Sangam: A Confluence of Knowledge Streams

The State of the Art in Fuzzy Data Envelopment Analysis

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dc.creator Emrouznejad, A.
dc.creator Tavana, M.
dc.creator Hatami-Marbini, A.
dc.date 2017-03-06T09:49:57Z
dc.date 2017-03-06T09:49:57Z
dc.date 2013-11-29
dc.date.accessioned 2023-02-22T17:04:45Z
dc.date.available 2023-02-22T17:04:45Z
dc.identifier Emrouznejad, A., Tavana, M. and Hatami-Marbini, A. (2013) The State of the Art in Fuzzy Data Envelopment Analysis. In: Performance Measurement with Fuzzy Data Envelopment Analysis
dc.identifier 9783642413711
dc.identifier http://link.springer.com/chapter/10.1007/978-3-642-41372-8_1
dc.identifier http://hdl.handle.net/2086/13395
dc.identifier https://doi.org/10.1007/978-3-642-41372-8_1
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/254455
dc.description 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.
dc.language en
dc.publisher Springer
dc.subject Data envelopment analysis
dc.subject Fuzzy sets theory
dc.title The State of the Art in Fuzzy Data Envelopment Analysis
dc.type Book chapter


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