Possibilistic Data Envelopment Analysis

Abstract

Data of problems in real world, fuzziness/impreciseness/absence appears due to various reasons very often. In such cases, difficulty in model building can be overcome by using fuzzy set theory and concepts. In this study, only situation of data lacking will be considered, Data envelopment analysis cannotbe used in the absence of one or more data in: model. If those absent datum or data can be supplied with possibilistic membership function, the problem is solved. In this study, a suggestion will be given about how to apply data envelopment analysis with the assistance of possibilistic membership function in real world problem with lacking data. Model problem has been chosen in the banking sector, which is a very popular area.

Fuzzy data envelopment analysis: A discrete approach

“…Table ( 1) shows the data which are also used in Guo and Tanaka [13]. Source: Guo and Tanaka (2001) Fuzzy efficiencies of DMUs using standard fuzzy DEA model (2) and with different α value solved by the method suggested in [24] is reported in Table (2).…”

“…Using the credibility approach they showed how the efficiency value for each DMU can be obtained as a representative of its possible range. A different approach based on possibility programming was developed in [1,18]. Inuiguchi and Tanino [15] applied the extension principle to define fuzzy efficiency score using DEA.…”

Inexact Discretionary Inputs in Data Envelopment Analysis

“…Alp [7] further extended the concept of possibilistic DEA by considering problems in handling real data such as fuzziness, impreciseness and incompleteness. In such cases, the difficulty in model building can be overcome by using fuzzy set theory and concepts.…”