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Ishizaka, A., & Lusti, M. (2006). How to derive priorities in AHP: a comparative study. Central European Journal of Operations Research, 14(4), 387–400. 
Added by: Klaus D. Goepel (06 Jun 2019 13:41:04 Asia/Singapore)   Last edited by: Klaus-admin (08 Jun 2019 02:32:52 Asia/Singapore)
Resource type: Journal Article
DOI: 10.1007/s10100-006-0012-9
BibTeX citation key: Ishizaka2006
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Categories: AHP/ANP
Keywords: Analytic Hierarchy Process (AHP), eigenvector method (EVM), geometric mean method, multi-criteria decision-making, Simulation
Creators: Ishizaka, Lusti
Publisher: Springer
Collection: Central European Journal of Operations Research
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A heated discussion has arisen over the “best” priorities derivation method. Using a
Monte Carlo simulation this article compares and evaluates the solutions of four
AHP ratio scaling methods: the right eigenvalue method, the left eigenvalue method,
the geometric mean and the mean of normalized values. Matrices with different dimensions
and degree of impurities are randomly constructed. We observe a high
level of agreement between the different scaling techniques. The number of ranking
contradictions increases with the dimension of the matrix and the inconsistencies.
However these contradictions affect only close priorities.
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