Inverse DEA-R by Using of the Frontier Changes

Inverse data envelopment analysis is an optimization problem to determine the optimal levels ofinputs and outputs. We suppose that the efficiency level of observed decision-making units will not change within the period of interest. It happens a lot in the real world that we have to dealwith ratio data, and we can no longer use the conventional DEA models to evaluate efficiency in these situations. In this research, we aim to extend Inverse DEA models to ratio data. For this purpose, we change the production frontier to gain the new target setting. We show the presented approach validity with an application example and present our results in the end.