3 accepted papers
Under the shift toward Industry 4.0, mass-customized manufacturing systems have introduced complex scheduling problems, such as the Flexible Job Shop Scheduling Problem (FJSSP). Recent Deep Reinforcement Learning (DRL)-based heuristics have shown promise, yet existing methods often suffer from two k
Reinforcement Learning (RL) has shown promising results in solving Job Scheduling Problems (JSPs), automatically deriving powerful dispatching rules from data without relying on expert knowledge. However, most RL-based methods train only a single decision-maker, which limits exploration capability a…
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 averag…