An Integrated Vehicle Routing and Vehicle Sequencing Problem at the Cross-Docking Centre: A Genetic Algorithm Approach

Cross-docking is one of the logistic strategies in the supply chain. To improve the efficiency of a supply chain, the literature review recommends jointly considering operational level problems together on cross-docking. Therefore, in this study, the vehicle routing problem with moving shipments and vehicle sequencing problem at the cross-docking center (CDC) which has only single-receiving and single-shipping door is integrated and abbreviated as VRSP-SD. On the one hand, the integrated VRSP-SD differs from the literature by considering internal operations such as unloading, moving and reloading shipments not only at suppliers/customers but also at the CDC. On the other hand, the novelty of VRSP-SD in sequencing vehicles by minimising the waiting-time of vehicles is based on the ‘arrival-time’ of inbound vehicles to the CDC and also based on the ‘route-quantity’ of outbound vehicles. Therefore, the objective of this study is to test the accuracy of the proposed genetic algorithm (GA) based meta-heuristic approach to solve VRSP-SD by optimising the total transportation cost which incur by routing vehicles, moving shipments and sequencing vehicles. A mixed integer quadratic programming model is developed to solve the integrated VRSP-SD. Since the numerical experiments reveal that the accuracy of the proposed GA is over 94% against the exact-optimal solution obtained by branch and bound algorithm, it is recommended to employ the proposed GA to solve VRSP-SD. Therefore, the industries which apply cross-docking strategy may benefit by utilizing this proposed GA to schedule the vehicles to the routes and to the doors of CDC.