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Membership maximization prioritization methods for fuzzy analytic hierarchy process

机译:模糊层次分析法的隶属度最大化排序方法

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摘要

Fuzzy analytic hierarchy process (FAHP) has increasingly been applied in many areas. Extent analysis method is the popular tool for prioritization in FAHP, although significant technical errors are identified in this study. With addressing the errors, this research proposes membership maximization prioritization methods (MMPMs) using different membership functions as the novel solutions. As a lack of research about effectiveness measurement on the crisp/fuzzy prioritization methods, this study proposes membership fitness index to evaluate the effectiveness of the prioritization methods. Comparisons with the other popular fuzzy/crisp prioritization methods including modified fuzzy preference programming, Direct least squares, and Eigen value are conducted and analyses indicate that MMPMs lead to much more reliable result in view of membership fitness index. A numerical example demonstrates the usability of MMPMs for FAHP, and thus MMPMs can effectively be applied to various decision analysis applications.
机译:模糊层次分析法(FAHP)已越来越多地应用于许多领域。范围分析方法是FAHP中进行优先级排序的常用工具,尽管在这项研究中发现了重大的技术错误。针对这些错误,本研究提出了使用不同隶属度函数作为新颖解决方案的隶属度最大化优先化方法(MMPM)。由于缺乏对脆性/模糊优先级排序方法的有效性度量的研究,本研究提出了隶属度适合度指数来评估优先级排序方法的有效性。与其他流行的模糊/酥脆优先排序方法进行了比较,包括改进的模糊偏好编程,直接最小二乘和本征值,分析表明,从成员资格指数来看,MMPM导致更可靠的结果。数值示例说明了MMPM在FAHP中的可用性,因此MMPM可以有效地应用于各种决策分析应用程序。

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