IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v63y2012i12p1773-1787.html
   My bibliography  Save this article

A visual interactive approach for scenario-based stochastic multi-objective problems and an application

Author

Listed:
  • E Balibek

    (1] Turkish Treasury, Ankara, Turkey[2] Middle East Technical University, Ankara, Turkey)

  • M Köksalan

    (Middle East Technical University, Ankara, Turkey)

Abstract

In many practical applications of stochastic programming, discretization of continuous random variables in the form of a scenario tree is required. In this paper, we deal with the randomness in scenario generation and present a visual interactive method for scenario-based stochastic multi-objective problems. The method relies on multi-variate statistical analysis of solutions obtained from a multi-objective stochastic problem to construct joint confidence regions for the objective function values. The decision maker (DM) explores desirable parts of the efficient frontier using a visual representation that depicts the trajectories of the objective function values within confidence bands. In this way, we communicate the effects of randomness inherent in the problem to the DM to help her understand the trade-offs and the levels of risk associated with each objective.

Suggested Citation

  • E Balibek & M Köksalan, 2012. "A visual interactive approach for scenario-based stochastic multi-objective problems and an application," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(12), pages 1773-1787, December.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:12:p:1773-1787
    as

    Download full text from publisher

    File URL: http://www.palgrave-journals.com/jors/journal/v63/n12/pdf/jors201225a.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/v63/n12/full/jors201225a.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. Murat Köksalan & Ceren Tuncer Şakar, 2016. "An interactive approach to stochastic programming-based portfolio optimization," Annals of Operations Research, Springer, vol. 245(1), pages 47-66, October.

    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:63:y:2012:i:12:p:1773-1787. 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.