Multi-objective reinforcement learning framework for dynamic flexible job shop scheduling problem with uncertain events

Hao Wang, Junfu Cheng, Chang Liu, Yuanyuan Zhang, Shunfang Hu, Liangyin Chen

Abstract: Highlights•Propose a new dynamic job shop scheduling problem including uncertain six events.•Develop a novel scheduling algorithm by using two deep improved Q-learning networks.•An improved local search algorithm is adopted to further optimize the results.