IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v60y2022i17p5155-5175.html
   My bibliography  Save this article

Joint decision of condition-based opportunistic maintenance and scheduling for multi-component production systems

Author

Listed:
  • Jie Gan
  • Wenyu Zhang
  • Siyu Wang
  • Xiaohong Zhang

Abstract

In this study, system maintenance and production scheduling are jointly decided to solve the problems of resource idleness and time cost increase due to system maintenance in the processing of production scheduling. For the multi-component system with economic dependence, a joint strategy of condition-based maintenance and production scheduling is formulated, which includes opportunistic maintenance, preventive maintenance, and corrective maintenance. On this basis, a joint decision model is established to minimise the total weighted expected completion time. Subsequently, all possible maintenance requirements and their corresponding probabilities for the multi-component system in the entire production scheduling process are deduced via the deterioration state space partition modelling method. Furthermore, the stationary probability density function of the joint state of the system is derived, and its numerical solution method is provided. Finally, taking the KS5 adjustable multi-axis tapping machine as an example, numerical experiments are conducted to verify the efficacy of the proposed strategy and the established model. Comparisons with previous strategies using different numbers of components and scheduling job scales indicate that the joint decision model yields better results.

Suggested Citation

  • Jie Gan & Wenyu Zhang & Siyu Wang & Xiaohong Zhang, 2022. "Joint decision of condition-based opportunistic maintenance and scheduling for multi-component production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5155-5175, September.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:17:p:5155-5175
    DOI: 10.1080/00207543.2021.1951447
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2021.1951447
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2021.1951447?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. Zhou, Yu & Zheng, Ran, 2024. "Capacity-based daily maintenance optimization of urban bus with multi-objective failure priority ranking," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    2. Li, Meiyan & Wu, Bei, 2024. "Optimal condition-based opportunistic maintenance policy for two-component systems considering common cause failure," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    3. Zhang, Wenyu & Zhang, Xiaohong & He, Shuguang & Zhao, Xing & He, Zhen, 2024. "Optimal condition-based maintenance policy for multi-component repairable systems with economic dependence in a finite-horizon," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Georgios K. Koulinas & Panagiotis D. Paraschos & Dimitrios E. Koulouriotis, 2024. "A machine learning framework for explainable knowledge mining and production, maintenance, and quality control optimization in flexible circular manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 737-759, September.

    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:tprsxx:v:60:y:2022:i:17:p:5155-5175. 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/TPRS20 .

    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.