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International Journal of Production Research Volume 58, 2020 - Issue 11
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Articles

Intelligent scheduling of discrete automated production line via deep reinforcement learning

Daming Shia Department of Automation, Tsinghua University, Beijing, People’s Republic of ChinaCorrespondence[email protected]
,
Wenhui Fana Department of Automation, Tsinghua University, Beijing, People’s Republic of China
,
Yingying Xiaob State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing, People’s Republic of China
,
Tingyu Linb State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing, People’s Republic of China
&
Chi Xingb State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing, People’s Republic of China
Pages 3362-3380 | Received 06 Apr 2019, Accepted 09 Jan 2020, Published online: 27 Jan 2020

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Shengluo Yang & Zhigang Xu. (2022) Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing. International Journal of Production Research 60:16, pages 4936-4953.
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Renke Liu, Rajesh Piplani & Carlos Toro. (2022) Deep reinforcement learning for dynamic scheduling of a flexible job shop. International Journal of Production Research 60:13, pages 4049-4069.
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Fei Qiao, Juan Liu & Yumin Ma. (2021) Industrial big-data-driven and CPS-based adaptive production scheduling for smart manufacturing. International Journal of Production Research 59:23, pages 7139-7159.
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Byeongseop Kim, Yongkuk Jeong & Jong Gye Shin. (2020) Spatial arrangement using deep reinforcement learning to minimise rearrangement in ship block stockyards. International Journal of Production Research 58:16, pages 5062-5076.
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Chen-Fu Chien, Yun-Siang Lin & Sheng-Kai Lin. (2020) Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor. International Journal of Production Research 58:9, pages 2784-2804.
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Sharareh Taghipour, Hamed A. Namoura, Mani Sharifi & Mageed Ghaleb. Real-time production scheduling using a deep reinforcement learning-based multi-agent approach. INFOR: Information Systems and Operational Research 0:0, pages 1-25.
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