Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree

Abstract

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instances. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock. • This research proposes a two-stage stochastic model for cross-docking with uncertain trucks arrival times. • The model minimizes penalty costs due to deliveries violating the retailers time-windows. • A tailored Stochastic Genetic Algorithm based on Scenario Tree (SGA-ST) is proposed to solve real-world instance. • SGA-ST outperforms the two-stage stochastic MILP formulation solved with commercial solvers. • The SGA-ST is implemented into a user-friendly decision-support tool to aid practitioners in cross-docking operational decisions.