IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v51y2019i4p406-421.html
   My bibliography  Save this article

Joint production and maintenance operations in smart custom-manufacturing systems

Author

Listed:
  • Jin Xu
  • Hoang M. Tran
  • Natarajan Gautam
  • Satish T. S. Bukkapatnam

Abstract

Machines in custom manufacturing environments with IoT (Internet-of-Things) capability are predicted to be pervading enterprises. However, there is a need to develop new algorithms that reap the benefits of such technologies. We consider a system where jobs with stochastic workloads arrive to a machine in an arbitrary fashion and upon arrival, their workload is revealed (enabled by IoT). The tool on the machine gets used up based on the speed at which the jobs are processed. Knowing that tool-replacement consumes a significant amount of time, we want to develop online algorithms that maximize the capacity of the machine by determining: (i) the speed at which each job is processed; and (ii) the epoch when the tool is replaced. We provide online approaches that leverage the ability to reveal workload in real-time and effectively balance future uncertainties. We derive asymptotic bounds for the online algorithm performance and show using numerical experimentation that a little revealed information could result in a tremendous improvement in performance. Our online algorithms also work under realistic conditions of non-stationary batch arrivals and correlated workloads. Our work opens up research directions for a variety of operational settings that may benefit from revealing stochastic quantities by mining information.

Suggested Citation

  • Jin Xu & Hoang M. Tran & Natarajan Gautam & Satish T. S. Bukkapatnam, 2019. "Joint production and maintenance operations in smart custom-manufacturing systems," IISE Transactions, Taylor & Francis Journals, vol. 51(4), pages 406-421, April.
    • Handle: RePEc:taf:uiiexx:v:51:y:2019:i:4:p:406-421
    DOI: 10.1080/24725854.2018.1511938
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24725854.2018.1511938
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24725854.2018.1511938?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. Li, Yao & He, Yihai & Liao, Ruoyu & Zheng, Xin & Dai, Wei, 2022. "Integrated predictive maintenance approach for multistate manufacturing system considering geometric and non-geometric defects of products," Reliability Engineering and System Safety, Elsevier, vol. 228(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:4:p:406-421. 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.