This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence imprecise vague data. In order to achieve this goal, data envelopment analysis (DEA) model, adjustable possibilistic programming (APP), chance-constrained (CCP) are applied propose new fuzzy (FNDEA) approach. The main advantages proposed FND...

The existing data envelopment analysis (DEA) models for measuring the relative efficiencies of a set of decision making units (DMUs) using various inputs to produce various outputs are limited to crisp data. The notion of fuzziness has been introduced to deal with imprecise data. Fuzzy DEA models are made more powerful for applications. This paper develops the measure of efficiencies in input o...

Many real-world problems require decision makers to consider multiple criteria when performing an analysis. One popular method used to analyze multicriteria decision problems is goal programming. When applying goal programming, it is often difficult if not impossible to determine the target values and unit penalty weights with any level of confidence. Thus, in many situations, managers and deci...

Data Envelopment Analysis (DEA) requires that the data for all inputs and outputs are known exactly. When some outputs and inputs are unknown decision variables, such as bounded and ordinal data, the DEA model becomes a nonlinear programming problem and is called imprecise DEA (IDEA). The nonlinear IDEA program can be converted into a linear program by an algorithm based upon scale transformati...

Data envelopment analysis which is a nonparametric technique for evaluating relative efficiency of the decision making units with multiple inputs and outputs, has been a very popular method among researchers. While this nonparametric technique is popular, it has some drawbacks such as lack of discrimination in efficient units and weights dispersion .The present study, which is a model based on ...

Data envelopment analysis (DEA) is a common technique in measuring the relative efficiency of a set of decision making units (DMUs) with multiple inputs and multiple outputs. ‎‎Standard DEA models are ‎‎quite limited models‎, ‎in the sense that they do not consider a DMU ‎‎at different times‎. ‎To resolve this problem‎, ‎DEA models with dynamic ‎‎structures have been proposed‎.‎In a recent pape...

In today's competitive environment, enterprises should use their resources correctly; they continuously improve themselves and work efficiently. It is important to evaluate the performances of units under same conditions in according each other, see current situations determine appropriate improvements necessary points. One commonly used approaches performance evaluation Data Envelopment Analys...

Data envelopment analysis (DEA) is a widely used benchmarking technique. Its strength stems from the fact that it can include several inputs and outputs of not necessarily same type to evaluate efficiency scores. Indeed, aforesaid method based on mathematical optimization. This paper constructs second-order conic optimization problem unifying DEA models. Moreover, presents an algorithm solves f...

in this paper, we show that inverse data envelopment analysis (dea) models can be used to estimate output with fuzzy data for a decision making unit (dmu) when some or all inputs are increased and deficiency level of the unit remains unchanged.