The state of the art in fuzzy data envelopment analysis

Ali Emrouznejad; Madjid Tavana; Adel Hatami-Marbini

doi:10.1007/978-3-642-41372-8-1

The state of the art in fuzzy data envelopment analysis

Ali Emrouznejad^*, Madjid Tavana, Adel Hatami-Marbini

^*Corresponding author for this work

Operations & Information Management

Research output: Chapter in Book/Published conference output › Chapter (peer-reviewed) › peer-review

Abstract

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 language	English
Title of host publication	Performance measurement with fuzzy data envelopment analysis
Editors	Ali Emrouznejad, Madjid Tavana
Place of Publication	Berlin (DE)
Publisher	Springer
Pages	1-45
Number of pages	45
Volume	309
ISBN (Electronic)	978-3-642-41372-8
ISBN (Print)	978-3-642-41371-1
DOIs	https://doi.org/10.1007/978-3-642-41372-8-1
Publication status	Published - 31 Dec 2014

Publication series

Name	Studies in fuzziness and soft computing
Publisher	Springer-Verlag
Volume	309
ISSN (Print)	1434-9922
ISSN (Electronic)	1860-0808

Keywords

data envelopment analysis
fuzzy sets
tolerance approach
alpha-level based approach
fuzzy ranking approach
possibility approach
fuzzy arithmetic
fuzzy random
type-2 fuzzy set

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10.1007/978-3-642-41372-8-1

Cite this

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The state of the art in fuzzy data envelopment analysis. / Emrouznejad, Ali; Tavana, Madjid; Hatami-Marbini, Adel.
Performance measurement with fuzzy data envelopment analysis. ed. / Ali Emrouznejad; Madjid Tavana. Vol. 309 Berlin (DE): Springer, 2014. p. 1-45 (Studies in fuzziness and soft computing; Vol. 309).

Research output: Chapter in Book/Published conference output › Chapter (peer-reviewed) › peer-review

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AU - Emrouznejad, Ali

AU - Tavana, Madjid

AU - Hatami-Marbini, Adel

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Y1 - 2014/12/31

N2 - 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.

AB - 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.

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KW - fuzzy sets

KW - tolerance approach

KW - alpha-level based approach

KW - fuzzy ranking approach

KW - possibility approach

KW - fuzzy arithmetic

KW - fuzzy random

KW - type-2 fuzzy set

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ER -

The state of the art in fuzzy data envelopment analysis

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Performance measurement with fuzzy data envelopment analysis

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