Generalized Fuzzy Inverse Data envelopment Analysis Models

Models in conventional Data Envelopment Analysis (DEA) applies accurate data to estimate effciency scores, whereas cases are frequently arisen in empirical studies with imprecise data. Inverse DEA models can be used to estimate inputs for a decision making unit (DMU) when some or all outputs and effciency level of this DMU are increased or preserved. This paper studies the inverse DEA for fuzzy data. Fuzzy data envelopment analysis (FDEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units. Although several approaches for solving fuzzy DEA models have been developed, numerous deciencies including the a-cut approaches and types of fuzzy numbers must still be improved. This paper proposes generalized inverse DEA in fuzzy data envelopment analysis. The practical application of these models is illustrated by a numerical example.