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.