Improving Discrimination Power in Data Envelopment Analysis Using Deviation Variables

Data Envelopment Analysis (DEA) has been proposed as a performance evaluative technique to measure the relative efficiency of decision-making units (DMUs) based on their respective multiple inputs and outputs. Lack of great discrimination power and poor weight dispersion has remained as the major issues in DEA. Hence, several methods were addressed in the literature as strategies to resolve the stated problems. However, there are some drawbacks to these methods too, which may lead to infeasible solutions. In order to address these drawbacks sufficiently, we extended the deviation variable form of classical DEA model by adding the lower bound to the input-output weights i.e. multi-criteria data envelopment analysis (MUDEA) developed in the late 1990s and proposed a procedure for ranking efficient units based on the deviation variables values framework. We further illustrated the performance of our proposed method against the alternative methods based on two numerical examples.