A fuzzy expected value approach under generalized data envelopment analysis

A fuzzy expected value approach under generalized data envelopment analysis

Fuzzy data envelopment analysis: A fuzzy expected value approach

Performance assessment often has to be conducted under uncertainty. This paper proposes a ‘‘fuzzy expected value approach’’ for data envelopment analysis (DEA) in which fuzzy inputs and fuzzy outputs are first weighted, respectively, and their expected values then used to measure the optimistic and pessimistic efficiencies of decision making units (DMUs) in fuzzy environments. The two efficienc...

Generalized Fuzzy Inverse Data envelopment Analysis Models

Traditional DEA models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. Inverse DEA models can be used to estimate inputs for a DMU when some or all outputs and efficiency level of this DMU are increased or preserved. this paper studies the inverse DEA for fuzzy data. This paper proposes generalized inverse DEA in fuzzy data envelopment anal...

Fuzzy data envelopment analysis: A discrete approach

Data envelopment analysis (DEA) as introduced by Charnes et al [3] is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input-output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency sc...

A Value Efficiency-Based Target Setting Approach in Data Envelopment Analysis

Basic models of Data Envelopment Analysis are intrinsically preference-free, in the sense that they consider all inputs and outputs and also all decision making units of the same importance. Although this property is beneficial in many ways, it has some drawbacks simultaneously, as the decision makers’ preferences are not taken into account in the process of evaluating units.  To overcome this ...