Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

Abstract

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

Publication DOI: https://doi.org/10.3233/jifs-200962
Divisions: Aston Business School > Operations & Information Management
Additional Information: Copyright ©2020 The Authors. Kazemi, Sajad et al. ‘Fuzzy Clustering of Homogeneous Decision Making Units with Common Weights in Data Envelopment Analysis’. 1 Jan. 2020 : 1 – 20. DOI: 10.3233/JIFS-200962
Uncontrolled Keywords: Clustering,Common set of weights (CSW),Data envelopment analysis,Fuzzy DEA,Non-homogeneous,Statistics and Probability,Engineering(all),Artificial Intelligence
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Related URLs: https://content ... stems/ifs200962 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021
Published Online Date: 2020-10-23
Accepted Date: 2020-10-01
Authors: Kazemi, Sajad
Mavi, Reza Kiani
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Kiani Mavi, Neda

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