IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v87y2023i2d10.1007_s10898-022-01227-y.html
   My bibliography  Save this article

Decision bounding problems for two-stage distributionally robust stochastic bilevel optimization

Author

Listed:
  • Xiaojiao Tong

    (Hunan First Normal University
    Xiangtan University)

  • Manlan Li

    (Xiangtan University)

  • Hailin Sun

    (Nanjing Normal University)

Abstract

Distributionally robust optimization (DRO) becomes a hot research topic in stochastic programming (SP) due to its characteristic for solving SP problems with incomplete distribution information. In this paper, we study a two-stage distributionally robust stochastic bilevel optimization problem (TDRBO) under the moment uncertainty. The TDRBO problem has a complicated optimization structure and is intractable. To this end, we first present the corresponding robust optimistic and pessimistic models as the bounds of the TDRBO problem. Then we use the decision rule approach to handle the bounded optimization problems under two situations of fixed recourse and random recourse. Finally, we obtain the tractable bounded optimization problems of the TDRBO problem. Some theoretical results are established and the numerical results are presented, showing that the approaches proposed in this paper are reasonable and effective.

Suggested Citation

  • Xiaojiao Tong & Manlan Li & Hailin Sun, 2023. "Decision bounding problems for two-stage distributionally robust stochastic bilevel optimization," Journal of Global Optimization, Springer, vol. 87(2), pages 679-707, November.
    • Handle: RePEc:spr:jglopt:v:87:y:2023:i:2:d:10.1007_s10898-022-01227-y
    DOI: 10.1007/s10898-022-01227-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-022-01227-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10898-022-01227-y?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.

    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:jglopt:v:87:y:2023:i:2:d:10.1007_s10898-022-01227-y. 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.