Production-Logistics Collaborative Scheduling in Dynamic Flexible Job Shops via Multi-Objective Deep Reinforcement Learning^*

Production scheduling and logistics scheduling are vital means for organizing manufacturing activities in flexible job shops. Given the intricate coupling relationship between them, corresponding collaborative scheduling becomes urgent need and challenging. Meanwhile, the actual manufacturing process is inevitably affected by disturbances, necessitating the consideration of dynamic environments. To this end, this study investigates a new production-logistics collaborative scheduling problem in dynamic flexible job shops (PLCSP-DFJS). The high-frequency disturbance of new job arrivals is incorporated into the PLCSP-DFJS, and two objectives, namely makespan and total logistics cost, are optimized. A multi-objective deep reinforcement learning (MODRL) method is presented to solve PLCSP-DFJS. In MODRL, a weight-decomposition and neighborhood-inheritance training mechanism is devised to obtain the near-optimal Pareto front, and a dual-level channel-driven framework capable of achieving decentralized decision-making of production and logistics is designed. The performance of MODRL is verified through experiments conducted in an aviation component production shop.