2009AIP conference proceedingsRequires access

A Mixed Prioritization Operators Strategy Using A Single Measurement Criterion For AHP Application Development

Kevin Kam Fung Yuen, Sio-Iong Ao, Alan H. S. Chan, Hideki Katagiri, Li Xu

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Abstract

The most appropriate prioritization method is still one of the unsettled issues of the Analytic Hierarchy Process, although many studies have been made and applied. Interestingly, many AHP applications apply only Saaty’s Eigenvector method as many studies have found that this method may produce rank reversals and have proposed various prioritization methods as alternatives. Some methods have been proved to be better than the Eigenvector method. However, these methods seem not to attract the attention of researchers. In this paper, eight important prioritization methods are reviewed. A Mixed Prioritization Operators Strategy (MPOS) is developed to select a vector which is prioritized by the most appropriate prioritization operator. To verify this new method, a case study of high school selection is revised using the proposed method. The contribution is that MPOS is useful for solving prioritization problems in the AHP.

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What this paper is about

The most appropriate prioritization method is still one of the unsettled issues of the Analytic Hierarchy Process, although many studies have been made and applied. Interestingly, many AHP applications apply only Saaty’s Eigenvector method as many studies have found that this method may produce rank reversals and have proposed various prioritization methods as alternatives. Some methods have been proved to be better than the Eigenvector method. However, these methods seem not to attract the attention of researchers. In this paper, eight important prioritization methods are reviewed. A Mixed Prioritization Operators Strategy (MPOS) is developed to select a vector which is prioritized by the most appropriate prioritization operator. To verify this new method, a case study of high school selection is revised using the proposed method. The contribution is that MPOS is useful for solving prioritization problems in the AHP.

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Available abstract

The most appropriate prioritization method is still one of the unsettled issues of the Analytic Hierarchy Process, although many studies have been made and applied. Interestingly, many AHP applications apply only Saaty’s Eigenvector method as many studies have found that this method may produce rank reversals and have proposed various prioritization methods as alternatives. Some methods have been proved to be better than the Eigenvector method. However, these methods seem not to attract the attention of researchers. In this paper, eight important prioritization methods are reviewed. A Mixed Prioritization Operators Strategy (MPOS) is developed to select a vector which is prioritized by the most appropriate prioritization operator. To verify this new method, a case study of high school selection is revised using the proposed method. The contribution is that MPOS is useful for solving prioritization problems in the AHP.

Key concepts: Analytic hierarchy process, Prioritization, Computer science, Rank (graph theory), Operator (biology), Selection (genetic algorithm), Process (computing), Mathematical optimization

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