A novel approach to combine the hierarchical and iterative techniques for solving capacitated location-routing problem

Abstract

This study focuses on the capacitated location-routing problem (CLRP), which is a combination of capacitated facility location problem and capacitated vehicle routing problem. We propose a novel approach to find solutions for CLRP, a common version of location routing problem, founded in the literature. We have proposed an enhanced version of particle swarm optimization (PSO), a swarm inspired metaheuristic to handle CLRP. The proposed approach consists of unique assignment techniques of the customers to the opened depots and a tri-fold PSO based searching strategy which combines the influence of both hierarchical and iterative techniques in order to find near optimal solutions to a CLRP. Two folds of PSO are for maintaining the global view of clustering the nodes and the remaining PSO fold keeps the local nature of prioritizing route construction cost while making a complete solution of CLRP, thus proposed approach preserves the influence of both hierarchical and iterative methods. Experimental performance evaluation of the proposed approach is compared to other particle swarm optimization based methods to solve benchmark instances available in literature which show better performance of the proposed method.

Keywords

• capacitated location-routing problem
• tri-fold particle swarm optimization
• combined hierarchical-iterative approach
• hub based clustering
• nearer neighbor based decoding technique