A new approach to rank decision making units with imprecise data

In this paper, we introduce a new method for ranking Decision Making Units (DMUs) in imprecise Data Envelopment analysis (IDEA). We use common weights methodology to IDEA and propose a method to fully ranking DMUs with interval data. In this method performance of all units in all of their statements are evaluated under a common conditions. Also the units which are not able to be efficient have the opportunity to compare with other DMUs. Here, The ranking criterion reflects well the performance of DMUs and make it more logical and comparative the ranking results. We implement our method on a real data set. Besides we implement Wang et al.’s method [11] and discuss the results.