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.


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    Title :

    Enhancing Ground Vehicle Route Planning with Multi-Drone Integration


    Additional title:

    EAI/Springer Innovations in Communication and Computing


    Contributors:

    Conference:

    International Congress of Electrical and Computer Engineering ; 2023 ; Bandirma, Türkiye November 22, 2023 - November 25, 2023



    Publication date :

    2024-03-19


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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