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首页> 外文期刊>Journal of testing and evaluation >Applying FQFD and Utility Representative Functions Under Fuzzy Environment for FMCDM
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Applying FQFD and Utility Representative Functions Under Fuzzy Environment for FMCDM

机译:在模糊环境下将FQFD和效用代表函数应用于FMCDM

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摘要

In this paper, we apply fuzzy quality function deployment (FQFD) and utility representative functions under fuzzy environment for fuzzy multi-criteria decision-making (FMCDM). In the FMCDM, criteria weights can present both consumers' opinions and experts' opinions. Consumers' opinions are expressed by fuzzy importance levels of customer needs, experts' opinions are presented by fuzzy relationship strength ratings for customer needs on skill needs, and the two opinions are integrated by FQFD. In addition to importance levels of customer needs and relationship strength ratings for customer needs on skill needs being fuzzy numbers, performance criteria ratings of alternatives are also displayed by fuzzy numbers in FMCDM. Thus, multiplying fuzzy numbers and aggregating several pooled fuzzy numbers formed by multiplying fuzzy numbers are critical on computation. Practically, the fuzzy computations are hard. In this paper, we use utility representative functions of multiplication and aggregation on fuzzy numbers to resolve the computation ties, especially for multiplying three fuzzy numbers. In addition, we compare the proposed method with other FMCDM methods to demonstrate our method's feasibility and rationality. Therefore, FMCDM can be finished easily and efficiently through associating FQFD with the utility representative functions of fuzzy numbers applied.
机译:在本文中,我们将模糊环境下的模糊质量函数展开(FQFD)和效用代表函数应用于模糊多准则决策(FMCDM)。在FMCDM中,标准权重既可以表示消费者的意见,也可以表示专家的意见。消费者的意见由客户需求的模糊重要性级别表示,专家的意见由针对技能需求的客户需求的模糊关系强度等级表示,而这两种意见由FQFD进行整合。除了客户需求的重要性级别和针对技能需求的客户需求的关系强度等级为模糊数之外,FMCDM中的模糊度还显示了备选方案的绩效标准等级。因此,将模糊数相乘并聚合通过将模糊数相乘而形成的多个合并的模糊数对计算至关重要。实际上,模糊计算是困难的。在本文中,我们使用模糊数乘法和聚合的效用代表函数来解决计算关系,尤其是将三个模糊数相乘时。此外,我们将提出的方法与其他FMCDM方法进行了比较,以证明我们的方法的可行性和合理性。因此,通过将FQFD与所应用的模糊数的效用表示函数相关联,可以轻松而有效地完成FMCDM。

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