This paper studies the distributed blocking flowshop scheduling problem (DBFSP) with new job insertions. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. This paper proposes a deep reinforcement learning (DRL) algorithm to optimize the job selection model, and makes local modifications on the basis of the original scheduling plan when new jobs arrive. The objective is to minimize the total completion time deviation of all products so that all jobs can be finished on time to reduce the cost of storage. Firstly, according to the definitions of the dynamic DBFSP problem, a DRL framework based on multi-agent deep deterministic policy gradient (MADDPG) is proposed. In this framework, a full schedule is generated by the VND algorithm before a dynamic event occurs. Meanwhile, all newly added jobs are reordered before the agents make decisions to select the one that needs to be scheduled most urgently. This research defines the observations, actions and reward calculation methods, and applies centralized training and distributed execution in MADDPG. Finally, a comprehensive computational experiment is carried out to compare the proposed method with the closely related and well performing methods. The results indicate that the proposed method can solve the dynamic DBFSP effectively and efficiently.     «

This paper studies the distributed blocking flowshop scheduling problem (DBFSP) with new job insertions. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. This paper proposes a deep reinforcement learning (DRL) algorithm to optimize the job selection model, and makes local modifications on the basis of the original scheduling plan when new jobs arrive. The objective is to minimize the tota...     »