A multi-objective optimization approach for DEA models in a fuzzy environment

Data envelopment analysis (DEA) is an important managerial tool for evaluating the performance of decision-making units (DMUs). The conventional DEA models are mostly in the static environment using deterministic/crisp data for input and output parameters. However, in real situations, input and output data cannot always be obtained accurately because of vagueness due to fluctuating market conditions. Such vagueness in input and output data can be tackled with fuzzy numbers. So, the aim of the current study is to extend the crisp DEA into fuzzy DEA (FDEA). In this study, the input and output data are considered as fuzzy numbers (FNs), particularly the triangular fuzzy numbers (TFNs). Then, a multi-objective approach is developed to solve the FDEA model to measure the performance efficiencies of DMUs. Further, the DMUs are ranked according to their efficiencies obtained. Finally, the developed FMODEA model and rankings of DMUs are illustrated with an application on real data 13 educational institutions in India.