Enhanced Cross-Dock Productivity: Combining Self-Driving Vehicles with Forklifts

Abstract

A cross-dock (CD) in a supply chain avoids storing goods that would be picked for orders soon after. Vehicles inbound to the CD are unloaded and their contents are re-sorted. Appropriate items are then loaded within a short time on outbound vehicles for shipment to customers. The CD material handling operations of unloading, sorting and loading are typically done “manually”, by forklifts with human operators. In this chapter, we consider the replacement of some or many forklifts by “Self-Driving Vehicles” (SDV). Can the resulting semi-automated material handling system attain the same or greater productivity as the fully manual system? At what cost (per unit of output)? We develop simulation models of two CDs, one purely manual and the other containing a mixture of forklifts and SDVs. Several CD performance measures are defined and estimated via simulation. For each CD, response surface methodology is employed to determine a near-optimal set of material handling equipment, when that CD is operated at a specified performance level.