Advances in inverse data envelopment analysis: empowering performance assessment.

Inverse DEA models may also be improved by considering uncertainty in data, for example developing stochastic inverse DEA or fuzzy inverse DEA. Data envelopment analysis (DEA) is a non-parametric optimization approach that was first introduced by [4] and is widely used for assessing the performance and comparative efficiency of decision-making units (DMUs) in both public and private sectors. Current state of knowledge in inverse DEA Unlike the conventional DEA, inverse DEA seeks to estimate input and/or output targets for DMUs to achieve efficiency targets. The introduction of inverse DEA created a new direction in the literature of DEA and provided great opportunities to solve novel inverse optimization problems that were beyond the scope of the standard DEA. [Extracted from the article]