A hybrid adaptive iterated local search with diversification control to the capacitated vehicle routing problem

Author(s)

Maximo, Vinicius R. ^[1] ; Nascimento, V, Maria C.

Affiliation

^[1] V, Univ Fed Sao Paulo, UNIFESP, Inst Ciencia & Tecnol, Av Cesare MG Lattes 1201, BR-12247014 Sao Jose Dos Campos, SP - Brazil

• Document type: Journal article
• Source: European Journal of Operational Research; v. 294, n. 3, p. 1108-1119, NOV 1 2021.
• Web of Science Citations: 0

Abstract

Metaheuristics are widely employed to solve hard optimization problems, like vehicle routing problems (VRP), for which exact solution methods are impractical. In particular, local search-based metaheuristics have been successfully applied to the capacitated VRP (CVRP). The CVRP aims at defining the minimum cost delivery routes for a given set of identical vehicles since each vehicle only travels one route and there is a single (central) depot. The best metaheuristics to the CVRP avoid getting stuck in local optima by embedding specific hill-climbing mechanisms such as diversification strategies into the solution methods. This paper introduces a hybridization of a novel adaptive version of Iterated Local Search with PathRelinking (AILS-PR) to the CVRP. The major contribution of this paper is an automatic mechanism to control the diversity step of the metaheuristic to allow it to escape from local optima. The results of experiments with 100 benchmark CVPR instances show that AILS-PR outperformed the state-of-the-art CVRP metaheuristics. (c) 2021 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 19/22067-6 - Learning strategies for heuristic search in combinatorial optimization problems
• Grantee: Mariá Cristina Vasconcelos Nascimento Rosset
• Support Opportunities: Scholarships abroad - Research
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC