Ranking efficient DMUs using minimizing distance in DEA

In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same efficiency score for different DMUs. Hence, there is a growing interest in ranking techniques yet. The main purpose of this paper is to overcome the lack of infeasibility and unboundedness in some DEA ranking methods. The proposed method is for ranking extreme efficient DMUs in DEA based on exploiting the leave-one out and minimizing distance between DMU under evaluation and virtual DMU.