A linear programming method for generating the most favorable weights from a pairwise comparison matrix

作者：Wang Ying Ming^*; Parkan Celik; Luo Ying

来源：Computers & Operations Research, 2008, 35(12): 3918-3930.

DOI：10.1016/j.cor.2007.05.002

• analytic hierarchy process
• multicriteria decision analysis
• data envelopment analysis
• most favorable weights
• rank preservation

This paper proposes a linear programming method for generating the most favorable weights (LP-GFW) from pairwise comparison matrices, which incorporates the variable weight concept of data envelopment analysis (DEA) into the priority scheme of the analytic hierarchy process (AHP) to generate the most favorable weights for the underlying criteria and alternatives on the basis of a crisp pairwise comparison matrix. The proposed LP-GFW method can generate precise weights for perfectly consistent pairwise comparison matrices and approximate weights for inconsistent pairwise comparison matrices, which are not too far from Saaty's principal right eigenvector weights. The issue of aggregation of local most favorable weights and rank preservation methods is also discussed. Four numerical examples are examined using the LP-GFW method to illustrate its potential applications and significant advantages over some existing priority methods.

• 出版日期2008-12
• 单位厦门大学; 福州大学