the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

Network data envelopment analysis (NDEA), one of the most important branches recent DEA developments, has been developed for examining decision making units (DMUs) a system with complex and internal component divisions. In this study we apply maximin strategy to network at two levels. At individual DMU level, evaluate system’s performance by maximizing minimum divisions efficiencies, which is b...

Network data envelopment analysis models assess efficiency of decision-making unit and its sections using historical data but fail to measure efficiency of its units and their internal stages in the future. In this paper we aim to measure efficiency of stages of bank branches and obtain efficiency trend of stages during the time, then to estimate their efficiency in the future therefore we can ...

‎‎in the data envelopment analysis (dea) the efficiency of the units‎ ‎can be obtained by identifying the degree of the importance of the‎ ‎criteria (inputs-outputs).in dea basic models‎, ‎challenges are zero‎ ‎and unequal weights of the criteria when decision‎- ‎making units are‎ ‎evaluated‎. ‎one of the strategies applied to deal with these problems‎ ‎is using common weights of the each input...

data envelopment analysis (dea) is a method used for measuring the efficiency of decision-making units‎. unlike the standard models‎, ‎which assume decision-making units to be a black box‎, ‎network data envelopment analysis focuses on the internal structure of these units‎. ‎some researchers have developed a two-stage method where all the inputs are entirely used in the first stage‎, ‎producin...