Optimizing cross-dock operations under uncertainty

Abstract

Cross-docking is an important transportation logistics strategy in supply chain management which reduces transportation costs, inventory holding costs, order-picking costs and response time. Careful planning is needed for successful cross-dock operations. Uncertainty in cross-dock problems is inevitable and needs to be addressed. Almost all research in the cross-dock area assumes determinism. This dissertation considers uncertainty in cross-dock problems and optimizes these problems under uncertainty. We consider uncertainty in processing times, using scenario-based and protection-based robust approaches. Using a heuristic method, we find a lower and upper bound and combine that with a meta-heuristic method to solve the problem. Also, we consider problems in two different industries (Goodwill and H-E-B) and address the uncertainties that happen frequently in their operations. The scenario-based robust optimization model for the unloading problem using a min max objective is presented with examples. A surrogate heuristic procedure is used to find a robust solution. Next, a two-space genetic algorithm, a meta-heuristic procedure, is applied to the unloading problem using the bounds obtained by the heuristic procedure. The results are closer to the optimal solution than those obtained by the two-space genetic algorithm without bounds. When compared with the regular genetic algorithm with bounds, the two-space algorithm performs well. The protection-based approach considers a limit on the number of coefficients allowed to change with data uncertainty, protecting against the degree of conservatism. The management of trucks and reduction of overtime pay in the cross-dock operations of Goodwill is addressed through two models and uncertainty is applied to those models. A combined cross-dock operations model together with demand is formulated and the uncertainties are discussed for H-E-B operations. This dissertation does not address the recycling operation within the cross-dock of Goodwill, or the uncertainty in H-E-B data.