Optimizing container operations at multimodal terminals: A literature review of machine/reinforcement learning methods

Bachelor thesis

The growing global container trade requires efficient handling and transportation of terminal containers to optimize the performance of inland container terminals and ports. As the demand for fast, efficient transshipment of terminal containers increases, innovative approaches are needed to improve measures such as task completion time, energy consumption, and overall operational efficiency. In multimodal terminals, the cranes generally serve the container ships, trucks, rail, and stacking areas. Unproductive movements of the cranes, for example in container relocation (reshuffling), should be minimized to improve the efficiency of the terminal. Intelligent methods such as machine/reinforcement learning can provide potential solutions. Therefore, this work aims to review the existing literature and develop innovative solutions for managing container relocation.

Interested in this topic? Please use the application form on our website!