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Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

Author

Listed:
  • Marco Wurster

    (Karlsruhe Institute of Technology (KIT))

  • Marius Michel

    (Karlsruhe Institute of Technology (KIT))

  • Marvin Carl May

    (Karlsruhe Institute of Technology (KIT))

  • Andreas Kuhnle

    (Karlsruhe Institute of Technology (KIT))

  • Nicole Stricker

    (Karlsruhe Institute of Technology (KIT))

  • Gisela Lanza

    (Karlsruhe Institute of Technology (KIT))

Abstract

Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases.

Suggested Citation

  • Marco Wurster & Marius Michel & Marvin Carl May & Andreas Kuhnle & Nicole Stricker & Gisela Lanza, 2022. "Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 575-591, February.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:2:d:10.1007_s10845-021-01863-3
    DOI: 10.1007/s10845-021-01863-3
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    References listed on IDEAS

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    1. Kenneth N. McKay & Frank R. Safayeni & John A. Buzacott, 1988. "Job-Shop Scheduling Theory: What Is Relevant?," Interfaces, INFORMS, vol. 18(4), pages 84-90, August.
    2. 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.
    3. Aytug, Haldun & Lawley, Mark A. & McKay, Kenneth & Mohan, Shantha & Uzsoy, Reha, 2005. "Executing production schedules in the face of uncertainties: A review and some future directions," European Journal of Operational Research, Elsevier, vol. 161(1), pages 86-110, February.
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    Cited by:

    1. Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).

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