A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity

This study presents a flexible neuro-fuzzy approach for location optimization of solar plants with possible complexity and uncertainty. The flexible approach is composed of artificial neural network (ANN) and fuzzy data envelopment analysis (FDEA). The intelligent approach of this study is applied for 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 the test of Normality. Also, several ANNs are developed through multi layer perceptron (MLP) for ranking of solar plants and the best one with minimum mean absolute percentage of error (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 the preferred FDEA at α = 0.3, where is the level of data uncertainty. 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, complexity and environmental uncertainty. This would help policy makers to identify the preferred Strategy for location optimization problems associated with solar plant units.

► This study is capable of handling complexity and non-linearity.
► It is capable of handling quantitative and qualitative indicators.
► It models qualitative indicators through fuzzy DEA.