IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v58y2020i14p4450-4470.html
   My bibliography  Save this article

An optimal integrated lot sizing and maintenance strategy for multi-machines system with energy consumption

Author

Listed:
  • Zied Hajej
  • Nidhal Rezg

Abstract

This paper proposes an integrated model for multi-machines dynamic lot sizing aiming to produce a single item, considering the energy consumption during the production horizon. The objective is to find, firstly, the optimal lot size as well as the number of machines that satisfy a random demand under given service level and secondly, maintenance plan depended to production planning to minimise the total production, energy and maintenance costs. In fact, the problem of energy consumption is one of the most evoked topics especially with the decision of many governments to reduce theirs (For example France is willing to reduce the total consumption by 20% by 2020). The keys of this study are to consider, firstly, the correlation between the forecasting of demand, the variation of the working machines as well as their production rates under energy constraint and secondly the correlation between the production cadences and the maintenance strategy of all machines.

Suggested Citation

  • Zied Hajej & Nidhal Rezg, 2020. "An optimal integrated lot sizing and maintenance strategy for multi-machines system with energy consumption," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4450-4470, July.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:14:p:4450-4470
    DOI: 10.1080/00207543.2019.1654630
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2019.1654630
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2019.1654630?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. Zhang, Nan & Cai, Kaiquan & Deng, Yingjun & Zhang, Jun, 2023. "Determining the optimal production–maintenance policy of a parallel production system with stochastically interacted yield and deterioration," Reliability Engineering and System Safety, Elsevier, vol. 237(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:tprsxx:v:58:y:2020:i:14:p:4450-4470. 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.