LAUSR

Data envelopment analysis (DEA), which has been widely used since it was introduced by Charnes et al. (J Econom 30:91–107, 1985 ), is an effective method for evaluating the relative efficiency of decision-making units. DEA models require accurate input data and output data; however, if no sample is available to estimate accurate data, then uncertain DEA is introduced. This paper reports on several new studies on uncertain DEA using the Hurwicz criterion, which attempts to find the intermediate area between extremes. Some uncertain DEA models, as well as their crisp equivalent models, are presented. Then, the Hurwicz ranking method is proposed based on these models, which can give an evaluation to all the decision-making units. By varying the parameter $$\beta $$ β in the Hurwicz criterion, which reflects the optimism of the decision maker, the new ranking method can exhibit various forms. Finally, an application to scientific personnel is provided to prove the advantage of the proposed method.