Author(s)

Emile Nawej Kayij, Joél Lema Makubikua, Justin Dupar Kampempe Busili

Faculty of Technological and Sciences, University of Kinshasa, Kinshasa, Democratic Republic of the Congo.

The main objective of this paper is to propose a new hybrid algorithm for solving the Bi objective green vehicle routing problem (BGVRP) from the BicriterionAnt metaheuristic. The methodology used is subdivided as follows: first, we introduce data from the GVRP or instances from the literature. Second, we use the first cluster route second technique using the k-means algorithm, then we apply the BicriterionAntAPE (BicriterionAnt Adjacent Pairwise Exchange) algorithm to each cluster obtained. And finally, we make a comparative analysis of the results obtained by the case study as well as instances from the literature with some existing metaheuristics NSGA, SPEA, BicriterionAnt in order to see the performance of the new hybrid algorithm. The results show that the routes which minimize the total distance traveled by the vehicles are different from those which minimize the CO₂ pollution, which can be understood by the fact that the objectives are conflicting. In this study, we also find that the optimal route reduces product CO₂ by almost 7.2% compared to the worst route.

Metaheuristics, Green Vehicle Routing Problem, Ant Colony Algorithm, Genetic Algorithms, Green Logistics

Kayij, E. , Makubikua, J. and Busili, J. (2023) New Hybrid Algorithm Based on BicriterionAnt for Solving Multiobjective Green Vehicle Routing Problem. American Journal of Operations Research, 13, 33-52. doi: 10.4236/ajor.2023.133003.