IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v62y2025i2d10.1007_s12597-024-00815-8.html
   My bibliography  Save this article

A Tri-level minimum information demand estimation model to the inverse p-median problem

Author

Listed:
  • Mehdi Zaferanieh

    (Hakim Sabzevari University)

  • Maryam Abareshi

    (Hakim Sabzevari University)

Abstract

In this paper, a novel inverse approach to the p-median problem is introduced in which the locations of p facilities and their supplies are known, while the demands of client nodes and the fraction allocated to each facility need to be estimated. To achieve this purpose, a tri-level programming problem is proposed. The primary objective of the first-level model is to minimize the sum of the squared differences between the estimated demand values and the observed target values. The second and third level problems together form a bi-level p-median model that incorporates the minimum information approach into the allocation phase. By substituting the optimality conditions of the third-level problem into the second one, a nonlinear bi-level mixed-integer model is obtained, which is addressed by using a particle swarm optimization algorithm. The added value of the tri-level model and the proposed method is verified by some small and large-sized examples.

Suggested Citation

  • Mehdi Zaferanieh & Maryam Abareshi, 2025. "A Tri-level minimum information demand estimation model to the inverse p-median problem," OPSEARCH, Springer;Operational Research Society of India, vol. 62(2), pages 877-904, June.
    • Handle: RePEc:spr:opsear:v:62:y:2025:i:2:d:10.1007_s12597-024-00815-8
    DOI: 10.1007/s12597-024-00815-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-024-00815-8
    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/s12597-024-00815-8?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:opsear:v:62:y:2025:i:2:d:10.1007_s12597-024-00815-8. 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.