基于语言z数和MSM算子的多属性群体决策集成方法

IF 0.6 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Knowledge-Based and Intelligent Engineering Systems Pub Date : 2023-08-22 DOI:10.3233/kes-221592
Bo Chen, Q. Cai, G. Wei, Zhiwen Mo
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引用次数: 0

摘要

本文在语言z数(LZN)环境中扩展了有序加权平均算子(OWA),增加了对数据本身分布的关注,并且该思想还可以与其他算子相结合。具体而言,对于数据的权重,考虑了属性的偏好和数据本身的分布,给出了区间分布诱导OWA算子(IDIOWA),并结合属性的权重得到了LZN环境下的区间分布诱导混合加权平均算子(LZIDIHWA)。然后介绍了该算子的一些优良性质。同时,将LZN区间分布诱导的OWA算子(LZIDIOWA)与LZN加权Maclaurin对称均值算子(LZWMSM)相结合,得到LZN区间分布诱导的加权Maclaurin对称均值算子(LZIDIWMSM),弥补了LZWMSM不能单独用于数据集成的缺陷。最后,将这两种算子用于多属性群体决策(MAGDM),并通过对比分析验证了其有效性。
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An integrated method for multi-attribute group decision-making based on the linguistic Z-number and MSM operators
This article extends the ordered weighted average operator (OWA) in the linguistic Z-number (LZN) environment, increasing attention to the distribution of data itself, and this idea can also be combined with other operators. Specifically, for the weight of data, this paper gives consideration to both the preference of attributes and the distribution of data itself, gives the interval distribution induced OWA operator (IDIOWA), and combines the weight of attributes to obtain the LZN interval distribution induced hybrid weighted average operator (LZIDIHWA) in LZN environment. Then, it introduces some good properties of this operator. At the same time, the LZN interval distribution induced weighted Maclaurin symmetric means operator (LZIDIWMSM) is obtained by combining LZN interval distribution induced OWA operator (LZIDIOWA) with the LZN weighted Maclaurin symmetric means operator (LZWMSM), which makes up for the defect that LZWMSM cannot be used for data integration alone. Finally, the two operators are used for multi-attribute group decision-making (MAGDM), and their effectiveness is verified by comparative analysis.
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22
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