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

G. Reza Nasiri (), Elnaz Miandoabchi () and Mohaddeseh Javadi ()
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G. Reza Nasiri: Alzahra University
Elnaz Miandoabchi: Logistics and Value Chain Research Group, Institute for Trade Studies and Research
Mohaddeseh Javadi: Alzahra University

Group Decision and Negotiation, 2025, vol. 34, issue 1, No 2, 7-34

Abstract: 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.

Keywords: DBSCAN; K-means; Supplier Selection; Resilience; Clustering (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10726-024-09903-y

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