关键词: 染色车间调度, 并行批处理机, 动态调度, 多智能体强化学习, 长短期记忆网络, 总拖期时间

Abstract: Aiming at the dynamic scheduling problem of textile dyeing workshop in which tasks release dynamically by orders,a Multi-Agent Recurrent Proximal Policy Optimization (MA-RPPO) reinforcement learning(RL) based fully reactive scheduling method was proposed by taking the minimum total tardiness time as the optimization goal.For the two sub-problems of group batching and the vats scheduling in the dyeing workshop,the batching agent and the scheduling agent were designed to group batches and schedule the vats.For the dynamics of dyeing tasks,Long Short Term Memory (LSTM) was introduced to extract workshop dynamic information and improve the adaptive ability of the agent;further the interaction mechanism between agents was proposed to achieve global optimization of two sub-problems.The relevant features of constraints and optimization goal were extracted,and the reward function was designed.The agents interacted with the dyeing workshop environment through dynamic scheduling mechanisms to learn the optimal scheduling strategy.The case study from a dyeing enterprise showed that the proposed method was better than some high-performance heuristic rules in different scales of problems,reducing the total tardiness time of products and improving the ability of timely delivery of enterprise effectively.

Key words: dyeing workshop scheduling, batch processing machine, dynamic scheduling, multi-agent reinforcement learning, long short term memory network, total tardiness