IDEAS home Printed from https://ideas.repec.org/h/spr/lnechp/978-3-319-20430-7_27.html
   My bibliography  Save this book chapter

Modeling Multi-Stage Decision Optimization Problems

In: Computational Management Science

Author

Listed:
  • Ronald Hochreiter

    (WU Vienna University of Economics and Business)

Abstract

Multi-stage optimization under uncertainty techniques can be used to solve long-term management problems. Although many optimization modeling language extensions as well as computational environments have been proposed, the acceptance of this technique is generally low, due to the inherent complexity of the modeling and solution process. In this paper a simplification to annotate multi-stage decision problems under uncertainty is presented—this simplification contrasts with the common approach to create an extension on top of an existing optimization modeling language. This leads to the definition of meta models, which can be instanced in various programming languages. An example using the statistical computing language R is shown.

Suggested Citation

  • Ronald Hochreiter, 2016. "Modeling Multi-Stage Decision Optimization Problems," Lecture Notes in Economics and Mathematical Systems, in: Raquel J. Fonseca & Gerhard-Wilhelm Weber & João Telhada (ed.), Computational Management Science, edition 1, pages 209-214, Springer.
    • Handle: RePEc:spr:lnechp:978-3-319-20430-7_27
    DOI: 10.1007/978-3-319-20430-7_27
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:lnechp:978-3-319-20430-7_27. 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.springer.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.