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- W4313443357 abstract "Reconfigurable manufacturing system (RMS) is commonly adopted under mass customization. For an RMS with dynamic events, how to real-timely schedule jobs and workshop configurations is still challenging. We studied the real-time and concurrent optimization of scheduling and reconfiguration for a dynamic reconfigurable flow shop with new job arrivals using deep reinforcement learning (DRL). The objective is to minimize the total tardiness cost of all jobs by properly configuring flow lines and scheduling jobs. The intelligent scheduling and reconfiguration system was established by designing the collaboration mechanism, training and execution procedures, and DRL-based decision models. In addition, a novel variant of deep Q-network (DQN)—expected deep Q-network (EDQN)—was proposed by calculating the exact expected state-action value rather than using an estimation. Furthermore, the T-step evaluation and multi-step learning were employed to reduce the estimation errors and improve the data efficiency, resulting in an EDQN_T algorithm. The training experiment shows the good learning efficiency of our proposed algorithms. The comparison experiment shows the proposed algorithms outperform several popular DRL algorithms and well-known priority dispatching rules in terms of solution quality. Besides, the decision time of trained DRL algorithms is only several milliseconds, which can fulfill the real-time scheduling under dynamics." @default.
- W4313443357 created "2023-01-06" @default.
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- W4313443357 date "2023-02-01" @default.
- W4313443357 modified "2023-10-01" @default.
- W4313443357 title "Real-time and concurrent optimization of scheduling and reconfiguration for dynamic reconfigurable flow shop using deep reinforcement learning" @default.
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- W4313443357 doi "https://doi.org/10.1016/j.cirpj.2022.12.001" @default.
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