guided construction with fragrance-scaled exploitation yields consistent, statistically validated improvements over all single-algorithm baselines. The algorithm provides a practical, scalable framework for sustainable last-mile logistics planning with computation times of 30–49 seconds remaining within daily scheduling horizons.", "keywords": [ "Metaheuristic", "Vehicle routing problem", "Scheduling (production processes)", "Benchmark (surveying)", "Population", "Routing (electronic design automation)", "Drone", "Greedy algorithm", "Truck" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3389/frsc.2026.1798928" }, { "id": "https://openalex.org/W4317651455", "doi": "10.2514/6.2023-2658", "title": "Sequential Cooperative Reinforcement Learning of Mothership Routing Problem", "year": 2023, "publication_date": "2023-01-19", "type": "conference-paper", "language": "en", "authors": [ "Jae‐Ho Lee", "Junwoo Park", "Dongwoo Lee", "Hyochoong Bang" ], "first_author": "Jae‐Ho Lee", "venue": "AIAA SCITECH 2023 Forum", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2023-2658.vid This paper proposes a sequential cooperative reinforcement learning for the vehicle routing problem which possess a moving depot (mothership) where the depot is bound for a designated destination. Drones which are considered as delivery vehicles and deployed from the moving depot should convey packages to the known customer within operation time of the mothership. Challenges here are to assign which drones to which customer at what point in the path of the mothership, as well as to determine the best route of the mothership and those of the drones that minimizes cumulative distance. Our solution approach is to separate constitutive rolls into respective agents instead of controlling everything in a centralized fashion using single policy network. Then the key contribution of this work is to train the collection of policies in a sequential cooperative fashion using alternated interaction among them. The suggested algorithm represents each policy well trained with increasing reward as iteration continues, moreover more stable than simultaneous reinforcement learning for each agent. The proposed design prevents affecting training of other agent. Also, algorithm makes proper gradient backpropagation of reward possible. On the vehicle routing problem with mothership, our approach outperforms a solution with simultaneous learning of two policy agent in solution quality and training time with computational cost.", "keywords": [ "Reinforcement learning", "Computer science", "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Path (computing)", "Operations research", "Real-time computing", "Mathematical optimization", "Artificial intelligence", "Computer network", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.2514/6.2023-2658" }, { "id": "https://openalex.org/W7141574873", "doi": "10.1371/journal.pone.0321917", "title": "Employing in-context learning prompts with large language models for drone routing in delivery services", "year": 2026, "publication_date": "2026-03-27", "type": "article", "language": "en", "authors": [ "Mahmoud Masoud", "Mohammed Mamdouh Zakaria Elhenawy", "Ahmed Abdelhay" ], "first_author": "Mahmoud Masoud", "venue": "PLoS ONE", "publisher": "Public Library of Science", "abstract": "Autonomous Aerial Vehicles (AAVs) - known as drones - employment in delivery services is one of the promising transformative technologies. The AAV industry has taken significant steps to develop drones to fulfill the needs of delivery services. However, AAVs have limitations related to the flight range and payload capacity. Therefore, drone route planning is crucial to reducing the effectiveness of these challenges. The recent emergence of Large Language Models (LLMs) has opened new possibilities for solving combinatorial problems using in-context learning (ICL). Unlike traditional machine learning models, LLMs can generate solutions without requiring task-specific fine-tuning by leveraging solved examples within their input prompts. In this study, we explore the application of LLMs to the Drone Routing Problem (DRP), leveraging various ICL strategies to generate optimized delivery routes. Our solution ensures that drone routes are planned to reduce the traveling distance for the full route. Notably, it ensures that drones don't mess any delivery points and fast delivery routes. Through extensive experimentation, we evaluate the effectiveness of different prompt engineering techniques in guiding LLMs to produce high-quality, non-hallucinated route plans. We compared our model results to heuristic-based generated routes to demonstrate the variation between our technique and other techniques. The results demonstrate that LLMs, when properly prompted, can reliably generate valid routing solutions, highlighting their potential as a flexible and adaptive tool for drone logistics planning. Project link: https://github.com/ahmed-abdulhuy/Solve-TSP-using-GPT3.5.git.", "keywords": [ "Drone", "Payload (computing)", "Routing (electronic design automation)", "Transformative learning", "Range (aeronautics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0321917&type=printable" }, { "id": "https://openalex.org/W7164559805", "doi": "10.1016/j.jestch.2026.102434", "title": "Performance analysis of a PV powered drone delivery network under realistic solar energy constraints", "year": 2026, "publication_date": "2026-06-12", "type": "article", "language": "en", "authors": [ "Roko Bračić", "Goran Krajačić", "Marko Mimica", "Zvonimir Šoša" ], "first_author": "Roko Bračić", "venue": "Engineering Science and Technology an International Journal", "publisher": "Elsevier BV", "abstract": "As the global shipping industry aims to reduce greenhouse gas emissions by 50 % by 2050, drone delivery networks present a promising solution for last-mile logistics. However, existing studies often rely on idealized assumptions regarding energy availability, overlooking the stochastic nature of renewable energy sources. This paper presents a comprehensive performance analysis of a PV-powered drone delivery network in the North Adriatic Sea, utilizing a discrete-time, agent-based simulation model. The study integrates real-world, time-series photovoltaic generation data to evaluate system throughput, energy consumption, and charging bottlenecks over a 48-hour period. Results indicate a severe performance gap between idealized and realistic scenarios: while an ideal system achieved a 100 % success rate, the introduction of realistic solar variability reduced the success rate to 51.7 % and caused significant queuing delays totaling over 223 h. Furthermore, the study reveals a counter-intuitive finding where increasing the number of chargers per hub during low-energy periods further degraded system performance by distributing scarce energy too thinly. A scalability analysis across fleet sizes of 30 to 240 drones demonstrates that proportional PV scaling restores performance at moderate fleet sizes, but a hub topology ceiling emerges at larger scales, indicating that routing and congestion management become the binding constraints beyond a critical fleet density. These findings underscore the critical necessity of incorporating energy intermittency constraints into the design of sustainable autonomous logistics infrastructure and identify techno-economic optimization of PV sizing and hub topology as key directions for future work.", "keywords": [ "Drone", "Solar powered", "Photovoltaic system", "Solar energy", "Energy (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.jestch.2026.102434" }, { "id": "https://openalex.org/W2901568424", "doi": "10.1007/978-3-030-01370-7_73", "title": "Unmanned Aerial Vehicles in Wireless Sensor Networks: Automated Sensor Deployment and Mobile Sink Nodes", "year": 2018, "publication_date": "2018-12-30", "type": "conference-paper", "language": "en", "authors": [ "Juan Marchal Gómez", "Thomas Wiedemann", "Dmitriy Shutin" ], "first_author": "Juan Marchal Gómez", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Wireless sensor network", "Computer science", "Software deployment", "Computer network", "Real-time computing", "Mobile wireless sensor network", "Key distribution in wireless sensor networks", "Sensor node", "Node (physics)", "Routing (electronic design automation)", "Wireless", "Embedded system" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-01370-7_73" }, { "id": "https://openalex.org/W3082216797", "doi": "10.1109/vlsidcs47293.2020.9179956", "title": "Optimizing FANET Routing Using a Hybrid Approach of Firefly Algorithm and ACO-Lévy Flight", "year": 2020, "publication_date": "2020-07-01", "type": "conference-paper", "language": "en", "authors": [ "Subhrapratim Nath", "Abhijit Paul", "Rupam Banerjee", "Swarnabja Bhaumik", "Jamuna Kanta Sing", "Subir Kumar Sarkar" ], "first_author": "Subhrapratim Nath", "venue": "2020 IEEE VLSI DEVICE CIRCUIT AND SYSTEM (VLSI DCS)", "publisher": null, "abstract": "Unmanned Aerial Vehicle (UAV) is an utter technological sensation that embraces the capability to hover peripatetically in the air. Multi UAV system designed on an effective but complex network needs a flying ad-hoc network (FANET) system to be employed. Due to the higher mobility degree and higher computational power FANET faces an enormous challenge in routing optimization. Other than conventional mobile ad-hoc network (MANET) protocols, metaheuristics-based Ant algorithm and Bee algorithm modifications have been developed as alternatives in FANET topology to exhibit high performance, but with limitations. A metaheuristics hybridization approach founded on Ant Colony Optimization-Levy flight and Firefly Algorithm is projected to make the AdHoc on demand distance vector (AODV) more adequate and thereby aiming in improved routing optimization in FANET system. Comparative analysis on the projected half brid algorithm and the preexisting routing algorithms (AODV and AntHocNet and BeeAdHoc) are conducted to overcome the Routing problem in FANET system.", "keywords": [ "Computer science", "Destination-Sequenced Distance Vector routing", "Firefly algorithm", "Wireless ad hoc network", "Ad hoc On-Demand Distance Vector Routing", "Distance-vector routing protocol", "Routing (electronic design automation)", "Ant colony optimization algorithms", "Metaheuristic", "Computer network", "Dynamic Source Routing", "Routing protocol" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/vlsidcs47293.2020.9179956" }, { "id": "https://openalex.org/W7167596829", "doi": "10.2139/ssrn.7075424", "title": "Routing Heterogeneous Drones with Automated Battery Swapping Machines for Multi-Customer Last-Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Rakesh Chowdhury", "Wenquan Dong", "Tanveer Hossain Bhuiyan", "Clara Novoa" ], "first_author": "Rakesh Chowdhury", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Scalability", "Payload (computing)", "Battery (electricity)", "Range (aeronautics)", "Linear programming", "Integer programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7075424" }, { "id": "https://openalex.org/W2763177753", "doi": "10.1109/ccta.2017.8062486", "title": "Cooperative surveillance in the presence of time sensitive data", "year": 2017, "publication_date": "2017-08-01", "type": "conference-paper", "language": "en", "authors": [ "Satyanarayana G. Manyam", "Kaarthik Sundar", "David W. Casbeer" ], "first_author": "Satyanarayana G. Manyam", "venue": "2017 IEEE Conference on Control Technology and Applications (CCTA)", "publisher": null, "abstract": "We consider a cooperative vehicle routing problem for surveillance and reconnaissance missions using an aerial and a ground vehicle, where the collected data needs to be processed in real-time or near real-time. The proposed framework assumes that the ground vehicle has the ability to communicate long range and the aerial vehicle has limited communication range. The vehicles' paths are constrained such that the aerial vehicle remains in contact with the ground vehicle when the aerial vehicle is surveilling a target. Under this constraint, as the aerial vehicle collects data from the targets, this data can then be transmitted in real-time to the base station via the ground vehicle relay. We present a mixed-integer linear programming formulation and develop a branch-and-cut algorithm to solve the resulting cooperative routing problem involving the two vehicles. The novelty of the algorithm is the ability to maintain communication between the ground vehicle and UAV, while allowing the ground vehicle to move as the UAV is deployed. The effectiveness of the proposed approach is corroborated through extensive computational experiments on several randomly generated instances.", "keywords": [ "Computer science", "Real-time computing", "Range (aeronautics)", "Integer programming", "Routing (electronic design automation)", "Linear programming", "Ground station", "Novelty", "Base station", "Vehicle routing problem", "Engineering", "Computer network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccta.2017.8062486" }, { "id": "https://openalex.org/W7160103783", "doi": "10.1007/978-3-032-19088-8_21", "title": "Path Optimization for UAV-Based Watershed Water Sampling: A Dynamic Vehicle Routing Approach for Discrete Points with Multi-constraints", "year": 2026, "publication_date": "2026-01-01", "type": "book-chapter", "language": "en", "authors": [ "Qianqian Shao", "Hao Yin", "Ziming Yan", "Xuemei Han" ], "first_author": "Qianqian Shao", "venue": "Environmental science and engineering", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Path (computing)", "Routing (electronic design automation)", "Watershed", "Minification", "Trajectory", "Control theory (sociology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-19088-8_21" }, { "id": "https://openalex.org/W4416230034", "doi": "10.1016/j.cie.2025.111669", "title": "Benders decomposition-based solution approach for periodic relief distribution with dynamic deprivation time modelling", "year": 2025, "publication_date": "2025-11-15", "type": "article", "language": "en", "authors": [ "Sanaz Khalaj Rahimi", "Donya Rahmani", "Tom Van Woensel" ], "first_author": "Sanaz Khalaj Rahimi", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Heuristic", "Routing (electronic design automation)", "Humanitarian Logistics", "Pickup", "Emergency management", "Duration (music)", "Relief Work" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://research.tue.nl/en/publications/11eb6d5e-d473-467d-a963-999ae025bc34" }, { "id": "https://openalex.org/W7161109098", "doi": "10.2139/ssrn.6766261", "title": "Routing Heterogeneous Drones with Automated Battery Swapping Machines for Multi-Customer Last-Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Rakesh Chowdhury", "Tanveer Hossain Bhuiyan", "Clara Novoa", "Wenquan Dong" ], "first_author": "Rakesh Chowdhury", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Routing (electronic design automation)", "Scalability", "Payload (computing)", "Battery (electricity)", "Range (aeronautics)", "Linear programming", "Integer programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6766261" }, { "id": "https://openalex.org/W4200067531", "doi": "10.1002/fsh.10696", "title": "Perspectives on the Use of Unmanned Aerial Vehicle Systems as Tools for Small-Scale Fisheries Research and Management", "year": 2021, "publication_date": "2021-12-15", "type": "article", "language": "en", "authors": [ "José Amorim Reis‐Filho", "Tommaso Giarrizzo" ], "first_author": "José Amorim Reis‐Filho", "venue": "Fisheries", "publisher": "Wiley", "abstract": "Abstract Unmanned aerial vehicles (UAVs) are increasingly used in marine wildlife research and have rapidly expanded across many scientific disciplines. As recent technological developments have improved the versatility and functionality of UAVs, the potential of these systems for research on small-scale and artisanal fisheries has grown considerably, although this potential is still underexploited. However, there is growing interest in the application of cost-effective UAVs for aerial surveys of fishery activities, but few empirical studies have produced reliable findings to aid the development of effective management actions. Common challenges for effective management include funding limitations and problems related to survey design, coverage, and implementation. In this context, aerial devices have the capacity to fill a niche for intensive fine-scale sampling in which data gathering strategies need to be tailored to specific operational requirements. Detailed documentation and monitoring of harvest processes and fisheries practices by UAVs can provide important insights into fishing gear and techniques (e.g., gillnets and seine nets), the operation of stationary artisanal fishing devices (e.g., traps and tidal fishing weirs), area coverage, fishery territories, and the number of fishers and/or fleet size. Here, we present and discuss the potential use of UAVs for surveying small-scale fishing activities as a complement to traditional research methods (e.g., fishery-dependent approaches, such as landing monitoring, electronic logbooks, dealer reports, dockside intercepts, and fishery cruises). We predict that the use of UAV as a fishery survey tool will become increasingly widespread as the technology and associated methods are refined and become more affordable with potential to generate significant advances in fishery management practices.", "keywords": [ "Scale (ratio)", "Fishery", "Fisheries management", "Environmental resource management", "Environmental science", "Business", "Geography", "Fishing", "Biology", "Cartography" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1002/fsh.10696" }, { "id": "https://openalex.org/W7163018740", "doi": "10.1080/21681015.2026.2678971", "title": "Data-driven robust optimization model for hybrid truck-drone last-mile delivery logistics", "year": 2026, "publication_date": "2026-06-01", "type": "article", "language": "en", "authors": [ "Reza Ahmadi Malakoot", "Rashed Sahraeian" ], "first_author": "Reza Ahmadi Malakoot", "venue": "Journal of Industrial and Production Engineering", "publisher": "Taylor & Francis", "abstract": "This research proposes a robust optimization model for a hybrid truck-drone delivery system under uncertain drone battery performance. The model introduces a one-to-one truck-drone pairing structure and formulates a hierarchical bi-objective mixed-integer linear program to (1) minimize the required fleet size and (2) minimize the total return time. A novel data-driven uncertainty modeling approach is employed, leveraging piecewise linear support vector clustering to formulate realistic uncertainty sets and mitigate over-conservatism. Findings from a real-world case study illustrate the model’s ability to maintain solution quality under uncertainty. Eliminating drones increases both truck count and delivery time by over 23%, highlighting the operational benefits of drone integration. This work advances last-mile logistics research by combining machine learning and robust optimization to address performance-risk trade-offs in hybrid delivery systems.", "keywords": [ "Robust optimization", "Robustness (evolution)", "Work (physics)", "Optimization problem", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/21681015.2026.2678971" }, { "id": "https://openalex.org/W4413346427", "doi": "10.2139/ssrn.5388230", "title": "Evaluating the Energy Savings from Truck-drone Hybrid Last-mile Delivery in Arbitrary Cities", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Seunghyun Baik", "Steven M. Willits", "Sebastian Scherer", "Junfeng Jiao", "Peter Yun Zhang" ], "first_author": "Seunghyun Baik", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Mile", "Drone", "Truck", "Last mile (transportation)", "Aeronautics", "Transport engineering", "Energy (signal processing)", "Computer science", "Automotive engineering", "Engineering", "Business", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5388230" }, { "id": "https://openalex.org/W4415125765", "doi": "10.22541/au.176038131.17106095/v1", "title": "Collaborative Truck-Drone Routing with Multi-visit via Surrogate-Assisted Bi-level Optimization", "year": 2025, "publication_date": "2025-10-13", "type": "preprint", "language": "en", "authors": [ "Ruonan Zhai", "Xuejun Zhang", "Yi Mei", "Wenbo Du", "Tong Guo", "Tao Song" ], "first_author": "Ruonan Zhai", "venue": null, "publisher": null, "abstract": "The development of drone techniques has extensive applications to fields like last-mile parcel delivery. For economic and efficiency advantages, collaborative truck-drone systems have attracted great attention and formulated as truck-drone routing problems. In this paper, a multi-visit traveling salesman problem with drones is investigated. We decompose it into a two-layer structure and formulate it as a new bi-level model (Bi-MTSPD). The major advantage of the bi-level model is its ability to represent the complex interactions between truck and drone routes by dispatching the drones under the premise of truck routes. To solve the bi-level model, we propose a novel surrogate-assisted bi-level optimization method. The upper-level subproblem can be treated as a traveling salesman problem with customer assignment, and high-quality truck routes are identified by the K-nearest neighbor-based surrogate model with newly proposed features in order to allocate more computational resources to promising upper-level solutions. For the lower-level drone location routing problem (DLR), a customized memetic algorithm is developed to optimize the drone routes for the corresponding truck route. Comprehensive experimental results show that the proposed algorithm significantly outperforms the existing state-of-the-art algorithms across most benchmark instances. Further analysis verifies the congruence of the bi-level optimization for MTSP-D and the effectiveness of upper-level and lower-level solvers.", "keywords": [ "Routing (electronic design automation)", "Drone", "Process (computing)", "Field (mathematics)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.authorea.com/doi/pdf/10.22541/au.176038131.17106095/v1" }, { "id": "https://openalex.org/W4283396185", "doi": "10.3390/math10132184", "title": "An Improved Intelligent Auction Mechanism for Emergency Material Delivery", "year": 2022, "publication_date": "2022-06-23", "type": "article", "language": "en", "authors": [ "Jie Zhang", "Yifan Zhu", "Tao Wang", "Weiping Wang", "Rui Wang", "Xiaobo Li" ], "first_author": "Jie Zhang", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Emergency material delivery is vital to disaster emergency rescue. Herein, the framework of the emergency material delivery system (EMDS) with the unmanned aerial vehicle (UAV) as the vehicle is proposed, and the problem is modeled into a multi-trip time-dependent dynamic vehicle routing problem with split-delivery (MTTDDVRP-SD) in combination with the rescue reality, which provides decision support for planning disaster relief material. Due to the universality of dynamic interference in the process of material delivery, an optimization algorithm based on the traditional intelligent auction mechanism is proposed to avoid system performance degradation or even collapse. The algorithm adds pre-authorization and sequential auction mechanisms to the traditional auction mechanism, where the pre-authorization mechanism improves the capability performance of the system when there is no interference during the rescue process and the sequential auction mechanism improves the resilience performance of the system when it faces interferences. Finally, considering three types of interference comprehensively, which includes new task generations, task unexpected changes and UAV’s number decreases, the proposed algorithm is compared with DTAP (DTA based on sequential single item auctions) and CBBA-PR (consensus-based bundle algorithms-partial replanning) algorithms under different dynamic interference intensity scenarios for simulation experimental from two perspectives of the capability performance and resilience performance. The results of Friedman’s test with 99% confidence interval indicate that the proposed algorithm can effectively improve the capability performance and resilience performance of EMDS.", "keywords": [ "Computer science", "Resilience (materials science)", "Emergency rescue", "Task (project management)", "Real-time computing", "Process (computing)", "Distributed computing", "Operations research", "Simulation", "Engineering", "Systems engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2227-7390/10/13/2184/pdf?version=1655973905" }, { "id": "https://openalex.org/W4229376982", "doi": "10.3390/su14095663", "title": "Nuclear Accident Emergency Response System: Radiation Field Estimation and Evacuation", "year": 2022, "publication_date": "2022-05-07", "type": "article", "language": "en", "authors": [ "Bo Chen", "Zhicheng Li", "Zaiyue Yang" ], "first_author": "Bo Chen", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In this paper, a nuclear accident emergency response system based on unmanned aerial vehicles (UAVs) and bus collaboration is designed for radiation field estimation and evacuation. When a nuclear accident occurs, the radiation field is estimated firstly using the measurements acquired by UAVs. Based on the Cramer–Rao Lower Bound (CRLB), the coordinate optimization combined with UAV routing is formulated as a nonconvex mixed integer nonlinear programming (MINLP) problem to maximize the estimation accuracy. Further, a two-stage solution procedure based on genetic algorithm (GA) is proposed to solve the above problem. Then, taking the predicted radiation field as input, personnel in the emergency planning zone (EPZ) are evacuated to shelters by buses. The evacuation routing problem for minimizing both the total radiation exposure and evacuation time is formulated as a mixed integer linear programming (MILP) problem, which is solvable with efficient commercial solvers, such as Gurobi and CPLEX. The simulation results indicate that the system can provide effective help for emergency management under the nuclear accident scenarios.", "keywords": [ "Integer programming", "Computer science", "Routing (electronic design automation)", "Mathematical optimization", "Genetic algorithm", "Emergency management", "Emergency response", "Integer (computer science)", "Linear programming", "Field (mathematics)", "Operations research", "Engineering" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/14/9/5663/pdf?version=1652086908" }, { "id": "https://openalex.org/W7113164412", "doi": null, "title": "Fixed Automated stations location and UAVs routing problems in urban road Networks: A tailored Branch-&-price algorithm", "year": null, "publication_date": null, "type": "article", "language": null, "authors": [ "Li, Hao", "Kang, Liujiang", "Sun, Huijun", "Wu, Jianjun", "Zhao, Yue", "Amihere, Samuel" ], "first_author": "Li, Hao", "venue": "RePEc: Research Papers in Economics", "publisher": "Federal Reserve Bank of St. Louis", "abstract": "Unmanned aerial vehicles (UAVs) serve as vital tools for collecting real-time traffic data, enabling rapid responses to events, and optimizing traffic management based on their cost-effectiveness, high flexibility, and wide coverage range advantages. The urban UAV cruising problem primarily focuses on selecting the locations of fixed automated station (FAS) and optimizing UAVs’ cruising routes. This paper introduces a strategy where UAVs can take off and land on different FASs to enhance urban UAVs’ cruising efficiency. Firstly, the problem is formulated as an integrated linear integer model (FU-ILP) which simultaneously determines the locations of FASs and the routes of UAVs. The objective is to minimize redundant cruising distance and cruising cycle time. The FU-ILP model takes into account UAV‘s flight capabilities, FAS’s signal coverage range, power requirements, and battery charging. Moreover, we design a tailored branch-and-price (TB&P) algorithm to decompose the FU-ILP model, which can reduce the variable scale for solving larger-scale cases. Finally, we test the model and the algorithm on the Sioux Falls Test Network. The TB&P algorithm improves computational efficiency by nearly 80%. Moreover, our strategy yields superior results compared to the strategy where UAVs must return to their take-off FASs for recharging. We also demonstrate that increasing the number of UAVs does not reduce redundant cruising distance in the whole network, but it can shorten the cruising cycle. These policy conclusions also apply to the case of Xiongan. This provides a basis for managers to calculate the minimum UAV’s configuration based on the cities’ actual cruising frequency requirements.", "keywords": [ "Range (aeronautics)", "Scale (ratio)", "Variable (mathematics)", "Routing (electronic design automation)", "Integer (computer science)", "Power (physics)", "Global Positioning System", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.sciencedirect.com/science/article/pii/S0965856424002490" }, { "id": "https://openalex.org/W7110891023", "doi": "10.6084/m9.figshare.29949661.v1", "title": "Logistics system design for hybrid commercial drones with integrated economic, social, and environmental cost considerations", "year": 2025, "publication_date": "2025-08-20", "type": "article", "language": null, "authors": [ "Nima Molavi (22092796)", "Yue Zhang (30585)", "Marcelo J. Alvarado-Vargas (22092799)" ], "first_author": "Nima Molavi (22092796)", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "Hybrid drones have been developed for commercial purposes due to their long range and high payload capacity. This research designs a multi-criteria logistics system for hybrid commercial drones with deterministic demand locations. An integrated location and routing problem is formulated as a mixed-integer linear program to minimise the total cost of the logistics system, comprising economic, social, and environmental costs. Results show that reducing social costs increases economic and environmental costs, particularly in densely populated areas. Due to the complexity of larger instances, the problem is decomposed into a location master problem and routing subproblems. The two models are compared through computational experiments, and an illustrative example is provided. The decomposed model accommodates larger instances; although its location master problem requires more time, routing subproblems run substantially faster. We recommend the decomposed model for large, dynamic settings where demand locations frequently change, and the integrated model for small, static settings.", "keywords": [ "Drone", "Payload (computing)", "Routing (electronic design automation)", "Vehicle routing problem", "Range (aeronautics)", "Hybrid system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/journal_contribution/Logistics_system_design_for_hybrid_commercial_drones_with_integrated_economic_social_and_environmental_cost_considerations/29949661" }, { "id": "https://openalex.org/W7135047858", "doi": "10.2139/ssrn.6362569", "title": "Enhancing Healthcare Access in Rural Appalachia: A Drone-truck System Powered by Deep Reinforcement Learning", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Sabrullah Deniz", "Zhenbo Wang", "Courtney C. Cronley", "Jennifer Tourville", "Daniel Wang" ], "first_author": "Sabrullah Deniz", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Truck", "Payload (computing)", "Process (computing)", "Reinforcement learning", "Markov decision process", "Resource (disambiguation)", "Resource allocation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6362569" }, { "id": "https://openalex.org/W4412548487", "doi": "10.1016/j.trc.2026.105920", "title": "Drone-enhanced last-mile medical delivery in rural areas: A weather-aware optimization approach", "year": 2026, "publication_date": "2026-08-18", "type": "article", "language": "en", "authors": [ "Pardis Bahmani", "Shakiba Enayati", "Sina Ansari", "Srikanth Gururajan", "Ziteng Wang" ], "first_author": "Pardis Bahmani", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Mile", "Last mile (transportation)", "Drone", "Computer science", "Meteorology", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5361264" }, { "id": "https://openalex.org/W7171775806", "doi": "10.1007/s10951-026-00879-y", "title": "Launching drones from a vessel: routing unmanned aerial vehicles from a linearly moving depot", "year": 2026, "publication_date": "2026-07-30", "type": "article", "language": "en", "authors": [ "Nils Boysen", "Helmut Sedding" ], "first_author": "Nils Boysen", "venue": "Journal of Scheduling", "publisher": "Springer