Research on the Plan of Container Train Operation in Sea-Rail Intermodal Transportation Under the Uncertain Demand

The chapter delves into the complexities of container train operations in sea-rail intermodal transportation, focusing on minimizing detention costs and operating expenses under uncertain demand. It introduces a multi-objective programming model that considers the unloading sequence of containers, train operation schemes, and container flow sources. The model is further enhanced by the Gaussian hidden Markov model to predict container flow, improving the robustness of the operation scheme. The genetic algorithm is employed to solve the model, providing a flexible and efficient solution that can reduce detention time and optimize train operations. The case study of Yangshan Port in Shanghai showcases the practical application of the model, highlighting its effectiveness in handling fluctuating container flow and minimizing operational costs.