Title: An improved genetic clustering algorithm for the multi-depot vehicle routing problem
Authors: Degang Xu; Renbin Xiao
Key Laboratory of Grain Information Processing and Control, (Henan University of Technology), Ministry of Education, Zhengzhou, China
School of Automation, Huazhong University of Science & Technology, Wuhan, China
Abstract: The multi-depot vehicle routing problem (MDVRP) is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. This paper considers not only the minimum distance required to deliver goods, but also the workload imbalance in terms of the distances travelled by the used vehicles and their loads. Thus, a new type of geometric shape based genetic clustering algorithm is proposed. A genetic algorithm based on this clustering technique is developed for the solution process of the multi-objective formulations of the MDVRP. A set of problems obtained from the literature is used to compare the efficiency of the proposed algorithm with the nearest neighbour algorithm so as to solve it. The computational experiments on data sets illustrate the efficiency and effectiveness of the proposed approach.
Keywords: vehicle routing problem; VRP; time windows; multi-objective optimisation; genetic algorithms; clustering algorithms; multi-depot vehicle routing.
Int. J. of Wireless and Mobile Computing, 2015 Vol.9, No.1, pp.1 - 7
Submission date: 14 Jul 2014
Date of acceptance: 22 Sep 2014
Available online: 14 Sep 2015