IDEAS home Printed from https://ideas.repec.org/a/spr/grdene/v34y2025i1d10.1007_s10726-024-09903-y.html
   My bibliography  Save this article

Developing a Two-Stage Decision-Making Method for Selecting and Clustering Suppliers Based on the Resilience Criteria

Author

Listed:
  • G. Reza Nasiri

    (Alzahra University)

  • Elnaz Miandoabchi

    (Logistics and Value Chain Research Group, Institute for Trade Studies and Research)

  • Mohaddeseh Javadi

    (Alzahra University)

Abstract

The selection of appropriate suppliers is a critical issue for the survival of a company in a competitive market environment and is also one of the most significant challenges for organizations. The present study proposes a method for supplier selection by organizing them using clustering techniques. In this study, suppliers are selected based on a set of resilience criteria. The Improved Best Worst Method was used to determine the weight of the criteria using GAMS software. The two clustering algorithms including K-means and DBSCAN were used in this study. The DBSCAN algorithm was used to identify the noise points as the K-means algorithm could not identify these points properly. Both algorithms were implemented in the MATLAB software considering a scenario with 30 suppliers and 22 resilience criteria. The criteria including raw material quality, delivery time of raw materials, and reliability have the highest priority. Based on the results, some managerial implications were also presented.

Suggested Citation

  • G. Reza Nasiri & Elnaz Miandoabchi & Mohaddeseh Javadi, 2025. "Developing a Two-Stage Decision-Making Method for Selecting and Clustering Suppliers Based on the Resilience Criteria," Group Decision and Negotiation, Springer, vol. 34(1), pages 7-34, February.
  • Handle: RePEc:spr:grdene:v:34:y:2025:i:1:d:10.1007_s10726-024-09903-y
    DOI: 10.1007/s10726-024-09903-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10726-024-09903-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/s10726-024-09903-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:grdene:v:34:y:2025:i:1:d:10.1007_s10726-024-09903-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.