Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA)

Abstract

This note is concerned with joint uses of imprecise data and assurance regions (ARs) in data envelopment analysis (DEA), referred to as assurance region-imprecise DEA (AR-IDEA). It has been developed to transform the AR-IDEA models into ordinary linear programming equivalents via scale transformations and variable alterations plus introducing dummy variables. In this note, we show one simpler approach for achieving linear programming equivalents only by variable alterations without rescaling as well as introducing dummy variables. We also provide findings in the use of imprecise data and AR conditions in DEA. This points out that some AR conditions do not affect the efficiency ratings under AR-IDEA.