Deep Reinforcement Learning for the Container Retrieval Problem

This study addresses the container retrieval problem (CRP) arising in automated container terminals. Containers are stacked in the storage yard, and to retrieve a container from below, the containers above it must be relocated. The CRP aims to generate an efficient retrieval plan for containers based on their given retrieval order, while minimizing the yard crane’s total working time. Due to the NP-hard nature of the problem, finding optimal solutions within practical time limits is challenging. To overcome this, we propose a deep reinforcement learning approach that generates near-optimal retrieval plans in a very short time. Our approach, based on an attention mechanism, is size-agnostic and adapts effectively to varying sizes of yard layouts. Through experiments on benchmark data, we have verified that our approach significantly outperforms existing methodologies in the literature. Furthermore, its strong performance across various problem scales demonstrates its high practicality for real-world applications.