Development of Intuitionistic Fuzzy Revenue Efficiency Models in DEA: An Application to the Indian Public Sector Banks

Data envelopment analysis (DEA) is a non-parametric linear programming (LP) based technique to measure the relative efficiencies of homogeneous decision-making units (DMUs). The classical models of DEA rely on crisp input-output data, which may not always be available in real-life scenarios. Due to the existence of uncertainty and vagueness in real-life data, the concept of intuitionistic fuzzy (IF) has been introduced to handle imprecise data. Here, the conventional revenue efficiency models of DEA are extended to the IF environment. Also, the lower and upper revenue efficiency models are developed using \(\alpha \) and \(\beta \) -cuts approaches. The input-output data and output prices are considered triangular intuitionistic fuzzy numbers (TIFNs). An application to the public sector banks of India is provided to illustrate the practicality of the proposed intuitionistic fuzzy revenue efficiency models (IFREMs). Data is collected from the official website of the Reserve Bank of India (RBI), Govt. of India, India.