Statistical Interval for Data Envelopment Analysis

Abstract

The techniques of data envelopment analysis (DEA) were largely studied. Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Conventional DEA models assume that input and output values should be certain (crisp data). However, the observed values of the input and output data in real-world situations are sometimes inexact, incomplete, vague, ambiguous or imprecise. Some researchers have proposed various methods for dealing with the imprecise and ambiguous data in DEA in the context of fuzzy (interval) data. In this paper, a statistical method based on arithmetic operations to solve fuzzy (interval) data envelopment analysis models (FDEA) can be improved. The suggested approach transforms the original data (crisp data) into interval data; in the form of upper and lower frontier data. Then, by using these upper and lower frontier data; the interval DEA efficiency scores can be achieved. This approach is applied on the real-life data and the results of application are efficient.