The superiority of DEAGP in ranking decision making units over DEA-AHP method: Utilizing relative closeness to ideal decision making units

Data envelopment analysis (DEA) is one of the most popular techniques for measuring relative efficiencies of various similar units. However, lack of opportunity to compare the decision making units (DMUs) on the same scale in DEA model can make it less practical to classify DMUs. In this paper, we present common weights for DMUs by applying a scientific methodology utilizing goal programming as one of multi criteria decision making (MCDM) techniques, thereby we deal with improving discrimination power for selecting the efficient DMUs. The paper investigates the validity of the ranking technique, an index called the relative closeness (RC) to the ideal DMU (IDMU). Finally, via a previously reported numerical example, the proposed data envelopment analysis-goal programming (DEAGP) model is compared with that obtained by the DEA-AHP.