Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering.

Cross-docking technology transships products from incoming vehicles directly to outgoing vehicles by using the warehouse as a temporary buffer instead of a place for storage and retrieval. The supply chain management (SCM) with cross-docks is both effective and efficient where no storage is facilitated at the cross-dock and the order-picking is replaced by fast consolidation. However, cross-docking involves interrelated operations such as vehicle routing and vehicle scheduling which require proper planning and synchronization. Traditional cross-docking methods treat the operations separately and overlook the potential advantage of cooperative planning. This paper proposes a bi-objective mathematical formulation for the cross-docking with the noted new challenges. As the addressed problem is highly constrained, we develop a cooperative coevolution approach consisting of Hyper-heuristics and Hybrid-heuristics for achieving continuous improvement in alternating objectives. The performance of our approach is illustrated with real geographical data and is compared with existing models. Statistical tests based on intensive simulations, including the convergence 95% confidence analysis and the worst-case analysis, are conducted to provide reliable performance guarantee.