A linear programming model to reduce rank violations while eliciting preference from pairwise comparison matrix

A linear programming model to reduce rank violations while eliciting preference from pairwise comparison matrix
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一种线性规划模型,可减少排名违规,同时从成对比较矩阵中得出偏好

DOI:
10.1080/01605682.2017.1409156
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发表时间:
2018-01
影响因子:
3.6
通讯作者:
Chenhua Li
Chenhua Li
中科院分区:
管理学4区
文献类型:
--
作者:
Kun Chen;Gang Kou;Chenhua Li

文献摘要

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摘要秩违反是判断两两比较矩阵排序向量序一致性的一个重要标准。流行的优先级排序方法,如特征值法,对数最小二乘法,加权最小二乘法,并没有保证最小的秩违反,即使与基数一致性检查。本文通过提出一个基于线性规划的程序来缩小这一差距,该程序可以更新由一种流行的优先级排序方法获得的优先级向量,并使用一种更好的方法来确保最小的秩违规。利用改进的线性规划模型中的秩保持性,该方法可以检查包含关于给定优先向量的不一致约束的可行性,从而确定存在具有减少的秩违反的更好的优先向量。经典的例子证明了所提出的程序获得更新的优先级向量的过程。
Abstract Rank violation is a crucial criterion to decide the ordinal consistency of a priority vector derived from a pairwise comparison matrix. Popular prioritisation methods, such as the eigenvalue method, logarithmic least squares, and weighted least squares have not guaranteed minimal rank violations even with cardinal consistency examination. This article closes this gap by proposing a linear-programming-based procedure to update a priority vector obtained by one of the popular prioritisation methods with a better one that ensures minimal rank violations. Taking advantage of rank preservation in an improved linear programming model, the proposed procedure can examine the feasibility of including inconsistent constraints with respect to a given priority vector, and hence determines the existence of a better priority vector with reduced rank violations. The process of obtaining updated priority vectors by the proposed procedure is demonstrated in classic examples.
DOI: 10.1016/j.amc.2011.06.006
发表时间: 2011-10
期刊: Appl. Math. Comput.
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