Abstract
Sekitani and Yamaki introduced two new concepts of self-evaluation value and non-self-evaluation value into AHP and showed that the eigenvalue method can be formulated as some mathematical programming problems with the ratios of the self-evaluation value to the non-self-evaluation value. This study develops a new discrepancy-minimization problem with the ratios of the self-evaluation value to the non-self-evaluation value and their reciprocals. We show that its optimal solution is identical to the principal eigenvector of the pairwise comparison matrix. We compare the analytical properties of the proposed optimization model with that of Harker method for the case of AHP with incomplete information.
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