为帮助企业更好地适应现实业务中的动态环境，研究了运输价格不确定性的多时段多式联运路径与存储协同优化模型。首先，在运输价格确定的环境下建立整数规划数学模型。其次，在运输价格不确定的环境下建立鲁棒优化模型，并将鲁棒优化模型转化为等价的线性鲁棒对等问题。随后，在传统k-短路算法、迭代贪婪（iterative greedy, IG）算法和自适应大邻域搜索算法（adaptive large neighbourhood search, ALNS）的基础上，提出了混合启发式算法MKIGALNS求解。最后，通过不同规模的算例实验，验证了所提出模型的正确性以及算法的有效性。实验结果表示，在10组算例中，不允许存储时的平均总运营成本为439 191元，允许存储时的平均总运营成本为391 378元，因此应当进行存储决策，有利于运营成本的降低。鲁棒实验结果表明，随着不确定预算取值的变化，总运营成本以及多时段多式联运运营策略发生变化，揭示了其内在联系。

In order to help enterprises better adapt to the dynamic environment in the real business, a multiperiod intermodal routing and storage co-optimization model with transport price uncertainty was investigated. Firstly, an integer programming mathematical model was established in the environment of transport price certainty. Secondly, a robust optimization model was established in the environment of uncertain transport prices, and the robust optimization model was transformed into an equivalent linear robust peer-to-peer problem. Subsequently, on the basis of the traditional k-shortest algorithm, iterative greedy algorithm (IG) and adaptive large neighbourhood search algorithm (ALNS), a hybrid heuristic algorithm of MKIGALNS was proposed to solve the problems. Finally, the correctness of the proposed model as well as the effectiveness of the algorithm were verified by different sizes of arithmetic case experiments. The experimental results indicate that in 10 sets of arithmetic cases, the average total operating cost is CNY 439 191 when storage is not allowed and CNY 391 378 when storage is allowed, so the storage decision should be made, which is conducive to the reduction of operating cost. And through the related robust experiments, the total operating cost as well as the multiperiod intermodal operation strategy changes with the change of the uncertain budget value, which reveals the intrinsic connection.