A comparative study among different parallel hybrid artificial intelligent approaches to solve the capacitated vehicle routing problem. (2018)

• Record Type: Journal Article
• Authors: Saeheaw, Teerapun; Charoenchai, Nivit
• Is Part Of: International journal of bio-inspired computation. Volume 11:Number 3(2018)
• Page Start: 171
• Page End: 191
• Publication Date: 2018
• Subjects: capacitated vehicle routing problem -- CVRP -- cuckoo search -- CS -- central force optimisation -- CFO -- chemical reaction optimisation -- CRO; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

The vehicle routing problem involves distribution management in the fields of transportation, distribution, and logistics, and it is one of the most important, and studied, combinatorial optimisation problems. The capacitated vehicle routing problem is an NP-hard problem, which was introduced by Dantzig and Ramser in 1959. The objective is to minimise the total distance and to maximise capacity for all of the vehicles. In this paper, the proposed parallel hybrid artificial intelligent approaches are based on cuckoo search that uses the positive features of two other optimisation techniques, central force optimisation and chemical reaction optimisation, for enhancing local search and improving the quality of the initial population, respectively. The motivation for this work is to improve the computational efficiency by getting even better results than the previous best known solutions, to study of the dynamics of various parameters of proposed approaches in searching optimum solutions, and to quicken the process of finding the optimal solution. The proposed approaches are tested on standard test instances from the literature. The test results demonstrate the effectiveness of the proposed approaches in solving the capacitated vehicle routing problem efficiently.