IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v27y2016i2d10.1007_s10845-013-0864-5.html
   My bibliography  Save this article

Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system

Author

Listed:
  • Xiao Wang

    (Huazhong University of Science and Technology
    Shenyang Aerospace University)

  • Hongwei Wang

    (Huazhong University of Science and Technology)

  • Chao Qi

    (Huazhong University of Science and Technology)

Abstract

This paper investigates the maintenance problem for a flow line system consisting of two series machines with an intermediate finite buffer in between. Both machines independently deteriorate as they operate, resulting in multiple yield levels. Resource constrained imperfect preventive maintenance actions may bring the machine back to a better state. The problem is modeled as a semi-Markov decision process. A distributed multi-agent reinforcement learning algorithm is proposed to solve the problem and to obtain the control-limit maintenance policy for each machine associated with the observed state represented by yield level and buffer level. An asynchronous updating rule is used in the learning process since the state transitions of both machines are not synchronous. Experimental study is conducted to evaluate the efficiency of the proposed algorithm.

Suggested Citation

  • Xiao Wang & Hongwei Wang & Chao Qi, 2016. "Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 325-333, April.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:2:d:10.1007_s10845-013-0864-5
    DOI: 10.1007/s10845-013-0864-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-013-0864-5
    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/s10845-013-0864-5?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. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    2. Tapas K. Das & Abhijit Gosavi & Sridhar Mahadevan & Nicholas Marchalleck, 1999. "Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning," Management Science, INFORMS, vol. 45(4), pages 560-574, April.
    3. D. G. Nguyen & D. N. P. Murthy, 1981. "Optimal Preventive Maintenance Policies for Repairable Systems," Operations Research, INFORMS, vol. 29(6), pages 1181-1194, December.
    4. David Edwards & Gary Holt & Frank Harris, 2000. "A model for predicting plant maintenance costs," Construction Management and Economics, Taylor & Francis Journals, vol. 18(1), pages 65-75.
    5. Kuo, Yarlin, 2006. "Optimal adaptive control policy for joint machine maintenance and product quality control," European Journal of Operational Research, Elsevier, vol. 171(2), pages 586-597, June.
    6. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    7. Kyriakidis, E.G. & Dimitrakos, T.D., 2006. "Optimal preventive maintenance of a production system with an intermediate buffer," European Journal of Operational Research, Elsevier, vol. 168(1), pages 86-99, January.
    8. Karamatsoukis, C.C. & Kyriakidis, E.G., 2010. "Optimal maintenance of two stochastically deteriorating machines with an intermediate buffer," European Journal of Operational Research, Elsevier, vol. 207(1), pages 297-308, November.
    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. Cheng, Jianda & Cheng, Minghui & Liu, Yan & Wu, Jun & Li, Wei & Frangopol, Dan M., 2024. "Knowledge transfer for adaptive maintenance policy optimization in engineering fleets based on meta-reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Wei, Shuaichong & Nourelfath, Mustapha & Nahas, Nabil, 2023. "Analysis of a production line subject to degradation and preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Orestes Llanes Santiago & Luis Anido Rifón, 2018. "Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1941-1952, December.
    4. Yang, Hongbing & Li, Wenchao & Wang, Bin, 2021. "Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    5. Qinming Liu & Ming Dong & Wenyuan Lv & Chunming Ye, 2019. "Manufacturing system maintenance based on dynamic programming model with prognostics information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1155-1173, March.
    6. Jorge Ribeiro & Pedro Andrade & Manuel Carvalho & Catarina Silva & Bernardete Ribeiro & Licínio Roque, 2022. "Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation," Mathematics, MDPI, vol. 10(9), pages 1-20, May.
    7. Johannes Dornheim & Lukas Morand & Samuel Zeitvogel & Tarek Iraki & Norbert Link & Dirk Helm, 2022. "Deep reinforcement learning methods for structure-guided processing path optimization," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 333-352, January.
    8. Zhang, Ning & Qi, Faqun & Zhang, Chengjie & Zhou, Hongming, 2022. "Joint optimization of condition-based maintenance policy and buffer capacity for a two-unit series system," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).
    10. Ye, Zhenggeng & Cai, Zhiqiang & Yang, Hui & Si, Shubin & Zhou, Fuli, 2023. "Joint optimization of maintenance and quality inspection for manufacturing networks based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    11. Zheng, Meimei & Su, Zhiyun & Wang, Dong & Pan, Ershun, 2024. "Joint maintenance and spare part ordering from multiple suppliers for multicomponent systems using a deep reinforcement learning algorithm," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Mohammadi, Reza & He, Qing, 2022. "A deep reinforcement learning approach for rail renewal and maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    13. Yuanju Qu & Zengtao Hou, 2022. "Degradation principle of machines influenced by maintenance," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1521-1530, June.
    14. 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.

