Optimization Strategy for Container Transshipment Between Yards at U-Shaped Sea-Rail Intermodal Terminal

The U-shaped automated container terminal (U-ACT) meets the requirements of sea-rail intermodal transportation with its unique layout. However, this layout also presents challenges, such as complex container transshipment planning and challenging equipment scheduling, which limit further improvements in overall efficiency. This paper focuses on the integrated scheduling of horizontal transportation and container-handling equipment for container transshipment at U-ACT. To minimize operation time and energy consumption while addressing path conflicts among container trucks, we designed a two-layer scheduling model to generate an optimal scheduling scheme for each automated device. Given the complexity of the problem, we developed a reinforcement learning-driven hyper-heuristic algorithm (RLHA) capable of efficiently searching for near-optimal solutions. Small-scale experiments demonstrate that our RLHA outperforms other algorithms, improving optimization results by 5.14% to 28.87% when the number of container operation tasks reaches 100. Finally, large-scale experiments were conducted to analyze key factors impacting sea-rail intermodal transport operations at U-ACT, providing a foundation for practical optimization.