Science+Business Media", "abstract": "Abstract Unmanned aerial vehicles (or drones) are expected to free urban areas from the negative impacts of traditional last-mile delivery by trucks and vans. To overcome one of the major operational challenges of drones, their limited range, existing concepts propose to launch them either from decentralized depots or trucks moving through the city center. Both solutions have drawbacks, so recent ideas suggest using river barges as mobile launch platforms. These ships act as linearly moving depots for drone launches, and this paper investigates the resulting routing problem when serving a given set of customers. First, we formulate the basic optimization problem and derive some structural properties through an in-depth computational complexity analysis. Based on these insights, we derive appropriate methods to attain exact, $$\\rho $$ ρ -approximative, or heuristic solutions, and apply them to evaluate the potential savings of delivery vans (benchmarked as a standalone delivery option and as a van-drone tandem) if, instead, drones are launched from ships or trains.", "keywords": [ "Drone", "Truck", "Routing (electronic design automation)", "Heuristic", "Vehicle routing problem", "Set (abstract data type)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10951-026-00879-y" }, { "id": "https://openalex.org/W2942064469", "doi": null, "title": "Unmanned Aerial Vehicle routing and trajectory optimisation problems", "year": 2018, "publication_date": "2018-09-01", "type": "dissertation", "language": "en", "authors": [ "Walton Pereira Coutinho" ], "first_author": "Walton Pereira Coutinho", "venue": "ePrints Soton (University of Southampton)", "publisher": "University of Southampton", "abstract": "In recent years, employing Unmanned Aerial Vehicles (UAV) to collect data and making measurements has gained popularity. Often, the use of UAVs allows for a reduction in costs and improvements of other performance criteria. The academic routing community has acknowledged the interest of companies and organisations in adopting UAVs in their operations. However, constraints due to the flight dynamics of UAVs have often been neglected. Finding feasible trajectories for UAVs in a routing problem is a complex task, but it is necessary to ensure the feasibility of the routes. In this thesis 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 subject to flight dynamics constraints. Motivated by a disaster assessment application, we propose a variant of the UAVRTOP, in which a fleet of autonomous aerial gliders is required to photograph a set of points of interest in the aftermath of a disaster. This problem is referred to as the Glider Routing and Trajectory Optimisation Problem (GRTOP). In this work, we propose a single-phase Mixed-Integer Non-linear Programming (MINLP) formulation for the GRTOP. Our formulation simultaneously optimises routes and the flight trajectories along these routes while the flight dynamics of the gliders are modelled as ordinary differential equations. We avoid dealing with non-convex dynamical constraints by linearising the gliders’ Equations of Motion (EOMs), reducing the proposed MINLP into a Mixed-Integer Second-Order Cone Programming (MISOCP) problem. Another contribution of this work consists of proposing a multi-phase MINLP formulation for a modified version of the GRTOP. We do not attempt to solve this formulation directly, instead we propose a hybrid heuristic method that is composed of two main building blocks: (i) a Sequential Trajectory Optimisation (STO) heuristic, designed to cope with the challenging task of finding feasible (flyable) trajectories for a given route; and (ii) a routing matheuristic, capable of generating routes that can be evaluated by STO. We perform computational experiments with real-life instances based on flood risk maps of cities in the UK as well as in a large number of randomly generated instances.", "keywords": [ "Computer science", "Trajectory", "Routing (electronic design automation)", "Integer programming", "Glider", "Vehicle routing problem", "Mathematical optimization", "Vehicle dynamics", "Operations research", "Engineering", "Mathematics", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://eprints.soton.ac.uk/426341/1/Final_thesis.pdf" }, { "id": "https://openalex.org/W4393077062", "doi": "10.1108/k-05-2023-0903", "title": "A heuristic-based benders decomposition for the hybrid relief distribution network minimizing deprivation cost of vulnerable areas", "year": 2024, "publication_date": "2024-03-22", "type": "article", "language": "en", "authors": [ "Sanaz Khalaj Rahimi", "Donya Rahmani" ], "first_author": "Sanaz Khalaj Rahimi", "venue": "Kybernetes", "publisher": "Emerald Publishing Limited", "abstract": "Purpose The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology. Design/methodology/approach Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques. Findings Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate. Originality/value Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.", "keywords": [ "Decomposition", "Heuristic", "Benders' decomposition", "Mathematical optimization", "Computer science", "Cybernetics", "Operations research", "Distribution (mathematics)", "Mathematical economics", "Artificial intelligence", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1108/k-05-2023-0903" }, { "id": "https://openalex.org/W4413356406", "doi": "10.1080/23249935.2025.2547758", "title": "Logistics system design for hybrid commercial drones with integrated economic, social, and environmental cost considerations", "year": 2025, "publication_date": "2025-08-20", "type": "article", "language": "en", "authors": [ "Nima Molavi", "Yue Zhang", "Marcelo J. Alvarado-Vargas" ], "first_author": "Nima Molavi", "venue": "Transportmetrica A Transport Science", "publisher": "Taylor & Francis", "abstract": "Hybrid drones have been developed for commercial purposes due to their long range and high payload capacity. This research designs a multi-criteria logistics system for hybrid commercial drones with deterministic demand locations. An integrated location and routing problem is formulated as a mixed-integer linear program to minimise the total cost of the logistics system, comprising economic, social, and environmental costs. Results show that reducing social costs increases economic and environmental costs, particularly in densely populated areas. Due to the complexity of larger instances, the problem is decomposed into a location master problem and routing subproblems. The two models are compared through computational experiments, and an illustrative example is provided. The decomposed model accommodates larger instances; although its location master problem requires more time, routing subproblems run substantially faster. We recommend the decomposed model for large, dynamic settings where demand locations frequently change, and the integrated model for small, static settings.", "keywords": [ "Drone", "Business", "Engineering", "Environmental economics", "Economics", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/23249935.2025.2547758" }, { "id": "https://openalex.org/W7165148309", "doi": "10.5267/j.ijiec.2026.5.008", "title": "Research on truck-drone collaborative emergency routing optimization considering road blockage in large-scale disaster scenarios", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Bo-Chen Wang", "Yu-Han Guo", "Chang-Ping He" ], "first_author": "Bo-Chen Wang", "venue": "International Journal of Industrial Engineering Computations", "publisher": "Growing Science", "abstract": "Large earthquakes often disrupt road networks, severely hindering the timely delivery of emergency supplies. This paper studies a truck–UAV collaborative emergency routing problem under road blockage conditions. We develop a mixed-integer programming model that coordinates truck and UAV operations to minimize total emergency response time while accounting for payload, endurance, demand satisfaction, and road repair constraints. To solve the problem efficiently, we propose an improved Variable Neighborhood Search algorithm with greedy initialization (VNS-G), together with a benchmark variant based on random initialization (VNS-R). Computational experiments on instances of different sizes are conducted to evaluate the proposed approach. The results show that VNS-G can obtain high-quality solutions close to those of CPLEX on small-scale instances. On medium-scale instances, it provides substantial computational savings while maintaining competitive solution quality, reducing computation time from 682–3496 s for CPLEX to 95–340 s in the tested cases. For large-scale instances, where exact optimization becomes computationally impractical, the proposed heuristic remains effective in generating feasible solutions within operationally meaningful time. A case study of the earthquake-prone Ya'an region further illustrates the model's practical applicability. Sensitivity analysis reveals a mechanism-based managerial insight: UAV endurance primarily affects reachability, whereas UAV payload more directly improves response efficiency by reducing the need for repeated sorties. This suggests that, once reachability is ensured, improving payload capacity is likely to generate greater operational benefits than further extending endurance.", "keywords": [ "Initialization", "Payload (computing)", "Benchmark (surveying)", "Reachability", "Heuristic", "Routing (electronic design automation)", "Truck", "Computation", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5267/j.ijiec.2026.5.008" }, { "id": "https://openalex.org/W7153195319", "doi": "10.48550/arxiv.2604.07514", "title": "Energy-Efficient Drone Logistics for Last-Mile Delivery: Implications of Payload-Dependent Routing Strategies", "year": 2026, "publication_date": "2026-04-08", "type": "preprint", "language": null, "authors": [ "Ziyue Li", "Qianwen", "Guo", "Paul M. Schonfeld" ], "first_author": "Ziyue Li", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Drone delivery is rapidly emerging as a cost-effective and energy efficient alternative for last-mile delivery. Unlike ground vehicles, a drone's energy consumption depends on its payload in addition to travel distance. This creates a unique environmental challenge for multi-stop delivery tours, as the drone's total weight, and therefore its energy consumption rate, dynamically changes after each delivery. This paper investigates a novel green drone routing problem focused on maximizing energy efficiency. Through a series of motivating examples and numerical experiments, we demonstrate that energy-aware routing leads to several counter-intuitive routing strategies that contradict traditional distance-minimization delivery: a longer route may actually consume less energy than a shorter one; separate single-customer tours can be superior to a multi-stop tour; and a heterogeneous fleet, with drones of varying sizes, can achieve greater efficiency by matching drone capacity to specific delivery demands. In the numerical study, the green routing strategy shows energy savings in 67% of the instances. For these cases, the average energy saving is 2.17%, with a maximum saving of 5.97%, compared to minimum distance routing. These findings highlight the potential for green drone routing strategies to improve the sustainability of last-mile delivery.", "keywords": [ "Drone", "Routing (electronic design automation)", "Payload (computing)", "Energy consumption", "Energy (signal processing)", "Efficient energy use" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2604.07514" }, { "id": "https://openalex.org/W7172244450", "doi": "10.21256/zhaw-37220", "title": "Launching drones from a vessel : routing unmanned aerial vehicles from a linearly moving depot", "year": 2026, "publication_date": "2026-07-30", "type": "article", "language": "en", "authors": [ "Nils Boysen", "Helmut Sedding" ], "first_author": "Nils Boysen", "venue": "Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences)", "publisher": "ZHAW Zurich University of Applied Sciences", "abstract": null, "keywords": [ "Drone", "Depot", "Routing (electronic design automation)", "Cruise missile", "Mobile robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/11475/37220" }, { "id": "https://openalex.org/W2901877719", "doi": "10.3103/s1068798x18100118", "title": "Smart Design of Driverless-Vehicle Routes in the Arctic and Far North", "year": 2018, "publication_date": "2018-10-01", "type": "article", "language": "en", "authors": [ "Yu. G. Kabaldin", "Д. А. Шатагин", "Andrey V. Kiselev", "M. V. Zhelonkin", "ALEXANDER A. GOLOVIN" ], "first_author": "Yu. G. Kabaldin", "venue": "Russian Engineering Research", "publisher": "Pleiades Publishing", "abstract": "Abstract An approach to the design of routes for driverless vehicles in the Arctic and Far North is discussed. The proposed approach employs artificial intelligence and drones.", "keywords": [ "Arctic", "Drone", "The arctic", "Engineering design process", "Computer science", "Engineering", "Ecology", "Geology", "Mechanical engineering", "Oceanography", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3103/s1068798x18100118" }, { "id": "https://openalex.org/W4210961145", "doi": "10.2139/ssrn.4031302", "title": "Dynamic Integrated Ground Vehicle and Drone Routing with Simultaneous Surveillance Coverage for Evading Intentional Disruption", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "seyed farid ghannadpour", "Fatemeh Zandieh", "Mohammad Mahdavi Mazdeh" ], "first_author": "seyed farid ghannadpour", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Computer science", "Routing (electronic design automation)", "Vehicle routing problem", "Aeronautics", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4031302" }, { "id": "https://openalex.org/W2899558197", "doi": null, "title": "Integrated location and routing problems: Models, algorithms and applications", "year": 2018, "publication_date": "2018-08-01", "type": "dissertation", "language": "en", "authors": [ "Andrea L. Arias" ], "first_author": "Andrea L. Arias", "venue": "ThinkTech (Texas Tech University)", "publisher": "Texas Tech University", "abstract": "The study of integrated location and routing problems is fundamental for developing efficient \\ndesigns of transportation networks in logistic applications. In this dissertation, we address two \\nalternative drone-assisted logistic operations in which the decisions of (1) what locations in the \\nnetwork need to be visited? and (2) in what order? are taken simultaneously. In the first problem, \\nwe consider an unmanned surface vehicle (USV) that needs to visit a subset of the given locations \\nin order to cover all locations not visited, at the minimum total travel time. It is assumed \\nthat the USV is loaded with a scanning device that can cover a specified range around it. We \\npropose to model this problem with a variant of the Shortest Covering Path Problem in which the \\nUSV can cover while in-transit, and develop a column generation-based heuristic by reformulating \\nthe mixed-integer linear model using Dantzig-Wolfe decomposition. An extensive computational \\nevaluation shows that our heuristic outperforms CPLEX on the most challenging instances, in both \\nsolution quality and required CPU time. In the second problem, we consider an application in lastmile \\ndelivery of parcels in which an unmanned aerial vehicle (UAV) is used in coordination with a \\ndelivery truck to deliver packages to a set of clients. It is assumed that the UAV is launched from \\nthe truck to deliver a parcel and then meets the truck again in a rendezvous location. It is allowed \\nfor the truck to visit other clients while the drone is flying, however, the UAV has a limited time \\non air since it is battery powered. The objective is to determine a coordinated tour for the truck \\nand the UAV, such that all the clients are served at the minimum total completion time of the route. \\nWe propose a mixed-integer formulation for this problem, and a coverage-based decomposition \\nheuristic. Computational experiments are conducted to test the performance of our heuristic. The \\nresults suggest that our approach is suitable to be used in practical applications with large-scale instances, since it is able to produce solutions of good quality in very short times when compared \\nto a dynamic programming approach from the academic literature.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Algorithm", "Routing algorithm", "Computer network", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/2346/74534" }, { "id": "https://openalex.org/W4411628874", "doi": "10.1016/j.tre.2025.104273", "title": "Assessing the sustainability of last-mile distribution strategies to manage expedited shipping with dynamic and stochastic demand", "year": 2025, "publication_date": "2025-06-25", "type": "article", "language": "en", "authors": [ "Anmol Pahwa", "Miguel Jaller" ], "first_author": "Anmol Pahwa", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "As many e-retailers compete through increasingly consumer-focused services, urban freight faces a critical need for sustainable alternate last-mile distribution practices. While previous research has investigated the performance of various distribution strategies under different planning and network design scenarios, their performance under the dynamic and stochastic conditions inherent to consumer-focused services (e.g., expedited deliveries) is not well understood. This study introduces a dynamic-stochastic last-mile network design (DS-LMND) problem, formulated as a multi-echelon capacitated location routing problem with time-windows (ME-C-LRP-TW), to address this gap. Consequently, this work develops a Monte Carlo simulation–optimization framework integrated with an adaptive large neighborhood search (ALNS) metaheuristic to solve the problem. Using this framework, the authors configure the distribution structure and simulate last-mile operations for each distribution strategy. For these configured distribution structures, this work determines economic viability, environmental efficiency, and social equity – the three pillars of sustainability. Further, the authors determine the impact of demand uncertainties on the sustainability of last-mile distribution through the value of information (VI) and coefficient of variance (CV) metrics. In doing so, this work a) estimates the efficacy of conventional distribution strategy; b) confirms the competitiveness of electric delivery vehicles; c) evaluates the effectiveness of crowdsourced delivery services; d) advances the case for consolidation-based multi-echelon distribution strategies; e) establishes the rationale for customer pickups; and f) develops the use case for drone and robots, to cater to dynamic and stochastic demand with expedited shipping.", "keywords": [ "Mile", "Sustainability", "Last mile (transportation)", "Transport engineering", "Business", "Demand management", "Environmental economics", "Operations research", "Engineering", "Economics", "Geography" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104273" }, { "id": "https://openalex.org/W4405738848", "doi": "10.2139/ssrn.5070781", "title": "Truck-Drone Delivery Route Optimization Modeling Based on a Pso-Vns Algorithm Considering Dynamic Demand Nodes", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Xunjie Gou", "Tong Li", "Haoyu Zhang", "Yuxuan Zhang", "Fumin Deng", "Zeshui Xu" ], "first_author": "Xunjie Gou", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Truck", "Computer science", "Drone", "Mathematical optimization", "Dynamic programming", "Algorithm", "Engineering", "Mathematics", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5070781" }, { "id": "https://openalex.org/W4283270669", "doi": "10.2514/6.2022-4028", "title": "Delivery Route Planning for Unmanned Aerial System in Presence of Recharging Stations", "year": 2022, "publication_date": "2022-06-20", "type": "conference-paper", "language": "en", "authors": [ "Ruifan Liu", "Hyo‐Sang Shin", "Minguk Seo", "Antonios Tsourdos" ], "first_author": "Ruifan Liu", "venue": "AIAA AVIATION 2022 Forum", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2022-4028.vid Existing variants of vehicle routing problems (VRPs) are incapable of describing real-world drone delivery scenarios in terms of drone physical restrictions, mission constraints, and stochastic operating environments. To that end, this paper proposes a specific drone delivery model with recharging (DDP-R) characterized by directional edges and stochastic edge costs subject to wind conditions. To address it, the DDP-R is cast into a Markov Decision Process (MDP) over a graph, with the next node chosen according to a stochastic policy based on the evolving observation. An edge-enhanced attention model (AM-E) is then suggested to map the optimal policy via the deep reinforcement learning (DRL) approach. AM-E comprises a succession of edge-enhanced dot-product attention layers which is designed with the aim of capturing the heterogeneous node relationship for DDP-Rs by incorporating adjacent edge information. Simulation shows that edge enhancement facilitates the training process, achieving superior performance with less trainable parameters and simpler architecture in comparison with other deep learning models. Furthermore, a stochastic drone energy cost model in consideration of winds is incorporated into validation simulations, which provides a practical insight into drone delivery problems. In terms of both non-wind and windy cases, extensive simulations demonstrate that the proposed DRL method outperforms state-of-the-art heuristics for solving DDP-R, especially at large sizes.", "keywords": [ "Drone", "Heuristics", "Markov decision process", "Computer science", "Reinforcement learning", "Enhanced Data Rates for GSM Evolution", "Markov process", "Node (physics)", "Edge computing", "Wind power", "Routing (electronic design automation)", "Real-time computing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dspace.lib.cranfield.ac.uk/bitstreams/b1959ff9-3cb9-414e-96e4-9e98e7c8b696/download" }, { "id": "https://openalex.org/W7164349973", "doi": "10.1080/00207543.2026.2685180", "title": "Integrating drones into post-disaster logistics: a two-echelon location routing model with split deliveries and heterogeneous fleet", "year": 2026, "publication_date": "2026-06-11", "type": "article", "language": "en", "authors": [ "Teena Thomas", "Chandrasekharan Rajendran", "Sharan Srinivas" ], "first_author": "Teena Thomas", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "The increasing frequency and severity of disasters highlight the need for responsive humanitarian logistics systems. In post-disaster settings, damaged infrastructure can delay conventional vehicle access, while drones can support timely relief delivery to locations with limited ground accessibility. This study introduces a two-echelon location-routing problem with multiple drones and split deliveries (2E-LRP-MD-SD) for coordinated ground vehicle and drone operations in post-disaster relief distribution. The problem jointly considers distribution centre selection, heterogeneous ground-vehicle deployment, multiple drones per vehicle, synchronised truck-drone routing, and split demand fulfillment. A mixed-integer linear programming formulation is developed to minimise delivery completion time while capturing accessibility asymmetry, capacity restrictions, drone endurance, and temporal synchronisation. To solve larger instances, a three-phase matheuristic, Prescriptive Analytics with Clustering and Optimisation (PACO), is proposed and benchmarked against a Variable Neighbourhood Search (VNS) metaheuristic. A modified subgradient-based lower-bound procedure supports solution-quality assessment. Computational experiments show that 2E-LRP-MD-SD reduces delivery completion time by 15%–42% relative to the ground-vehicle-only system, and that PACO consistently outperforms VNS. The sensitivity analyses show that split deliveries improve efficiency, while drone speed, fleet size, payload–endurance trade-offs, and demand intensity affect delivery completion time. The results inform configuration of truck-drone relief systems under capacity, accessibility, and demand-surge constraints.", "keywords": [ "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Key (lock)", "Facility location problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2026.2685180" }, { "id": "https://openalex.org/W2375705934", "doi": null, "title": "GIS-based Unmanned Air Vehicle Route Planning", "year": 2003, "publication_date": "2003-02-05", "type": "article", "language": "en", "authors": [ "Su Dol Kang", "Jingnan Liu", "Yan Li" ], "first_author": "Su Dol Kang", "venue": null, "publisher": null, "abstract": "The mechanism of how flying geographical environment affects unmanned air vehicle (UAV)route planning has been investigated.A method of GIS-based UAV route planning is proposed,and its architecture and work flows have been studied.The results of use case show that the method can provide an solution to UAV route planning problem.", "keywords": [ "Route planning", "Computer science", "Architecture", "Work (physics)", "Motion planning", "Transport engineering", "Real-time computing", "Engineering", "Geography", "Artificial intelligence", "Robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://ch.whu.edu.cn/en/article/id/4802" }, { "id": "https://openalex.org/W4405350483", "doi": "10.2139/ssrn.5054711", "title": "Optimizing Vehicle-Mounted Multi-Uav Wildfire Patrol Routing Problem Based on Fusion Clustering and Route Planning Algorithms", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Wenjun Li", "Jiawen Hu", "Wenjun Li", "Jianxin Jia", "Domokos Esztergár‐Kiss" ], "first_author": "Wenjun Li", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Vehicle routing problem", "Cluster analysis", "Route planning", "Routing (electronic design automation)", "Computer science", "Routing algorithm", "Fusion", "Algorithm", "Real-time computing", "Transport engineering", "Artificial intelligence", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5054711" }, { "id": "https://openalex.org/W2791302085", "doi": "10.48550/arxiv.1803.02723", "title": "Heterogeneous Vehicles Routing for Water Canal Damage Assessment", "year": 2018, "publication_date": "2018-03-07", "type": "preprint", "language": "en", "authors": [ "Di Deng", "Tao Pang", "Prasanth Palli", "Fang Shu", "Kenji Shimada" ], "first_author": "Di Deng", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In Japan, inspection of irrigation water canals has been mostly conducted manually. However, the huge demand for more regular inspections as infrastructure ages, coupled with the limited time window available for inspection, has rendered manual inspection increasingly insufficient. With shortened inspection time and reduced labor cost, automated inspection using a combination of unmanned aerial vehicles (UAVs) and ground vehicles (cars) has emerged as an attractive alternative to manual inspection. In this paper, we propose a path planning framework that generates optimal plans for UAVs and cars to inspect water canals in a large agricultural area (tens of square kilometers). In addition to optimality, the paths need to satisfy several constraints, in order to guarantee UAV navigation safety and to abide by local traffic regulations. In the proposed framework, the canal and road networks are first modeled as two graphs, which are then partitioned into smaller subgraphs that can be covered by a given fleet of UAVs within one battery charge. The problem of finding optimal paths for both UAVs and cars on the graphs, subject to the constraints, is formulated as a mixed-integer quadratic program (MIQP). The proposed framework can also quickly generate new plans when a current plan is interrupted. The effectiveness of the proposed framework is validated by simulation results showing the successful generation of plans covering all given canal segments, and the ability to quickly revise the plan when conditions change.", "keywords": [ "Computer science", "Plan (archaeology)", "Routing (electronic design automation)", "Integer programming", "Operations research", "Path (computing)", "Transport engineering", "Engineering", "Algorithm", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1803.02723" }, { "id": "https://openalex.org/W4408350510", "doi": "10.1016/j.ejor.2025.02.032", "title": "Cyclic stochastic two-echelon inventory routing for an application in medical supply", "year": 2025, "publication_date": "2025-03-12", "type": "article", "language": "en", "authors": [ "Alexander Rave", "Pirmin Fontaine", "Heinrich C. Kuhn" ], "first_author": "Alexander Rave", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": "Drug availability in clinics is essential for patient services, whose demand for medication is uncertain. Thus, clinics must have a variety of drugs available, leading to high inventory holding costs. In Germany, it is common for a larger central clinic to take over the procurement of drugs and distribute them to smaller surrounding clinics, which results in a two-echelon network structure. The clinics, however, operate according to their inventory policy as they plan independently. Additionally, the inventory policies include instant replenishment orders to avoid shortages, which can be executed by various vehicles, such as vans or aerial drones, because the orders only involve a few medications. We present a two-stage stochastic program for a multi-product two-echelon inventory routing problem with stochastic demands. We decide on the cost-optimal cyclic delivery patterns and reorder points for the clinics with instant replenishment orders as recourse decision. Further, we introduce an adaptive large neighborhood search with problem-specific operators that modify the routing, delivery periods, and reorder points. We present a case study at a large German clinic that supplies multiple surrounding clinics and plans to integrate drone instead of van deliveries for emergency resupply. Our integrated approach leads to cost savings of 57% for the surrounding clinics and 18% for the central clinic. Using drone delivery compared to van delivery, the average stock of medication at surrounding clinics can be reduced, resulting in a total cost decrease of 29% while maintaining medication availability. • Deciding on the cost-optimal delivery days, routing, and reorder points for clinic supply. • Introducing a two-stage stochastic program that includes emergency resupply as a recourse decision. • Developing an ALNS that executes an algorithm for determining reorder points in each iteration. • Presenting a novel case study that leads to multiple managerial insights.", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Supply chain", "Operations research", "Business", "Operations management", "Mathematical optimization", "Economics", "Mathematics", "Computer network", "Marketing" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2025.02.032" }, { "id": "https://openalex.org/W7160256900", "doi": "10.1016/j.rineng.2026.110873", "title": "A CNN-Driven Mission Planning Framework for Vessel-Drone Collaborative Logistics in Multi-Island Port Environments", "year": 2026, "publication_date": "2026-05-04", "type": "article", "language": "en", "authors": [ "Xinghan Sun", "Xiaoyu Lei", "D H Zhang" ], "first_author": "Xinghan Sun", "venue": "Results in Engineering", "publisher": "Elsevier BV", "abstract": "The fragmented terrain and limited infrastructure in multi-island areas present significant engineering challenges for port logistics, including high operational costs, prolonged transport cycles, and excessive port congestion. To address these issues, this paper proposes a vessel-drone collaborative logistics system. Recognizing that existing vehicle routing models fail to account for island obstacles, we develop a mission planning framework grounded in Mixed-Integer Linear Programming (MILP) and a Convolutional Neural Network (CNN)-driven range optimizer. The framework involves: (1) modeling island terrain and port distribution with optional vessel routes via path planning; (2) formulating a MILP model for transport planning; and (3) applying a CNN-driven post-processing framework that uses constraint relaxation and en-route strategies to resolve conflicts between terrain obstacles and drone range limits. Numerical experiments validate the method’s effectiveness and provide planning examples for real-world scenarios. Compared with traditional vessel-only transport, the proposed approach reduces total system cost by 6.44%–7.98% and mission completion time by 16.45%–17.87% across varying island densities, with time savings of 17.65%–24.16% under different port berthing durations. These results underscore the engineering value of embedding ML-driven optimization into real-world port logistics systems, offering a scalable pathway toward intelligent and sustainable maritime operations in multi-island areas.", "keywords": [ "Port (circuit theory)", "Terrain", "Drone", "Range (aeronautics)", "Plan (archaeology)", "Routing (electronic design automation)", "Time horizon", "Linear programming", "Scalability" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.rineng.2026.110873" }, { "id": "https://openalex.org/W4293216187", "doi": "10.17760/d20429171", "title": "A geometric optimization approach to the location and routing problems in modern economy", "year": 2021, "publication_date": "2021-01-01", "type": "dissertation", "language": "en", "authors": [ "Dinghao Ma" ], "first_author": "Dinghao Ma", "venue": null, "publisher": null, "abstract": "With the development of technology, human life has seen major changes throughout the twentieth century and the last two decades in the twenty-first century. New modern ideas and technologies have been continuously adopted into our everyday life. In recent years, an increasing number of people and companies are using unmanned aerial vehicles (UAVs), electric vehicles (EVs), and sharing economy services. The literature of geometric optimization research has been focusing more on the traditional logistics and transportation problems and less on the combination of them with modern technologies. This dissertation aims to study, analyze, and solve different location and routing problems in the context of modern technologies, such as UAVs, EVs, and sharing economy platforms using a geometric optimization approach, which combines optimization with computational geometry. This dissertation studies three different projects in this direction. In the first project, we consider finding the optimal location of several charging stations for EVs in a convex polygonal region to minimize the maximum ratio between the distance traveled by EVs and the distance traveled by the internal combustion engine vehicles (ICEVs). We propose an algorithm to find the optimal solution for the problem of finding the optimal location of one charging station under this setting and then we provide efficient algorithms for finding good sub-optimal solutions when we have several charging stations. In the second project, we study a delivery routing problem where we combine the operation of a delivery truck with UAVs (a.k.a. drones) to serve all demand points in a coordinated system. In this problem, the drone can pick up a package from the truck and deliver it to a demand point and then travel back to the truck for charging its battery or initiating another delivery, while the truck is delivering packages at the same time. In the third project, we study a delivery routing problem where drones are used in a sharing economy platform to deliver packages. A delivery service carries a large batch of packages to a neighborhood using a truck and then crowdsourced drones deliver the packages to their final destinations. The problem is analyzed under different settings and several optimization models and algorithms have been developed to solve it.