Performance assessment and optimization of decision making units with an integrated artificial neural network algorithm

This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN algorithm is able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). However, the proposed algorithm is capable of handling outliers and noise. This is shown by two examples related to outlier situations. It is also capable of performing optimization analysis and forecasting for a given set of data. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority.