IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v56y2018i14p4968-4982.html
   My bibliography  Save this article

Workforce grouping and assignment with learning-by-doing and knowledge transfer

Author

Listed:
  • Huan Jin
  • Mike Hewitt
  • Barrett W. Thomas

Abstract

We consider a workforce allocation problem in which workers learn both by performing a job and by observing the performance of and interacting with co-located colleagues. As a result, an organisation can benefit from both effectively assigning individuals to jobs and grouping workers into teams. A challenge often faced when solving workforce allocation models that recognise learning is that learning curves are non-linear. To overcome this challenge, we identify properties of an optimal solution to a non-linear programme for grouping workers into teams and assigning the resulting teams to sets of jobs. With these properties identified, we reformulate the non-linear programme to a mixed integer programme that can be solved in much less time. We analyse (near-)optimal solutions to this model to derive managerial insights.

Suggested Citation

  • Huan Jin & Mike Hewitt & Barrett W. Thomas, 2018. "Workforce grouping and assignment with learning-by-doing and knowledge transfer," International Journal of Production Research, Taylor & Francis Journals, vol. 56(14), pages 4968-4982, July.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:14:p:4968-4982
    DOI: 10.1080/00207543.2018.1424366
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2018.1424366
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2018.1424366?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. Cavagnini, Rossana & Hewitt, Mike & Maggioni, Francesca, 2020. "Workforce production planning under uncertain learning rates," International Journal of Production Economics, Elsevier, vol. 225(C).
    2. Ausseil, Rosemonde & Ulmer, Marlin W. & Pazour, Jennifer A., 2024. "Online acceptance probability approximation in peer-to-peer transportation," Omega, Elsevier, vol. 123(C).
    3. Henao, César Augusto & Mercado, Yessica Andrea & González, Virginia I. & Lüer-Villagra, Armin, 2023. "Multiskilled personnel assignment with k-chaining considering the learning-forgetting phenomena," International Journal of Production Economics, Elsevier, vol. 265(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:56:y:2018:i:14:p:4968-4982. 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.