A Novel Inverse DEA-R Model for Inputs/Outputs Estimation

In this paper, we propose inverse data envelopment anal- ysis (DEA) models in the presence of ratio data. We present the inputs/output estimation process based on ratio based DEA (DEA- R) models. We rst present a multiple objective linear programming (MOLP) model to determine the level of inputs based on the perturbed outputs, assuming that the relative effciency of the under evaluation decision making unit (DMU) preserve. We also present the relation- ship between the Pareto solutions of the proposed MOLP model and the optimal level of inputs and outputs of the new DMU. We presented criterion models to determine the effciency of the new DMU in the in- puts/output estimation process based on inverse DEA-R models in the presence of ratio data. We showed that in the presence of ratio data the selection of criterion model can be important, in order to we provide a new criterion model in the inputs/output estimation process in the presence of ratio data, and so on the amount of calculations is reduced. We have shown that the results for the new criterion model are the same as the existing criterion model presented in the paper.