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Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems

Author

Listed:
  • Lea Kaven

    (Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University)

  • Philipp Huke

    (Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University)

  • Amon Göppert

    (Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University)

  • Robert H. Schmitt

    (Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University
    Fraunhofer Institute for Production Technology IPT)

Abstract

Manufacturing systems are undergoing systematic change facing the trade-off between the customer's needs and the economic and ecological pressure. Especially assembly systems must be more flexible due to many product generations or unpredictable material and demand fluctuations. As a solution line-less mobile assembly systems implement flexible job routes through movable multi-purpose resources and flexible transportation systems. Moreover, a completely reactive rearrangeable layout with mobile resources enables reconfigurations without interrupting production. A scheduling that can handle the complexity of dynamic events is necessary to plan job routes and control transportation in such an assembly system. Conventional approaches for this control task require exponentially rising computational capacities with increasing problem sizes. Therefore, the contribution of this work is an algorithm to dynamically solve the integrated problem of layout optimization and scheduling in line-less mobile assembly systems. The proposed multi agent deep reinforcement learning algorithm uses proximal policy optimization and consists of a decoder and encoder, allowing for various-sized system state descriptions. A simulation study shows that the proposed algorithm performs better in 78% of the scenarios compared to a random agent regarding the makespan optimization objective. This allows for adaptive optimization of line-less mobile assembly systems that can face global challenges.

Suggested Citation

  • Lea Kaven & Philipp Huke & Amon Göppert & Robert H. Schmitt, 2024. "Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3917-3936, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02309-8
    DOI: 10.1007/s10845-023-02309-8
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    References listed on IDEAS

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    1. 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, Taylor & Francis Journals, vol. 58(16), pages 5062-5076, July.
    2. Meir J. Rosenblatt, 1986. "The Dynamics of Plant Layout," Management Science, INFORMS, vol. 32(1), pages 76-86, January.
    3. Azalia Mirhoseini & Anna Goldie & Mustafa Yazgan & Joe Wenjie Jiang & Ebrahim Songhori & Shen Wang & Young-Joon Lee & Eric Johnson & Omkar Pathak & Azade Nova & Jiwoo Pak & Andy Tong & Kavya Srinivasa, 2021. "A graph placement methodology for fast chip design," Nature, Nature, vol. 594(7862), pages 207-212, June.
    4. Junyoung Park & Jaehyeong Chun & Sang Hun Kim & Youngkook Kim & Jinkyoo Park, 2021. "Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning," International Journal of Production Research, Taylor & Francis Journals, vol. 59(11), pages 3360-3377, June.
    5. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
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