A Memetic Algorithm for the Multi-Depot Vehicle Routing Problem

Wenhan Shao, Zhouxing Su, Junwen Ding, Zhipeng Lü

Abstract: Multi-depot vehicle routing problem (MDVRP) is a variant of the classical VRP, which includes several depots with a fleet of homogeneous vehicles to serve each customer exactly once while satisfying the vehicle capacity and duration constraints. We propose a memetic algorithm called GVTS-DPX which hybridizes the granular variable tabu search (GVTS) with the depot partition crossover (DPX) for solving the MDVRP, where GVTS combines tabu search and the granular neighborhoods with variable neighborhood descent, while DPX treats the solution as the collection of depots and partitions the depots into two groups covering the most customers. The main contributions of this study include proposing the DPX operator, reforming several existing move types used for the VRP and its variants, and designing a granular variable neighborhood consisting of a total of 21 kinds of move types. Experimental results on 33 public MDVRP instances indicate that GVTS-DPX is competitive with the state-of-the-art algorithms in the literature.