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Complexity
Volume 2017, Article ID 1702506, 15 pages
https://doi.org/10.1155/2017/1702506
Research Article

Parameter Tuning for Local-Search-Based Matheuristic Methods

1Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
2Instituto Tecnológico Metropolitano, Calle 73 No. 76A-374, Vía al Volador, Medellín, Colombia
3Universidad de Playa Ancha, Casilla 34-V, Valparaíso, Chile
4Universidad Diego Portales, 8370109 Santiago, Chile
5CIMFAV-Facultad de Ingeniería, Universidad de Valparaíso, 2374631 Valparaíso, Chile

Correspondence should be addressed to Guillermo Cabrera-Guerrero; lc.vcup@arerbac.omrelliug

Received 20 June 2017; Revised 2 October 2017; Accepted 25 October 2017; Published 31 December 2017

Academic Editor: Kevin Wong

Copyright © 2017 Guillermo Cabrera-Guerrero et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and continuous decision variables. One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. In general, we say that both mixed integer (non)linear programming problems and combinatorial optimisation problems can be addressed using this strategy. Beside the number of parameters researchers need to adjust when using heuristic methods, additional parameters arise when using matheuristic methods. In this paper we focus on one particular parameter, which determines the size of the subproblem. We show how matheuristic performance varies as this parameter is modified. We considered a well-known NP-hard combinatorial optimisation problem, namely, the capacitated facility location problem for our experiments. Based on the obtained results, we discuss the effects of adjusting the size of subproblems that are generated when using matheuristics methods such as the one considered in this paper.