Abstract
While data envelopment analysis (DEA) models are very sensitiveto possible data errors, in many real applications, the data of production processes cannot be precisely measured. Fuzzy DEA is asuccessful method that allows to deal with imprecise data in DEA. In this paper, we develop fuzzy version of the input relaxation model introduced in Jahahshahloo and Khodabakhshi [15] by using some ranking methods based on the comparison of α-cuts. The resulting auxiliary crisp problems can be solved by the usual DEA software. It is shown that, using a numerical example, how the proposed model become specially useful for detecting sensitive decision-making units.