Data Envelopment Analysis (DEA) is a nonparametric method for identifying sources and estimating the mount of inefficiencies contained in inputs and outputs produced by Decision Making Units (DMUs). DEA requires that the data for all inputs and outputs should be known exactly, but under many qualifications, exact data are inadequate to model real-life situations. So these data may have different structures such as bounded data, interval data, and fuzzy data. Moreover, the main assumption in all DEA is that input and output values are positive, but we confront many cases that discount this condition producing negative data. The purpose of this paper is to compute efficiency for DMUs, which permits the presence of intervals which can take both negative and positive values.

No abstract