IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v57y2019i15-16p5239-5283.html
   My bibliography  Save this article

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

Author

Listed:
  • Isabel Mundi
  • M. M. E. Alemany
  • Raúl Poler
  • Vicente S. Fuertes-Miquel

Abstract

Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.

Suggested Citation

  • Isabel Mundi & M. M. E. Alemany & Raúl Poler & Vicente S. Fuertes-Miquel, 2019. "Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 5239-5283, August.
  • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:15-16:p:5239-5283
    DOI: 10.1080/00207543.2019.1566665
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2019.1566665
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2019.1566665?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. Jann Michael Weinand & Kenneth Sorensen & Pablo San Segundo & Max Kleinebrahm & Russell McKenna, 2020. "Research trends in combinatorial optimisation," Papers 2012.01294, arXiv.org.

    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:57:y:2019:i:15-16:p:5239-5283. 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.