A ranking system based on inverse data envelopment analysis

Inverse (DEA) is an approach to estimate the expected input/output variation levels when the efficiency score reminds unchanged. Essentially, finding most efficient decision-making units (DMUs) or ranking units is an important problem in DEA. A new ranking system for ordering extreme efficient units based on inverse DEA is introduced in this article. In the adopted method, here the amount of required increment of inputs by increasing the outputs of the unit under evaluation is obtained through the proposed models. By obtaining these variations, this proposed methodology enables the researcher to rank the efficient DMUs in an appropriate manner. Through the analytical theorem, it is proved that suggested models here are feasible. These newly introduced models are validated through a data set of commercial banks and a numerical example.