A Novel Ranking Method Based on Uncertain DEA Model

Proposing a full ranking method to rank all the homogeneous decision-making units (DMUs) is one of the important issues in data envelopment analysis (DEA) field. It should be noted that one of the main challenges in using traditional ranking approaches to real-world applications is the presence of noise and uncertainty in some of the input/output data of DMUs. Thus, in this paper, the novel ranking method for DMUs under uncertain environment is presented. using DEA method, super efficiency technique, and uncertainty theory. Moreover, validation and verification of the proposed uncertain super-efficiency data envelopment analysis (USEDEA) model is illustrated by applying a numerical example