IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v66y2015i9p1471-1480.html
   My bibliography  Save this article

Two empirical uncertain models for project scheduling problem

Author

Listed:
  • Chunxiao Ding

    (Nanjing University of Science and Technology, Nanjing, China)

  • Yuanguo Zhu

    (Nanjing University of Science and Technology, Nanjing, China)

Abstract

The project scheduling problem with uncertain activity durations is considered, and two types of models for uncertain project scheduling problems are established according to different management requirements. These models are transformed to their crisp forms, which may be solved by classical optimization methods. For the models that could not be transformed to their crisp forms, an uncertain simulation is employed to approximate uncertain functions. Finally, two numerical examples are given to illustrate the usefulness of proposed models.

Suggested Citation

  • Chunxiao Ding & Yuanguo Zhu, 2015. "Two empirical uncertain models for project scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(9), pages 1471-1480, September.
  • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:9:p:1471-1480
    as

    Download full text from publisher

    File URL: http://www.palgrave-journals.com/jors/journal/v66/n9/pdf/jors2014115a.pdf
    File Function: Link to full text PDF
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: http://www.palgrave-journals.com/jors/journal/v66/n9/full/jors2014115a.html
    File Function: Link to full text HTML
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Xiaoxia Huang & Liying Song, 2018. "An emergency logistics distribution routing model for unexpected events," Annals of Operations Research, Springer, vol. 269(1), pages 223-239, October.
    2. Bordley, Robert F. & Keisler, Jeffrey M. & Logan, Tom M., 2019. "Managing projects with uncertain deadlines," European Journal of Operational Research, Elsevier, vol. 274(1), pages 291-302.
    3. Jian Zhou & Yujiao Jiang & Athanasios A. Pantelous & Weiwen Dai, 2023. "A systematic review of uncertainty theory with the use of scientometrical method," Fuzzy Optimization and Decision Making, Springer, vol. 22(3), pages 463-518, September.

    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:pal:jorsoc:v:66:y:2015:i:9:p:1471-1480. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    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.