Optimization of Transportation Routes with Multiple Drones: A Proposal Using Machine Learning, Metaheuristics, and Exact Solutions

  • Ricardo Vieira Chagas de Oliveira PUC-GO
  • Alexandre Ribeiro PUC-GO
  • Maria José Pereira Dantas PUC-GO

Abstract


The increasing utilization of drones for last-mile deliveries, motivated by the reduction of delivery time and maintenance costs, as well as by the mitigation of CO2 emissions, demands robust solutions for the multiple drone transport route optimization problem (Covering Salesman Problem with Multiple Drones – CSPMD). This article proposes a multilayer solution to this problem, in an approach that integrates Machine Learning (ML), metaheuristic, and dynamic programming. The obtained results indicate that the use of specific metaheuristics, in the context of this multilayer framework, constitutes a promising strategy for solving the CSPMD. The performance evaluation of these metaheuristics indicated that the Genetic Algorithm outperformed in small instances, achieving a cost up to 0.49% lower, while Simulated Annealing proved superior in large instances, with a cost up to 1.93% lower.

Keywords: Route Optimization, Drones, CSPMD, Machine Learning, Metaheuristics

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Published
2025-12-04
OLIVEIRA, Ricardo Vieira Chagas de; RIBEIRO, Alexandre; DANTAS, Maria José Pereira. Optimization of Transportation Routes with Multiple Drones: A Proposal Using Machine Learning, Metaheuristics, and Exact Solutions. In: REGIONAL SCHOOL ON INFORMATICS OF GOIÁS (ERI-GO), 13. , 2025, Luziânia/GO. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 165-174. DOI: https://doi.org/10.5753/erigo.2025.17111.