Recently, some researches have been carried out in the context of using data envelopment analysis (DEA) models to generate local weights of alternatives from pairwise comparison matrices used in the analytic hierarchy process (AHP). One of these models is the DEAHP. The main drawback of the DEAHP is that it generates counter-intuitive priority vectors for inconsistent pairwise comparison matric...

-Need of robust multi criteria decision making techniques are becoming essential in day today life for decision makers to make appropriate decisions. This paper attempts to explore robustness through rank reversal properties of the Data Envelopment Analytic Hierarchy Process (DEAHP) in decision making. DEAHP uses the principles of Data Envelopment Analysis (DEA) to generate the local weights of...

DEAHP as a weight derivation procedure for analytic hierarchy process (AHP) has been found suffering from some significant drawbacks. Recently, Mirhedayatian and Saen (2011) [5] proposed a new procedure entitled Revised DEAHP for AHP weight derivation [S.M. Mirhedayatian, R.F. Saen, A new approach for weight derivation using data envelopment analysis in the analytic hierarchy process, Journal o...

The data envelopment analytical hierarchy process (DEAHP) is a type of the analytical hierarchy process (AHP), which used the concept of data envelopment analysis (DEA) for generating LW from the judgment matrices and aggregating them to be final weight (FW) in AHP. In general, the judgment scales in AHP is built based on decision maker. It is unrealistic to expect that the decision maker has c...

Data envelopment analysis (DEA) is proposed in this paper to generate local weights of alternatives from pair-wise comparison judgment matrices used in the analytic hierarchy process (AHP). The underlying assumption behind the approach is explained, and some salient features are explored. It is proved that DEA correctly estimates the true weights when applied to a consistent matrix formed using...