The State of the Art in Fuzzy Data Envelopment Analysis

Date

2013-11-29

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Book chapter

Peer reviewed

Yes

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.

Description

Keywords

Data envelopment analysis, Fuzzy sets theory

Citation

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

Rights

Research Institute

Centre for Enterprise and Innovation (CEI)