IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i2d10.1007_s10845-021-01863-3.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01863-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-021-01863-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Framinan, Jose M. & Ruiz, Rubén, 2010. "Architecture of manufacturing scheduling systems: Literature review and an integrated proposal," European Journal of Operational Research, Elsevier, vol. 205(2), pages 237-246, September.
    2. Altekin, F. Tevhide & Bukchin, Yossi, 2022. "A multi-objective optimization approach for exploring the cost and makespan trade-off in additive manufacturing," European Journal of Operational Research, Elsevier, vol. 301(1), pages 235-253.
    3. Alexey Matveev & Varvara Feoktistova & Ksenia Bolshakova, 2016. "On Global Near Optimality of Special Periodic Protocols for Fluid Polling Systems with Setups," Journal of Optimization Theory and Applications, Springer, vol. 171(3), pages 1055-1070, December.
    4. Ilkyeong Moon & Sanghyup Lee & Moonsoo Shin & Kwangyeol Ryu, 2016. "Evolutionary resource assignment for workload-based production scheduling," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 375-388, April.
    5. Shichang Xiao & Zigao Wu & Hongyan Dui, 2022. "Resilience-Based Surrogate Robustness Measure and Optimization Method for Robust Job-Shop Scheduling," Mathematics, MDPI, vol. 10(21), pages 1-22, October.
    6. 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.
    7. Faicel Hnaien & Taha Arbaoui, 2023. "Minimizing the makespan for the two-machine flow shop scheduling problem with random breakdown," Annals of Operations Research, Springer, vol. 328(2), pages 1437-1460, September.
    8. P J Kalczynski & J Kamburowski, 2004. "Generalization of Johnson's and Talwar's scheduling rules in two-machine stochastic flow shops," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1358-1362, December.
    9. Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo & Newman, Alexandra, 2024. "A target-time-windows technique for project scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 314(2), pages 792-806.
    10. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    11. Selcuk Goren & Ihsan Sabuncuoglu & Utku Koc, 2012. "Optimization of schedule stability and efficiency under processing time variability and random machine breakdowns in a job shop environment," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(1), pages 26-38, February.
    12. Koltai, Tamás, 2009. "Robustness of a production schedule to inventory cost calculations," International Journal of Production Economics, Elsevier, vol. 121(2), pages 494-504, October.
    13. Alejandra Duenas & Dobrila Petrovic, 2008. "An approach to predictive-reactive scheduling of parallel machines subject to disruptions," Annals of Operations Research, Springer, vol. 159(1), pages 65-82, March.
    14. De', Rahul & May, Jerrold H, 1998. "Using Operational Planning Horizons for Determining Setup Changes," Omega, Elsevier, vol. 26(5), pages 581-592, October.
    15. Victor Portougal & David J. Robb, 2000. "Production Scheduling Theory: Just Where Is It Applicable?," Interfaces, INFORMS, vol. 30(6), pages 64-76, December.
    16. Hazır, Öncü & Ulusoy, Gündüz, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," International Journal of Production Economics, Elsevier, vol. 223(C).
    17. Boysen, Nils & Briskorn, Dirk & Schwerdfeger, Stefan, 2019. "Matching supply and demand in a sharing economy: Classification, computational complexity, and application," European Journal of Operational Research, Elsevier, vol. 278(2), pages 578-595.
    18. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
    19. Han, Xiao-le & Lu, Zhi-qiang & Xi, Li-feng, 2010. "A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1327-1340, December.
    20. Qiulan Zhao & Jinjiang Yuan, 2017. "Rescheduling to Minimize the Maximum Lateness Under the Sequence Disruptions of Original Jobs," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-12, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:33:y:2022:i:2:d:10.1007_s10845-021-01863-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.