IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v70y2022i3p1822-1834.html
   My bibliography  Save this article

Distributionally Robust Linear and Discrete Optimization with Marginals

Author

Listed:
  • Louis Chen

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Will Ma

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Karthik Natarajan

    (Engineering Systems and Design, Singapore University of Technology and Design, Singapore 487372, Singapore)

  • David Simchi-Levi

    (Institute for Data, Systems, and Society, Department of Civil and Environmental Engineering, and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Zhenzhen Yan

    (School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore)

Abstract

In this paper, we study linear and discrete optimization problems in which the objective coefficients are random, and the goal is to evaluate a robust bound on the expected optimal value, where the set of admissible joint distributions is assumed to be specified only up to the marginals. We study a primal-dual formulation for this problem, and in the process, unify existing results with new results. We establish NP-hardness of computing the bound for general polytopes and identify two sufficient conditions: one based on a dual formulation and one based on sublattices that provide a class of polytopes where the robust bounds are efficiently computable. We discuss several examples and applications in areas such as scheduling.

Suggested Citation

  • Louis Chen & Will Ma & Karthik Natarajan & David Simchi-Levi & Zhenzhen Yan, 2022. "Distributionally Robust Linear and Discrete Optimization with Marginals," Operations Research, INFORMS, vol. 70(3), pages 1822-1834, May.
    • Handle: RePEc:inm:oropre:v:70:y:2022:i:3:p:1822-1834
    DOI: 10.1287/opre.2021.2243
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2021.2243
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2021.2243?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
    ---><---

    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:inm:oropre:v:70:y:2022:i:3:p:1822-1834. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.