A fuzzy data envelopment analysis for clustering operating units with imprecise data

Saber Saati, Adel Hatami-Marbini, Madjid Tavana, Per J. Agrell

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency of peer operating units that employ multiple inputs to produce multiple outputs. Several DEA methods have been proposed for clustering operating units. However, to the best of our knowledge, the existing methods in the literature do not simultaneously consider the priority between the clusters (classes) and the priority between the operating units in each cluster. Moreover, while crisp input and output data are indispensable in traditional DEA, real-world production processes may involve imprecise or ambiguous input and output data. Fuzzy set theory has been widely used to formalize and represent the impreciseness and ambiguity inherent in human decision-making. In this paper, we propose a new fuzzy DEA method for clustering operating units in a fuzzy environment by considering the priority between the clusters and the priority between the operating units in each cluster simultaneously. A numerical example and a case study for the Jet Ski purchasing decision by the Florida Border Patrol are presented to illustrate the efficacy and the applicability of the proposed method.

Original languageEnglish
Pages (from-to)29-54
Number of pages26
JournalInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
Volume21
Issue number1
DOIs
Publication statusPublished - 1 Feb 2013
Externally publishedYes

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