Inefficiency evaluation with an additive dea model under imprecise data, an application on iauk departments

In imprecise data envelopment analysis (IDEA) (Cooper et al. [4]), the corresponding DEA models become non-linear and an important problem is to transform them into a linear programming one. In most of the current approaches to this problem, the number of decision variables increases dramatically, and usually the favorable results of these models are taken in several occasions. In this paper an additive DEA model is employed to evaluate the technical inefficiency of decision making units (DMUs) under imprecise data. The non-linear DEA model is transformed into an equivalent linear one, then the translation invariant property is used and a one-stage approach is introduced in this inefficiency evaluation. The approach rectifies the computational burden of previous methods in applications.