Abstract:

Facing the operation practice of cross-border e-commerce enterprises under the background of the “Belt and Road”，a collaborative optimization strategy for multi-time procurement and multimodal transportation considering cost factors such as procurement， transportation， transshipment， and storage costs incurred for early arrival is proposed. 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 under the uncertain environment of procurement costs and also develops linear robust equivalence models through mathematical transformation to improve the efficiency of problem solving.To solve the above problem， a hybrid heuristic algorithm called KIGALNS is proposed， based on the K-shortest path algorithm， Iterative Greedy algorithm （IG）， and Adaptive Large Neighborhood Search （ALNS）. Then， through the different size case experiments verify the validity of the proposed model， the effectiveness of the algorithm and the collaborative optimization strategy. Finally， through a series of numerical experiments show that the purchasing strategy helps to save operating cost in advance， the robust model can well deal with multimodal transport path optimization problem such as purchasing cost uncertainty， in promoting the transport link authors efficiency at the same time， considering purchasing plan and the influence of uncertain factors， provide theoretical guidance for enterprise procurement - transport joint decision and scientific solutions.

Key words: multi-period, multimodal transportation, procurement strategy, robust optimization, hybrid heuristic algorithm