Machine preventive replacement policy for serial production lines based on reinforcement learning

In the manufacturing industry, the random failures of machines introduce unexpected disruptions to the production. It is preferable to replace those aged machines with new ones before they fail. In this paper, the machine preventive replacement problem in serial production lines is discussed. To obtain an optimal machine replacement policy, the problem is formulated as a reinforcement learning problem and solved with the Q-learning algorithm. A reward function is proposed based on the production loss evaluation of the serial production lines. The data-driven modeling of serial production lines is used during the training process of the agent. A simulation study is conducted to evaluate the efficiency and effectiveness of the proposed method.