Integrated mutation strategies in a biologically inspired algorithm to solve the vehicle routing problem with moving shipments at the cross-docking centre

This study is an extended version of previously developed model for the Vehicle Routing Problem with Moving Shipments at the Cross-Docking Centre (VRPCD-MS). In the earlier study, some internal operations at the crossdocking centre were integrated and only the small-scale instances were used to obtain the exact-optimal solution. Nevertheless, in this study a biologically inspired approach to reach near-optimal solution to the large-scale instances of VRPCD-MS is proposed. Therefore, Genetic Algorithm (GA) with integrated-mutation strategies ‘SWAP’ and ‘REVERSION’ to solve VRPCD-MS is employed in the current study. The ‘SWAP’ mutation strategy was already applied in the literature of VRPCD-MS and the current solutions by ‘REVERSION’ mutation strategy is compared with it. When the GA with SWAP, and GA with REVERSION mutation strategies are applied separately, the solutions of the benchmark problems reveal that there is no strong evidence to recommend any of those two approaches is better than the other. The two-sample t-test also confirms the similar result that, there is no significant difference in solutions by applying these two mutation strategies alone. The numerical experiments conclude that, up to 30% improvement in the solution can be achieved by the integrated mutation strategies in the proposed biologically inspired approach which is nearly 15% on average.