Enhancing Ground Vehicle Route Planning with Multi-Drone Integration
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
2nd International Congress of Electrical and Computer Engineering, ICECENG 2023 -- 22 November 2023 through 25 November 2023 -- Bandirma -- 309799
Anahtar Kelimeler
Heuristic Solution Approach, Hybrid Genetic Algorithm, Machine Learning, Multiple Drone, Route Problem
Kaynak
EAI/Springer Innovations in Communication and Computing
WoS Q Değeri
Scopus Q Değeri
Q3
