IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v53y2015i5p1487-1502.html
   My bibliography  Save this article

Task assignment under uncertainty: stochastic programming and robust optimisation approaches

Author

Listed:
  • Lu Zhen

Abstract

The assignment of tasks to teams is a challenging combinatorial optimisation problem. The uncertainty in the tasks’ execution processes further complicates the assignment decisions. This study investigates a variant of the typical assignment problem, in which each task can be divided into two parts, one is deterministic and the other is uncertain with respect to their workloads. From the stochastic perspective, this paper proposes both a stochastic programming model that can cope with arbitrary probability distributions of tasks’ random workload requirements, and a robust optimisation model that is applicable to situations in which limited information about probability distributions is available. An example of its application in the software project management is given. Some numerical experiments are also performed to validate the effectiveness of the proposed models and the relationships between the two models.

Suggested Citation

  • Lu Zhen, 2015. "Task assignment under uncertainty: stochastic programming and robust optimisation approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 53(5), pages 1487-1502, March.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:5:p:1487-1502
    DOI: 10.1080/00207543.2014.951094
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2014.951094
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2014.951094?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. Gülpınar, Nalan & Çanakoğlu, Ethem & Branke, Juergen, 2018. "Heuristics for the stochastic dynamic task-resource allocation problem with retry opportunities," European Journal of Operational Research, Elsevier, vol. 266(1), pages 291-303.

    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:53:y:2015:i:5:p:1487-1502. 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.