    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. Xiang, Yisha, 2013. "Joint optimization of X¯ control chart and preventive maintenance policies: A discrete-time Markov chain approach," European Journal of Operational Research, Elsevier, vol. 229(2), pages 382-390.
    2. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2023. "Improving classical optimal age-replacement policies for degrading items," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    3. Dehayem Nodem, F.I. & Kenné, J.P. & Gharbi, A., 2011. "Simultaneous control of production, repair/replacement and preventive maintenance of deteriorating manufacturing systems," International Journal of Production Economics, Elsevier, vol. 134(1), pages 271-282, November.
    4. Dimitrakos, T.D. & Kyriakidis, E.G., 2008. "A semi-Markov decision algorithm for the maintenance of a production system with buffer capacity and continuous repair times," International Journal of Production Economics, Elsevier, vol. 111(2), pages 752-762, February.
    5. Kurt, Murat & Kharoufeh, Jeffrey P., 2010. "Optimally maintaining a Markovian deteriorating system with limited imperfect repairs," European Journal of Operational Research, Elsevier, vol. 205(2), pages 368-380, September.
    6. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    7. Hong, H.P. & Zhou, W. & Zhang, S. & Ye, W., 2014. "Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 276-288.
    8. Olde Keizer, Minou & Teunter, Ruud, 2014. "Opportunistic condition-based maintenance and aperiodic inspections for a two-unit series system," Research Report 14033-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    9. Guo, Chiming & Wang, Wenbin & Guo, Bo & Si, Xiaosheng, 2013. "A maintenance optimization model for mission-oriented systems based on Wiener degradation," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 183-194.
    10. A Grall & M Fouladirad, 2008. "Maintenance decision rule with embedded online Bayesian change detection for gradually non-stationary deteriorating systems," Journal of Risk and Reliability, , vol. 222(3), pages 359-369, September.
    11. Fouladirad, Mitra & Grall, Antoine, 2011. "Condition-based maintenance for a system subject to a non-homogeneous wear process with a wear rate transition," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 611-618.
    12. Poppe, Joeri & Basten, Rob J.I. & Boute, Robert N. & Lambrecht, Marc R., 2017. "Numerical study of inventory management under various maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 262-273.
    13. Mitra Fouladirad & Antoine Grall, 2015. "Monitoring and condition-based maintenance with abrupt change in a system’s deterioration rate," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(12), pages 2183-2194, September.
    14. Bouvard, K. & Artus, S. & Bérenguer, C. & Cocquempot, V., 2011. "Condition-based dynamic maintenance operations planning & grouping. Application to commercial heavy vehicles," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 601-610.
    15. Liu, Biyu & Pang, Jie & Yang, Haidong & Zhao, Yilin, 2024. "Optimal condition-based maintenance policy for leased equipment considering hybrid preventive maintenance and periodic inspection," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    16. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    17. Estelle Deloux & Yann Dijoux & Mitra Fouladirad, 2012. "Generalization of the proportional hazards model for maintenance modelling and optimization," Journal of Risk and Reliability, , vol. 226(5), pages 439-447, October.
    18. Karamatsoukis, C.C. & Kyriakidis, E.G., 2010. "Optimal maintenance of two stochastically deteriorating machines with an intermediate buffer," European Journal of Operational Research, Elsevier, vol. 207(1), pages 297-308, November.
    19. Wang, Xiaolin & Balakrishnan, Narayanaswamy & Guo, Bo, 2014. "Residual life estimation based on a generalized Wiener degradation process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 13-23.
    20. Ponchet, Amélie & Fouladirad, Mitra & Grall, Antoine, 2010. "Assessment of a maintenance model for a multi-deteriorating mode system," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1244-1254.

    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:27:y:2016:i:2:d:10.1007_s10845-013-0864-5. 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.