Título: Biased Random-key Genetic Algorithm for theHybrid Vehicle-drone Routing Problem for Pick-upand Delivery
Autores: Anderson Zudio, Igor Machado Coelho and Luiz Satoru Ochi
Resumo:
The Hybrid Vehicle drone Routing Problem (HVDRP) was recently introduced as an extension of the classic Vehicle Routing Problem (VRP). In this version, one vehicle is equipped with multiple drones to serve customers with demands for pick-up and delivery. The vehicle travels between stations that serve as parking locations to dispatch drones to attend clients. The drones have limitations in their maximum flight range and carrying capacity. We propose a BRKGA algorithm to solve HVDRP with a decoder component specially tailored to find feasible solutions. The proposed method is empirically analyzed in solution quality through a test set that a mixed-integer programming (MIP) model implementation can optimally solve in reasonable computation time. The computational result shows that the best solution found by BRKGA for each instance of the test set matches the solution quality devised by the MIP implementation. The data also show that the proposed algorithm achieves the best solution consistently through many independent executions. The instance set used and its respective best solutions attained for this work are publicly available.
Palavras-chave:
BRKGA, HVDRP, Metaheuristic.
Páginas: 6
Código DOI: 10.21528/CBIC2021-107
Artigo em pdf: CBIC_2021_paper_107.pdf
Arquivo BibTeX: CBIC_2021_107.bib