Stochastic parallel machine scheduling using reinforcement learning

被引：6

作者：

Julaiti J. ^{[1
]}

Oh S.-C. ^{[2
]}

Das D. ^{[1
]}

Kumara S. ^{[1
]}

机构：

[1] The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Penn State University, State College, PA

[2] General Motors R&D, Warren, MI

来源：

Journal of Advanced Manufacturing and Processing | 2022年 / 4卷 / 04期

关键词：

deep deterministic policy gradient; job scheduling; parallel scheduling; reinforcement learning;

D O I：

10.1002/amp2.10119

中图分类号：

学科分类号：

摘要：

In a high-mix and low-volume manufacturing facility, heterogeneous jobs introduce frequent reconfiguration of machines which increases the chance of unplanned machine breakdowns. As machines are often nonidentical and their performance degrades over time, it is critical to consider the heterogeneity and non-stationarity of the machines during scheduling. We propose a reinforcement learning-based framework with a novel sampling method to train the agent to schedule heterogeneous jobs on non-stationary unreliable parallel machines to minimize weighted tardiness. The results indicate that the new sampling approach expedites the learning process and the resulting policy significantly outperforms static dispatching rules. © 2022 American Institute of Chemical Engineers.

引用

共 40 条

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