An interactive method for dynamic intuitionistic fuzzy multi-attribute group decision making
An interactive method for dynamic intuitionistic fuzzy multi-attribute group decision making
复制标题
动态直觉模糊多属性群决策的交互方法
DOI：
10.1016/j.eswa.2011.06.022
复制
发表时间：
2011-11
影响因子：
8.5
通讯作者：
Wang, Li
中科院分区：
文献类型：
--
作者：
Su, Zhi-xin;Chen, Ming-yuan;Xia, Guo-ping;Wang, Li
研究了多个决策者在不同时期提供的所有属性值均为直觉模糊数的动态直觉模糊多属性群决策问题，提出了一种交互式求解方法.该方法首先利用动态直觉模糊加权平均（DIFWA）算子将不同时期的个体直觉模糊决策矩阵集结为个体集体直觉模糊决策矩阵，然后采用直觉模糊TOPSIS方法计算各方案对各决策者的个体相对贴近度系数，得到各方案的个体排序。然后，利用混合加权平均（HWA）算子将所有个体的相对贴近度系数聚合为各方案的总体相对贴近度系数，得到方案的总体排序，从而选出最优方案。此外，斯皮尔曼相关系数的总体排名和个人排名的方案计算来衡量群体偏好的共识水平。最后，数值例子来说明所开发的方法。
This paper investigates the dynamic intuitionistic fuzzy multi-attribute group decision making (DIF-MAGDM) problems, in which all the attribute values provided by multiple decision makers (DMs) at different periods take the form of intuitionistic fuzzy numbers (IFNs), and develops an interactive method to solve the DIF-MAGDM problems. The developed method first aggregates the individual intuitionistic fuzzy decision matrices at different periods into an individual collective intuitionistic fuzzy decision matrix for each decision maker by using the dynamic intuitionistic fuzzy weighted averaging (DIFWA) operator, and then employs intuitionistic fuzzy TOPSIS method to calculate the individual relative closeness coefficient of each alternative for each decision maker and obtain the individual ranking of alternatives. After doing so, the method utilizes the hybrid weighted averaging (HWA) operator to aggregate all the individual relative closeness coefficients into the collective relative closeness coefficient of each alternative and obtain the aggregate ranking of alternatives, by which the optimal alternative can be selected. In addition, the spearman correlation coefficient for both the aggregate ranking and individual ranking of alternatives is calculated to measure the consensus level of the group preferences. Finally, a numerical example is used to illustrate the developed method.
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影响因子：
--
作者：
ZADEH, LA
通讯作者：
ZADEH, LA
DOI：
10.1142/s0218488508005418
发表时间：
2008-08
期刊：
Int. J. Uncertain. Fuzziness Knowl. Based Syst.
影响因子：
--
作者：
Dengfeng Li;Yongchun Wang
通讯作者：
Dengfeng Li;Yongchun Wang
DOI：
10.1049/pbpo161e_ch3
发表时间：
2021-07
期刊：
Artificial Intelligence for Smarter Power Systems: Fuzzy logic and neural networks
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1142/s0218488507004686
发表时间：
2007-06
期刊：
Int. J. Uncertain. Fuzziness Knowl. Based Syst.
影响因子：
--
作者：
Z. Xu
通讯作者：
Z. Xu
DOI：
10.1002/(sici)1098-111x(199601)11:1
发表时间：
1996-01
影响因子：
7
作者：
R. Yager
通讯作者：
R. Yager