Enhancing Ground Vehicle Route Planning with Multi-Drone Integration

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dc.contributor.authorBakirci, Murat
dc.contributor.authorÖzer, Muhammed Mirac
dc.date.accessioned2025-03-17T12:22:44Z
dc.date.available2025-03-17T12:22:44Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description2nd International Congress of Electrical and Computer Engineering, ICECENG 2023 -- 22 November 2023 through 25 November 2023 -- Bandirma -- 309799
dc.description.abstractThis study aims to address the challenge of coordinating time and location for route planning in ground vehicles during simultaneous disaster response missions, utilizing a hybrid genetic algorithm based on machine learning. The proposed method combines both precise and heuristic solutions by integrating exact solution approaches while managing multiple drones during concurrent response efforts. The research evaluates the performance of these methods and assesses the impact of using multiple drones on disaster response times. The results unequivocally demonstrate the effectiveness of the hybrid genetic algorithm in solving small-/medium-sized problems involving drones. Furthermore, the study examines variables such as the number of drones and battery life, thereby elaborating their influence on response times and aiding in strategic decision-making. Compared to the TSP solution, single drone integration reduced response time by 30%, while two drones achieved a 45% reduction, and three drones a 50% reduction. Furthermore, an increase in the number of drones has been accompanied by a decrease in the workload for each drone, allowing for a reduction in battery capacity requirements in an inversely proportional manner to the increase in the number of drones. Moreover, this research underscores the adaptability of the hybrid genetic algorithm (HGA) in addressing the Vehicle Routing Problem with Multiple Drones (VRP-mD), a complex simultaneous deployment issue involving ground vehicles and multiple drones. This finding represents a significant contribution, expanding the potential of hybrid algorithms in tackling larger and more intricate distribution challenges. This approach holds promise for broader applications in solving complex intervention problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
dc.identifier.doi10.1007/978-3-031-52760-9_8
dc.identifier.endpage117
dc.identifier.isbn978-303152759-3
dc.identifier.issn2522-8595
dc.identifier.scopus2-s2.0-85189497033
dc.identifier.scopusqualityQ3
dc.identifier.startpage103
dc.identifier.urihttps://doi.org/10.1007/978-3-031-52760-9_8
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1330
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofEAI/Springer Innovations in Communication and Computing
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectHeuristic Solution Approach
dc.subjectHybrid Genetic Algorithm
dc.subjectMachine Learning
dc.subjectMultiple Drone
dc.subjectRoute Problem
dc.titleEnhancing Ground Vehicle Route Planning with Multi-Drone Integration
dc.typeConference Object

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