A characteristic of data envelopment analysis (DEA) is to allow individual decision making units (DMUs) to select the most advantageous weights in calculating their efficiency scores. This flexibility, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes using a multiple objective...

data envelopment analysis (dea) is a wide range of mathematical models that used for measuring the relative efficiency of a set of harmonic units whit similar inputs and outputs. this model calculates some different weight for input and output variables in each decision making model. this variation in similar weights caused some critiques to this method. from 1991 some linear and nonlinear mode...

data envelopment analysis (dea) with considering the best condition for each decision making unit (dmu) assesses the relative efficiency for it and divides a homogenous group of dmus in to two categories: efficient and inefficient, but traditional dea models can not rank efficient dmus. although some models were introduced for ranking efficient dmus, franklin lio & hsuan peng (2008), proposed a...

In data envelopment analysis (DEA), mul-tiplier and envelopment CCR models eval-uate the decision-making units (DMUs) under optimal conditions. Therefore, the best prices are allocated to the inputs and outputs. Thus, if a given DMU was not efficient under optimal conditions, it would not be considered efficient by any other models. In the current study, using common weights in DEA, a number of...

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are large number objects, non-homogeneity significantly influences scores that leads unfair ranking DMUs. The aim this study deal w...

Data envelopment analysis (DEA) is a powerful technique for performance evaluation of decision making units (DMUs). One of the main objectives that is followed in performance evaluation is discriminating among efficient DMUs to provide a complete ranking of DMUs. DEA successfully divides them into two categories: efficient DMUs and inefficient DMUs. The DMUs in the efficient category have ident...