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Modular production control using deep reinforcement learning: proximal policy optimization

Author

Listed:
  • Sebastian Mayer

    (Technische Hochschule Ingolstadt)

  • Tobias Classen

    (Technical University of Munich)

  • Christian Endisch

    (Technische Hochschule Ingolstadt)

Abstract

EU regulations on $$\textit{CO}_2$$ CO 2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design.

Suggested Citation

  • Sebastian Mayer & Tobias Classen & Christian Endisch, 2021. "Modular production control using deep reinforcement learning: proximal policy optimization," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2335-2351, December.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-021-01778-z
    DOI: 10.1007/s10845-021-01778-z
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    References listed on IDEAS

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    1. Andreas Kuhnle & Jan-Philipp Kaiser & Felix Theiß & Nicole Stricker & Gisela Lanza, 2021. "Designing an adaptive production control system using reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 855-876, March.
    2. Yu-Fang Wang, 2020. "Adaptive job shop scheduling strategy based on weighted Q-learning algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 417-432, February.
    3. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
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