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Article Dans Une Revue International Transactions in Operational Research Année : 2023

Improving neighborhood exploration into MOEA/D framework to solve a bi-objective routing problem

Résumé

Local search (LS) algorithms are efficient metaheuristics to solve combinatorial problems. The performance of LS highly depends on the neighborhood exploration of solutions. Many methods have been developed over the years to improve the efficiency of LS on different problems of operations research. In particular, the exploration strategy of the neighborhood and the exclusion of irrelevant neighboring solutions are design mechanisms that have to be carefully considered when tackling NP-hard optimization problems. A MOEA/D framework including an LSbased mutation and knowledge discovery mechanisms is the core algorithm used to solve a bi-objective vehicle routing problem with time windows (bVRPTW) where the total traveling cost and the total waiting time of drivers have to be minimized. We enhance the classical LS exploration strategy of the neighborhood from the literature of scheduling and propose new metrics based on customers distances and waiting times to reduce the neighborhood size. We conduct a deep analysis of the parameters to give a fine-tuning of the MOEA/D framework adapted to the LS variants and to the bVRPTW. Experiments show that the proposed neighborhood strategies lead to better performance on both Solomon's and Gehring and Homberger's benchmarks.
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Dates et versions

hal-04299349 , version 1 (13-02-2024)

Identifiants

Citer

Clément Legrand, Diego Cattaruzza, Laetitia Jourdan, Marie-Eléonore Kessaci. Improving neighborhood exploration into MOEA/D framework to solve a bi-objective routing problem. International Transactions in Operational Research, 2023, Developments in Metaheuristics, 30 (2), pp.1179 - 1180. ⟨10.1111/itor.13223⟩. ⟨hal-04299349⟩
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