--Author's abstract", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Context (archaeology)", "Truck", "Drone", "Computer science", "Operations research", "Optimization problem", "Engineering", "Transport engineering", "Geography", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://repository.library.northeastern.edu/files/neu:4f16j633h/fulltext.pdf" }, { "id": "https://openalex.org/W4417066762", "doi": "10.2139/ssrn.5871916", "title": "A Bi-Level Heuristic Approach for the Truck-Drone Last-Mile Delivery Problem with Dynamic PID Control", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": null, "authors": [ "Heictor Costa", "Fernando José Von Zuben" ], "first_author": "Heictor Costa", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Control theory (sociology)", "Heuristic", "Control (management)", "Control system", "PID controller", "Optimal control" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5871916" }, { "id": "https://openalex.org/W7213437499", "doi": "10.3390/math14183362", "title": "Two-Stage Stochastic Location–Routing for Vehicle–Drone Blood Delivery with Blood Compatibility-Based Substitution", "year": 2026, "publication_date": "2026-09-16", "type": "article", "language": "en", "authors": [ "Chengyang Huo", "Yueyue Huang", "Yazhou Guo" ], "first_author": "Chengyang Huo", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Timely blood delivery, particularly the delivery of red blood cells (RBCs), is critical to emergency medical services (EMS), but supplies of specific blood types are often insufficient. To mitigate these shortages, we consider compatible substitution under the ABO-Rh transfusion rules, which allow selected donor blood types to meet compatible recipient demand when identical-type units are insufficient. We formulate a two-stage stochastic location–routing model for joint vehicle–drone blood delivery that minimizes the expected urgency-weighted average arrival time. In the first stage, the model selects cluster centers that serve as drone launch-and-recovery sites. In the second stage, it jointly determines vehicle routes, drone deliveries, and blood allocations after the uncertainty is revealed. We develop a problem-specific adaptive large neighborhood search (ALNS) to address the computational complexity arising from the interaction among location, routing, and compatible blood-allocation decisions. The numerical experiments yield three main findings: (i) ALNS achieves an average best-run deviation of 0.404% and a ten-run average deviation of 4.516% relative to exact benchmarks for small instances and best-known solutions for larger instances, while solving instances with up to 50 hospitals in under 19 s per run; (ii) full compatibility maintains 100% full-demand feasibility across all tested A+ shortage levels; and (iii) the exact value of the stochastic solution (VSS) is non-negative for the small instances, while heuristic VSS estimates indicate that stochastic planning becomes more valuable as demand uncertainty increases. These findings provide decision support for EMS blood delivery under supply shortages and operational uncertainty.", "keywords": [ "Economic shortage", "Heuristic", "Standard deviation", "Expected value", "Blood transfusion", "Stochastic programming", "Substitution (logic)", "Blood units" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/math14183362" }, { "id": "https://openalex.org/W3198599734", "doi": "10.13016/m2nfgv-az4q", "title": "Security Issues in Unmanned Aerial Vehicle Routing Protocols.", "year": 2020, "publication_date": "2020-01-20", "type": "article", "language": "en", "authors": [ "Adekeye Adegoke" ], "first_author": "Adekeye Adegoke", "venue": "Maryland Shared Open Access Repository (USMAI Consortium)", "publisher": null, "abstract": "With the rapidly increasing demand for portable and flexible communications which has led to an evolution in networking between unmanned aerial vehicles, the potential attack surface for unmanned aerial vehicles has also increased drastically. In Unmanned Aerial Vehicle Ad Hoc networks, UAVs collaborate in order to relay data to each other or share data with each other or the Ground control station, this provides an avenue for malicious attackers to compromise the integrity of these networks. There have already been numerous security analyses of unmanned aerial vehicle routing protocols; yet, these analyses have not been combined with each other sufficiently. Since There is a lack of a defined central authority, securitizing the routing process in Unmanned aerial vehicle UAV communication becomes a challenging task. In this thesis, we carry out a comprehensive survey on the security challenges, potential threats, and countermeasures for unmanned aerial vehicle routing protocols.", "keywords": [ "Computer science", "Computer security" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/11603/22938" }, { "id": "https://openalex.org/W4205871489", "doi": "10.1109/acomp53746.2021.00011", "title": "Multi-Agent Collaborative Framework for Automated Agriculture", "year": 2021, "publication_date": "2021-11-01", "type": "conference-paper", "language": "en", "authors": [ "Ankit Kumar", "Shishir N.Y. Kolathaya", "Debasish Ghose" ], "first_author": "Ankit Kumar", "venue": null, "publisher": null, "abstract": "The use of internet-connected devices, especially small multi-rotor Unmanned Aerial Vehicles (UAVs), in scientific data gathering and applications is quite widespread. But due to limited intervention capability, the UAVs alone fail to automate agricultural tasks completely. Thereby, we propose a centralized framework capable of handling a heterogeneous mixture of UAVs and UGVs to cater to the needs of automating agriculture efficiently. The framework’s core is a novel heuristic decision module that creates new tasks by visually analyzing the farm and solves a vehicle routing problem to allocate it to agents optimally. It is also equipped with supporting modules to monitor their operation and, in case of failures, help them recover autonomously based on the task and agent assessment. The framework is used in three significant agricultural applications, namely yield prediction and drought stress detection in a simulated environment using ROS and Gazebo, and 3D mapping of a real farm. These applications demonstrate the use of the multi-agent collaborative framework in identifying agricultural tasks on a farm and executing them.", "keywords": [ "Computer science", "Agriculture", "Collaborative software", "Multi-agent system", "Knowledge management", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/acomp53746.2021.00011" }, { "id": "https://openalex.org/W2004778904", "doi": "10.1117/12.357165", "title": "Visibility-constrained routing of unmanned aerial vehicles", "year": 1999, "publication_date": "1999-04-23", "type": "conference-paper", "language": "en", "authors": [ "Keith R. Buck", "Richard R. Gassner", "Aubrey B. Poore", "Xin Yan" ], "first_author": "Keith R. Buck", "venue": "Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE", "publisher": "SPIE", "abstract": "Standard vehicle routing problems have been studied for decades in fields such as transportation, manufacturing, and commodity distribution. In this work, we proposed a variation of these problems that arise in routing Unmanned Aerial Vehicles (UAV's) in the presence of terrain obscuration. Specifically, the UAV must visit a location from which the object on the ground in mountainous regions can be viewed without actually flying over the object. Numerical results are presented for near optimal and real time algorithms which have been developed using Lagrangian relaxation techniques. Directions for future work that include priorities, time windows, and routing multiple UAV's with periodic and dynamic changes in the object locations are discussed.", "keywords": [ "Computer science", "Terrain", "Routing (electronic design automation)", "Visibility", "Vehicle routing problem", "Real-time computing", "Object (grammar)", "Work (physics)", "Lagrangian relaxation", "Simulation", "Artificial intelligence", "Mathematical optimization" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.357165" }, { "id": "https://openalex.org/W7202069755", "doi": "10.3390/a19080666", "title": "Grid-Aware Bi-Level Optimization for Truck–Drone Routing: Integrating Grid Feasibility and Shadow Pricing", "year": 2026, "publication_date": "2026-08-10", "type": "article", "language": "en", "authors": [ "Heictor Alves de Oliveira Costa", "Fernando José Von Zuben" ], "first_author": "Heictor Alves de Oliveira Costa", "venue": "Algorithms", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Electric trucks operating as mobile depots for delivery drones are promising for last-mile logistics, yet fleet electrification makes depot charging a critical issue governed by distribution-grid limits. Existing truck–drone routing formulations omit the electrical network, treating energy as exogenous, while grid-aware routing models overlook the combinatorial structure of mobile-depot drone synchronization. This paper introduces an energy-aware bi-level framework for the truck–drone routing problem that closes this gap. A distribution-grid leader solves slot-wise alternating current (AC) optimal power flow (OPF) under time-varying base loads and line deratings, returning a grid-feasible energy headroom and shadow prices. A logistics follower then co-optimizes truck routes, drone sorties, and ramp-constrained charging against this effective price, within a multi-objective cost structure. A damped fixed-point iteration couples the two levels, communicating grid scarcity through a single price signal without the logistics layer solving power-flow equations. On a Tokyo-inspired 100-customer instance with a stressed IEEE 33-bus feeder, the framework confines charging to slots with genuine headroom, reaching at most 81% loading and returning the fleet fully charged, whereas a grid-blind baseline reaches 109% loading. This comparison validates shadow pricing as an effective coordination mechanism.", "keywords": [ "Drone", "Grid", "Truck", "Electrification", "Routing (electronic design automation)", "Electricity pricing", "Electricity", "Power flow", "Demand response" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/a19080666" }, { "id": "https://openalex.org/W4230270421", "doi": "10.1109/apeie.2016.7806891", "title": "Modeling of bio-inspired algorithms AntHocNet and BeeAdHoc for Flying Ad Hoc Networks (FANETS)", "year": 2016, "publication_date": "2016-10-01", "type": "conference-abstract", "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", "Swarm behaviour", "Optimized Link State Routing Protocol", "Swarm intelligence", "Routing protocol" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/apeie.2016.7806891" }, { "id": "https://openalex.org/W7048856700", "doi": null, "title": "Méthodes d'approximation au problème de routage de véhicule pour une gestion de flotte de drones", "year": 2019, "publication_date": "2019-12-04", "type": "dissertation", "language": "en", "authors": [ "Setyawan Ajie Sukarno" ], "first_author": "Setyawan Ajie Sukarno", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "Nowadays, drone plays a big role in civilian purposes, and it will getting bigger and more important in the future. Because of it’s flexibility and versatility, the application of drone is more extensive. Incertain fields of implementation, applying a team of drones will improve the effectiveness and efficiency of the application, such as in search and rescue, military purpose, agriculture and surveillance. Recently, there is a challenging issue to manage a team of drones in order to achieve the given mission. This issue opens many research opportunities, and our project is made to answer this challenge, to develop a platform for managing a fleet of drones. Among several approaches, vehicle routing problem (VRP) is one of a considered study to answer this challenge, in order to allocate the tasks and find the best path for each drone, with several constraints to be considered. There are many methods to solve VRP, and could be categorized into two groups i.e. exact and approximation method. But since VRP is classified as an NP-hard optimization problem, an approximation method is considered to be implemented in this project. Genetic Algorithm (GA), an approximation method which designed by an inspiration to the evolutionary ideas of genetic and natural selection, is applied in this project, since it is one of most used algorithm to solve VRP, among several approximation method. We observed that GA is suitable to be implemented in this project, but when the number of to-be-visited-points is hugely augmented, the number of iterations to get a satisfactory result would be extremely increased. This issue led us to hybridize GA with Clarke and Wright’s saving algorithm (SA) in order to generate the initial population, so that it is no longer randomly generated as usually done. Eventually, this proposed hybrid method can improve the performance of the algorithm very satisfactorily, and reduce the number of iteration more than 90%. Furthermore, a dynamic scenario in VRP is taken into account in this work i.e. an emerge of one or several new points that appear once the mission is already launched, and require a visit by a single drone. To deal with this dynamic scenarios, a Reverse Open Vehicle Routing Problem (ROVRP) is considered to be implemented. We decide to choose a heuristic method in solving the ROVRP, as it is classified as an NP-hard optimization problem, and we prefer to apply Clarke and Wright’s Saving Algorithm (SA) in this project, due to their speed and simplicity. In our point of view, speed is one most considerable thing in choosing algorithm to solve dynamic scenario in VRP. Our proposed method is devided into two phases i.e. clustering and routing. And the experimental results show that our proposed method can give more than 95% accuracy. In order to simulate and investigate the proposed methods, a Graphical User Interface (GUI) is developed. There are some available framework to develop this tool, and Netlogo is considered as the chosen framework.", "keywords": [ "Drone", "Genetic algorithm", "Vehicle routing problem", "Order (exchange)", "Path (computing)", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://theses.hal.science/tel-03156305v1/document" }, { "id": "https://openalex.org/W2909381294", "doi": "10.2514/6.2019-1796", "title": "An Extended Savings Algorithm for UAS-based Delivery Systems", "year": 2019, "publication_date": "2019-01-06", "type": "conference-paper", "language": "en", "authors": [ "Younghoon Choi", "Youngjun Choi", "Simon I. Briceño", "Dimitri N. Mavris" ], "first_author": "Younghoon Choi", "venue": "AIAA Scitech 2019 Forum", "publisher": null, "abstract": "This paper presents an extended savings algorithm for a package delivery system using unmanned aircraft systems (UAS). The savings algorithm as a heuristic method solves a vehicle routing problem (VRP) that is commonly formulated by an operational plan for each vehicle. In general, package delivery systems need to establish an operational plan based on demand and preferred time to be visited for each customer. In UAS-based delivery systems, however, capacity and traveling time constraints must be additionally considered to create their operational schedules because of limited payload capacity and short endurance of unmanned aerial vehicles (UAVs). Because of these limitations, UAVs should be reused during operation hours to reduce acquisition costs. Thus, a recharging strategy should be included in the operational planning process. However, conventional savings algorithms cannot capture those properties at once because they have mainly focused on delivery systems with conventional vehicles such as trucks and passenger/cargo aircraft that have different vehicle features and operational characteristics, such as the endurance/speed of a vehicle and recharging strategy. To overcome the limitations of the conventional approaches, this paper proposes the extended savings algorithm, which can concurrently reflect the characteristics of both delivery systems and UAVs. To demonstrate the proposed extended savings algorithm this paper preforms numerical simulations with two representative scenarios in Annapolis, MD and Macon, GA.", "keywords": [ "Computer science", "Algorithm", "Algorithm design" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://repository.gatech.edu/bitstreams/b6f65600-2039-46c2-9467-ea13ecc00dec/download" }, { "id": "https://openalex.org/W2325397583", "doi": "10.2514/6.2012-2455", "title": "Dynamic Allocation of Unmanned Aerial Vehicles with Communication Constraints", "year": 2012, "publication_date": "2012-06-19", "type": "conference-paper", "language": "en", "authors": [ "Chelsea M. Sabo", "Kelly De Oliveira Cohen" ], "first_author": "Chelsea M. Sabo", "venue": "Infotech@Aerospace 2012", "publisher": null, "abstract": "A subset of Vehicle Routing Problems (VRPs) address the problem in which a vehicle is required to service targets by picking them up at an origin and deliver them to their destination. With respect to surveillance functions, this becomes a realistic problem as UAVs are restricted by operating range, data rate, Anti-Jam margins, and cost. Therefore, UAVs must be allocated to pickup targets and then deliver them to a communication range to be able to transmit information back to a command and control HQ. Research on similar problems illustrates the importance of studying dynamic scenarios as most of these problems are dynamic in nature (similar to this one). To maximize the information return to the communication range, the objective of allocating the UAVs is such that the total service time (pickup and delivery) of all the targets is minimized. A heuristic solution has been applied to this problem in previous work showing that good result can be obtained for the static problem. The focus of this research is to extend the work to operate in a dynamic scenario where requests arrive according to a stochastic process. It is shown that a solution to the static problem is also a solution to the dynamic problem. To address how the solution would perform in a dynamic environment, the scalability of the static solution is analyzed. Furthermore, the performance is analyzed under varying degrees of dynamism. Results show that the short execution times of the heuristic solution allow it to be used in a dynamic setup and show good performance under varying arrival rates. Furthermore, the boundaries for the regions of light and heavy load cases were identified and presented.", "keywords": [ "Computer science", "Remotely operated underwater vehicle", "Vehicle dynamics", "Real-time computing", "Aeronautics", "Aerospace engineering", "Mobile robot", "Engineering", "Robot", "Artificial intelligence" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2012-2455" }, { "id": "https://openalex.org/W7167690074", "doi": "10.48550/arxiv.2607.06066", "title": "Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency", "year": 2026, "publication_date": "2026-07-07", "type": "preprint", "language": null, "authors": [ "Manish Kolachalam", "Rani Malhotra" ], "first_author": "Manish Kolachalam", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility. In this study, we address a dynamic, online variant combining elements of the VRP and the Orienteering Problem (OP), in which a fleet of vehicles must maximise cumulative reward collected within a fixed time horizon while continuously replanning as new tasks arrive. We propose and evaluate a reward-density heuristic for dynamic multi-vehicle assignment, referred to as the Efficiency heuristic. We evaluate this formulation across two application domains: autonomous drone task allocation and urban taxi dispatch, across multiple fleet sizes and task scales. The proposed method is compared with four classical construction heuristics and three metaheuristic algorithms (Adaptive Large Neighbourhood Search, Genetic Algorithm, and Simulated Annealing), all evaluated under identical conditions. Across all tested configurations, the Efficiency heuristic matches the solution quality of the best metaheuristic algorithms while requiring two to three orders of magnitude less planning time, establishing Pareto dominance over all competing methods on the reward-versus-compute frontier. These findings suggest a practical design principle for real-time allocation and dispatch systems: in dynamic, time-constrained routing environments, carefully designed greedy heuristics can match the output of sophisticated search procedures at a fraction of the computational cost, making them preferable for online deployment.", "keywords": [ "Heuristics", "Metaheuristic", "Orienteering", "Pareto principle", "Vehicle routing problem", "Heuristic", "Greedy algorithm", "Time horizon", "Multi-objective optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2607.06066" }, { "id": "https://openalex.org/W7113295553", "doi": null, "title": "Pathways to Efficient and Equitable Solutions for Large-Scale Routing Problems", "year": 2025, "publication_date": "2025-06-11", "type": "dissertation", "language": null, "authors": [ "Sobhanan, Abhay" ], "first_author": "Sobhanan, Abhay", "venue": "Digital Commons - University of South Florida (University of South Florida)", "publisher": "University of South Florida", "abstract": "This dissertation addresses large-scale optimization problems in transportation emerging from hierarchical decision-making, equitable workload allocation, and innovative routing logistics. It presents three sets of contributions, each detailed in a separate chapter, and offers computational tools and insights to advance both the theory and practice of transportation systems. The first work introduces a deep learning-enhanced genetic algorithm framework for solving the Hierarchical Vehicle Routing Problems (HVRPs). Traditional optimization approaches to such problems require extensive evaluation of multiple lower-level routing solutions and are computationally intensive. Our innovative method integrates a genetic algorithm with a pretrained graph neural network, which is trained on solutions from the HGS CVRP solver, to effectively predict vehicle routing costs resulting from upper level decisions without explicitly solving each scenario. Extensive computational experiments validate the effectiveness of this approach, demonstrating significant computational savings while maintaining high-quality solutions across various datasets. The second work proposes a practical equity-oriented optimization framework tailored specifically for crowdshipping platforms within the last-mile delivery systems. Recognizing that equitable workload distribution and fair compensation are crucial for maintaining a sustainable pool of crowdworkers, our framework utilizes bi-objective optimization to balance equity against operational costs. To effectively solve this problem, we propose Hybrid NSGA-II. Numerous experiments are conducted to quantify the trade-offs between cost and equity, revealing that modest sacrifices in efficiency can lead to substantial equity improvements. Our findings provide actionable insights for gig-economy platforms, highlighting optimal strategies to maximize fairness with minimal cost increases, and guiding workforce management by analyzing the effects of varying crowdshipper pool sizes. The third problem extends the classical Rural Postman Problem (RPP) to a mixed-fleet scenario involving multiple trucks and drones, with the objective of minimizing makespan. Trucks act as mobile depots, deploying drones to service required arcs while also serving the arcs they visit themselves. To navigate the complexity inherent in this problem, we introduce a Hybrid Genetic Algorithm with a chromosome structure that encodes required arc sequences and vehicle assignments, accompanied by multiple dedicated local searches. Comprehensive evaluations on large-scale instances demonstrate the scalability, effectiveness, and real-time applicability of our method, underscoring significant improvements achievable through truck-drone integration in arc-routing logistics.", "keywords": [ "Vehicle routing problem", "Workload", "Equity (law)", "Genetic algorithm", "Routing (electronic design automation)", "Multi-objective optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://digitalcommons.usf.edu/etd/11009" }, { "id": "https://openalex.org/W7104011076", "doi": "10.5281/zenodo.17530844", "title": "AI-Powered Digital Twin System for Post-Earthquakes Search and Rescue Operations Using Satellite and Drone Imagery", "year": 2025, "publication_date": "2025-11-05", "type": "conference-paper", "language": "en", "authors": [ "Berkay Toker", "Diaa Gadelmavla", "Mirkan Kalkan", "A. M. M. A. Allam" ], "first_author": "Berkay Toker", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This paper proposes an artificial intelligence (AI) powered digital twin system design to assist the search and rescue process by rapidly analysing high resolution post-disaster satellite and drone images. The system is capable of detecting damaged or collapsed buildings, as well as closed roads. This is achieved through the utilisation of deep learning algorithms, which are trained using high-resolution annotated drone or satellite images. These algorithms constitute a layer that is integrated into a digital twin model. This will enable the digital twin model to optimise the deployment of search and rescue process to damaged areas and determine optimal vehicle routes in real time. A dataset has been developed by integrating open-source annotated datasets, and the model selection and development phase has commenced. The subsequent phases of this work entail the development and training of an AI model in conjunction with the creation and integration of a digital twin, which is to be based on geospatial data services in conjunction with the AI model. Furthermore, a prototype will be tested in simulated earthquake scenarios. The present work delineates the overarching architecture of the system, its objectives, methodologies, and prospective advantages.", "keywords": [ "Drone", "Software deployment", "Search and rescue", "Process (computing)", "Satellite", "Geospatial analysis", "Task (project management)", "Systems architecture" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.17530844" }, { "id": "https://openalex.org/W7104034984", "doi": "10.5281/zenodo.17530845", "title": "AI-Powered Digital Twin System for Post-Earthquakes Search and Rescue Operations Using Satellite and Drone Imagery", "year": 2025, "publication_date": "2025-11-05", "type": "conference-paper", "language": "en", "authors": [ "Berkay Toker", "Diaa Gadelmavla", "Mirkan Kalkan", "A. M. M. A. Allam" ], "first_author": "Berkay Toker", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This paper proposes an artificial intelligence (AI) powered digital twin system design to assist the search and rescue process by rapidly analysing high resolution post-disaster satellite and drone images. The system is capable of detecting damaged or collapsed buildings, as well as closed roads. This is achieved through the utilisation of deep learning algorithms, which are trained using high-resolution annotated drone or satellite images. These algorithms constitute a layer that is integrated into a digital twin model. This will enable the digital twin model to optimise the deployment of search and rescue process to damaged areas and determine optimal vehicle routes in real time. A dataset has been developed by integrating open-source annotated datasets, and the model selection and development phase has commenced. The subsequent phases of this work entail the development and training of an AI model in conjunction with the creation and integration of a digital twin, which is to be based on geospatial data services in conjunction with the AI model. Furthermore, a prototype will be tested in simulated earthquake scenarios. The present work delineates the overarching architecture of the system, its objectives, methodologies, and prospective advantages.", "keywords": [ "Drone", "Software deployment", "Search and rescue", "Process (computing)", "Satellite", "Geospatial analysis", "Task (project management)", "Systems architecture" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.17530845" }, { "id": "https://openalex.org/W4310040242", "doi": "10.3390/app122311995", "title": "UGAVs-MDVR: A Cluster-Based Multicast Routing Protocol for Unmanned Ground and Aerial Vehicles Communication in VANET", "year": 2022, "publication_date": "2022-11-24", "type": "article", "language": "en", "authors": [ "Waqar Farooq", "Saif ul Islam", "Muazzam Ali Khan", "Saad Ur Rehman", "Usman Ali Gulzari", "Jalil Boudjadar" ], "first_author": "Waqar Farooq", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols.", "keywords": [ "Multicast", "Computer network", "Computer science", "Vehicular ad hoc network", "Network packet", "Dissemination", "Routing protocol", "Protocol (science)", "Overhead (engineering)", "Throughput", "Wireless ad hoc network", "Scheme (mathematics)" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/12/23/11995/pdf?version=1669346004" }, { "id": "https://openalex.org/W3037947901", "doi": "10.1109/icusai47366.2019.9124729", "title": "A Coverage Path Planning Method Based on Multiple Solar Powered Unmanned Aerial Vehicles", "year": 2019, "publication_date": "2019-11-01", "type": "conference-paper", "language": "en", "authors": [ "Nannan Du", "Zichao Zhang", "Jian Chen", "Xuzan Liu", "Kai Zhang", "Ben Ma" ], "first_author": "Nannan Du", "venue": null, "publisher": null, "abstract": "This paper proposes a solution to solve the coverage path planning (CPP) problem of multi-solar unmanned aerial vehicles (UAVs), and the research content of this paper is as follows. First, the energy production model and consumption model of the solar UAV are established, and according to this model, the flying speed of the solar UAV are determined. Besides, the energy flow efficiency is defined to evaluate the energy utilization efficiency of this operation. After that, transform the CPP problem into vehicle routing problem (VRP), and a mixed integer linear programming (MILP) model is established to determine the flight path of each UAV with the objective of minimizing operation time. In this method, the solar UAV is used for CPP, which solves the problem of short flight time of traditional UAV. Operation time is used as the objective function to solve the CPP, so that the task can be completed as soon as possible. And the calculation of energy flow efficiency can be used to evaluate the operation efficiency of solar UAV.", "keywords": [ "Computer science", "Energy consumption", "Integer programming", "Linear programming", "Path (computing)", "Motion planning", "Efficient energy use", "Solar energy", "Energy (signal processing)", "Real-time computing", "Mathematical optimization", "Simulation" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icusai47366.2019.9124729" }, { "id": "https://openalex.org/W7201915590", "doi": null, "title": "Multi-objective vehicle routing with unmanned aerial vehicles using evolutionary algorithms", "year": 2026, "publication_date": "2026-07-15", "type": "article", "language": "el", "authors": [ "Ασημίνα Ρόδη" ], "first_author": "Ασημίνα Ρόδη", "venue": "Νημερτής", "publisher": null, "abstract": "Η παρούσα διπλωματική εργασία εξετάζει το πολυκριτηριακό Πρόβλημα Δρομολόγησης Οχημάτων με Αυτόνομα Εναέρια Οχήματα (drones) – Vehicle Routing Problem with Drones (VRPD) σε περιβάλλον παράδοσης τελευταίου μιλίου (last-mile delivery). Το σύστημα περιλαμβάνει ομογενή στόλο φορτηγών, καθένα από τα οποία συνεργάζεται με ένα αποκλειστικά αντιστοιχισμένο drone. Λαμβάνονται υπόψη περιορισμοί χωρητικότητας, χρονικός συγχρονισμός φορτηγού–drone, δυναμική αυτονομία πτήσης, ελαστικά χρονικά παράθυρα και χρονικά εξαρτώμενη ταχύτητα των φορτηγών.