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- W2914098981 abstract "Real-time batching in job shops decides 1) whether to start processing a batch or to wait for more jobs joining the batch, and 2) which batch should be processed first. It is addressed as a sequential decision-making problem and formalized based on Markov decision processes. By adding a dummy batch, which means no batches are selected and all batches wait for additional jobs, the first decision-making is generalized to the second. A simulation-based neural fitted Q learning is introduced to solve the Markov decision processes and build a decision maker. The well-trained decision maker decides which batch in a batch list should be processed first at each decision epoch. The experiment results show that the proposed approach outperforms some other decision rules." @default.
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- W2914098981 date "2018-12-09" @default.
- W2914098981 modified "2023-10-07" @default.
- W2914098981 title "Real-time batching in job shops based on simulation and reinforcement learning" @default.
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- W2914098981 doi "https://doi.org/10.5555/3320516.3320910" @default.
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