关键词: 智能车间, 自适应调度, 强化学习, Q学习

Abstract: To reduce the influence of dynamic uncertainties on the production performance of smart shop floor,a self-adaptive scheduling method based on Q-learning was proposed.A self-adaptive scheduling framework based on reinforcement learning was designed for the smart shop floor.The Q-learning algorithm was adopted in this framework to train the scheduling model autonomously through the agent-environment interactive trial and error mechanism.In addition,the scheduling model would be dynamically updated according to the changes in the production environment to generate the optimal decision trajectory,which could guide the operation of the shop floor.The proposed method was validated on a semiconductor production line model,namely the MiniFab model.Experimental results demonstrated that the proposed method could effectively respond to the changes in the production environment of the smart shop floor,adjusting the scheduling decision in real time throughout the production process.As a result,the comprehensive performance was optimized,and time and labor costs were significantly reduced.

Key words: smart shop floor, self-adaptive scheduling, reinforcement learning, Q-learning