Το πρόβλημα διατυπώνεται με τρεις αντικειμενικές συναρτήσεις ελαχιστοποίησης: τη συνολική ενεργειακή κατανάλωση των φορτηγών, το συνολικό κόστος διανομής και τον χρόνο ολοκλήρωσης. Για την επίλυσή του αναπτύχθηκαν δύο αλγοριθμικές προσεγγίσεις, οι οποίες συνδυάζουν τη μεταευρετική πολυκριτηριακής βελτιστοποίησης Non-dominated Sorting Genetic Algorithm II (NSGA-II), με δυο ευρέως διαδεδομένες μεταευρετικές τοπικής αναζήτησης, την Adaptive Large Neighborhood Search (ALNS) και την VariableNeighborhood Search (VNS), αντίστοιχα.Η πειραματική αξιολόγηση πραγματοποιήθηκε σε 160 στιγμιότυπα διαφορετικού μεγέθους και χωρικής κατανομής, τόσο υπό κοινό αριθμό γενεών όσο και υπό κοινό χρονικό όριο. Επιπλέον, ο NSGA-II–ALNS εξετάστηκε σε σύνολα 75 και 100 πελατών, στα οποία ο NSGA-II–VNS αδυνατούσε να ανταπεξέλθει εντός λογικών χρονικών ορίων. Η αξιολόγηση βασίστηκε στους δείκτες Hypervolume και Spread, στο μέγεθος του πρώτου μη κυριαρχούμενου μετώπου στο τελικό σύνολο λύσεων, στις τιμές των αντικειμενικών συναρτήσεων και στον χρόνο εκτέλεσης.Τα αποτελέσματα έδειξαν ότι ο NSGA-II–VNS παρουσιάζει πλεονεκτήματα σε ορισμένα προβλήματα μεσαίας κλίμακας, αλλά απαιτεί σημαντικά μεγαλύτερο υπολογιστικό χρόνο.Αντίθετα, ο NSGA-II–ALNS ολοκληρώνει τις εκτελέσεις ταχύτερα, παράγει συνήθως μεγαλύτερα μη κυριαρχούμενα μέτωπα και εμφανίζει ευνοϊκότερη συνολική ισορροπία μεταξύ ποιότητας λύσεων και υπολογιστικού κόστους. Ως εκ τούτου, για το εξεταζόμενο πειραματικό πλαίσιο προκρίνεται ο NSGA-II–ALNS ως η καταλληλότερη συνολική προσέγγιση.", "keywords": [ "Vehicle routing problem", "Drone", "Genetic algorithm", "Sorting", "Evolutionary algorithm", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/10889/32586" }, { "id": "https://openalex.org/W4416286048", "doi": "10.2139/ssrn.5700524", "title": "Integrated Planning of Drone-Based Disaster Relief: Facility Location, Inventory Prepositioning, and Fleet Operations under Uncertainty", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": null, "authors": [ "Yuyang Ma", "Karmel S. Shehadeh", "Man Yiu Tsang" ], "first_author": "Yuyang Ma", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Dependency (UML)", "Sizing", "USable", "Set (abstract data type)", "Plan (archaeology)", "Facility location problem", "Emergency management" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5700524" }, { "id": "https://openalex.org/W4416145302", "doi": "10.2139/ssrn.5737889", "title": "Optimization of Last-Mile Logistics Delivery Routes for Ground-Vehicle and Drone Parallel Distribution from Pre-Warehouses Considering Customer Priorities", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": null, "authors": [ "Zuning Zhang", "Hui Wang", "Lingxuan Liu", "Zhongjin Wang", "Xiaohan Liu", "Jie Tian" ], "first_author": "Zuning Zhang", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Distribution (mathematics)", "Key (lock)", "Distribution center" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5737889" }, { "id": "https://openalex.org/W7203757480", "doi": "10.1049/itr2.70302", "title": "Dynamic Multimodal Routing With Drones Under Communication Uncertainty in Emergency Logistics: A Hybrid Reinforcement Learning–Heuristic Approach", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Yimeng Zhang", "Shuyang Zhu", "Yixuan Li", "Xianbo Tan", "Zhiyuan Liu", "Hua Tang", "Mi Gan", "Xiaobo Liu", "Ruixue Ai" ], "first_author": "Yimeng Zhang", "venue": "IET Intelligent Transport Systems", "publisher": "Institution of Engineering and Technology", "abstract": "ABSTRACT The coordinative use of ground vehicles (e.g., trains and trucks) and drones has emerged as a promising approach to enhance the efficiency and flexibility of emergency logistics. However, communication interference can severely undermine such coordination. To address this challenge, this study investigates a dynamic multimodal routing problem with drones under communication uncertainty in emergency logistics. The duration of the communication interference is considered an uncertain parameter. A mixed‐integer programming model is formulated with a bi‐level objective. The upper‐level objective is to ensure order fulfillment rate, and the lower‐level objective is to minimise cost. To address the computational challenges of this complex problem, a hybrid approach is developed, which integrates reinforcement learning (RL) and adaptive large neighbourhood search (ALNS) within an event‐triggered rolling‐horizon framework. In this approach, RL continuously learns from the real‐time data and guides the ALNS procedure to generate optimal routing decisions dynamically. The proposed approach is evaluated against a benchmark method, demonstrating superior effectiveness and adaptability. Furthermore, a series of experiments are conducted to analyse the effects of numbers of training iteration and the influence of different communication interference durations. The results confirm that the proposed approach improves the efficiency and reliability of emergency logistics under communication uncertainty.", "keywords": [ "Reinforcement learning", "Drone", "Flexibility (engineering)", "Benchmark (surveying)", "Routing (electronic design automation)", "Reliability (semiconductor)", "Duration (music)", "Interference (communication)", "Dynamic programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1049/itr2.70302" }, { "id": "https://openalex.org/W4293850932", "doi": "10.48550/arxiv.1805.04417", "title": "Cooperative Planning for Fuel-constrained Aerial Vehicles and\\n Ground-based Refueling Vehicles for Large-Scale Coverage", "year": 2018, "publication_date": "2018-05-11", "type": "preprint", "language": null, "authors": [ "Parikshit Maini", "Kaarthik Sundar", "Sivakumar Rathinam", "P. B. Sujit" ], "first_author": "Parikshit Maini", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Low cost Unmanned Aerial Vehicles (UAVs) need multiple refuels to accomplish\\nlarge area coverage. The number of refueling stations and their placement plays\\na vital role in determining coverage efficiency. In this paper, we propose the\\nuse of a ground-based refueling vehicle (RV) to increase the operational range\\nof a UAV in both spatial and temporal domains. Determining optimal routes for\\nthe UAV and RV, and selecting optimized locations for refueling to aid in\\nminimizing coverage time is a challenging problem due to different vehicle\\nspeeds, coupling between refueling location placement, and the coverage area at\\neach location. We develop a two-stage strategy for coupled route planning for\\nUAV and RV to perform a coverage mission. The first stage computes a minimal\\nset of refueling sites that permit a feasible UAV route. In the second stage,\\nmultiple Mixed-Integer Linear Programming (MILP) formulations are developed to\\nplan optimal routes for the UAV and the refueling vehicle taking into account\\nthe feasible set of refueling sites generated in stage one. The performance of\\ndifferent formulations is compared empirically. In addition, computationally\\nefficient heuristics are developed to solve the routing problem. Extensive\\nsimulations are conducted to corroborate the effectiveness of proposed\\napproaches.\\n", "keywords": [ "Heuristics", "Integer programming", "Range (aeronautics)", "Set (abstract data type)", "Computer science", "Linear programming", "Plan (archaeology)", "Scale (ratio)", "Routing (electronic design automation)", "Mathematical optimization", "Engineering", "Algorithm" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1805.04417" }, { "id": "https://openalex.org/W7203794106", "doi": "10.4108/eetsis.13700", "title": "Reinforcement Learning Method for Collaborative Multi-Objective Path Planning of IIoT Drones and Unmanned Vehicles in Random Demand and Dynamic Delivery Environments", "year": 2026, "publication_date": "2026-08-19", "type": "article", "language": null, "authors": [ "Li Qiao", "Changkai Xu", "Bin Ni" ], "first_author": "Li Qiao", "venue": "ICST Transactions on Scalable Information Systems", "publisher": "European Alliance for Innovation", "abstract": "INTRODUCTION: Dynamic Industrial Internet of Things (IIoT) delivery is characterized by stochastic order arrivals, heterogeneous UAV–UGV capabilities, and real-time demand updates, making conventional static routing methods insufficient for collaborative delivery planning. Existing UAV trajectory-planning and truck–drone routing studies usually focus on either single-platform trajectory control or offline vehicle routing, and they rarely integrate stochastic IIoT demand updating, UAV–UGV rendezvous coordination, operational constraints, and multi-objective decision-making into a unified reinforcement-learning framework.OBJECTIVES: To address this limitation, this study proposes a guidance-factor-enhanced MADDPG framework for collaborative multi-objective path planning of UAVs and UGVs in random-demand and dynamic delivery environments.METHODS: Order arrivals are modeled by a Poisson process, while order location, payload, priority, and time-window attributes are generated through multivariate random distributions and updated by IIoT terminals every 5 s. Payload, endurance, speed, rendezvous, and task-sequencing constraints are incorporated into a normalized multi-objective function considering delivery time, delivery cost, demand satisfaction, and UAV endurance loss. A distance–demand–priority guidance factor and a reconstructed reward function are further introduced to alleviate sparse-reward effects and guide agents toward high-value demand regions.RESULTS: Simulation and ablation results show that the reconstructed reward is essential for stable learning, while the guidance factor significantly accelerates convergence. Under 40 IIoT nodes, the proposed method converges after approximately 2,000 episodes, whereas the model without guidance information requires about 5,000 episodes. Comparative experiments further show that the proposed method improves system throughput and collaborative delivery efficiency while maintaining feasible UAV trajectories.CONCLUSION: The proposed framework provides an adaptive reinforcement-learning solution for UAV–UGV cooperative logistics in dynamic IIoT environments.", "keywords": [ "Reinforcement learning", "Motion planning", "Routing (electronic design automation)", "Trajectory", "Drone", "Throughput", "Function (biology)", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://publications.eai.eu/index.php/sis/article/download/13700/4266" }, { "id": "https://openalex.org/W2377269668", "doi": null, "title": "Route Planning for Unmanned Aerial Vehicles Based on Particle Swarm Optimization", "year": 2010, "publication_date": "2010-01-01", "type": "article", "language": "en", "authors": [ "Yafu Wang" ], "first_author": "Yafu Wang", "venue": "Jisuanji fangzhen", "publisher": "China Aerospace Science and Industry Corporation", "abstract": "The Unmanned Aerial Vehicles(UAV) path planning is the key technology in UAV mission system and the technology guarantee that UAV can flight by itself.A route planning for unmanned aerial vehicles based on particle swarm optimization(PSO) is presented.Incorporating constrains into the algorithm,a desirable vehicle route can be generated and the efficiency can be greatly improved.The PSO is introduced to get a global optimized route.The experiment results demonstrated that this method can complete planning mission efficiently and obtain a desirable route.", "keywords": [ "Particle swarm optimization", "Route planning", "Key (lock)", "Motion planning", "Computer science", "Swarm behaviour", "Real-time computing", "Engineering", "Artificial intelligence", "Algorithm", "Robot", "Transport engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-JSJZ201008011.htm" }, { "id": "https://openalex.org/W7165748672", "doi": "10.71097/ijsat.v17.i2.11320", "title": "AI-Driven Vehicle Route Optimization Using GNN and Multi-Agent Reinforcement Learning", "year": 2026, "publication_date": "2026-06-06", "type": "article", "language": null, "authors": [ "Himanshu Seth" ], "first_author": "Himanshu Seth", "venue": "International Journal on Science and Technology", "publisher": null, "abstract": "The Vehicle Routing Problem (VRP) is a fundamental challenge in intelligent transportation systems due to its NP-hard complexity and sensitivity to dynamic, real-world constraints. The rapid expansion of e-commerce, urban congestion, and sustainability requirements has rendered traditional routing approaches inadequate, as they rely on static heuristics and lack adaptability to time-varying environments. In this paper, we propose a novel Graph-Enhanced Multi-Agent Reinforcement Learning (GEMARL) framework for dynamic vehicle route optimization in heterogeneous logistics networks. The proposed approach integrates graph neural networks (GNNs) to model spatiotemporal dependencies in transportation graphs with decentralized multi-agent reinforcement learning (MARL) to enable scalable, real-time decision making. The routing problem is formulated as a constrained multi-objective optimization task that jointly minimizes delivery time, operational cost, and carbon emissions under capacity, time-window, and energy constraints. The effectiveness of the proposed framework is validated through four representative case studies, including last-mile truck-drone delivery, urban electric vehicle (EV) logistics, multi-echelon supply chain coordination, and drone fleet management. Extensive experimental evaluations demonstrate that GEMARL achieves up to 30% reduction in delivery time, 25% improvement in energy efficiency, and 40% reduction in emissions compared to conventional optimization techniques and state-of-the-art learning-based methods. These results highlight the potential of hybrid AI-driven approaches to enable scalable, adaptive, and sustainability-aware logistics systems. Future research directions include quantum-enhanced optimization and integration with smart city infrastructures.", "keywords": [ "Reinforcement learning", "Heuristics", "Adaptability", "Vehicle routing problem", "Routing (electronic design automation)", "Optimization problem", "Reduction (mathematics)", "Task (project management)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.71097/ijsat.v17.i2.11320" }, { "id": "https://openalex.org/W3209547981", "doi": "10.1109/smc53803.2021.9569938", "title": "Involving Motorbikes in Blood Pickup Services: A Mathematical Modelling Perspective", "year": 2021, "publication_date": "2021-09-14", "type": "conference-paper", "language": "en", "authors": [ "Rafi Naufal Al Mochtari Pohan", "Hans Bastian Wangsa", "Setyo Tri Windras Mara", "Achmad Pratama Rifai", "Nur Mayke Eka Normasari" ], "first_author": "Rafi Naufal Al Mochtari Pohan", "venue": null, "publisher": null, "abstract": "The inclusion of smaller-size vehicles to enhance the performance of logistics systems has been a new trend in logistics, spanning from bicycles to drones. On the other hand, the motorbike has long been known as a common transportation mode, especially in developing countries, which opens the possibility to implement this vehicle mode in a healthcare logistics environment. Therefore, in this article, we discuss the possibility to involve a motorbike in the blood pickup service system. Here, we propose to mathematically model the system as a blood pickup routing problem with heterogeneous vehicle modes (BPRP-HV). Numerical experiments are then conducted, and managerial insights are derived from the results.", "keywords": [ "Pickup", "Vehicle routing problem", "Perspective (graphical)", "Computer science", "Mode (computer interface)", "Routing (electronic design automation)", "Service (business)", "Drone", "Distributed computing", "Computer network", "Human–computer interaction", "Business" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/smc53803.2021.9569938" }, { "id": "https://openalex.org/W4285483531", "doi": "10.48550/arxiv.2207.06155", "title": "Multi-Depot Multi-Trip Vehicle Routing with Total Completion Time Minimization", "year": 2022, "publication_date": "2022-07-13", "type": "preprint", "language": "en", "authors": [ "Tiziana Calamoneri", "Federico Corò", "Simona Mancini" ], "first_author": "Tiziana Calamoneri", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned aerial vehicles (UAVs) are aircraft whose flights can be fully autonomous without any provision for human intervention. One of the most useful and promising domains where UAVs can be employed is natural disaster management. In this paper, we focus on an emergency scenario and propose the use of a fleet of UAVs that help rescue teams to individuate people needing help inside an affected area. We model this situation as an original graph theoretical problem called Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Times minimization (MDMT-VRP-TCT); we go through some problems already studied in the literature that appear somehow similar to it and highlight the differences, propose a mathematical formulation for our problem as a MILP, design a matheuristic framework to quickly solve large instances, and experimentally test its performance. Beyond the proposed application, our solution works in any case in which a multi-depot multi-trip vehicle routing problem must be solved.", "keywords": [ "Vehicle routing problem", "Minification", "Computer science", "Depot", "Routing (electronic design automation)", "Operations research", "Route planning", "Natural disaster", "Emergency vehicle", "Mathematical optimization", "Transport engineering", "Real-time computing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2207.06155" }, { "id": "https://openalex.org/W7163610448", "doi": null, "title": "Dynamic and Stochastic Vehicle Routing Problems:A reinforcement learning approach", "year": 2026, "publication_date": "2026-06-02", "type": "other", "language": "en", "authors": [ "Dawei Chen" ], "first_author": "Dawei Chen", "venue": "TU/e Research Portal", "publisher": null, "abstract": "The rise of on-demand delivery services has fundamentally transformed the way commodities are acquired, aiming to connect local supply with real-time demand through digital platforms. This \"instant fulfillment\" paradigm, encompassing meal delivery, grocery delivery, and parcel services, has placed unprecedented pressure on urban logistics systems. These systems operate within dynamic and uncertain environments where travel times in urban road networks are time-dependent and stochastic due to congestion, peak hours, and unexpected incidents. Furthermore, customer requests arrive randomly over time, requiring immediate response and efficient routing. Traditional exact and heuristic algorithms, while effective for static problems, face a severe computational bottleneck in these dynamic contexts. Evaluating a vast number of potential future scenarios is time-consuming, often leading to decisions that are already obsolete by the time they are generated. To address these challenges, this thesis proposes the use of Deep Reinforcement Learning (DRL) for three variants of the Dynamic Vehicle Routing Problem (VRP). Reinforcement learning provides a \"train offline, execute online\" mechanism that allows for real-time decision-making once the model is trained. The research identifies seven specific challenges in urban logistics: the need for fast decision-making, the generalization across different scenarios, time-dependent and stochastic travel times, stochastic order arrivals, tight delivery time windows, multi-vehicle coordination, and the complexity of order consolidation. By addressing these challenges, the thesis seeks to answer how real-time decisions can be made to align with long-term objectives and how to design RL models that efficiently capture the complex spatial-temporal features of urban traffic. The first study focuses on the Dynamic Traveling Salesman Problem with Time-dependent and Stochastic Travel Times (DTSP-TSTT). This research addresses the fundamental problem of a single vehicle navigating through a network where travel times vary based on the time of day and are subject to random fluctuations. The core innovation is the Dynamic Graph Temporal Attention (DGTA) model. This model consists of a spatial-temporal encoder and a dynamic encoder. The spatial-temporal encoder utilizes embedding layers to map node coordinates and time-slot indices into high-dimensional representations, capturing the underlying traffic patterns. The dynamic encoder then updates these representations based on real-time update, such as the actual travel times. A spatial-temporal decoder, featuring specialized spatial and temporal pointers, evaluates the relevance of nodes and the impact of future traffic conditions to select the next destination. Numerical results demonstrate that DGTA-RL significantly outperforms rolling-window heuristics and other RL models, improving average route duration by up to 32% in large-scale scenarios while maintaining execution in seconds. The second study expands the scope to the Dynamic Vehicle Routing and Scheduling Problem (DVRSP) with stochastic request times and time-dependent travel times. Unlike the first study, this variant involves a fleet of vehicles and the added complexity of \"scheduling\"—deciding exactly when a vehicle should depart from a location. This is crucial in environments where waiting at a depot or a customer site might allow the vehicle to avoid a subsequent traffic peak, thereby reducing total travel time. To solve this, the thesis introduces the Dynamic Spatial-Temporal Multi-Pointer (DSTMP) model. A key feature of this model is the Reversed LSTM (Long Short-Term Memory) network, which processes future time-dependent travel time data from the future back to the present. This architectural choice ensures that the model remains focused on immediate, actionable information while being aware of future congestion trends. The multi-pointer decoder simultaneously determines the next stop and the optimal departure time. Using a real-world case study from the Vienna road network, the research shows that flexible scheduling can drastically reduce operational costs. The DSTMP-RL model achieves high-quality solutions in a fraction of the time required by traditional Rolling Approximate Dynamic Programming (ADP) methods. The third study tackles the Online Meal Delivery Problem (OMDP), which represents one of the most complex and high-pressure applications of urban logistics. The OMDP is characterized by massive order volumes, extremely tight time windows, and uncertainties in meal preparation times, travel times, and service durations. This problem is modeled as a Partially Observable Markov Decision Process (POMDP) because, in real-world operations, the dispatcher may not have perfect information regarding when a courier will actually complete a current task. To manage this, the thesis develops a lightweight RL model utilizing problem-based embeddings. Instead of raw data, the model encodes derived features such as expected time costs and \"slack time\" (the buffer remaining before a deadline). A parallel decoder is implemented to handle pickup and delivery actions simultaneously, which implicitly optimizes order consolidation, the practice of grouping multiple orders for a single courier to increase efficiency. Validated against a large-scale dataset from Meituan, the RL approach demonstrated a 21% improvement in service quality over greedy benchmarks. The study also identifies that a one-minute decision interval provides an optimal balance between system performance and computational overhead. In conclusion, the thesis demonstrates that reinforcement learning is a powerful tool for solving dynamic and stochastic vehicle routing problems in urban environments. By designing specialized neural network architectures like DGTA and DSTMP, the research successfully captures the nuances of time-dependent traffic and random events. The primary practical implication is th", "keywords": [ "Reinforcement learning", "Bottleneck", "Heuristic", "Vehicle routing problem", "Order (exchange)", "Routing (electronic design automation)", "Generalization", "Vehicle dynamics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://research.tue.nl/files/388669693/20260602_Chen_Dawei_hf.pdf" }, { "id": "https://openalex.org/W7161725529", "doi": "10.1109/icnwc68145.2026.11518153", "title": "Smart Hydrogen Microgrids for Powering Polar Vehicles and Drones Using IoT and Satellite-Assisted Intelligent Networks in Extreme Environments", "year": 2026, "publication_date": "2026-04-06", "type": "conference-paper", "language": null, "authors": [ "Mekha K Babu", "R Mamat", "Noreffendy Tamaldin", "Adrian Valentin Boicea", "S.Shanmuga Priya", "K. Sudhakar" ], "first_author": "Mekha K Babu", "venue": null, "publisher": null, "abstract": "Extreme environments such as Antarctica create major challenges for generating, storing, and transporting energy. Persistent sub-zero temperatures, remote locations, and limited infrastructure make reliable power supply difficult. Because of this, many polar stations still depend on diesel generators. While these systems are familiar and relatively dependable, they are not environmentally sustainable and require complicated fuel logistics, including long-distance transport and seasonal storage. To address these issues, this study introduces a Smart Hydrogen Microgrid designed to support energy needs for polar ground vehicles and autonomous aerial drones. The concept combines renewable energy, hydrogen-based storage, and intelligent communication networks into a single integrated system. In this framework, hybrid solar and wind resources generate electricity that is used for hydrogen production through water electrolysis. This approach provides a clean method for storing energy over long periods, which is particularly valuable in remote regions with variable renewable supply. The hydrogen produced is later converted back into electricity using fuel cells. This electricity powers electric vehicles and unmanned aerial systems used for logistics, surveillance, and environmental monitoring. At the same time, an Internet of Things (IoT) sensing layer continuously tracks renewable power output, hydrogen storage levels, vehicle operation, and key environmental conditions. Since communication in polar regions is often unreliable, the system also incorporates satellite-assisted connectivity combined with edge computing. This setup allows stable data exchange and supports near real-time decision-making even when network access becomes intermittent. Within the overall architecture, intelligent control algorithms manage energy distribution, optimize vehicle routing, and maintain system performance under changing environmental and operational conditions. Overall, the proposed framework strengthens energy resilience, improves operational reliability, and reduces dependence on fossil fuel supply chains. It also aligns with current developments in smart microgrids, renewable energy integration, and intelligent energy management systems. Although designed for polar research stations, the approach can be scaled and adapted for other remote or infrastructure-limited environments.", "keywords": [ "Drone", "Internet of Things", "Key (lock)", "Polar", "Intelligent Network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icnwc68145.2026.11518153" }, { "id": "https://openalex.org/W4412486729", "doi": "10.2514/6.2025-3411", "title": "Bandit-Enabled Dynamic Vehicle Dispatch and Routing in Hazardous Environments", "year": 2025, "publication_date": "2025-07-16", "type": "conference-paper", "language": "en", "authors": [ "Jimin Choi", "Max Z. Li" ], "first_author": "Jimin Choi", "venue": null, "publisher": null, "abstract": "This paper presents a decision-making framework for autonomous vehicle dispatch in hazardous environments with uncertain and evolving risk levels. The system integrates a Bayesian Upper Confidence Bound (BUCB) sensing strategy with task-specific vehicle routing problems with profits (VRPP), enabling adaptive coordination of unmanned aerial vehicles (UAVs) for hazard sensing and unmanned ground vehicles (UGVs) for cleaning. Using VRPP allows selective site visits under resource constraints by assigning each site a profit value that reflects sensing or cleaning priorities. Site-level hazard beliefs are maintained through a time-weighted Bayesian update. BUCB scores guide UAV routing to balance exploration and exploitation under uncertainty, while UGV routes are optimized to maximize expected hazard reduction under resource constraints. Simulation results show that the proposed method consistently reduces cumulative hazard, maintains accurate belief tracking, and scales effectively across different problem settings. The results underscore the importance of uncertainty-aware planning and joint routing strategies for reliable hazard mitigation.", "keywords": [ "Hazardous waste", "Routing (electronic design automation)", "Computer science", "Vehicle routing problem", "Computer network", "Engineering", "Waste management" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2025-3411" }, { "id": "https://openalex.org/W4394788184", "doi": "10.2139/ssrn.4793504", "title": "A Bi-Objective Truck Traveling Salesman Problem with Drone and Boat Considering 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": [ "Download", "Drone", "Travelling salesman problem", "Equity (law)", "Perspective (graphical)", "Computer science", "Distribution (mathematics)", "Operations research", "Computer security", "Engineering", "World Wide Web", "Political science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4793504" }, { "id": "https://openalex.org/W4386449999", "doi": "10.1109/iccc57788.2023.10233540", "title": "A Dynamic and Local Perspective: A Reliable Q-Routing Protocol based on Fuzzy Logic for FANETs", "year": 2023, "publication_date": "2023-08-10", "type": "conference-paper", "language": "en", "authors": [ "Penghui Hu", "Jianwen Wang", "Yirui Huang", "Yan Ma", "Cunzhuo Zhao", "Chaofan He" ], "first_author": "Penghui Hu", "venue": null, "publisher": null, "abstract": "Flying Ad-hoc Networks (FANETs) have found extensive applications in various fields, such as