Integrating inverse data envelopment analysis and neural network to preserve relative efficiency values

The present paper is an attempt to integrate inverse Data Envelopment Analysis (DEA) and Artificial Neural Network (ANN) for a large dataset with multiple Decision Making Units (DMUs). The purpose of this study is to determine the best possible values of inputs for a large number of DMUs when their output levels are changed and their efficiency values remain unchanged. When the ANN is used to develop inverse DEA, it is not necessary to solve the inverse DEA model for every single DMU. Therefore, this approach can save the computer's memory and the CPU time especially for very large scale datasets. To illustrate the ability of the proposed methodology, a set of 600 Iranian bank branches is used.