Deep reinforcement learning for Intermodal transport optimization considering uncertainty

Intermodal transport is an important part of international trade, and poor connectivity between transport modes can significantly increase costs. To minimize these costs, we propose a novel two-stage optimization model that combines transport mode selection and route planning. However, previous studies often ignore the uncertainty of node detention service time. To address this issue, we extend the robust optimization theory and introduce a two-stage robust planning model. The model is illustrated based on a simulation case. The study shows that even if the transport mode remains unchanged, uncertain parameter changes can lead to a significant drop in service levels. Based on a deep reinforcement learning algorithm, two improvement strategies are combined to improve the algorithm efficiency. In addition, optimizing transport mode selection can reduce carbon emissions without sacrificing transportation efficiency. This study provides valuable theoretical support for the low-carbon transformation of intermodal transport enterprises.