Dynamic Crane Scheduling with Reinforcement Learning for a Steel Coil Warehouse

This paper tackles the dynamic crane scheduling problem in a steel coil warehouse, involving tasks such as coil storage, retrieval, and shuffling. Tasks arrive dynamically with precedence relations, while multiple cranes share a track, necessitating collision avoidance. We aim to minimize the average task waiting time by allocating tasks to cranes and optimizing their execution sequence. Unlike prior research focusing on static scenarios or rule-based heuristics, we introduce a real-time, reinforcement learning-based algorithm. We propose a policy network based on graph neural networks to effectively handle precedence relations and global information. Experimental results demonstrate its superiority over traditional heuristics, such as dispatching rules in dynamic scenarios.