A new method based on data envelopment analysis to derive weight vector in the group analytic hierarchy process

Abstract

In this study, we propose a new method based on Data Envelopment Analysis (DEA) for the weight vector derivation from the pair-wise comparison matrices in the group AHP that is called DEA-WDGD. In this method, we can use both interval importance weight and relative importance weight for each decision maker. In this method, solving only one Linear Programming (LP) model is enough for the local weights derivation of several pair-wise comparison matrices in the group decision making and there is no need to normalize the derived weight vector. Three numerical examples are examined. Also, the DEA-WDGD is compared with the DEA method which has been recently proposed for weights derivation in the group AHP. The results show that DEA-WDGD provides better weights. The Simple Additive Weighting (SAW) method is utilized to aggregate local weights without needing to normalize them.