Collaborative Vehicle Routing and Scheduling with Cross-Docks Under Uncertainty

Inspired by successful cross-docking applications in the prevailing supply chain management (SCM) business such as Wal-Mart, FedEx, and Home Depot, we propose a new collaborative vehicle routing and scheduling model with cross-dock under uncertainty. Conventional cross-docking models treat the vehicle routing and docking scheduling components as independent subproblems, and intend to solve them separately. The obtained result may overlook the potential benefits from solving them as a whole. Moreover, little literature has addressed the uncertainty scenarios such as vehicle failure, traffic condition, and changing demand. This paper presents collaborative computing for optimization of the integrated problem consisting of vehicle routing and docking scheduling. We further propose collaborative service rules for handling the uncertainty. An illustrative solution with a 50-vertex problem is used to manifest the superiority of our model over existing ones. Worst-case statistical analysis is conducted to show the worst performance that could be obtained by our method after a specific number of repetitive runs.