Multi-Agent Reinforcement Learning for Dynamic Parallel-Machine Scheduling with Human–Robot Collaboration and Worker Fatigue
This study investigates a dynamic parallel-machine scheduling problem with human–robot collaboration (DPMS-HRC), where dynamically arriving jobs are assigned to eligible workstations and processed by workers, robots, or human–robot collaboration based on real-time system states. Worker-involving modes improve operation...