IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v51y2019i7p777-791.html
   My bibliography  Save this article

Flexible preventative maintenance for serial production lines with multi-stage degrading machines and finite buffers

Author

Listed:
  • Yunyi Kang
  • Feng Ju

Abstract

In production systems, machines are typically subject to degradation, which is a gradual and accumulating process that can influence the performance of the production systems. In this work, we focus on the flexible preventative maintenance problem on serial production lines with multi-stage degrading machines and finite buffers. Condition-based maintenance decisions are first investigated for a two-machine-one-buffer system, considering machine degradation stages and the buffer level. The optimal maintenance policy is obtained using Markov decision models. For longer lines, approximation methods are developed based on the results from the two machine case. Specifically, an iterative state and machine aggregation approach is developed to find the optimal preventative maintenance policy for each machine in large systems. Numerical experiments show that the proposed method outperforms the state-of-the-art.

Suggested Citation

  • Yunyi Kang & Feng Ju, 2019. "Flexible preventative maintenance for serial production lines with multi-stage degrading machines and finite buffers," IISE Transactions, Taylor & Francis Journals, vol. 51(7), pages 777-791, July.
    • Handle: RePEc:taf:uiiexx:v:51:y:2019:i:7:p:777-791
    DOI: 10.1080/24725854.2018.1562283
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24725854.2018.1562283
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24725854.2018.1562283?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.

    Citations

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


    Cited by:

    1. Yang, Ao & Qiu, Qingan & Zhu, Mingren & Cui, Lirong & Chen, Weilin & Chen, Jianhui, 2022. "Condition-based maintenance strategy for redundant systems with arbitrary structures using improved reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(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. Zhou, Yifan & Li, Bangcheng & Lin, Tian Ran, 2022. "Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Ye, Zhenggeng & Yang, Hui & Cai, Zhiqiang & Si, Shubin & Zhou, Fuli, 2021. "Performance evaluation of serial-parallel manufacturing systems based on the impact of heterogeneous feedstocks on machine degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

    More about this item

    Statistics

    Access and download statistics

    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:taf:uiiexx:v:51:y:2019:i:7:p:777-791. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    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.