agriculture, security, rescue operations, and military or civilian operations. However, ensuring reliable routing and energy efficiency remains a critical challenge in FANETs due to the high mobility of drones and limited wireless resources. This paper investigates a general routing problem, specifically how battery-limited UAVs can route data reliably over multiple hops using only local information. We propose a multi-hop exploration model based on the local time-varying graph. We highlight the diminishing information freshness during hop-by-hop exploration, which limits the perceived validity of the network topology. This limitation often leads to unstable transmission when making routing decisions. To maximize routing reliability in long-term exploration, we formulate an appropriate optimization problem. Additionally, we introduce reinforcement learning and fuzzy logic into the routing protocol design, named FQR. To evaluate the quality of wireless links using a lightweight solution, we employ a fuzzy logic approach that considers multiple metrics, including relative mobility, resident energy, and link quality, as reliability features. We enhance the exploration process by incorporating information from two-hop neighbors in the reward definition. Furthermore, we design a variable discount factor to differentiate the validity of different local links. The proposed method is evaluated through simulations in NS3, utilizing an actual UAV trajectory model. The results demonstrate that our approach outperforms traditional strategies regarding packet delivery ratio, end-to-end delay, and network lifetime.", "keywords": [ "Computer science", "Routing protocol", "Computer network", "Network packet", "Reliability (semiconductor)", "Fuzzy logic", "Distributed computing", "Artificial intelligence" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iccc57788.2023.10233540" }, { "id": "https://openalex.org/W2896939873", "doi": "10.1109/ijcnn.2018.8489344", "title": "Logistics SLA optimization service for transportation in smart cities", "year": 2018, "publication_date": "2018-07-01", "type": "conference-paper", "language": "en", "authors": [ "Edcarllos Santos", "Puca Huachi Vaz Penna", "Igor Machado Coelho", "Heder Dorneles Soares", "Luiz Satoru Ochi", "Luidi Gelabert Simonetti" ], "first_author": "Edcarllos Santos", "venue": null, "publisher": null, "abstract": "A Service-Level Agreement (SLA) usually refers to computational services (e.g., cloud/web services), indicating contract goals and expected Quality of Service (QoS), recently extended for transportation problems called Logistics SLA. Transportation problems are being systematically studied for Smart City (SC) applications due to its huge importance: public transportation services, drone delivery services, battery recharging for electric vehicles, and also transportation for private companies considering real-time traffic information. Many of these transportations problems involve not only one-way deliveries, but also pickups, forming a set of routes with desired QoS such as maximum route length/time, delivery/pickup sequences and time-windows. In order to achieve all desired QoS, while minimizing routing distances, this problem can be seen as an extension of the challenging Vehicle Routing Problem (VRP), which is known to be NP-Hard. Computational intelligence strategies such as metaheuristics are often employed to find near-optimal solutions in short computational times. In this paper, we deal with a practical industrial problem involving employees transportation to a workplace in a Brazilian metropolis, involving minimization of operational costs, achievement of QoS requirements and visualization of the routes.", "keywords": [ "Quality of service", "Computer science", "Metaheuristic", "Vehicle routing problem", "Service (business)", "Minification", "Public transport", "Cloud computing", "Operations research", "Routing (electronic design automation)", "Transport engineering", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ijcnn.2018.8489344" }, { "id": "https://openalex.org/W7112569873", "doi": null, "title": "REAL-TIME SOLUTIONS OF ROBUST ENERGY-AWARE UNMANNED AERIAL VEHICLE ROUTING", "year": 2023, "publication_date": "2023-06-01", "type": "dissertation", "language": null, "authors": [ "Cotney, Tyler W." ], "first_author": "Cotney, Tyler W.", "venue": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)", "publisher": "Naval Postgraduate School", "abstract": "The Marine Corps’ force design priorities call for a small mobile force capable of conducting expeditionary advanced basing operations (EABO). Unmanned aerial vehicles (UAV) are the perfect platform to support this mission, but the Marine Corps will need an energy-aware routing system capable of solving a system of multiple depots and UAVs while considering weather forecasts. Due to limited internal energy available on UAVs, units must carefully consider energy consumption rates when routing assets. Weather variance can significantly impact energy requirements and must be accounted for to produce a robust energy-feasible solution. Works by Jatho, Haller, and Won, in 2020, 2021, and 2022, respectively, laid the groundwork for this thesis model by creating a final multiple depot vehicle routing problem (MDVRP) that accounts for an ensemble weather forecast. Large instances of the model can take days to solve and cannot support real-time operations. This thesis reduces the computation time of the final model by several orders of magnitude to support real-time use at a practical scale. We achieve this by implementing several warm start techniques to improve overall solve time. We also created additional data and an automated scenario builder to quickly compare complex sustainment networks and emerging UAV technologies.", "keywords": [ "Routing (electronic design automation)", "Energy consumption", "Energy (signal processing)", "Computation", "Variance (accounting)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/10945/72145" }, { "id": "https://openalex.org/W7160997210", "doi": "10.31004/jutin.v9i1.55995", "title": "Integrasi Analisis Bibliometrik Scopus–VOSviewer dan Simulasi Diskrit (ProModel) dalam Evaluasi Kinerja Distribusi Drone Last-Mile", "year": 2026, "publication_date": "2026-01-20", "type": "article", "language": null, "authors": [ "Ok Rangga Ahyar", "Putri Parinduri", "Rahmat Fadhillah", "Syarifuddin Solin", "Khairul Nizan Aulia" ], "first_author": "Ok Rangga Ahyar", "venue": "Jurnal Teknik Industri Terintegrasi", "publisher": "Universitas Pahlawan Tuanku Tambusai", "abstract": "The results of the co-occurrence, co-authorship, and citation analyses indicate that existing studies predominantly emphasize technical development, artificial intelligence integration, communication systems, and sustainability aspects, while quantitative operational performance modeling remains limited. Based on the identified research gaps, a discrete event simulation model was developed in promodel to represent operational processes, including order reception, routing, loading, flight operations, battery charging or swapping, and return cycles. Several policy scenarios were tested by varying fleet size, charging station capacity, and demand patterns. Simulation results demonstrate that system performance is significantly influenced by charging infrastructure capacity, resource allocation, and scheduling configuration, directly affecting delivery time, throughput, and drone utilization. The integration of bibliometric mapping and simulation modeling provides a comprehensive decision-support framework that bridges conceptual research trends with practical operational optimization in last-mile drone logistics.", "keywords": [ "Drone", "Scheduling (production processes)", "Discrete event simulation", "Conceptual model", "Resource (disambiguation)", "Resource allocation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journal.universitaspahlawan.ac.id/index.php/jutin/article/download/55995/33694" }, { "id": "https://openalex.org/W7171401211", "doi": "10.1007/978-3-032-29680-1_25", "title": "A Review of Constraint Programming Applications to Integrated Scheduling and Routing Problems", "year": 2026, "publication_date": "2026-07-27", "type": "conference-paper", "language": "en", "authors": [ "Anass El-Yaakoubi" ], "first_author": "Anass El-Yaakoubi", "venue": "Lecture notes in networks and systems", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Scheduling (production processes)", "Scalability", "Constraint programming", "Resource constraints", "Synchronization (alternating current)", "Job shop scheduling", "Resource (disambiguation)", "Constraint satisfaction" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-29680-1_25" }, { "id": "https://openalex.org/W7168949343", "doi": "10.82992/intellectum/handle.10818.5051", "title": "Optimización del ruteo sostenible de drones y vehículos para entregas de última milla en Chía, Cundinamarca", "year": 2025, "publication_date": "2025-10-23", "type": "dissertation", "language": "es", "authors": [ "Carlos Steven Rodríguez Salcedo" ], "first_author": "Carlos Steven Rodríguez Salcedo", "venue": "Intellectum (Universidad de La Sabana)", "publisher": "Universidad de La Sabana", "abstract": "This study presents a mixed-integer linear programming (MILP) optimization model for the last-mile routing problem supported by ground vehicles and drones, with a focus on operational sustainability in urban environments. The case study is developed in the municipality of Chía, Cundinamarca. The project begins with a systematic literature review that identified the main trends and gaps in the formulation of similar problems.", "keywords": [ "Sustainability", "Linear programming", "Focus (optics)", "Investment (military)", "Drone", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://intellectum.unisabana.edu.co/items/5d0b5cb5-46a2-47ce-b823-2f8a65d97b25/request-a-copy?bitstream=3531a839-1f1b-4d4c-8ad1-9f631608d27b" }, { "id": "https://openalex.org/W7203995480", "doi": "10.1016/j.asoc.2026.116286", "title": "Hybrid permutation-coded genetic algorithms and parallel implementations for the drone safety-aware electric vehicle traveling salesman problem", "year": 2026, "publication_date": "2026-08-22", "type": "article", "language": "en", "authors": [ "Ritesh Kumari", "Sakshi Goyal", "Amrata Gill", "Prem Prakash Vuppuluri", "C. Patvardhan" ], "first_author": "Ritesh Kumari", "venue": "Applied Soft Computing", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Implementation", "Travelling salesman problem", "Genetic algorithm", "Vehicle routing problem", "Electric vehicle" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.asoc.2026.116286" }, { "id": "https://openalex.org/W2330815914", "doi": "10.1504/ijidss.2015.075476", "title": "Integrated vehicle routing: a case study of unmanned aerial vehicle decision making", "year": 2015, "publication_date": "2015-01-01", "type": "article", "language": "en", "authors": [ "Varun Kalyan", "Subhashini Ganapathy", "Srinidhi Narayanan", "Raymond R. Hill" ], "first_author": "Varun Kalyan", "venue": "International Journal of Intelligent Defence Support Systems", "publisher": null, "abstract": "Time critical vehicle routings, such as with unmanned aerial vehicle (UAV) routing, are considered as integrated location routing problems where the objective is to simultaneously optimise the location and the routing. Although computer systems are good at providing solutions for optimised routing, they may not have the expert knowledge as that of human operators. Human operators play an important role in ensuring the safety and in achieving operational effectiveness in such systems. Problems such as increased human error, lack of situational awareness, and opacity from poorly automated systems remain; particularly in scenarios where human operators must make decisions in time-pressured planning. Hence, we study joint cognitive problem solving for a class of problems related to supervisory control of integrated vehicle routing. Results indicate that the graphical representation of the routing alternatives led to quicker evaluation time by the operator than tabular representation.", "keywords": [ "Routing (electronic design automation)", "Situation awareness", "Vehicle routing problem", "Class (philosophy)", "Computer science", "Static routing", "Situation analysis", "Engineering", "Operations research", "Systems engineering", "Computer network", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijidss.2015.075476" }, { "id": "https://openalex.org/W2332613663", "doi": "10.2514/6.2008-874", "title": "Flight Trajectory Optimization for Multiple Autonomous Glider Unmanned Aerial Vehicles", "year": 2008, "publication_date": "2008-01-07", "type": "conference-paper", "language": "en", "authors": [ "Nazli E. Kahveci", "Pétros Ioannou" ], "first_author": "Nazli E. Kahveci", "venue": "46th AIAA Aerospace Sciences Meeting and Exhibit", "publisher": null, "abstract": "Today’s Unmanned Aerial Vehicles (UAVs) are used for a variety of missions including surveillance, reconnaissance, search and rescue, border patrol, law enforcement and disaster management. In order to conduct surveillance operations unmanned aircraft are equipped with downward pointing cameras monitoring the environment. Multiple aircraft can be assigned a cooperative mission providing a better surveillance platform in comparison to a single UAV in service. The automation of the vehicles in the formation, however, becomes a complicated task possibly demanding higher energy requirements. Soaring flight strategies thus appear to be effective for large area observation. In this paper we consider multiple glider UAVs and study the static soaring flight formulation in the form of a multiple-Vehicle Routing Problem (mVRP). The proposed guidance algorithm promises high level of autonomy for a UAV formation and guarantees energy efficient flight trajectories for the mission performance optimization. The flight path planning mechanism is demonstrated through a simulation scenario involving a team of unpowered UAVs being navigated along near-optimal flight trajectories and performing climb maneuvers to utilize rising air currents.", "keywords": [ "Glider", "Trajectory", "Computer science", "Trajectory optimization", "Remotely operated underwater vehicle", "Aeronautics", "Aerospace engineering", "Artificial intelligence", "Mobile robot", "Computer vision", "Engineering", "Robot" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2008-874" }, { "id": "https://openalex.org/W4200006473", "doi": "10.3390/s21248188", "title": "Algorithms for Smooth, Safe and Quick Routing on Sensor-Equipped Grid Networks", "year": 2021, "publication_date": "2021-12-08", "type": "article", "language": "en", "authors": [ "Giovanni Andreatta", "Carla De Francesco", "Luigi De Giovanni" ], "first_author": "Giovanni Andreatta", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Automation plays an important role in modern transportation and handling systems, e.g., to control the routes of aircraft and ground service equipment in airport aprons, automated guided vehicles in port terminals or in public transportation, handling robots in automated factories, drones in warehouse picking operations, etc. Information technology provides hardware and software (e.g., collision detection sensors, routing and collision avoidance logic) that contribute to safe and efficient operations, with relevant social benefits in terms of improved system performance and reduced accident rates. In this context, we address the design of efficient collision-free routes in a minimum-size routing network. We consider a grid and a set of vehicles, each moving from the bottom of the origin column to the top of the destination column. Smooth nonstop paths are required, without collisions nor deviations from shortest paths, and we investigate the minimum number of horizontal lanes allowing for such routing. The problem is known as fleet quickest routing problem on grids. We propose a mathematical formulation solved, for small instances, through standard solvers. For larger instances, we devise heuristics that, based on known combinatorial properties, define priorities, and design collision-free routes. Experiments on random instances show that our algorithms are able to quickly provide good quality solutions.", "keywords": [ "Routing (electronic design automation)", "Heuristics", "Computer science", "Context (archaeology)", "Column generation", "Automation", "Static routing", "Grid", "Distributed computing", "Collision avoidance", "Collision", "Routing protocol" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/21/24/8188/pdf?version=1638957723" }, { "id": "https://openalex.org/W4285224733", "doi": "10.1109/tase.2022.3181512", "title": "A Lagrangian Algorithm for Multiple Depot Traveling Salesman Problem With Revisit Period Constraints", "year": 2022, "publication_date": "2022-06-17", "type": "article", "language": "en", "authors": [ "Drew D. Scott", "Satyanarayana G. Manyam", "David W. Casbeer", "Manish Kumar" ], "first_author": "Drew D. Scott", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This work presents a Multiple Depot Traveling Salesman Problem with revisit period constraints. The revisit period constraints are relevant to persistent routing applications, where these constraints represent maximum time between successive visits to a target. This problem is first posed as a Mixed Integer Linear Program. The coupling constraints in the primal problem are then relaxed via Lagrangian relaxation. Minimizing the resulting Lagrangian over the primal variables can be separated into an individual subproblem for each salesman. An algorithm to solve the subproblems to near-optimality that scales well for larger instances is presented. The dual function is then maximized, where three different dual update methods are studied: the subgradient method, the ellipsoid algorithm, and a bundle algorithm. Further, a primal reconstruction algorithm is presented to reconstruct feasible solutions from the solution of the dual algorithm. The quality of these solutions are compared to the optimal solutions obtained from the CPLEX MILP optimizer. The results of extensive numerical testing show that the dual algorithm presented was computationally efficient, capable of finding high quality solutions, and scale well compared to CPLEX. Further, it was seen that the bundle method used showed better convergence than the other update methods within the dual algorithm. Note to Practitioners— This work was motivated by the problem of routing UAVs in the presence of timing constraints, specifically motivated by military applications. This problem has constraints on the frequency at which targets are visited, such that high priority targets must be visited more frequently. Similar prior work exists in the literature for a variety of routing problems for different timing or resource constraints. Techniques which are successful on one set of constraints may be less useful when applied to different constraints. We present a Lagrangian based approach to the multiple depot traveling salesman variant. The results show the utility of a Lagrangian based technique to this problem, as both the solution quality and computation time scale well with problem size. The Lagrangian based technique presented here is seen to provide, on average, high quality solutions with improved computational time over a branch-and-bound algorithm, which solves the problem exactly. While the solutions returned from the algorithm are on average of good quality, they do exhibit some variance. Alternative algorithms may give more consistent results. The technique presented in this paper is general and may be applied to other routing problems where the revisit period constraints are applied. Future research includes application of this method to dynamic environments, where the targets’ states are unknown to the UAVs except when being monitored. Further, alternative techniques can be developed for this problem and compared to both the branch-and-bound and the Lagrange algorithm presented here.", "keywords": [ "Travelling salesman problem", "Lagrangian", "Traveling purchaser problem", "Bottleneck traveling salesman problem", "Mathematical optimization", "2-opt", "Period (music)", "Depot", "Algorithm", "Lagrangian relaxation", "Computer science", "Mathematics" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tase.2022.3181512" }, { "id": "https://openalex.org/W7111844738", "doi": null, "title": "ROBUST ENERGY-AWARE UNMANNED AERIAL VEHICLE ROUTING USING ENSEMBLE WEATHER FORECASTS", "year": 2022, "publication_date": "2022-06-01", "type": "dissertation", "language": null, "authors": [ "Won, David D." ], "first_author": "Won, David D.", "venue": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)", "publisher": "Naval Postgraduate School", "abstract": "The Marine Corps seeks to develop energy-aware unmanned aerial vehicle (UAV) routing for last-mile logistics resupply. UAVs have limited range and time on station to execute their assigned mission. To optimize the delivery of supplies to dispersed units, users must optimally utilize the internal energy onboard the UAV while considering external factors such as weather and priorities of resupply requests. Energy-aware UAV routing will increase Marine Corps logistics capabilities during expeditionary advanced base operations (EABO). The current EABO construct places forces within the threat rings of adversary weapon systems. Use of UAVs can allow dispersed forces to operate in the adversary’s threat rings without the stoppage of logistical support. This thesis builds upon the two-layer framework developed in previous theses by Jatho (2020) and Haller (2021) to include ensemble weather forecasts and partial delivery of supplies. The first layer, which solves the boundary value problem to obtain optimal trajectories between all nodes in the network, is solved for each member of the ensemble forecast. The second layer consists of a stochastic vehicle routing problem using the cost matrix from the first layer. This thesis also introduces the notion of partial delivery of supplies in the second layer to allow demand nodes to request multiple packages of supplies that can be delivered by multiple UAVs. Finally, this thesis analyzes various case studies and corresponding results.", "keywords": [ "Routing (electronic design automation)", "Range (aeronautics)", "Adversary", "Boundary (topology)", "Construct (python library)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/10945/70783" }, { "id": "https://openalex.org/W4388103307", "doi": "10.2139/ssrn.4589264", "title": "Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Lishuai Li", "Xinyu He", "Yanfang Mo", "Zhankun Sun", "S. Joe Qin" ], "first_author": "Lishuai Li", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Computer science", "Drone", "Flow network", "Graph theory", "Algorithm", "Graph", "Theoretical computer science", "Mathematics", "Mathematical optimization", "Combinatorics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4589264" }, { "id": "https://openalex.org/W4313447398", "doi": "10.48550/arxiv.2212.14624", "title": "An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards", "year": 2022, "publication_date": "2022-12-30", "type": "preprint", "language": "en", "authors": [ "Ruifan Liu", "Hyo‐Sang Shin", "Yan, Binbin", "Antonios Tsourdos" ], "first_author": "Ruifan Liu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties. Existing task coordination algorithms either ignore the stochastic process or suffer from the computational intensity. Taking advantage of the weakly coupled feature of the problem and the opportunity for coordination in advance, we propose a decentralized auction-based coordination strategy using a newly formulated score function which is generated by forming the problem into task-constrained Markov decision processes (MDPs). The proposed method guarantees convergence and at least 50% optimality in the premise of a submodular reward function. Furthermore, for the implementation on large-scale applications, an approximate variant of the proposed method, namely Deep Auction, is also suggested with the use of neural networks, which is evasive of the troublesome for constructing MDPs. Inspired by the well-known actor-critic architecture, two Transformers are used to map observations to action probabilities and cumulative rewards respectively. Finally, we demonstrate the performance of the two proposed approaches in the context of drone deliveries, where the stochastic planning for the drone league is cast into a stochastic price-collecting Vehicle Routing Problem (VRP) with time windows. Simulation results are compared with state-of-the-art methods in terms of solution quality, planning efficiency and scalability.", "keywords": [ "Computer science", "Submodular set function", "Scalability", "Markov decision process", "Mathematical optimization", "Vehicle routing problem", "Context (archaeology)", "Operations research", "Distributed computing", "Markov process", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2212.14624" }, { "id": "https://openalex.org/W7166044761", "doi": "10.2139/ssrn.7002043", "title": "Unmanned aerial vehicles-based search routing problem with environmental risks in disaster response", "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", "Vehicle routing problem", "Route planning", "Orienteering", "Disaster response" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7002043" }, { "id": "https://openalex.org/W4417054325", "doi": "10.1109/cdc57313.2025.11312588", "title": "Bi-Level Route Optimization and Path Planning with Hazard Exploration", "year": 2025, "publication_date": "2025-12-09", "type": "conference-paper", "language": "en", "authors": [ "Jimin Choi", "Grant Stagg", "Cameron K. Peterson", "Max Z. Li" ], "first_author": "Jimin Choi", "venue": null, "publisher": null, "abstract": "Effective risk monitoring in dynamic environments such as disaster zones requires an adaptive exploration strategy to detect hidden threats. We propose a bilevel unmanned aerial vehicle (UAV) monitoring strategy that efficiently integrates high-level route optimization with low-level path planning for known and unknown hazards. At the high level, we formulate the route optimization as a vehicle routing problem (VRP) to determine the optimal sequence for visiting known hazard locations. To strategically incorporate exploration efficiency, we introduce an edge-based centroidal Voronoi tessellation (CVT), which refines baseline routes using pseudo-nodes and allocates path budgets based on the UAV’s battery capacity using a line segment Voronoi diagram. At the low level, path planning maximizes information gain within the allocated path budget by generating kinematically feasible B-spline trajectories. Bayesian inference is applied to dynamically update hazard probabilities, enabling the UAVs to prioritize unexplored regions. Simulation results demonstrate that edgebased CVT improves spatial coverage and route uniformity compared to the node-based method. Additionally, our optimized path planning consistently outperforms baselines in hazard discovery rates across a diverse set of scenarios.", "keywords": [ "Motion planning", "Voronoi diagram", "Path (computing)", "Hazard", "Routing (electronic design automation)", "Particle swarm optimization", "Set (abstract data type)", "Bayesian probability" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2503.24044" }, { "id": "https://openalex.org/W7204635021", "doi": "10.2139/ssrn.7371806", "title": "Mothership-based truck-multi-drone cooperative delivery to moving targets: An event-driven multi-agent reinforcement learning approach", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Kai Yan", "Shiwei He", "Rui Song", "Rixin Zhao", "Jinjin Cai", "Shaokun Yuan" ], "first_author": "Kai Yan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Reinforcement learning", "Markov decision process", "Task (project management)", "Truck", "Computation", "Baseline (sea)", "Service (business)", "Matching (statistics)", "Feature (linguistics)", "Dimension (graph theory)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7371806" }, { "id": "https://openalex.org/W2147366262", "doi": "10.1109/med.2008.4602182", "title": "An emergency refuelling problem over a dynamically changing environment in the context of Unmanned Aerial Vehicles", "year": 2008, "publication_date": "2008-06-01", "type": "conference-paper", "language": "en", "authors": [ "Georgios P. Kladis", "J.T. Economou", "Antonios Tsourdos", "Brian A. White", "K. Knowles" ], "first_author": "Georgios P. Kladis", "venue": null, "publisher": null, "abstract": "In general routing problems are adapted to the application domain while incorporating constraints and special conditions. Depending on the problem, the classical static shortest path algorithm may be proved unrealized due to insufficient energy reserves. Motivated from the analysis in (Economou et al., 2007) where graph theory tools were utilized in the UAV (unmanned aerial vehicle) context for the static routing problem. The paper presents methods for determining the shortest path while conserving propulsion energy for the overall mission when a dynamically changing environment is concerned. Additional constraints can be incorporated when an adequate refuelling station is also needed in order to reach a goal, thus including real world conditions in the methodology. The later is considered using location theory tools. The overall methodology is illustrated through a simple simulation example where a UAV, with finite fuel reserves, has a task to traverse a dynamically changing environment from a particular starting point towards a goal and passing through an intermediate refuelling point, while minimizing energy requirements.", "keywords": [ "Traverse", "Computer science", "Shortest path problem", "Context (archaeology)", "Routing (electronic design automation)", "Propulsion", "Graph theory", "Vehicle routing problem", "Domain (mathematical analysis)", "Task (project management)", "Mathematical optimization", "Real-time computing" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/med.2008.4602182" }, { "id": "https://openalex.org/W7116701459", "doi": "10.1007/s10696-025-09648-z", "title": "Combining multiple delivery systems for urban logistics by ALNS-based route planning", "year": 2025, "publication_date": "2025-12-22", "type": "article", "language": "en", "authors": [ "Barbara Himstedt", "Frank Meisel" ], "first_author": "Barbara Himstedt", "venue": "Flexible Services and Manufacturing Journal", "publisher": "Springer Science+Business Media", "abstract": "Abstract Diesel-powered delivery vans still dominate the parcel delivery sector. However, due to their negative environmental impact, delivery service providers are urged to switch to more innovative and eco-friendly delivery vehicles like cargo bikes, delivery robots, or drones. Unfortunately, relying on only one type of delivery system is not always feasible, either because the customer’s site lacks the requirements or because the parcel size is too large. A possible solution is to combine multiple such delivery systems. In this context, this paper focuses on a problem in city logistics based on the two-echelon vehicle routing problem. To accommodate a diverse range of delivery vehicles, it involves deploying various vehicle types in the second echelon, direct delivery by first-echelon vehicles, transfer of second-echelon vehicles and parcels at satellite locations, and preference constraints for customers and vehicle types. To solve the problem, we propose an Adaptive Large Neighborhood Search (ALNS) heuristic that features problem-specific local search and destroy operators. Experiments are conducted for an urban delivery area in Hamburg, Germany. They show that the ALNS can handle various fleet compositions and instance sizes. Our findings indicate that the combination of delivery systems can lead to significant cost savings compared to traditional van delivery and that customer preferences heavily influence the optimal fleet composition.", "keywords": [ "Routing (electronic design automation)", "Vehicle routing problem", "Heuristic", "Service delivery framework", "Delivery Performance", "Service (business)", "Delivery system" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10696-025-09648-z.pdf" }, { "id": "https://openalex.org/W4312301810", "doi": "10.1109/case49997.2022.9926704", "title": "A Reactive Energy-Aware Rendezvous Planning Approach for Multi-Vehicle Teams", "year": 2022, "publication_date": "2022-08-20", "type": "conference-paper", "language": "en", "authors": [ "Kenny