Artikel
Designing an adaptive production control system using reinforcement learning
Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring significant human domain knowledge and, hence, do not match the dynamic environment of manufacturing companies. Data-driven reinforcement learning (RL) showed compelling results in applications such as board and computer games as well as first production applications. This paper addresses the design of RL to create an adaptive production control system by the real-world example of order dispatching in a complex job shop. As RL algorithms are “black box” approaches, they inherently prohibit a comprehensive understanding. Furthermore, the experience with advanced RL algorithms is still limited to single successful applications, which limits the transferability of results. In this paper, we examine the performance of the state, action, and reward function RL design. When analyzing the results, we identify robust RL designs. This makes RL an advantageous control system for highly dynamic and complex production systems, mainly when domain knowledge is limited.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Intelligent Manufacturing ; ISSN: 1572-8145 ; Volume: 32 ; Year: 2020 ; Issue: 3 ; Pages: 855-876 ; New York, NY: Springer US
- Classification
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Ingenieurwissenschaften und Maschinenbau
- Subject
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Reinforcement learning
Production control
Adaptivity
Semiconductor industry
- Event
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Geistige Schöpfung
- (who)
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Kuhnle, Andreas
Kaiser, Jan-Philipp
Theiß, Felix
Stricker, Nicole
Lanza, Gisela
- Event
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Veröffentlichung
- (who)
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Springer US
- (where)
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New York, NY
- (when)
-
2020
- DOI
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doi:10.1007/s10845-020-01612-y
- Last update
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10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Kuhnle, Andreas
- Kaiser, Jan-Philipp
- Theiß, Felix
- Stricker, Nicole
- Lanza, Gisela
- Springer US
Time of origin
- 2020
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