Ranking Efficient DMUs with Stochastic Data by Considering Inefficient Frontier

Data Envelopment Analysis (DEA) models which evaluate the effeciency of a set of decision making units (DMUs) are unable to discriminate between effeciency DMUs. The problem of discriminating between these effecient DMUs is an interesting subject. A large number of methods for fully ranking both effecient and ineffecient DMUs have been proposed. Through real world applications, analysis may encounter data that are not deterministic or on have a stochastic essence but whose distribution can be defined by collecting data in successive periods and by statistical methods. In this paper, a method for ranking stochastic effecient DMUs is suggested which is based on the full ineffecient frontier method. Using a numerical example, we will demonstrate how to use the result