Chour", "Jean-Paul Reddinger", "James M. Dotterweich", "Marshal Childers", "James D. Humann", "Sivakumar Rathinam", "Swaroop Darbha" ], "first_author": "Kenny Chour", "venue": "2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)", "publisher": null, "abstract": "This paper addresses a type of rendezvous recharging problem involving a team of unmanned aerial and ground vehicles (UAVs, UGVs). UAVs are tasked with long-term surveillance and reactivity to events over a wide area, but are subject to energy constraints limiting their operation. UGVs can support UAVs by replenishing their battery via docking. The presented problem is a generalization of well-known NP-Hard problems in vehicle routing. We present a multi-level framework that decouples the challenging problem into smaller sub-problems for solving. The resulting framework is one that interleaves planning and execution for a team of vehicles, allowing for long-term operation and reactivity. We also present simulation results to validate our proposed approach.", "keywords": [ "Rendezvous", "Computer science", "Limiting", "Generalization", "Routing (electronic design automation)", "Distributed computing", "Motion planning", "Vehicle dynamics", "Real-time computing", "Computer network", "Robot", "Artificial intelligence" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/case49997.2022.9926704" }, { "id": "https://openalex.org/W4392485307", "doi": null, "title": "Distribution management problem : case of vehicles routing with collection and delivery of goods in a multimodal network", "year": 2020, "publication_date": "2020-11-27", "type": "dissertation", "language": "fr", "authors": [ "Hamdi Radhoui" ], "first_author": "Hamdi Radhoui", "venue": "theses.fr (ABES)", "publisher": null, "abstract": "La distribution du fret urbain est essentielle au fonctionnement des économies urbaines.Cependant, il contribue de manière significative à des problèmes tels que la congestion dutrafic et la pollution de l'environnement. Afin de répondre aux défis de la société moderne,l'organisation des processus logistiques nécessite des effets d'optimisation synergique desprocessus physiques et l'application de technologies innovantes. L'accent est mis surl'optimisation continue des processus dans le but de fournir au client final des services plusrapides, de meilleure qualité et plus rentables. L'objectif principal de cette recherche est decontribuer à l'écologisation de la distribution de fret urbain, qualité et rapidité des serviceset minimisation des coûts du transport en développant de nouveaux modèles mathématiqueset algorithmes concernant le problème de collecte et livraison de marchandises. Ainsi, cettethèse montre l’importance de l’intégration du drone avec les camions dans la distribution dequelques marchandises.", "keywords": [ "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Distribution (mathematics)", "Operations research", "Engineering", "Computer network", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.theses.fr/2020NORMLH28/document" }, { "id": "https://openalex.org/W4285306201", "doi": "10.1109/lcomm.2022.3182657", "title": "Autonomous Target Search Using Unmanned Aerial Vehicles in the Presence of Signal Noise", "year": 2022, "publication_date": "2022-06-13", "type": "article", "language": "en", "authors": [ "Jesse Pietz", "Joseph Hubert Wilck IV", "Soren Christensen", "Mitchell Melville", "J. Quinn Stank", "Sarah Zaiser" ], "first_author": "Jesse Pietz", "venue": "IEEE Communications Letters", "publisher": "IEEE Communications Society", "abstract": "A search vehicle routing algorithm is presented, inspired by thermalling in glider operations, for use in unmanned aerial vehicle (UAV) search routing problems where radio frequency signal noise affects target geolocation. We conduct ground testing experiments and observe that signal strength significantly deviates from its mean value — 10-20% variation in our experiments — at each distance epoch away from a target of interest. The thermalling algorithm uses straight-line segments and relatively simple guidance logic to generate routes that appear promising for fixed-wing, short endurance and autonomous UAV routing applications. We compare the simulated performance of the thermalling algorithm to a stochastic gradient descent algorithm using 1,000 randomly generated problem instances and show that, when considering signal readings and distance traveled as performance metrics, the thermalling algorithm performs better in 100% and 37% of problem instances respectively.", "keywords": [ "Glider", "Computer science", "Geolocation", "SIGNAL (programming language)", "Noise (video)", "Routing (electronic design automation)", "Real-time computing", "Algorithm", "Gradient descent", "Computer vision", "Artificial intelligence" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/lcomm.2022.3182657" }, { "id": "https://openalex.org/W4404103745", "doi": "10.1080/00207543.2024.2422818", "title": "Enhancing offshore parcel delivery efficiency through vessel-unmanned aerial vehicle collaborative routing", "year": 2024, "publication_date": "2024-11-06", "type": "article", "language": "en", "authors": [ "Yantong Li", "Xingqi Wang" ], "first_author": "Yantong Li", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "Offshore oil and gas production is vital for global energy supply, but it faces logistical challenges due to the high costs and inefficiencies of traditional supply methods. This paper introduces the vessel-unmanned aerial vehicle (UAV) routing problem with multiple visits in a single flight (VURP-M), a novel logistical model that addresses aimed at enhancing the replenishment of offshore platforms with small, essential items. The VURP-M allows the UAV to perform multiple visits during a single flight, optimising the delivery process. To tackle the VURP-M, we propose two mixed-integer second-order cone programs that capture the problem's complexities. Given its NP-hard nature, we employ an adaptive large neighbourhood search (ALNS) method, featuring a segmented initialisation process and problem-specific operators guided by a rule-based mechanism to improve solution efficiency. The ALNS formulates an initial solution by solving a travelling salesman problem to create a giant tour, which is then used to group sequential targets into multiple UAV flights. The subsequent optimal resolution of a SOCP determines the take-off and landing points for each flight. Subsequently, the ALNS refines the initial solution through destroy and repair operators, enhancing the search for superior sequences and allocation schemes. The effectiveness of our approach is demonstrated through a real-world case study and numerical experiments on random instances featuring up to 100 platforms. The results offer implications of the collaborative vessel-UAV model for the offshore logistics industry.", "keywords": [ "Submarine pipeline", "Marine engineering", "Routing (electronic design automation)", "Vehicle routing problem", "Computer science", "Environmental science", "Transport engineering", "Engineering", "Computer network" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2024.2422818" }, { "id": "https://openalex.org/W4387914298", "doi": "10.1109/codit58514.2023.10284483", "title": "Van and Unmanned-Aerial-Vehicle Cooperative Routing Problem with Time Windows", "year": 2023, "publication_date": "2023-07-03", "type": "conference-paper", "language": "en", "authors": [ "Hongqi Li", "Zhuopeng Zhan", "Zhiqi Wang" ], "first_author": "Hongqi Li", "venue": null, "publisher": null, "abstract": "Because of the significant real-world benefits of using cargo vans with unmanned aerial vehicles (UAVs) for deliveries, we define the van and unmanned-aerial-vehicle cooperative routing problem with time windows (VURP-TW). The VURP-TW is characterized by synchronization in route, classified customers, direct delivery, time windows, multiple vans, and multiple UAVs carried by each van. Customers along van routes, which are named satellites in this paper, are selected for vans dispatching or retrieving the carried UAVs. A satellite means a UAV launch/retrieval location. The synchronization in route enables UAVs launching from vans to return to paired vans at satellites instead of the depot. We develop mixed integer linear programming formulations to obtain optimal solutions for small- and medium-scale instances. We propose an adaptive large neighborhood search (ALNS) heuristic. The results of the computational experiments prove the effectiveness of the model and the heuristic.", "keywords": [ "Computer science", "Heuristic", "Synchronization (alternating current)", "Vehicle routing problem", "Drone", "Routing (electronic design automation)", "Integer programming", "Scale (ratio)", "Satellite", "Real-time computing", "Linear programming", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/codit58514.2023.10284483" }, { "id": "https://openalex.org/W4367293741", "doi": "10.1117/12.2665844", "title": "Deep learning-based heterogeneous system for autonomous navigation", "year": 2023, "publication_date": "2023-04-27", "type": "conference-abstract", "language": "en", "authors": [ "Timothy Sellers", "Tingjun Lei", "Daniel W. Carruth", "Chaomin Luo" ], "first_author": "Timothy Sellers", "venue": null, "publisher": null, "abstract": "In order to face the everyday growing population in today’s world, the deployment of autonomous vehicles is a promising direction for precision agriculture. Autonomous vehicles (AVs) have been developed and deployed for various agricultural needs such as field planting, harvesting, soil collection, and crop data collection. One method of achieving those task is complete coverage path planning (CCPP), which constructs a continuous path that covers a wide area of interest. However, in a large farm with multiple fields, those tasks have been extremely complicated and computationally expensive on a navigation system when utilizing a single AV. A heterogeneous system is proposed to sense the fields and solve the navigation and routing problem within multi-field path planning. We developed a deep learning-based routing scheme for Unmanned Aerial Vehicles (UAVs) to sense mature crops for harvest. The deep learning routing scheme utilizes a goal embedding feature and coordinate position feature to generate an optimal path for the Unmanned Aerial Vehicles, which allows them to find several candidate solutions. A deep learning-based complete coverage path planning (DL-CCPP) navigation scheme is also proposed for our Unmanned Ground Vehicle (UGVs) to navigate through the fields and collect the mature crops within them. The DL-CCPP uses UAV’s images in its deep learning network to construct the CCPP path from the AV coordinates.", "keywords": [ "Computer science", "Motion planning", "Real-time computing", "Artificial intelligence", "Routing (electronic design automation)", "Software deployment", "Population", "Deep learning", "Path (computing)", "Distributed computing", "Computer network", "Robot" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2665844" }, { "id": "https://openalex.org/W7204481209", "doi": "10.64960/easr.2026.265202", "title": "Fuzzy multimodal vehicle routing for last-mile flood relief: A scenario-based model for Chiang Mai", "year": 2026, "publication_date": "2026-08-27", "type": "article", "language": null, "authors": [ "Kanyarat Phutthanawong", "Takashi Irohara", "Israt Jahan Hridi", "Chawis Boonmee" ], "first_author": "Kanyarat Phutthanawong", "venue": "Engineering and Applied Science Research", "publisher": "Khon Kaen University", "abstract": "This research addresses the logistical challenges of last-mile relief delivery during flood disasters in Chiang Mai, Thailand. We propose a scenario-based fuzzy multi-depot, multi-trip vehicle routing problem model with split deliveries and integrated multimodal accessibility across varying flood severity levels. The model incorporates fuzzy logic to handle uncertainty by transforming it into an equivalent auxiliary crisp model through triangular membership functions. The objective function aims to minimize total transportation time across five flood scenarios. Results demonstrate that community accessibility requirements vary significantly with flood severity: low water levels can be served entirely by trucks, while severe flooding necessitates intensive boat operations, significantly increasing response times and trip frequencies. Sensitivity analysis reveals that strategic boat-drone collaboration in Scenario 4 can reduce the number of trips by 2 and travel time by up to 3.7%, while significantly simplifying boat routing complexity. The study provides actionable insights for disaster management agencies, emphasizing the critical role of scenario-based planning, multimodal fleet deployment, and robust decision-making under uncertainty.", "keywords": [ "Flood myth", "Fuzzy logic", "Flooding (psychology)", "Chiang mai", "Routing (electronic design automation)", "TRIPS architecture", "Vehicle routing problem", "Sensitivity (control systems)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ph01.tci-thaijo.org/index.php/easr/article/download/265202/176337" }, { "id": "https://openalex.org/W7128367456", "doi": "10.1007/s10479-026-07089-x", "title": "Optimizing Cooperative Carrier-Vehicle Routing with Iterative Second-Order Cone Programming Method", "year": 2026, "publication_date": "2026-02-09", "type": "article", "language": "en", "authors": [ "Ning-Wen Yang", "Maharani Rizki Larasati", "I-Lin Wang", "Makoto Yamashita" ], "first_author": "Ning-Wen Yang", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": "This study focuses on optimizing a cooperative delivery system that combines a large-capacity but slow carrier and two fast yet capacity-limited unmanned aerial vehicles (UAVs). The primary objective is to minimize the carrier’s total travel time while ensuring all delivery points in predetermined order are serviced by the UAVs. We formulate this problem with an emphasis on the flight patterns of the two UAVs. We propose a novel optimization approach that breaks down the complex problem into manageable subproblems by considering pairs of UAV flights. This decomposition enables more efficient computation and better scalability than a prior approach. Numerical experiments demonstrate that in a test instance with 20 delivery points, our method reduces computation time by over 90% and decreases the carrier’s travel time by approximately 40% compared to an existing approach. These results suggest significant potential for improving the efficiency of cooperative delivery systems, which can be applied to logistics and various vehicle coordination problems.", "keywords": [ "Scalability", "Computation", "Theory of computation", "Routing (electronic design automation)", "Decomposition", "Optimization problem", "Drone", "Benders' decomposition" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10479-026-07089-x.pdf" }, { "id": "https://openalex.org/W3127382316", "doi": "10.1515/9783110691276-007", "title": "7 Internet of things platform for smart farming", "year": 2021, "publication_date": "2021-02-08", "type": "book-chapter", "language": "en", "authors": [ "K.S. Krishnaveni", "E. Radhamani", "Preethi K R" ], "first_author": "K.S. Krishnaveni", "venue": null, "publisher": null, "abstract": "Internet of things (IoT)-based smart farming is a mechanism initiated with various sensors to monitor the temperature, soil pH, moisture, and so on of the farm and automate the irrigation system to provide high quality and quantity of agricultural products. Innovative smart farming applications will enhance the productivity with reduced wastage, efficient farm vehicle routes, and optimal fertilizer usage. The benefits and technologies required for implementing smart farming are initially focused in this chapter. Then, the role of IoT in transforming the agriculture; cases where IoT is used, for example, precision farming, agriculture robotics and drones, livestock monitoring, smart greenhouse, smart irrigation technology, farm management information system, weather monitoring system (WMS), smart logistics and warehousing, waste management; and solutions to agricultural issues are analyzed. With novel end to end intelligent process and advanced business process execution, the products will reach the supermarkets in the fastest time possible with less operational cost and increased product value. This real-life implementation of IoT solutions in agriculture will perfectly demonstrate the growth of global smart agriculture into remote areas and highlight the benefits that are achieved.", "keywords": [ "Agriculture", "Internet of Things", "Process (computing)", "Agricultural engineering", "Precision agriculture", "Productivity", "Engineering", "Business", "Computer science", "Computer security", "Geography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1515/9783110691276-007" }, { "id": "https://openalex.org/W4397017351", "doi": "10.1007/s11081-024-09893-5", "title": "Trajectory optimization of unmanned aerial vehicles in the electromagnetic environment", "year": 2024, "publication_date": "2024-05-17", "type": "article", "language": "en", "authors": [ "Anvarbek Atayev", "Jörg Fliege", "Alain B. Zemkoho" ], "first_author": "Anvarbek Atayev", "venue": "Optimization and Engineering", "publisher": "Springer Science+Business Media", "abstract": "Abstract We consider a type of routing problems common in defence and security, in which we control a fleet of unmanned aerial vehicles (UAVs) that have to reach one or more target locations without being detected by an adversary. Detection can be carried out by a variety of sensors (radio receivers, cameras, personnel, etc) placed by the adversary around the target sites. We model the act of detecting a UAV from first principles by noting that sensors work by monitoring frequencies in the electromagnetic spectrum for signals or noise emitted. By this, we are able to provide a flexible and versatile nonlinear optimisation framework in which the problem is modeled as a novel trajectory optimisation problem with paths of the UAVs as continuous arcs in an Euclidean space. The flexibility of our approach is exhibited by the fact that we can easily consider various relevant objectives, among them minimising the overall probability of detection and maximising the location error that the adversary experiences when trying to locate our UAVs. Our model is also versatile enough to consider the act of jamming, in which one or more of our UAVs intentionally send out signals to interfere with the operations of the adversary’s sensors. Numerical results show the flexibility of our framework, and that we can solve realistic instances of this problem type.", "keywords": [ "Trajectory", "Trajectory optimization", "Computer science", "Aerospace engineering", "Artificial intelligence", "Physics", "Engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s11081-024-09893-5" }, { "id": "https://openalex.org/W2887440347", "doi": null, "title": "Unmanned aerial vehicle route planning on a dynamically changing waypoint basedmap for exploration purposes", "year": 2009, "publication_date": "2009-12-17", "type": "conference-paper", "language": "en", "authors": [ "Georgios P. Kladis", "J.T. Economou", "Antonios Tsourdos", "Brian A. White", "K. Knowles" ], "first_author": "Georgios P. Kladis", "venue": "CERES (Cranfield University)", "publisher": "Cranfield University", "abstract": "In the included work the Unmanned Aerial Vehicle (UAV) mission is represented by energy graphs motivated by the analysis in [1]. The problem of the shortest path routing is revisited when a dynamically changing environment is considered. It is assumed that information about the map is received while on flight due to events. In addition, UAVs are required, while on mission, to \"scout\" areas of interest which involves extracting as much intelligence as possible and traversing it in the most safe flyable means. Hence, the UAV should be capable of integrating knowledge from a variety of sources and re-plan its mission accordingly in order to fulfil objectives. Motivated by the previous, depending on the decision making process, the notion of a \"temporary\" optimum path can be of physical and functional sense. The problem is modeled as a multistage decision making process, where each stage is triggered by an event and is characterized by a current starting point, an area for reconnaissance purposes and a final destination. Hence, given the current availability between paths, the objective is to devise a policy that leads from an origin or current known location to a destination node while traversing the unknown region of interest with the minimal energy demand.", "keywords": [ "Waypoint", "Aeronautics", "Computer science", "Remotely operated underwater vehicle", "Remote sensing", "Mobile robot", "Real-time computing", "Engineering", "Geography", "Artificial intelligence", "Robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dspace.lib.cranfield.ac.uk/handle/1826/4080" }, { "id": "https://openalex.org/W4200428875", "doi": "10.1016/j.urbmob.2021.100011", "title": "Approach for simulating vehicle-based supply of sellable data products in smart cities – Parking space data as a use case", "year": 2021, "publication_date": "2021-12-16", "type": "article", "language": "en", "authors": [ "Marko Palviainen", "Ville Kotovirta" ], "first_author": "Marko Palviainen", "venue": "Journal of Urban Mobility", "publisher": "Elsevier BV", "abstract": "Mobile devices, sensors, 5G networks, digital platforms and data marketplaces provide emerging possibilities for smart cities and data business. This paper introduces a novel Data Market Simulation (DMS) approach that assists data suppliers in the following: optimising vehicle-based supply of sellable data and creation of value from data; evaluating vehicle routes and the quality of data that the vehicles on these routes will produce; and reviewing how well the routes will serve the potential data users before beginning the supply of data in cities. The DMS approach supports iterative development and optimisation of routes and route parameters. If the goals are not achieved in the route plan, the data user can use the previous simulation results as a start point, develop the routes and route parameters in the simulation model, and then evaluate the updated route plan in simulations. The route development and simulation can be continued until the desired targets are met in the route plan. The DMS approach is evaluated in a small-scale experiment that simulated the use of autonomous busses and drones for the supply of parking space data in the LuxTurrim5G+’s smart pole pilot network in the city of Espoo in Finland.", "keywords": [ "Computer science", "Plan (archaeology)", "Drone", "Big data", "Quality (philosophy)", "Space (punctuation)", "Scale (ratio)", "Point (geometry)", "Transport engineering", "Real-time computing", "Data mining", "Engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S266709172100011X/pdf" }, { "id": "https://openalex.org/W7133160609", "doi": "10.70675/b90f39c1z06aez479ez8948z62d4786c3e87", "title": "Résolution de problèmes de tournées de véhicules avec drones en logistique urbaine : une approche basée sur les méthodes d’optimisation exactes et approchées", "year": 2026, "publication_date": "2026-02-04", "type": "dissertation", "language": "en", "authors": [ "Sylvain Lichau" ], "first_author": "Sylvain Lichau", "venue": "HAL (Le Centre pour la Communication Scientifique Directe)", "publisher": "Centre National de la Recherche Scientifique", "abstract": "Drones represent a promising complement to delivery logistics. They offer a fundamentally different performance profile than conventional trucks. They have lower operating costs and higher speeds, at the expense of reduced range and capacity. In scenarios where these limitations are manageable, drones can fully replace traditional delivery trucks. However, in most practical applications, these complementary vehicle types should be strategically combined to leverage their respective operational strengths. This thesis mainly proposes exact mathematical optimization methods for routing problems with drones. The first chapter gives an overview of the literature on routing problems with drones, in the field of operations research, with an emphasis on exact optimization methods. We highlight the variety of problems addressed by researchers and the need to increase the size of the instances solved to optimality. The second chapter briefly recalls the concepts of mathematical programming required for understanding branch-cut-and-price algorithms. We also describe the branch-cut-and-price algorithm used in this thesis, including state-of-the-art components and the labeling algorithm used to solve the pricing problems. The third chapter addresses the Two-Echelon Vehicle Routing Problem with Drones. We propose a new set-partitioning model, in which partial routes corresponding to drone movements are enumerated using a dynamic program. We propose an adaptation of the well-known rounded capacity cuts, specifically tailored to address this problem. In addition, we propose a heuristic branch-cut-and-price based on the exact algorithm. Computational experiments show that the exact algorithm significantly increases the size of optimally solved instances. To further evaluate the model, the algorithm is then extended to address the Park-and-Loop Routing Problem. The fourth chapter addresses the Truck-based Drone Delivery Routing Problem with Time Windows. We develop an exact branch-cut-and-price algorithm and a labeling algorithm to solve 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. Computational experiments exhibit the efficiency of the proposed approaches. We also propose new challenging instances with increased drone capacity. The fifth chapter studies a variant of the Vehicle Routing Problem with Time Windows, which integrates two operationally relevant features: flexible departure times from the depot and strict limits on route duration. Although more generic, this problem can be assimilated to a drone routing problem in which the route duration limits the drone’s range. We develop an exact branch-cut-and-price algorithm, featuring a bidirectional labeling algorithm as its primary component for solving the complex pricing subproblem. We also show how the related problem of minimizing total route duration can be treated. Computational experiments confirm the algorithm’s performance.", "keywords": [ "Leverage (statistics)", "Routing (electronic design automation)", "Drone", "Heuristic", "Vehicle routing problem", "Complement (music)", "Range (aeronautics)", "Variety (cybernetics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://theses.hal.science/tel-05529816v1/file/LICHAU_SYLVAIN_2026.pdf" }, { "id": "https://openalex.org/W7202386890", "doi": "10.1177/03611981261467338", "title": "Highways in the Sky: Unmanned Aerial Vehicle Routing Under Dynamic Wind Profiles in Low-Altitude Urban Airspace", "year": 2026, "publication_date": "2026-08-13", "type": "article", "language": "en", "authors": [ "Rawda Ahmed", "Khaled F. Abdelghany", "Ernest Huffman" ], "first_author": "Rawda Ahmed", "venue": "Transportation Research Record Journal of the Transportation Research Board", "publisher": "SAGE Publishing", "abstract": "This paper develops a wind-aware routing framework for low-altitude small unmanned aerial vehicles (UAVs) operating under FAA Part 107 with maximum takeoff weight under 55 lb and altitudes at or below 400 ft above ground level, and evaluates how spatiotemporal wind influences travel time and energy consumption. The framework integrates wind-vector projections into a four-dimensional time-expanded network and applies a time-dependent Pareto-label-correcting algorithm to generate non-dominated routes between drone ports (i.e., hubs/staging nodes). Controlled experiments vary wind heading, vertical shear, and spatial heterogeneity in a representative urban corridor to isolate the role of wind structure. Results show that tailwinds simultaneously reduce travel time and energy, headwinds increase both, and time-varying winds shift the relative efficiency of early versus late departures. The findings highlight that ignoring wind can substantially increase energy consumption or lead to delay penalties. The framework provides UAV operators with actionable guidance on departure timing and altitude selection and establishes a reproducible benchmark for assessing air mobility corridor performance under realistic wind conditions.", "keywords": [ "Wind speed", "Routing (electronic design automation)", "Takeoff", "Wind power", "Benchmark (surveying)", "Drone", "Energy consumption", "Global wind patterns" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/03611981261467338" }, { "id": "https://openalex.org/W2739911716", "doi": "10.11575/prism/24855", "title": "A Net Present Cost Minimization Framework for Wireless Sensor Networks", "year": 2016, "publication_date": "2016-01-01", "type": "dissertation", "language": "en", "authors": [ "Kevin Dorling" ], "first_author": "Kevin Dorling", "venue": "Libraries and Cultural Resources (University of Calgary)", "publisher": "University of Calgary", "abstract": "Minimizing the cost of deploying and operating a wireless sensor network (WSN) involves deciding how to partition a budget between competing expenses such as node hardware, energy, and labour. To determine if funds are given to a specific project or invested elsewhere, companies often use interest rates to sum the project's cash flows in terms of present-day dollars. This provides an incentive to defer expenditures when possible and use the returns to reduce future costs. In this thesis, a framework is proposed for minimizing the net present cost (NPC) of a WSN by optimizing the number of, cost of, and time between expenditures. The proposed framework balances competing expenses and defers expenditures when possible. A similar strategy does not appear to be available in the literature, and has likely not been developed in industry as no commercial WSN operators currently exist. In general, NPC minimization is a non-linear, non-convex optimization problem. However, if the time until the next expenditure is linearly proportional to the cost of the current expenditure, and the number of maintenance cycles is known in advance, the problem becomes convex and can be solved to global optimality. If non-deferrable recurring costs are low, then evenly spacing the expenditures can provide near-optimal results. The NPC minimization framework is most effective when non-deferrable recurring costs, such as labour, are low. High labour costs limit the number of times that a WSN operator can use the returns from investing deferrable costs to decrease future expenditures. This thesis therefore proposes vehicle routing problems (VRPs) to reduce labour costs by delivering nodes with drones. Unlike similar VRPs, drone costs are reduced by reusing vehicles, and low-cost, feasible routes are ensured by modelling energy consumption as a function of drone battery and payload weight. The problems are modelled as mixed integer linear programs (MILPs). As these MILPs are NP-hard, simulated annealing algorithms are proposed for finding sub-optimal solutions to large instances of the problems.", "keywords": [ "Wireless sensor network", "Minification", "Computer science", "Net (polyhedron)", "Computer network", "Mathematics", "World Wide Web" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/11023/3179" }, { "id": "https://openalex.org/W7154984156", "doi": "10.2139/ssrn.6617700", "title": "Multi-Agent Deep Reinforcement Learning for Dynamic Depot Returns in Close Enough Multi-Vehicle Routing", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Francesca Guerriero", "Francesco Paolo Saccomanno" ], "first_author": "Francesca Guerriero", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Heuristic", "Reinforcement learning", "Solver", "Routing (electronic design automation)", "Set (abstract data type)", "Sequence (biology)", "Heuristics", "Discretization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6617700" }, { "id": "https://openalex.org/W4406495889", "doi": "10.1109/tase.2025.3530425", "title": "Parallel Inspection Route Optimization With Priorities for 5G Base Station Networks", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Xiangqi Dai", "Zhenglin Liang" ], "first_author": "Xiangqi Dai", "venue": "IEEE Transactions on Automation Science and Engineering", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "5G base station networks generate numerous alarms daily. With the increasing demand for digital services, it