An Enhanced Russell Measure in DEA with interval data

Traditional data envelopment analysis (DEA) models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. In real world situations, however, this assumption may not always be true. When some inputs and outputs are unknown decision variables, such as interval data, ordinal data, and ratio bounded data, the DEA model is called imprecise DEA (IDEA). In this paper, we develop a new approach based upon the Enhanced Russell Measure (ERM) for dealing with interval data in DEA.