Ranking Effcient DMUs with Stochastic Data by Considering Inecient Frontier

Data envelopment analysis (dea) models which evaluate the efficiency of a set of decisionmaking units (dmus) are unable to discriminate between efficient dmus. the problemof discriminating between these efficient dmus is an interesting subject. a large numberof methods for fully ranking both efficient and inefficient dmus have been proposed.through real world applications, analysis may encounter data that are not deterministicor on have a stochastic essence but whose distribution can be dened by collecting data insuccessive periods and by statistical methods. in this paper, a method for ranking stochastic efficient dmus is suggested which is based on the full inefficient frontier method. usinga numerical example, we will demonstrate how to use the result.