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Deep reinforcement learning based preventive maintenance policy for serial production lines
یادگیری تقویتی عمیق مبتنی بر سیاست یشگیری برای خطوط تولید متوالی-2020 In the manufacturing industry, the preventive maintenance (PM) is a common practice to reduce random
machine failures by replacing/repairing the aged machines or parts. The decision on when and where the
preventive maintenance needs to be carried out is nontrivial due to the complex and stochastic nature of
a serial production line with intermediate buffers. In order to improve the cost efficiency of the serial production
lines, a deep reinforcement learning based approach is proposed to obtain PM policy. A novel
modeling method for the serial production line is adopted during the learning process. A reward function
is proposed based on the system production loss evaluation. The algorithm based on the Double Deep QNetwork
is applied to learn the PM policy. Using the simulation study, the learning algorithm is proved
effective in delivering PM policy that leads to an increased throughput and reduced cost. Interestingly,
the learned policy is found to frequently conduct ‘‘group maintenance” and ‘‘opportunistic maintenance”,
although their concepts and rules are not provided during the learning process. This finding further
demonstrates that the problem formulation, the proposed algorithm and the reward function setting
in this paper are effective. Keywords: Preventive maintenance | Production loss | Deep reinforcement learning | Serial production line | Group maintenance | Opportunistic maintenance |
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