Emrouznejad, A., M. Tavana, A. Hatami-Marbini (2014) \u201cThe State of the Art in Fuzzy Data\u00a0Envelopment Analysis\u201d, in \u201cPerformance Measurement with Fuzzy Data Envelopment Analysis\u201d published\u00a0in Studies in Fuzziness and Soft Computing 309: 1:48, Springer-Verlag.<\/p>\n
Data envelopment analysis (DEA) is a methodology for measuring the\u00a0relative efficiencies of a set of decision making units (DMUs) that use multiple\u00a0inputs to produce multiple outputs. Crisp input and output data are fundamentally\u00a0indispensable in conventional DEA. However, the observed values of the input and\u00a0output data in real-world problems are sometimes imprecise or vague. Many\u00a0researchers have proposed various fuzzy methods for dealing with the imprecise\u00a0and ambiguous data in DEA. This chapter provides a taxonomy and review of\u00a0the fuzzy DEA (FDEA) methods. We present a classification scheme with six\u00a0categories, namely, the tolerance approach, the a-level based approach, the fuzzy\u00a0ranking approach, the possibility approach, the fuzzy arithmetic, and the fuzzy\u00a0random\/type-2 fuzzy set. We discuss each classification scheme and group the\u00a0FDEA papers published in the literature over the past 30 years.<\/p>\n
[ Download1<\/a>\u00a0\u00a0 or\u00a0\u00a0 Download2<\/a> ]<\/p>\n","protected":false},"excerpt":{"rendered":" Emrouznejad, A., M. Tavana, A. Hatami-Marbini (2014) \u201cThe State of the Art in Fuzzy Data\u00a0Envelopment Analysis\u201d, in \u201cPerformance Measurement with Fuzzy Data Envelopment Analysis\u201d published\u00a0in Studies in Fuzziness and Soft Computing 309: 1:48, Springer-Verlag. Data envelopment analysis (DEA) is a methodology for measuring the\u00a0relative efficiencies of a set of decision making units (DMUs) that use … <\/p>\n