a new data envelopment analysis (dea) model to determine the most efficient decision making unit (dmu) with imprecise data

sohrabi and nalchigar (2010) proposed a new data envelopment analysis (dea) model to identify the most efficient decision-making unit (dmu) in presence of imprecise data. in this paper, it is shown that the proposed model is not able to determine the most efficient dmu and is randomly introduced an efficient dmu. in addition, it is shown that this model determines the most efficient dmu in the case of variable return to scale having the same drawback and may be infeasible in some cases. to overcome the drawbacks, some new integrated dea models are developed. in addition, to find and rank the other most efficient dmus, an algorithm is proposed. by using the model developed in this paper, the decision maker can find the most efficient dmu by solving only one linear integer programming. the applicability of the proposed model is indicated using imprecise data for 18 suppliers.