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Note on a new optimization based approach for estimating priority weights and related consistency index

机译:注意基于新的基于优化的方法,用于估算优先级权重和相关的一致性指标

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The generation of priority vectors from pairwise comparison matrices is an essential part of the Analytic Hierarchy Process. Perhaps the most popular approach for deriving the priority weights is the right eigenvalue method (REV) which was proposed by Saaty. Despite its popularity some shortcomings of the REV have been reported in literature. Among the alternative approaches one can find the statistical estimation techniques, methods founded on constrained optimization models and models based on fuzzy description of decision maker preferences. In this paper new optimization techniques for deriving priority weights are introduced. In the proposed approach, the constrained optimization models are based on the same idea which underlies the REV. The properties of the resulting prioritization techniques are studied via computer simulations. This study demonstrates that the new methods perform very well in comparison with other popular techniques known from literature. What is especially important, the new approach provides the decision maker with a meaningful index that can be used to measure consistency of his/her judgments. The new index is closely related to the well-known Saaty's consistency index Cl, but in difference to the latter, it can be applied to both reciprocal as well as nonreciprocal comparison matrices. Hence the new index can be considered as a natural extension of the CI to all types of matrices. Some additional advantages resulting from the new approach are discussed and illustrated by numerical examples.
机译:从成对比较矩阵生成优先级向量是分析层次过程的重要组成部分。推导优先权重的最流行方法也许是Saaty提出的正确的特征值方法(REV)。尽管它很流行,但是REV的一些缺点在文献中已有报道。在替代方法中,可以找到统计估计技术,基于约束优化模型的方法以及基于决策者偏好的模糊描述的模型。本文介绍了用于推导优先权重的新优化技术。在提出的方法中,约束优化模型基于REV背后的同一思想。通过计算机仿真研究了所得优先级排序技术的属性。这项研究表明,与文献中已知的其他流行技术相比,新方法的性能非常好。尤为重要的是,新方法为决策者提供了有意义的索引,可用于衡量其判断的一致性。新指数与众所周知的Saaty一致性指数Cl密切相关,但是与后者不同的是,它既可以应用于倒数比较矩阵,也可以应用于不可倒数比较矩阵。因此,可以将新索引视为CI对所有类型矩阵的自然扩展。数值示例讨论并说明了新方法带来的一些其他优点。

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