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Production control with Reinforcement Learning for a matrix-structured production system.

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Title: Production control with Reinforcement Learning for a matrix-structured production system.
Authors: Steinbacher, L. M.1,2 (AUTHOR) stb@biba.uni-bremen.de, Wegmann, T.2 (AUTHOR), Freitag, M.1,2 (AUTHOR)
Source: International Journal of Production Research. Jun2025, Vol. 63 Issue 11, p4114-4136. 23p.
Subjects: Reinforcement learning, Production control, Markov processes, Autonomous vehicles, Automobile industry
Abstract: With increasing product complexities in mass customisation in the automotive industry, the downsides of conventional production concepts like flow production get more pronounced. Their inability to deal with cycle time losses adequately opens up possibilities for new concepts like matrix-structured production (MSP). Due to the immanent dynamics of matrix-structured production, control concept like takt binding or control stands are no longer sufficient to achieve near-optimal performance. The application of Reinforcement Learning (RL) to solve this problem emerged in the recent years. In particular, routing and dispatching tasks have been solved by applying RL. As both tasks influence each other's performance, a combined RL approach is developed. Therefore, a car body construction is simulated to test different modelled Markov processes, algorithms, and rewards. The new approach is validated against common heuristics regarding logistic performance and relevant metrics for operating autonomous guided vehicle fleets. For this, RL systems are designed and compared. The combined approach of production control in terms of dispatching jobs and routing autonomous guided vehicles achieved equivalent performance to heuristics. Still, it excelled in fleet operation metrics, like reduced live or deadlocks. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jun2025, Vol. 63 Issue 11, p4114-4136. 23p.
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  Data: With increasing product complexities in mass customisation in the automotive industry, the downsides of conventional production concepts like flow production get more pronounced. Their inability to deal with cycle time losses adequately opens up possibilities for new concepts like matrix-structured production (MSP). Due to the immanent dynamics of matrix-structured production, control concept like takt binding or control stands are no longer sufficient to achieve near-optimal performance. The application of Reinforcement Learning (RL) to solve this problem emerged in the recent years. In particular, routing and dispatching tasks have been solved by applying RL. As both tasks influence each other's performance, a combined RL approach is developed. Therefore, a car body construction is simulated to test different modelled Markov processes, algorithms, and rewards. The new approach is validated against common heuristics regarding logistic performance and relevant metrics for operating autonomous guided vehicle fleets. For this, RL systems are designed and compared. The combined approach of production control in terms of dispatching jobs and routing autonomous guided vehicles achieved equivalent performance to heuristics. Still, it excelled in fleet operation metrics, like reduced live or deadlocks. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2024.2436126
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      – Code: eng
        Text: English
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        PageCount: 23
        StartPage: 4114
    Subjects:
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Production control
        Type: general
      – SubjectFull: Markov processes
        Type: general
      – SubjectFull: Autonomous vehicles
        Type: general
      – SubjectFull: Automobile industry
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      – TitleFull: Production control with Reinforcement Learning for a matrix-structured production system.
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            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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