is vital to inspect and rectify anomalies to uphold user satisfaction. This study explores the potential of unmanned aerial vehicle (UAV) empowered opportunistic inspection based on alarm data. We formulate the inspection routing problem as a prioritized traveling salesman problem (PTSP) encompassing two categories of base stations. Priority is assigned to stations generating more alarms, while others are subject to opportunistic inspection. To expedite large-scale opportunistic inspection routes, we introduce a novel transformer-based parallelizable routing algorithm (TPRA). TPRA is an intelligent optimization that orchestrates multiple parallelized constrained reinforcement learning algorithms. Through balancing spectral clustering, the large-scale graph is segmented into manageable subgraphs. For each subgraph, the prioritized inspection routing problem is formulated as a constrained Markov decision process and optimized by transformer-based reinforcement learning in parallel. The optimized subgraphs are then merged using an adaptive large neighborhood search approach. Through parallel computing, our approach achieves as much as 75% reduction in computation time, while concurrently generating shorter routes. The approach is implemented in real-world cases to validate its efficacy. Note to Practitioners—The rapid expansion of 5G infrastructure underscores the critical need for advanced technology and maintenance strategies. Base stations are often placed at high altitudes to ensure line-of-sight connectivity, which poses difficulties for maintenance, particularly in challenging terrains. UAVs offer a promising solution for faster and safer inspection and rectification. The designed approach utilizes reinforcement learning in parallel to optimize UAV inspection routes in an opportunistic manner. This method strategically prioritizes inspection routes based on the real-time base station alarm data, ensuring a swift and effective response to potential issues. Trained in simulated scenarios, the model requires few adjustments for real-world deployment, making it readily implementable in 5G networks. Beyond the potential of the 5G network, the approach also unlocks new value across various types of service in the low-altitude economy.", "keywords": [ "Base station", "Computer science", "Base (topology)", "Computer network", "Engineering" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tase.2025.3530425" }, { "id": "https://openalex.org/W7154845068", "doi": "10.1016/j.cie.2026.112032", "title": "Hybrid framework for multi-UExV routing with time windows and capacity constraints", "year": 2026, "publication_date": "2026-04-18", "type": "article", "language": "en", "authors": [ "Ramdhan Nugraha", "Adriansyah Dwi Rendragraha", "Soo Young Shin" ], "first_author": "Ramdhan Nugraha", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Payload (computing)", "Routing (electronic design automation)", "Vehicle routing problem", "Energy (signal processing)", "Key (lock)", "Static routing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2026.112032" }, { "id": "https://openalex.org/W4224942749", "doi": "10.48550/arxiv.2204.11477", "title": "Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer", "year": 2022, "publication_date": "2022-04-25", "type": "preprint", "language": "en", "authors": [ "Kaiwen Li", "Tao Zhang", "Rui Wang", "Ling Wang" ], "first_author": "Kaiwen Li", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The unmanned aerial vehicle (UAV) plays an vital role in various applications such as delivery, military mission, disaster rescue, communication, etc., due to its flexibility and versatility. This paper proposes a deep reinforcement learning method to solve the UAV online routing problem with wireless power transfer, which can charge the UAV remotely without wires, thus extending the capability of the battery-limited UAV. Our study considers the power consumption of the UAV and the wireless charging process. Unlike the previous works, we solve the problem by a designed deep neural network. The model is trained using a deep reinforcement learning method offline, and is used to optimize the UAV routing problem online. On small and large scale instances, the proposed model runs from four times to 500 times faster than Google OR-tools, the state-of-the-art combinatorial optimization solver, with identical solution quality. It also outperforms different types of heuristic and local search methods in terms of both run-time and optimality. In addition, once the model is trained, it can scale to new generated problem instances with arbitrary topology that are not seen during training. The proposed method is practically applicable when the problem scale is large and the response time is crucial.", "keywords": [ "Reinforcement learning", "Computer science", "Flexibility (engineering)", "Solver", "Heuristic", "Wireless", "Routing (electronic design automation)", "Wireless power transfer", "Scale (ratio)", "Artificial intelligence", "Distributed computing", "Real-time computing" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2204.11477" }, { "id": "https://openalex.org/W7155209221", "doi": "10.1016/j.tre.2026.104891", "title": "A tandem mother-daughter-vessel routing problem for maintenance activities in offshore wind farms", "year": 2026, "publication_date": "2026-04-22", "type": "article", "language": "en", "authors": [ "Chandra Ade Irawan", "Saı̈d Salhi", "Giampaolo Buticchi", "Hing Kai Chan" ], "first_author": "Chandra Ade Irawan", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Offshore wind power", "Submarine pipeline", "Tandem", "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.tre.2026.104891" }, { "id": "https://openalex.org/W4417073282", "doi": "10.1007/s12065-025-01120-z", "title": "Reactivable ripple spreading algorithm for solving post-flood disaster inspection routing problem", "year": 2025, "publication_date": "2025-12-06", "type": "article", "language": "en", "authors": [ "Shilin Yu", "Zihan Li", "Songtao Hu", "Yuantao Song" ], "first_author": "Shilin Yu", "venue": "Evolutionary Intelligence", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Flood myth", "Benchmark (surveying)", "Routing (electronic design automation)", "Situation awareness", "Emergency management", "Path (computing)", "Ripple", "Relay", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s12065-025-01120-z" }, { "id": "https://openalex.org/W4298131504", "doi": "10.48550/arxiv.2106.08379", "title": "Routing Problem for Unmanned Aerial Vehicle Patrolling Missions -- A\\n Progressive Hedging Algorithm", "year": 2021, "publication_date": "2021-06-15", "type": "preprint", "language": null, "authors": [ "Sudarshan Rajan", "Kaarthik Sundar", "Natarajan Gautam" ], "first_author": "Sudarshan Rajan", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The paper presents a two-stage stochastic program to model a routing problem\\ninvolving an Unmanned Aerial Vehicle (UAV) in the context of patrolling\\nmissions. In particular, given a set of targets and a set of supplemental\\ntargets corresponding to each target, the first stage decisions involve finding\\nthe sequence in which the vehicle has to visit the set of targets. Upon\\nreaching each target, the UAV collects information and if the operator of the\\nUAV deems that the information collected is not of sufficient fidelity, then\\nthe UAV has to visit all the supplemental targets corresponding to that target\\nto collect additional information before proceeding to visit the next target.\\nThe problem is solved using a progressive hedging algorithm and extensive\\ncomputational results corroborating the effectiveness of the proposed model and\\nthe solution methodology is presented.\\n", "keywords": [ "Patrolling", "Context (archaeology)", "Computer science", "Set (abstract data type)", "Fidelity", "Routing (electronic design automation)", "Real-time computing", "Operations research", "Algorithm", "Engineering", "Computer network", "Telecommunications" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2106.08379" }, { "id": "https://openalex.org/W7130595577", "doi": "10.48550/arxiv.2602.15841", "title": "Close-enough general routing problem for multiple unmanned aerial vehicles in monitoring missions", "year": 2026, "publication_date": "2026-01-21", "type": "preprint", "language": null, "authors": [ "Huan Liu", "Michel Gendreau", "Binjie Xu", "Guohua Wu", "Yi Gu" ], "first_author": "Huan Liu", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In this paper, we introduce a close-enough multi-UAV general routing problem (CEMUAVGRP) where a fleet of homogeneous UAVs conduct monitoring tasks containing nodes, each of which has its disk neighborhood, and edges, aiming to minimize the total distance. A two-phase iterative method is proposed, partitioning the CEMUAVGRP into a general routing phase where a satisfactory route including required nodes and edges for each UAV is obtained without considering the disk neighborhoods of required nodes, and a close-enough routing phase where representative points are optimized for each required node in the determined route. To be specific, a variable neighborhood descent (VND) heuristic is proposed for the general routing phase, while a second-order cone programming (SOCP) procedure is applied in the close-enough routing phase. These two phases are performed in an iterative fashion under the framework of an adaptive iterated local search (AILS) algorithm until the predefined termination criteria are satisfied. Extensive experiments and comparative studies are conducted, demonstrating the efficiency of the proposed AILS-VND-SOCP algorithm and the superiority of disk neighborhoods.", "keywords": [ "Routing (electronic design automation)", "Iterated local search", "Heuristic", "Node (physics)", "Variable (mathematics)", "Link-state routing protocol", "Destination-Sequenced Distance Vector routing", "Static routing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2602.15841" }, { "id": "https://openalex.org/W3185043512", "doi": "10.2514/6.2021-2455", "title": "System of Systems Simulation Driven Wildfire Fighting Aircraft Design", "year": 2021, "publication_date": "2021-07-28", "type": "conference-paper", "language": "en", "authors": [ "Prajwal Shiva Prakasha", "Björn Nagel", "San Kilkis", "Nabih Naeem", "Patrick Ratei" ], "first_author": "Prajwal Shiva Prakasha", "venue": "AIAA AVIATION 2021 FORUM", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2021-2455.vid Large wildfires are increasingly occurring phenomenon in several since the past few years. The suppression of wildfires is complex considering heterogeneous independent constituent systems operating together to monitor, mitigate, and suppress the fire. In addition, the management of the disaster response involve multiple institutions in collaboration. Recognition of this wildfire fighting scenario, as a System of Systems (SoS) is valid. Aerial vehicles may play a big role in firefighting considering monitoring and suppression at early stages when the fire is still small. Thus, there is scope for designing a new Unmanned Aerial Vehicle (UAV) with a payload of 250 kg to 500 kg for aerial forest fire suppression, using a SoS wildfire simulation driven aircraft design approach, where the individual optimum performance of a system, especially of a new aircraft for firefighting, does not guarantee optimum overall firefighting mission effectiveness. Whereas an optimum combination of fleet, technology and operational tactics can effectively suppress fire. For this reason, this research focuses on four different aspects: 1) Applying the inverse design paradigm to a wildfire suppression air vehicle by coupling a fire propagation cellular automata model with a stochastic agent-based simulation of an evolved firefighting SoS. An efficient SoS framework to Evaluate fleet performance. 2) Four System of systems – system – subsystem interlinking research questions are addressed with corresponding sensitivity results. The impact of wildfire based on vehicle fleet size, vehicle architecture (Tiltrotor, Compound Heli, Multirotor or Lift cruise), payload carrying capability, response time and cruise speed. 3) The evolution of perfect combination of aerial vehicle fleet with different vehicle architectures, technologies and performances using simulations. 4) Obtaining a set of system level (aircraft level) Measures of Performance (MoP) for the large suppression UAVs that produce improved SoS-level Measures of Effectiveness (MoE) during an initial attack quantified by containment time and total fire burnt area. As addressed by research questions and results. The response time and Number of Aircraft has large impact on success of the firefighting mission. As the time advantage deteriorate, the wild fire expands exponentially.", "keywords": [ "Firefighting", "Payload (computing)", "Computer science", "Systems engineering", "Aeronautics", "Cruise", "Engineering", "Simulation", "Aerospace engineering", "Computer security" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2021-2455" }, { "id": "https://openalex.org/W4412471483", "doi": "10.3934/jimo.2025113", "title": "A leader-wingman-inspired cooperative-competitive optimization algorithm for sustainable part collection routing problem", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Honghui Wang", "Binghai Zhou" ], "first_author": "Honghui Wang", "venue": "Journal of Industrial and Management Optimization", "publisher": "American Institute of Mathematical Sciences", "abstract": "Due to the challenge of just-in-time part collection in the automobile industry, as well as the growing interest in energy conservation, this paper focuses on the design of the sustainable part collection routes with flexible time windows and heterogeneous fleet. A bi-objective integer programming model is therefore established, aiming to minimize the total energy consumption for executing all collection tasks and the total penalty costs imposed on early and late deliveries. To efficiently solve this problem which is inherently NP-hard, a novel metaheuristic optimization algorithm based on the leader-wingman framework is proposed. The algorithm employs dual populations, one denoted as 'leaders' and the other as 'wingmen', to mimic the cooperation and competition mechanisms in drone operations. This division of labor encourages relentless improvement and adaptability throughout the optimization process, striking a balance between exploiting high-potential regions and exploring uncharted territories. Moreover, a two-stage repair mechanism is designed to repair the infeasible solutions. Finally, computational experiments reveal that the proposed algorithm outperforms the other five state-of-the-art algorithms in both solution quality and convergence speed, and the use of heterogeneous fleet could achieve significant reductions in energy consumption over a homogeneous one. Moreover, a specific case is applied to demonstrate the practicality of the proposed model and algorithm.", "keywords": [ "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Routing algorithm", "Mathematical optimization", "Operations research", "Algorithm", "Computer network", "Mathematics", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3934/jimo.2025113" }, { "id": "https://openalex.org/W4401242015", "doi": "10.52058/2786-6025-2024-7(35)-501-512", "title": "ANT ALGORITHM APPLICATION FOR ROUTE PLANNING FOR UNMANNED AERIAL VEHICLES (UAVs)", "year": 2024, "publication_date": "2024-08-02", "type": "article", "language": "uk", "authors": [ "Oleksandr M. Kryvonos", "Oleksandr Yatsenko", "Hlieb Firanskyi" ], "first_author": "Oleksandr M. Kryvonos", "venue": "Наука і техніка сьогодні", "publisher": null, "abstract": "Creating routes for drones is a convoluted process hence this objective requires to design optimal itineraries for safe interaction between groups of remotely piloted aerial systems (RPAS).Also, drone exploitation opens up completely new perspectives in logistic control systems because, in comparison with a ground vehicle, RPAS uses airspace as its primary road flying directly to the designated point.However, it must be maintained that a such small aircraft is vulnerable to weather conditions and does not have an excessive amount of energy which a drone operator always has to keep in mind when he intends to take off.Thereby, the problem of route design consists of numerous restrictions and demands optimal algorithms for efficient navigation.The article is devoted to the actual problem of using unmanned aerial vehicles in human activity.In particular, the article considers the task of routing unmanned aerial vehicles, which is a complex optimisation problem involving the determination of optimal routes for a group of UAVs to perform various tasks.The subject environment of the problem is characterised, which may include the following components: unmanned aerial vehicle, objects and recharging stations.It describes several existing analogues of platforms that can be used for route planning using unmanned aerial vehicles, in particular DJI Terra and Altitude Angel.The authors consider the most common types of vehicle route optimisation problems (Capacitated Vehicle Routing Problem, VRP with Time Windows, Multiple Depot VRP).The paper formulates a mathematical statement of the UAV routing problem.№ 7(35) 2024 502Considerable attention is paid to the ant algorithm and its modifications.The authors define the importance and prospects for further research on the use of heuristic algorithms for controlling semi-automated systems.", "keywords": [ "Route planning", "Computer science", "Drone", "Artificial intelligence", "ANT", "Aeronautics", "Real-time computing", "Algorithm", "Engineering", "Transport engineering", "Computer network", "Biology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://perspectives.pp.ua/index.php/nts/article/download/13465/13530" }, { "id": "https://openalex.org/W2946836529", "doi": null, "title": "ASSIGNMENT OF A FLEET OF UMMANNED AERIAL VEHICLES TO MULTI TARGETS USING GENETIC ALGORITHMS", "year": 2010, "publication_date": "2010-01-01", "type": "article", "language": "en", "authors": [ "Baha Pakkan", "Murat Ermiş" ], "first_author": "Baha Pakkan", "venue": "DOAJ (DOAJ: Directory of Open Access Journals)", "publisher": null, "abstract": "Capacity Constrained Vehicle Routing Problem (CCVRP), a combinatorial optimization problem seeking to service a number of customers with a fleet of vehicles which have limited carrying capacity of the goods that must be delivered. In this study, a Genetic Algorithms (GA) based solution method is developed to help Turkish Air Force Command aiming to detect possible foes by using Unmanned Air Vehicles (UAVs) in a more manageable and efficient manner. A user interface is designed to gather targets’ data from the Google Maps server and to import into Matlab database. The proposed novel GA method tries to optimize the path of each UAV which flies on. The results of computational experiments in order to evaluate the effectiveness of our GA method are presented. This study might be used for future online/real time target/route planning researches.", "keywords": [ "Computer science", "Genetic algorithm", "Algorithm", "Artificial intelligence", "Machine learning" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doaj.org/article/3154eb33360948f08595534616a00a43" }, { "id": "https://openalex.org/W2921228217", "doi": "10.1007/978-3-030-14984-0_27", "title": "Route Planning for Teams of Unmanned Aerial Vehicles Using Dubins Vehicle Model with Budget Constraint", "year": 2019, "publication_date": "2019-01-01", "type": "conference-paper", "language": "en", "authors": [ "David Zahrádka", "Robert Pěnička", "Martin Saska" ], "first_author": "David Zahrádka", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Orienteering", "Computer science", "GRASP", "Benchmark (surveying)", "Vehicle routing problem", "Mathematical optimization", "Constraint (computer-aided design)", "Motion planning", "Greedy algorithm", "Path (computing)", "Routing (electronic design automation)", "Robot" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-14984-0_27" }, { "id": "https://openalex.org/W7118625127", "doi": "10.1117/12.3096389", "title": "Truck–UAV dynamic collaborative delivery route optimization", "year": 2026, "publication_date": "2026-01-08", "type": "conference-paper", "language": null, "authors": [ "Zhiqiang Tian", "Shengping Liu", "Qi peng Song", "Yacheng Lan", "Jiabao Liang" ], "first_author": "Zhiqiang Tian", "venue": null, "publisher": null, "abstract": "To address the challenges of high costs and low efficiency in last-mile logistics, this study investigates a truck–drone collaborative routing problem that incorporates dynamic rendezvous points and time synchronization. An integer programming model is formulated with the objective of minimizing travel and waiting costs, while a dynamic rendezvous mechanism is introduced. A two-level interactive optimization framework is proposed: in the outer layer, a Genetic Algorithm (GA) determines the truck routes; in the inner layer, an improved Dung Beetle Optimization (DBO) algorithm generates the drone delivery paths, enabling efficient spatiotemporal coordination between trucks and drones. Simulation results demonstrate that the proposed method significantly reduces total system costs compared with single-truck delivery and conventional static collaboration. Across test instances of different scales, the dynamic coordination strategy achieves an average cost reduction of about 25% over static strategies, with more pronounced advantages in scenarios featuring widely dispersed and large customer sets. Applicable to e-commerce logistics, emergency delivery, and remote last-mile distribution, this approach helps logistics companies optimize resources and reduce costs.", "keywords": [ "Truck", "Rendezvous", "Integer programming", "Routing (electronic design automation)", "Genetic algorithm", "Vehicle routing problem", "Dynamic programming", "Reduction (mathematics)", "Optimization problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3096389" }, { "id": "https://openalex.org/W4415402735", "doi": "10.3390/app152011249", "title": "Integrated Two-Stage Optimization of Strategic Unmanned Aerial Vehicle Allocation and Operational Scheduling Under Demand Uncertainty", "year": 2025, "publication_date": "2025-10-21", "type": "article", "language": "en", "authors": [ "Xiaojin Zheng", "Shengkun Qin", "Yanxia Zhang", "Jiazhen Huo" ], "first_author": "Xiaojin Zheng", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The rapid growth of e-commerce has intensified the need for efficient last-mile delivery, making unmanned aerial vehicles (UAVs) a promising solution. However, despite their potential, practical deployment remains limited by how to effectively plan depot locations and UAV fleet sizes under stochastic customer demands with probabilistic same-day modifications. Existing approaches often address the strategic and operational decisions separately, leading to inefficiencies and infeasible solutions in practice. This study develops a unified two-stage decision framework integrating strategic depot location and UAV fleet allocation with operational assignment and scheduling. Three strategic models are considered: a deterministic model, a stochastic model solved via Sample Average Approximation (SAA), and a robust optimization model. Operational decisions assign UAV trips to realized requests while respecting time-slot and UAV availability constraints. Deterministic and SAA models are solved directly as integer programs, whereas the robust model is tackled via a logic-based Benders decomposition framework, with all approaches evaluated through simulation. The results show that the robust model provides overly conservative solutions, resulting in higher costs; the deterministic model minimizes cost but risks service failures; and the SAA approach balances cost and service across demand scenarios. The findings demonstrate the value of jointly considering strategic and operational decisions in UAV delivery design and provide practical guidance for UAV logistics operators. The proposed framework helps firms select appropriate planning models that align with their risk tolerance and service reliability goals, thereby improving the feasibility and competitiveness of UAV-based delivery systems.", "keywords": [ "Probabilistic logic", "Scheduling (production processes)", "Software deployment", "Stochastic programming", "Reliability (semiconductor)", "Service (business)", "Robust optimization", "Operational planning", "Integer programming", "Time horizon" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/15/20/11249/pdf?version=1761025148" }, { "id": "https://openalex.org/W7138838614", "doi": "10.1109/jiot.2026.3675581", "title": "Multitruck Multidrone Collaborative Delivery via EG-GAT Embedding Multiagent DRL in Rural Areas", "year": 2026, "publication_date": "2026-03-19", "type": "article", "language": null, "authors": [ "Huamei Qi", "Jiaxing Liu", "Xiaoheng Deng", "Hairong Lin", "Jinsong Gui", "Jingjing Zhang", "Shaohua Wan" ], "first_author": "Huamei Qi", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The vast geographic coverage and sparse customer distribution in rural areas lead to inefficiency in traditional last-mile delivery. Truck-drone collaborative delivery systems have emerged as a promising solution to these rural logistics challenges. Accordingly, we introduce a multi-truck multi-drone collaborative delivery framework. Within this framework, we propose a novel graph embedding module—the edge-gated graph attention network (EG-GAT)—which incorporates multi-dimensional edge features into the attention mechanism and introduces a learnable gating module for adaptive multi-head fusion. We further propose a graded flexible time window mechanism, which permits limited service advancement or deferral while applying graded incentive-penalty structures. This approach better captures the temporal flexibility inherent in rural customer service requirements. The resulting multi-objective truck-drone routing problem is modeled as a rewardmaximization task and solved using multi-agent proximal policy optimization (MAPPO) under a centralized-training with decentralized-execution framework. Extensive experimental results demonstrate that the proposed method outperforms other approaches. Furthermore, studies assess the individual effects of graded flexible time window settings and objective function weight coefficients on the optimization performance of collaborative delivery. Finally, we evaluate the practical advantages of our proposed model using real-world rural road cases.", "keywords": [ "Embedding", "Flexibility (engineering)", "Probabilistic logic", "Adaptability", "Enhanced Data Rates for GSM Evolution", "Graph", "Mobile telephony", "Service (business)", "Minification" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2026.3675581" }, { "id": "https://openalex.org/W4408020671", "doi": "10.1016/j.cie.2025.111005", "title": "Electric vehicle charging on the go via unmanned aerial vehicle: Optimal charging policy", "year": 2025, "publication_date": "2025-02-27", "type": "article", "language": "en", "authors": [ "Byeongmok Kim", "Seokcheon Lee" ], "first_author": "Byeongmok Kim", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Electric vehicle", "Automotive engineering", "Aeronautics", "Engineering", "Computer science", "Electrical engineering", "Physics", "Power (physics)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111005" }, { "id": "https://openalex.org/W4403704119", "doi": "10.1007/978-3-031-71397-2_17", "title": "Survey on Sensing, Modelling and Reasoning Aspects in Military Autonomous Systems", "year": 2024, "publication_date": "2024-10-24", "type": "conference-paper", "language": "en", "authors": [ "Eveline Kalff", "Thomas M. Maaiveld", "Maarten P. D. Schadd", "J.M. Voogd", "Louis F. Weyland" ], "first_author": "Eveline Kalff", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Artificial intelligence", "Data science", "Human–computer interaction" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-71397-2_17" }, { "id": "https://openalex.org/W7171963702", "doi": "10.1109/access.2026.3719107", "title": "Priority-Aware Unmanned Aerial Vehicles Route Planning for Forest Fire Monitoring Using Bayesian-Tuned Hybrid Metaheuristics", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Amalia Utamima", "Naufal Firjatulloh Fano" ], "first_author": "Amalia Utamima", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Forest and land fires remain a recurring environmental challenge in large forested regions, where spatially dispersed hotspots make timely monitoring and mitigation difficult. Unmanned aerial vehicles (UAVs) provide a flexible platform for rapid aerial surveillance, but effective route planning must account for travel distance, endurance limits, hotspot distribution, and inspection priorities. This study proposes a priority-aware UAV route-planning framework for forest fire monitoring, with experimental evaluation based on hotspot data from Kalimantan, Indonesia. The framework includes hotspot organization, supports short-horizon replanning across two consecutive monitoring cycles, and generates map-based visual outputs to support operational interpretation. To efficiently solve the planning problem, this study adopts an evolutionary computation perspective by developing a Bayesian-optimized GA–VNS method that combines Genetic Algorithm search with Variable Neighborhood Search intensification. Experimental results on the Ketapang and Melawi datasets show that the Bayesian-tuned GA-VNS consistently delivers feasible routing performance, with the largest distance reduction reaching 19.4% over GA in the Melawi Cycle 2 case. Sensitivity analysis further shows that hotspot prioritization improves the inspection order of high-priority locations while preserving strong routing performance. Overall, the findings suggest that the proposed framework provides practical decision-support value for wildfire monitoring and UAV deployment planning.", "keywords": [ "Route planning", "Metaheuristic", "Fire detection", "Firefighting", "Search and rescue" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2026.3719107" }, { "id": "https://openalex.org/W4367019978", "doi": "10.2139/ssrn.4421477", "title": "Cyclic Stochastic Inventory Routing Planning with Reorder Points and Recourse Decision for an Application in Medical Supply", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Alexander Rave", "Pirmin Fontaine", "Heinrich C. Kuhn" ], "first_author": "Alexander Rave", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Operations research", "Procurement", "Computer science", "Reorder point", "Stockout", "Business", "Operations management", "Supply chain", "Lead time", "Inventory control", "Inventory theory", "Variety (cybernetics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4421477" }, { "id": "https://openalex.org/W7123350283", "doi": "10.1016/j.apenergy.2026.127373", "title": "Load restoration strategy for post-disaster distribution networks considering cyber-physical-traffic coupling with multi-resource collaboration", "year": 2026, "publication_date": "2026-01-12", "type": "article", "language": "en", "authors": [ "Ying Wang", "Chunming Liu", "Yulong Zhao", "Xinyu Li", "Yuhan Wu" ], "first_author": "Ying Wang", "venue": "Applied Energy", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Scheduling (production processes)", "Flexibility (engineering)", "Fault management", "Load balancing (electrical power)", "Situation awareness", "Software deployment", "Telecommunications network", "Fault tolerance" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.apenergy.2026.127373" }, { "id": "https://openalex.org/W3152620107", "doi": "10.14569/specialissue.2018.090163", "title": "A Randomized Heuristic Algorithm for Cyclic Routing of UAVs", "year": 2018, "publication_date": "2018-01-01", "type": "article", "language": "en", "authors": [ "Cheng Siang Lim", "Shell Ying Huang" ], "first_author": "Cheng Siang Lim", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) have been increasingly used in military and civilian applications. Even though the UAV routing problems have similarities with Vehicle Routing Problems, there are still many problems where effective and efficient solutions are lacking. We propose a randomized heuristic algorithm for the cyclic routing of a single UAV. The UAV is required to visit a set of target areas where the time interval between consecutive visits to each area cannot exceed its relative deadline. The PSPACE-complete problem has a solution whose length