Robust optimization of a procurement and routing strategy for multiperiod multimodal transport in an uncertain environment

This paper proposes a collaborative optimization strategy for multiperiod procurement and multimodal transportation that considers cost factors such as procurement, transportation, transshipment, and storage costs incurred for early arrival. A mixed-integer planning model is established to minimize the overall operating costs of cross-border e-commerce enterprises by arranging procurement, transportation, and storage strategies. Considering the fluctuation of procurement costs with the market environment, this study constructs robust optimization models and develops linear robust equivalence models through mathematical transformation to improve the efficiency of problem solving. A hybrid heuristic algorithm, KIGALNS, is proposed to solve this problem. Finally, a series of numerical experiments are conducted to show that our robust model can better address multimodal transportation path optimization problems such as procurement cost uncertainty. In addition, the correctness of the proposed model and the effectiveness of the algorithm and collaborative optimization strategy were verified. Finally, the case analysis shows that the early procurement strategy helps reduce total operating costs, and the robust model can effectively handle multimodal transportation path optimization problems such as uncertain procurement costs. While promoting cost reduction and efficiency improvement in transportation, the proposed approach comprehensively considers the impact of procurement plans and uncertain factors, providing theoretical guidance and scientific solutions for joint decision-making in enterprise procurement transportation.