A hybrid particle swarm optimization algorithm for the capacitated location routing problem

Abstract

Purpose

The purpose of this paper is to solve the capacitated location routing problem (CLRP), which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions, using a hybrid particle swarm optimization (PSO) algorithm.

Design/methodology/approach

PSO, which is a population-based metaheuristic, is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP.

Findings

The algorithm is tested on a set of instances available in the literature and gave good quality solutions, results are compared to those obtained by other metaheuristic, evolutionary and PSO algorithms.

Originality/value

Local search is a time consuming phase in hybrid PSO algorithms, a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase, moves are applied directly to particles, a clear decoding method is adopted to evaluate a particle (solution) and there is no need to re-encode solutions in the form of particles after applying local search.

Citation

Kechmane, L., Nsiri, B. and Baalal, A. (2018), "A hybrid particle swarm optimization algorithm for the capacitated location routing problem", International Journal of Intelligent Computing and Cybernetics, Vol. 11 No. 1, pp. 106-120. https://doi.org/10.1108/IJICC-03-2017-0023