Priority estimation in the AHP through maximization of correlation coefficient

This paper proposes a correlation coefficient maximization approach (CCMA) for estimating priorities from a pairwise comparison matrix. The priorities are supposed to be as highly correlated with each column of a pairwise comparison matrix as possible. Such priorities are not unique and can be determined in different ways. Two optimization models are therefore suggested for determining further the priorities, one of which leads to an analytic solution. Theorems about the CCMA are developed and its potential applications are illustrated with two numerical examples.