A Cross-Dock (CD) is a synchronized unit of a supply chain network, used to sort the goods received from inbound trucks (from a warehouse or factory), and load those products to outbound trucks (for delivery of the goods to retail stores in the supply chain
network). Most cross-docks use forklifts, and other manual material handling equipment (MHE) to process the goods on pallets received from inbound trucks. Those pallets are sorted and loaded onto outbound trucks. With the advancements in robotics, it could be bene cial to employ semi-automated material handling techniques in a CD, rather than solely relying on manual material handling. In this thesis, the scope of self-driving vehicles (SDVs) in one such semi-automated cross-dock facility is studied. We compare the cases of purely manual and semi-automated material handling in a cross-dock.
Using simulation, we modelled two cross-dock facilities, one with forklifts only and with a mixture of forklifts and SDVs. Simulation was thus employed to mimic the CD's material handling process, to compare the two MHE con gurations. Then the built cross-dock simulation models were optimized using the response surface methodology and mixed integer non-linear programming (MINLP), to achieve the optimal MHE con guration for those facilities operating with the desired levels of performance metrics.
Thereby the manual and semi-automated cross-dock with similar performance (and optimal MHE con gurations) are compared and the scope of SDVs in a cross-dock is evaluated. Conclusions are given, and opportunities for further research are presented
