The state of the art in fuzzy data envelopment analysis

Ali Emrouznejad, Madjid Tavana, Adel Hatami-Marbini

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

86 Citations (Scopus)


Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. This chapter provides a taxonomy and review of the fuzzy DEA (FDEA) methods. We present a classification scheme with six categories, namely, the tolerance approach, the α-level based approach, the fuzzy ranking approach, the possibility approach, the fuzzy arithmetic, and the fuzzy random/type-2 fuzzy set. We discuss each classification scheme and group the FDEA papers published in the literature over the past 30 years.

Original languageEnglish
Title of host publicationPerformance Measurement with Fuzzy Data Envelopment Analysis
EditorsAli Emrouznejad, Madjid Tavana
PublisherSpringer Verlag
Number of pages45
Volume309 STUDFUZZ
ISBN (Electronic)9783642413728
ISBN (Print)9783642413711, 9783662509845
Publication statusPublished - 17 Dec 2013
Externally publishedYes

Publication series

NameStudies in Fuzziness and Soft Computing
PublisherSpringer Verlag
Volume309 STUDFUZZ
ISSN (Print)1434-9922
ISSN (Electronic)1860-0808


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