3MO-AHP:通过单目标、多目标或多目标质量度量来减少不一致的方法

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
C. Floriano, Valdecy Pereira, Brunno e Souza Rodrigues
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引用次数: 5

摘要

虽然多准则技术层次分析法(AHP)已经成功地应用于许多领域,无论是选择或排序备选方案,还是为一组标准推导优先向量(权重),但如果配对比较矩阵(PCM)具有不一致的比较,即一致性比(CR)大于0.1,则使用该技术存在一个显著的缺点,即无法验证最终解决方案。针对不一致性问题的研究已经有很多,但很少有研究试图满足不同的质量指标,即最小不一致性(fMI)、调整后的两两比较总数(fNC)、原始秩保持(fKT)、最小平均权值调整(fWA)以及原始PCM与调整后的PCM之间的最小L1矩阵范数(fLM)。设计/方法论/方法方法定义为四个步骤:首先,决策者应该选择她/他希望使用的质量度量,范围从一个到所有的质量度量。第二步,对PCM进行编码,用于多目标优化算法(MOOA),每对比较都可以单独调整。在第三步中,作者从得到的帕累托最优前沿生成了具有期望质量度量的一致解。最后,决策者选择最适合自己问题的解决方案。值得注意的是,由于决策者可以选择一个(单目标),两个(多目标),三个或更多(多目标)质量度量,并非所有mooa都可以处理或在单目标或多目标问题中表现良好。统一非排序算法III (U-NSGA III)是最适合这种场景的MOOA,因为它是专门为处理单目标、多目标和多目标问题而设计的。两种质量措施的使用不能保证调整后的PCM与原PCM相似;因此,如果目标是保持原有的PCM特性,决策者应该考虑使用更多的质量度量。原创性/价值多目标方法第一次将CR降低到具有考虑一个或多个质量度量的能力的一致水平,并允许决策者单独调整每个两两比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3MO-AHP: an inconsistency reduction approach through mono-, multi- or many-objective quality measures
PurposeAlthough the multi-criteria technique analytic hierarchy process (AHP) has successfully been applied in many areas, either selecting or ranking alternatives or to derive priority vector (weights) for a set of criteria, there is a significant drawback in using this technique if the pairwise comparison matrix (PCM) has inconsistent comparisons, in other words, a consistency ratio (CR) above the value of 0.1, the final solution cannot be validated. Many studies have been developed to treat the inconsistency problem, but few of them tried to satisfy different quality measures, which are minimum inconsistency (fMI), the total number of adjusted pairwise comparisons (fNC), original rank preservation (fKT), minimum average weights adjustment (fWA) and finally, minimum L1 matrix norm between the original PCM and the adjusted PCM (fLM).Design/methodology/approachThe approach is defined in four steps: first, the decision-maker should choose which quality measures she/he wishes to use, ranging from one to all quality measures. In the second step, the authors encode the PCM to be used in a many-objective optimization algorithm (MOOA), and each pairwise comparison can be adjusted individually. The authors generate consistent solutions from the obtained Pareto optimal front that carry the desired quality measures in the third step. Lastly, the decision-maker selects the most suitable solution for her/his problem. Remarkably, as the decision-maker can choose one (mono-objective), two (multi-objective), three or more (many-objectives) quality measures, not all MOOAs can handle or perform well in mono- or multi-objective problems. The unified non-sorting algorithm III (U-NSGA III) is the most appropriate MOOA for this type of scenario because it was specially designed to handle mono-, multi- and many-objective problems.FindingsThe use of two quality measures should not guarantee that the adjusted PCM is similar to the original PCM; hence, the decision-maker should consider using more quality measures if the objective is to preserve the original PCM characteristics.Originality/valueFor the first time, a many-objective approach reduces the CR to consistent levels with the ability to consider one or more quality measures and allows the decision-maker to adjust each pairwise comparison individually.
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来源期刊
Data Technologies and Applications
Data Technologies and Applications Social Sciences-Library and Information Sciences
CiteScore
3.80
自引率
6.20%
发文量
29
期刊介绍: Previously published as: Program Online from: 2018 Subject Area: Information & Knowledge Management, Library Studies
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