may be exponential. Our algorithm tries to compute a feasible cyclic route while trying to keep short the cycle time. Our tests of 57 instances of the problem show that the algorithm has good effectiveness and efficiency.", "keywords": [ "Routing (electronic design automation)", "Heuristic", "Set (abstract data type)", "Computer science", "Algorithm", "Vehicle routing problem", "Interval (graph theory)", "Randomized algorithm", "Mathematical optimization", "Mathematics", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://thesai.org/Downloads/FTC2017/63_Paper_453-A_Randomized_Heuristic_Algorithm.pdf" }, { "id": "https://openalex.org/W4234623351", "doi": "10.1007/978-3-319-32193-6_79-2", "title": "UAV Logistic Support Definition", "year": 2021, "publication_date": "2021-01-01", "type": "book-chapter", "language": "en", "authors": [ "Sergio Chiesa", "Marco Fioriti" ], "first_author": "Sergio Chiesa", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Maintainability", "Relevance (law)", "Reliability (semiconductor)", "Computer science", "Systems engineering", "Sizing", "Risk analysis (engineering)", "Operations research", "Reliability engineering", "Engineering", "Software engineering", "Power (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-319-32193-6_79-2" }, { "id": "https://openalex.org/W3166788467", "doi": "10.1002/spe.3008", "title": "Big data analytics in Industry 4.0 ecosystems", "year": 2021, "publication_date": "2021-06-11", "type": "article", "language": "en", "authors": [ "Gagangeet Singh Aujla", "Radu Prodan", "Danda B. Rawat" ], "first_author": "Gagangeet Singh Aujla", "venue": "Software Practice and Experience", "publisher": "Wiley", "abstract": "The emergence of advanced technologies has triggered a sweeping digital transformation in the industrial ecosystem. The cutting-edge technologies (like, Internet of things, big data, artificial intelligence, drones, cyber-physical systems, and augmented reality, and computer vision) are key enablers of this industrial revolution. Industry 4.0 has reshaped the conventional manufacturing and production processes into automated operations and workflows. This industrial transition is fueled by advanced computing (cloud and edge computing), analytic (big data analytics and computational analytics), intelligent (machine and deep learning), and communication (programmable and intelligent networks) infrastructure and technologies. The collection, aggregation, analysis, and processing of big data generated from the industrial periphery (like manufacturing equipment and maintenance systems) enable real-time decision-making and autonomous opportunities. However, the voluminous size, variability, and frequency of this data bring a wide array of disputes and oppositions in the resource-limited Industrial systems. Moreover, the continuous decision-making workflow in production and manufacturing segments increases the sharing of data across different functions, systems, and organizational boundaries. For this reason, cloud computing and big data technologies (Hadoop and Map-Reduce) can improve the anticipated response and reaction times. Industry 4.0 will lead toward more devices enriched with embedded computing platforms which boost the capabilities of the overall workflow. But, this also leads towards an increased communication and interaction between these devices which can end up in various challenges for the underlying network infrastructure. However, the conventional communication protocols may end up in various performance bottlenecks which in turn can increase the threat from different kinds of attacks and security challenges. Concluding the above discussion, the industrial ecosystem would rely on two entities: (1) users or infrastructure (physical world) and (2) cloud-enabled algorithms and autonomous systems (virtual world) that are connected through advanced and autonomous communication technologies. The driving force behind the success of these industrial ecosystems relies on the efficient gathering/collection, analysis, and storage of data generated by smart devices and sensors. Under this domain, big data analytics is set to be driving predictive manufacturing and provide timely detection of anomalies and system failures to predict product quality. In this way, big data is bound to play a prominent role in driving the industrial ecosystem. Even more, the only reason for this concern is not limited to the volume of data but the major concern is the contribution of this data for the design and implementation of efficient industrial processes and policies. The interpretation and understanding of the available data help to the design of efficient processes and policies related to industrial systems. The focus of this special issue is to present novel and seminal contributions around the important issues and challenges related to big data management and analytics for industrial 4.0 ecosystems. It provides ground-breaking research from academia and industry, that emphasizes the novel solutions, applications, tools, software, and algorithms designed to handle the industrial big data. A substantial number of submissions were received for the special issue. The papers were reviewed by at least three reviewers and underwent a rigorous two rounds of reviews. After the completion of the peer review process, we have accepted 10 seminal contributions related to big data analytics for Industry 4.0. All the accepted papers either discuss the recent solutions related to big data analytics or proposes an innovative way of handling big data across diverse infrastructure deployments. The outline of these contributions discussed below. The first paper titled \"An Efficient Scheme for Secure Feature Location using Data Fusion and Data Mining in IOT Environment\" by Balaji et al.1 proposes a secure feature location approach based on data fusion and data mining to overcome the challenges of the existing textual and dynamic approaches. The first step in this approach involves the removal of repeated test cases followed by the selection of important attributes. The artificial flora optimization algorithm was used to remove the repeated test cases. After this, the Caesar Cipher-RSA algorithm was used to encrypt the selected attributes, and thereafter a score value was assigned to them. This score value acts as an input to the K-mean algorithm to normalize it using the min-max approach. The evaluation results show that the proposed approach is superior in comparison to existing variants. The second paper titled \"Data Dimensionality Reduction Techniques for Industry 4.0: Research Results, Challenges, and Future Research Directions\" by Chhikara et al.2 provides a comprehensive survey on dimensionality reduction techniques. This survey discussed various data dimensionality techniques, analyzed them, and provided a thorough comparison based on different factors and parameters. The survey provided an understanding of the applicability of dimensionality reduction techniques in group or stand-alone use cases. The third paper titled \"Deep-Q Learning-based Heterogeneous Earliest Finish Time Scheduling Algorithm for Scientific Workflows in Cloud\" by Kaur et al.3 proposes a workflow scheduling approach wherein a deep-Q learning mechanism is used. This deep-Q mechanism was based on a heterogeneous earliest-finish-time algorithm was designed to amalgamate the deep learning approach with the heuristic approach for task scheduling. The evaluations were performed on a workflow simulator and the results depict the superiority of the proposed approach in contrast to the existing algorithms in terms of makespan and speed. The fourth pap", "keywords": [ "Big data", "Analytics", "Data science", "Ecosystem", "Data analysis", "Computer science", "Business", "Data mining", "Ecology" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/spe.3008" }, { "id": "https://openalex.org/W3170658350", "doi": null, "title": "Optimizing Satellite Locations for a Multi-Echelon Last Mile Distribution Network to utilize Alternative Delivery Vehicles for Last Mile Delivery", "year": 2020, "publication_date": "2020-08-06", "type": "other", "language": "en", "authors": [ "Abhinav Goyal" ], "first_author": "Abhinav Goyal", "venue": "DSpace@MIT (Massachusetts Institute of Technology)", "publisher": "Massachusetts Institute of Technology", "abstract": "The growing urban population over the past few years has created many challenges for last mile distribution, such as traffic congestion, pollution, and lack of parking space availability. With the advent of e-commerce industry, the volumes for last mile delivery are growing consistently. As firms struggle to provide competitive delivery commitments to the end customers, they are exploring alternative delivery methods, such as drones and e-cargo bikes to navigate urban areas efficiently while addressing pollution and traffic concerns. However, range and capacity constraints associated with such alternative delivery modes restrict the operations that can be carried out with such vehicles. Hence, firms are re-designing their last mile distribution strategies to adapt to the constraints posed by these delivery modes. One such strategy is to deploy a multi-echelon distribution network, using satellite nodes near customer locations that allow for transshipment. The large conventional trucks deliver parcels to a satellite, from whereon the parcels are cross-docked into lighter vehicles (such as e-cargo bikes) which perform the final delivery. This project introduces a mixed integer linear programming model for a two-echelon delivery network, to determine the optimal count and locations of satellites for a large parcel company. The transactional data for deliveries and pickups associated with one parcel center has been used to develop and test the model. The first-tier transportation, that is from parcel center to satellites, has been designed as a location routing problem. The second-tier transportation, that is from satellites to customer delivery points, has been designed as an allocation problem. The model tries to minimize the associated costs with the satellite operation, optimizing the fixed cost of establishing a satellite against the cost of distance travelled and transit time. Traffic considerations and road network distances have been accounted for by using real road network data for distance calculations, and transit times adjusted for traffic conditions across multiple hours during the day. The model returns the count of satellites to be established, along with their respective locations and vehicle routes for first tier transportation. Finally, the model maps all the customers to their respective satellites to achieve an optimum distribution cost.", "keywords": [ "Mile", "Last mile (transportation)", "Computer science", "Satellite", "Telecommunications", "Engineering", "Geography", "Geodesy" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1721.1/126489" }, { "id": "https://openalex.org/W4417364680", "doi": "10.1049/sbra563e_ch10", "title": "Optimizing UAV battery usage for supporting underwater acoustic networks", "year": 2025, "publication_date": "2025-12-16", "type": "book-chapter", "language": "en", "authors": [ "Muhammad Mudasir Atif", "Ahmed Hussain", "Seung Yeob Nam" ], "first_author": "Muhammad Mudasir Atif", "venue": null, "publisher": null, "abstract": "The need for marine surveillance, as well as underwater communication, has brought attention to the use of unmanned aerial vehicles (UAVs) and developed the concept of underwater acoustic sensor networks (UASNs) enhanced by UAVs. The batteries on UAVs often hinder their performance, mainly when they are required to travel far into remote regions or by sea. This chapter discusses the issues connected with batteries and suggests that defining optimal paths is a good way to solve them. Several methods such as smart routing, smart positioning of docking stations, and energy-saving communication methods are studied to reduce power consumption. Clustering algorithms, gaining-sharing knowledge (GSK) and the capacitated vehicle routing problem (CVRP) techniques are used to make planning efficient. The method conserves power, but fulfills requirements such as UAV memory and its allotted airtime. Using adaptation algorithms and wind information made in real time helps reduce energy use. The chapter also considers other options, such as solar drones, platforms in the sky known as high-altitude platforms (HAPs), and charging stations on the water. The emphasis is on using AI-based systems for forecasting, guiding energy management, and correcting the path of a ship on its own. Although the chapter has a good technical model, it does not discuss underwater humidity, team efforts between UAVs and autonomous underwater vehicles (AUVs), what happens if an AUV fails, and the unreliable quality of GPS at sea. The following areas could be improved in the future with swarm intelligence, harvesting of renewable energy, and the use of non-rusty materials. In general, the research in this chapter covers how to make UAVs more sustainable for ocean missions, helping shape the future of collecting marine data and surveilling the environment with intelligent aircraft.", "keywords": [ "Underwater", "Global Positioning System", "Sonar", "Swarm behaviour", "Motion planning", "Adaptation (eye)", "Cluster analysis", "Underwater acoustic communication", "Wind power" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1049/sbra563e_ch10" }, { "id": "https://openalex.org/W7203648495", "doi": "10.3390/rs18162753", "title": "UAV Remote Sensing Mission Scheduling for Vessel Emission Monitoring in Emission Control Areas", "year": 2026, "publication_date": "2026-08-15", "type": "article", "language": "en", "authors": [ "Yunxiang Shu", "Haoran Li", "Qixiu Cheng", "Shuaian Wang" ], "first_author": "Yunxiang Shu", "venue": "Remote Sensing", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Maritime emissions in Emission Control Areas pose significant environmental and health concerns, and effective monitoring is essential for regulatory compliance. This paper addresses the scheduling of drones as a remote sensing platform for inspecting vessel emissions in these areas. A mixed-integer linear programming framework is proposed based on a time-expanded network representation. The model incorporates three practical features of the inspection environment. First, vessels are assumed to follow uniform linear motion within a narrow approach channel, enabling a closed-form expression for drone flying times. Second, inspection sub-windows are introduced to account for exhaust plume interference that may affect detection accuracy. Third, a time-dependent weighting scheme is adopted to encourage early inspections. The resulting optimisation model is solved directly using Gurobi. Computational experiments on instances with 30 vessels and a fleet of 15 drones demonstrate that the proposed formulation achieves an average optimality gap of 0.01%, with an average solution time of 5.91 s. Sensitivity analyses reveal that fleet size beyond 15 drones yields negligible additional benefit, while raising drone speed from 20 to 35 knots enables multi-vessel inspection tours and yields a relative increase of 85% in the number of vessels inspected. The results indicate that the proposed framework provides effective support for drone-based vessel inspection scheduling in Emission Control Areas.", "keywords": [ "Drone", "Scheduling (production processes)", "Weighting", "Linear programming", "Command and control", "Remote control" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/rs18162753" }, { "id": "https://openalex.org/W2808720005", "doi": "10.2991/ncce-18.2018.163", "title": "Path Planning-aiding System of Unmanned Aerial Vehicle in Freight Transportation", "year": 2018, "publication_date": "2018-01-01", "type": "conference-paper", "language": "en", "authors": [ "Yujia Zhang", "Shaobo Zhai", "Dongmei Wang", "Long Fei Chen" ], "first_author": "Yujia Zhang", "venue": "Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)", "publisher": null, "abstract": "This paper deals with the Unmanned Aerial Vehicle (UAV) Routing and Scheduling problem applied in logistics, with the implementation of Dynamic Mixed Integer Linear Programming (MILP) to tackle the problem.The MILP accounts for multiply constraints and objectives based on the demand of practicality and flexibility in real logistic operation: the minimal traveling cost and shortest delivery time, division of cold chain and ordinary logistics, aerial vehicle capacity, delivery and pickup requirement, the damage rate of cold chain goods, time window, etc. Formulations are proposed by modifying the vehicle routing problem with time window (VRPTW) with the increased time and space complexity.Then computer experiment is carried out by applying a commercial linear equation solver Gurobi.The output of proposed model involves the optimal routes, which shows a highly-acceptable solution with high-efficiency.", "keywords": [ "Motion planning", "Path (computing)", "Transport engineering", "Computer science", "Traffic management", "Automotive engineering", "Engineering", "Robot", "Artificial intelligence", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://download.atlantis-press.com/article/25896641.pdf" }, { "id": "https://openalex.org/W3182492452", "doi": "10.1145/3449726.3459526", "title": "Optimising pheromone communication in a UAV swarm", "year": 2021, "publication_date": "2021-07-07", "type": "conference-paper", "language": "en", "authors": [ "Daniel H. Stolfi", "Matthias R. Brust", "Grégoire Danoy", "Pascal Bouvry" ], "first_author": "Daniel H. Stolfi", "venue": "Proceedings of the Genetic and Evolutionary Computation Conference Companion", "publisher": null, "abstract": "Communications in multi-robot systems is a key factor, especially when aiming for real-world applications. In this article we address the optimisation of the communications in a swarm of unmanned aerial vehicles for surveillance applications. More precisely a genetic algorithm is introduced to optimize the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which enhance the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage.", "keywords": [ "Ant colony optimization algorithms", "Computer science", "Swarm behaviour", "Key (lock)", "Chaotic", "Mobile robot", "Robot", "Swarm intelligence", "Pheromone", "Ant colony", "Genetic algorithm", "Distributed computing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dl.acm.org/doi/pdf/10.1145/3449726.3459526" }, { "id": "https://openalex.org/W7214150906", "doi": "10.71548/18363", "title": "Three-Dimensional UAV Path Planning for Last-Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "dissertation", "language": "en", "authors": [ "Sarah Hamed" ], "first_author": "Sarah Hamed", "venue": "MacSphere (McMaster University)", "publisher": "McMaster University", "abstract": "There is an increasing interest in using unmanned aerial vehicles (UAVs), or drones, for last-mile delivery, with cities competing to achieve faster and more efficient delivery systems. However, the usage of UAVs is limited due to battery capacity and the need to navigate through a complex three-dimensional environment. This thesis aims to advance UAV operational algorithms for last-mile delivery applications. The thesis comprises two related studies using the same five path planning algorithms: Dijkstra, A*, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Rapidly-exploring Random Tree star (RRT*). The first study is focused on UAVs routing problem through evaluating the algorithms based on their ability to create efficient paths in a three dimensional environment. The second study is focused on an energy-based problem, where a UAV energy model is integrated into the algorithms to analyze energy-efficient 3D path planning, and the contributing factors for UAV energy consumption.", "keywords": [ "Motion planning", "Path (computing)", "Particle swarm optimization", "Energy (signal processing)", "Routing (electronic design automation)", "Genetic algorithm", "Random tree", "Tree (set theory)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.71548/18363" }, { "id": "https://openalex.org/W7161579746", "doi": "10.1007/978-981-95-7648-7_16", "title": "A UGV-UAV Cooperative Navigation Framework: Obstacle-Aware Planning for Perception-Limited UGVs Enhanced by LiDAR-Based UAV Mapping", "year": 2026, "publication_date": "2026-01-01", "type": "conference-paper", "language": "en", "authors": [ "Haoran Xie", "Xuting Duan", "Feiyang Zhao", "Qi Wang", "Sifan Wu", "Yongzhuo Yu" ], "first_author": "Haoran Xie", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Field (mathematics)", "Motion planning", "Mobile robot", "Robot", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-95-7648-7_16" }, { "id": "https://openalex.org/W7212151883", "doi": "10.1002/cpe.70942", "title": "Evaluating Waypoint‐Based and Flocking‐Based Mobility Models for Military UAV Swarm Coverage", "year": 2026, "publication_date": "2026-09-01", "type": "article", "language": "en", "authors": [ "Sumayah A. Almuntasheri", "Bander A. Jabr", "Mohammed J. F. Alenazi" ], "first_author": "Sumayah A. Almuntasheri", "venue": "Concurrency and Computation Practice and Experience", "publisher": "Wiley", "abstract": "ABSTRACT UAV swarms are increasingly used in military surveillance and area coverage missions, where the chosen mobility model is critical for determining the efficacy of drone coordination and operational area coverage. This paper presents a comparative analysis of two UAV swarm mobility models: A waypoint‐based leader–follower model and a flocking‐based decentralized model. The waypoint‐based model is evaluated across five formation patterns (line, V‐shape, triangle, diamond, and circle), while the flocking‐based model is tested using four mobility patterns (line, zigzag, patrol, and random). The waypoint‐based model is evaluated for point target missions, while the flocking‐based model is evaluated for area coverage missions. A scalability analysis across various swarm sizes is also applied to the flocking‐based model. The results indicate that within the leader–follower model, the circle formation achieves perfect waypoint coverage (100%), whereas the line formation attains the highest total area coverage (744,675 m 2 ). Conversely, in the flocking‐based model, the line pattern yields the highest performance, reaching 100% coverage with 32 drones. Scalability analysis further reveals that the line pattern achieves 95% coverage with as few as 10 drones, while the zigzag pattern fails to reach this threshold regardless of fleet size. These findings offer practical guidelines for selecting mobility models tailored to specific military mission requirements.", "keywords": [ "Waypoint", "Swarm behaviour", "Scalability", "Drone", "Mobility model", "Line (geometry)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1002/cpe.70942" }, { "id": "https://openalex.org/W4388927317", "doi": "10.1016/j.ifacol.2023.10.212", "title": "Stability focused evaluation and tuning of special ground vehicle tracking algorithms", "year": 2023, "publication_date": "2023-01-01", "type": "article", "language": "en", "authors": [ "Peter H. Bauer", "Mihály Nagy", "Gergely István Kuna", "Ádám Kisari", "E. Simonyi", "Antal Hiba", "István Drotár" ], "first_author": "Peter H. Bauer", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "This paper deals with a special tracking problem when a ground vehicle should be tracked by a multicopter flying ahead of the vehicle. Pre-designed vehicle route is assumed and the UAV stops or slows down at every intersection to react to route changes. After introducing the problem, the methods applied in a real flight demonstration in the Smart City module of ZalaZONE proving ground are presented. Then new methods are introduced to possibly improve performance. The main focus of the article is the evaluation of the stability of the methods and the provision of tuning guidelines. All of the introduced methods is tuned based-on the guidelines considering real ground vehicle test data and the high fidelity simulation of the applied multicopter. The two best methods are compared in detail and guidelines of their applicability are provided.", "keywords": [ "Intersection (aeronautics)", "Stability (learning theory)", "Computer science", "Tracking (education)", "Fidelity", "Algorithm", "Simulation", "Aerospace engineering", "Engineering", "Machine learning" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2023.10.212" }, { "id": "https://openalex.org/W4407414837", "doi": "10.2514/6.2025-1612", "title": "Cooperative Mission Allocation in MUM-T Search and Destroy Operation With Transformer-Based Deep Reinforcement Learning", "year": 2025, "publication_date": "2025-01-03", "type": "conference-paper", "language": "en", "authors": [ "Bo Hoon Moon", "Jun Hyeok Ji", "HyeonJun Lee", "Aye Aye Maw", "Jae-Woo Lee" ], "first_author": "Bo Hoon Moon", "venue": null, "publisher": null, "abstract": "In complex and diverse mission environments such as MUM-T (Manned-Unmanned-Teaming) systems, mission allocation of air assets must be conducted quickly and efficiently by the operator. This study addresses the mission allocation problem in WASD (Wide Area Search and Destroy) operations involving manned helicopters and Unmanned Air Vehicles (UAVs). In this scenario, UAVs are tasked with surveilling multiple unidentified Region of Interests (ROIs), while the manned helicopter conducts strike operations on enemy targets based on the gathered reconnaissance data. To enhance reconnaissance efficiency and operational safety, the Standoff-Sweep methodology is employed for UAVs, enabling systematic mission distance calculation and complete coverage of ROIs while maintaining a safe standoff distance. The mission allocation problem in each operational phase is formulated as combinatorial optimization problems, specifically variants of the Vehicle Routing Problem (VRP) such as the Split-Delivery VRP (SDVRP) and Capacitated VRP (CVRP). To solve these problems using deep reinforcement learning, a Policy Neural Network incorporating an Encoder-Decoder architecture with Multi-Head Attention mechanisms is trained using the REINFORCE-baseline algorithm. The proposed methodology enables the acquisition of approximate solutions to the mission allocation problem formulated as a combinatorial optimization problem in a shorter time compared to traditional heuristic algorithms.", "keywords": [ "Reinforcement learning", "Transformer", "Reinforcement", "Computer science", "Artificial intelligence", "Engineering", "Electrical engineering", "Voltage", "Structural engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2025-1612" }, { "id": "https://openalex.org/W4382050865", "doi": "10.1109/icuas57906.2023.10156192", "title": "Spatiotemporal VRP for Collision-free Multi-UAV Inspection Planning", "year": 2023, "publication_date": "2023-06-06", "type": "conference-paper", "language": "en", "authors": [ "Jaehan Im", "Youngjoo Kim" ], "first_author": "Jaehan Im", "venue": null, "publisher": null, "abstract": "The study proposes a method for planning optimal, collision-free routes for multiple UAVs for infrastructure inspections. The conventional approach of using the Vehicle Routing Problem (VRP) has proven to be inadequate due to the complexity of the routing problem and the difficulty in considering inter-vehicle conflict situations. To address these challenges, the Spatiotemporal VRP algorithm is introduced, which considers temporal occupation information over sparse inspection graphs. The proposed algorithm is capable of handling large-sized graphs with several hundreds of nodes and has been shown to be effective in finding feasible solutions without any failure through a series of Monte-Carlo experiments and a case study. The results of the case study demonstrate the potential of the proposed algorithm to be adapted to real-world scenarios and provide a promising solution for optimizing UAV inspection routes.", "keywords": [ "Vehicle routing problem", "Computer science", "Routing (electronic design automation)", "Collision", "Route planning", "Monte Carlo method", "Mathematical optimization", "Mathematics", "Computer network", "Computer security" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/icuas57906.2023.10156192" }, { "id": "https://openalex.org/W1880609699", "doi": "10.48550/arxiv.1504.02151", "title": "A Linear Time Algorithm for the $3$-neighbour Traveling Salesman Problem on Halin graphs and extensions", "year": 2015, "publication_date": "2015-04-08", "type": "preprint", "language": "en", "authors": [ "Brad Woods", "Abraham P. Punnen", "Tamon Stephen" ], "first_author": "Brad Woods", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The Quadratic Travelling Salesman Problem (QTSP) is to find a least cost Hamilton cycle in an edge-weighted graph, where costs are defined on all pairs of edges contained in the Hamilton cycle. This is a more general version than the commonly studied QTSP which only considers pairs of adjacent edges. We define a restricted version of QTSP, the $k$-neighbour TSP (TSP($k$)), and give a linear time algorithm to solve TSP($k$) on a Halin graph for $k\\leq 3$. This algorithm can be extended to solve TSP($k$) on any fully reducible class of graphs for any fixed $k$ in polynomial time. This result generalizes corresponding results for the standard TSP. TSP($k$) can be used to model various machine scheduling problems as well as an optimal routing problem for unmanned aerial vehicles (UAVs).", "keywords": [ "Travelling salesman problem", "Combinatorics", "Mathematics", "Hamiltonian path", "Graph", "Time complexity", "Discrete mathematics", "Algorithm" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1504.02151" }, { "id": "https://openalex.org/W3123641920", "doi": "10.20944/preprints201809.0487.v1", "title": "Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance and Reconnaissance", "year": 2018, "publication_date": "2018-09-25", "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", "Integer programming", "Heuristic", "Drone", "Base (topology)", "Military Base", "Real-time computing", "Operations research", "Artificial intelligence", "Computer network", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/manuscript/201809.0487/v1/download" }, { "id": "https://openalex.org/W7117869352", "doi": "10.1109/tits.2025.3647141", "title": "M-SIALNS for Air–Ground Collaborative Inspection: Spatio-Temporal Conflict Mitigation in Complex Bi-Layer Networks", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "M. Zhang", "Yuanhao Xu", "Xuewei Yu", "Chunyan Zhang", "Jianlei Zhang" ], "first_author": "M. Zhang", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Multi-vehicle–UAV collaborative inspection systems face critical challenges in complex environments, where spatiotemporal node conflicts, coupled scheduling, and congestion severely affect operational efficiency. To address these issues, we define the Multi-UAV–Multi-Vehicle Collaborative Inspection Vehicle Routing Problem in a Bi-Layer Road Network (MUMV-CIVRP-BLRN). The model captures realistic inspection scenarios through (i) spatiotemporal node-conflict constraints that prevent simultaneous vehicle access to occupied nodes and (ii) flexible UAV operations, enabling chained multi-task sorties and cross-vehicle recovery. To solve this NP-hard problem, we propose a Multi-Strategy Improved Adaptive Large Neighborhood Search (M-SIALNS) algorithm. Beyond standard ALNS frameworks, M-SIALNS incorporates a cluster-based initialization method, task-chain destroy–repair operators, and air–ground coordination strategies specifically tailored for the bi-layer structure. These strategies enhance global search, solution feasibility, and robustness. Comprehensive experiments on benchmark datasets and a power-grid case study demonstrate the advantages of M-SIALNS. Compared with state-of-the-art algorithms, it reduces inspection duration by 1.6%–11.0%, consistently delivering statistically significant improvements. Ablation and sensitivity analyses confirm the contribution of tailored operators and provide managerial insights into optimal fleet configurations and resource allocation thresholds. This work advances both the theoretical modeling of bi-layer vehicle–UAV routing and its practical deployment in large-scale inspection missions.", "keywords": [ "Initialization", "Software deployment", "Benchmark (surveying)", "Routing (electronic design automation)", "Resource allocation", "Vehicle routing problem", "Node (physics)", "Resource (disambiguation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tits.2025.3647141" } ]