An improved genetic clustering algorithm for the multi-depot vehicle routing problem

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.