Improving of Efficiency in Data Envelopment Analysis with Interval Data

Aim of this research is study efficiency of Decision Making Units (DMUs) with interval data using Data Envelopment Analysis (DEA) models. The DEA is a widely applied approach for measuring the relative efficiencies of a set of DMUs which uses multiple inputs to produce multiple outputs. An assumption underlying DEA is that all the data are known exactly. But in reality, many factors cannot be measured in a precise manner. In recent years, in different applications of DEA, inputs and outputs have been observed whose values are indefinite. Such data are called imprecise. Imprecise data can be probabilistic, interval, ordinal, qualitative or fuzzy. In this study, we investigate an interval DEA model, in the case that the inputs and outputs are located within the bounded intervals. The resulting model is non-linear and then we convert it to a linear one. Also minimal variations of input and output intervals are computed to achieve to full efficiency. Indeed, we propose a new method for improving of efficiency classifications of DMUs with interval data in data envelopment analysis.