A Reinforcement Learning Method for Automated Guided Vehicle Dispatching and Path Planning Considering Charging and Path Conflicts at an Automated Container Terminal

The continued growth of international maritime trade has driven automated container terminals (ACTs) to pursue more efficient operational management strategies. In practice, the horizontal yard layout in ACTs significantly enhances transshipment efficiency. However, the more complex horizontal transporting system calls for an effective approach to enhance automated guided vehicle (AGV) scheduling. Considering AGV charging and path conflicts, this paper proposes a multi-agent reinforcement learning (MARL) approach to address the AGV dispatching and path planning (VD2P) problem under a horizontal layout. The VD2P problem is formulated as a Markov decision process model. To mitigate the challenges of high-dimensional state-action space, a multi-agent framework is developed to control the AGV dispatching and path planning separately. A mixed global–individual reward mechanism is tailored to enhance both exploration and corporation. A proximal policy optimization method is used to train the scheduling policies. Experiments indicate that the proposed MARL approach can provide high-quality solutions for a real-world-sized scenario within tens of seconds. Compared with benchmark methods, the proposed approach achieves an improvement of 8.4% to 53.8%. Moreover, sensitivity analyses are conducted to explore the impact of different AGV configurations and charging strategies on scheduling. Managerial insights are obtained to support more efficient terminal operations.