The State of the Art in Fuzzy Data Envelopment Analysis

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 a-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. An earlier version of this chapter was published as Hatami-Marbini et al. [1]. A. Emrouznejad (&) Aston Business School, Aston University, Birmingham, UK e-mail: [email protected] M. Tavana Lindback Distinguished Chair of Information Systems and Decision Sciences, Business Systems and Analytics Department, La Salle University, Philadelphia, PA 19141, USA e-mail: [email protected] URL: http://tavana.us M. Tavana Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, 33098 Paderborn, Germany A. Hatami-Marbini Louvain School of Management, Center of Operations Research and Econometrics (CORE), Université catholique de Louvain, L1.03.01 1348 Louvain-la-Neuve, Belgium e-mail: [email protected] A. Emrouznejad and M. Tavana (eds.), Performance Measurement with Fuzzy Data Envelopment Analysis, Studies in Fuzziness and Soft Computing 309, DOI: 10.1007/978-3-642-41372-8_1, Springer-Verlag Berlin Heidelberg 2014 1