A Flexible Neural Network-Fuzzy Data Envelopment Analysis Approach for Location optimization of Solar Plants with Uncertainty and Complexity

This study presents a flexible approach for optimization the location of solar plants. It is composed of artificial neural network (ANN) and fuzzy data envelopment analysis (FDEA). The intelligent approach of this study is applied to an actual location optimization of solar plants in Iran. First, FDEA is validated by DEA, and then it is used for ranking of solar plant units (SPUs) and the best α -cut is selected based on test of Normality. Also, several ANNs are developed through multi layer perceptron (MLP) for ranking of solar plants and the best one with minimum MAPE is selected for further considerations. Finally, the preferred model (FDEA or ANN) is selected based on test of Normality. The implementation of the flexible approach for solar plants in Iran identifies FDEA at α = 0.3. This indicates that the data are collected from the uncertain and fuzzy environment. This is the first study that presents a flexible approach for identification of optimum location of solar plants with possible noise, non-linearity, vague and fuzzy environment. The proposed approach of this study is ideal for solar plants location optimization with possible complexity, ambiguity and uncertainty. This would help policy makers to identify the preferred policy for location optimization problems associated with solar plant units.