A New Hybrid Methodology Based on Data Envelopment Analysis and Neural Network for Optimization of Performance Evaluation

There are numerous models of data envelopment analysis (DEA) for solving the effciency evaluation a set of homogeneous Decision-Making Units (DMUs) that use similar sources to produce similar outputs. However, the effciency boundary in these models is very sensitive to outliers and random factors. In this way, researchers have always sought a method that, in addition to having the high exibility of nonparametric methods, compensates for the weaknesses of this view. The approach suggested by scholars in this regard is the use of a combination of Articial Neural Network (ANN) and DEA. In this paper, a new method of combining ANN and DEA (ANN-DEA) presented in which the input and output values for a large number of DMUs determined as neural network inputs. It can be seen that the use of the neural network to solve the data envelopment analysis problem does not require solving the model for each DMU, and therefore compared with the conventional method, in the proposed algorithm processing time and memory usage signicantly reduced. We have also compared the new model with the existing approach of ANN-DEA. To illustrate the ability of the proposed methodology some case studies are used, including a set of 500 Iranian bank branches. The results indicate a high accuracy and less computational time of the proposed hybrid model and have practical outcomes for decision makers.