New approach for ranking efficient DMUs based on Euclidean norm in data envelopment analysis

Abstract

Data envelopment analysis (DEA) is a widely used technique for measuring the relative efficiencies of decision-making unit (DMUs) with multiple inputs and multiple outputs. In many applications, ranking of DMUs is an important and essential procedure to decision makers in DEA, especially when there are extremely efficient DMUs. Basic DEA models usually give the same efficiency score for some DMUs. Hence, it is necessary to rank of all extreme DMUs. The main purpose of this study is to propose an appropriate method in order to overcome the drawbacks in several methods for ranking DMUs based on the DEA concept. In the present paper, we propose a model for ranking extreme efficient DMUs in DEA by super efficiency technique and Euclidean norm (l2-norm). The presented method in this paper is able to overcome the existing obstacles in some methods. As regards, the proposed model is into nonlinear programming form, a linear model is suggested to approximate the nonlinear model.