Fuzzy C-Means based Data Envelopment Analysis for Mitigating the Impact of Units' Heterogeneity

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dc.cclicenceN/Aen
dc.contributor.authorRazavi Hajiagha, S.H.
dc.contributor.authorHashemi, S.S.
dc.contributor.authorAmoozad Mahdiraji, Hannan
dc.date.accessioned2020-11-13T11:47:21Z
dc.date.available2020-11-13T11:47:21Z
dc.date.issued2016-03-07
dc.description.abstractPurpose Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.en
dc.exception.ref2021codes252cen
dc.funderNo external funderen
dc.identifier.citationHajiagha, S. H. R., Hashemi, S. S., and Mahdiraji, H. A. (2016). Fuzzy C-means based data envelopment analysis for mitigating the impact of units’ heterogeneity. Kybernetes, 45 (3), pp. 536-551en
dc.identifier.doihttps://doi.org/10.1108/K-07-2015-0176
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/20438
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherEmeralden
dc.titleFuzzy C-Means based Data Envelopment Analysis for Mitigating the Impact of Units' Heterogeneityen
dc.typeArticleen

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