Freight forwarding companies commonly encounter difficulties in handling uncertainties, especially demand uncertainty under the circumstances of no sufficient historical data or accurate forecasting approach. A two-stage robust optimization method is proposed for service network design under demand uncertainty. We employ probability-free uncertainty sets to illuminate the potential scenarios and develop a column-and-constraint generation approach as the solution method to solve the introduced robust models exactly. As indicated by the numerical results, the algorithm we proposed herein performs better than the Benders decomposition approach in terms of computing speed and quality of the solution. Comparative results demonstrate the robustness of the proposed models. We also analyze the structural properties of robust solutions, which provide operational flexibility against uncertainty.
A Benders decomposition-based approach for logistics service network design
