基于高度残缺信息的分布式语言信任网络群决策方法

doi:10.12305/j.issn.1001-506X.2022.06.17

Abstract

Abstract:

Aiming at the missing distributed linguistic trust social matrix and highly incomplete evaluation information, a new group decision-making method for distributed linguistic trust network is proposed. Firstly, a distributed linguistic trust propagation model is proposed to fill the missing values in the distributed linguistic trust social matrix. Secondly, the individual trust centrality and the group trust centrality based on the distributed linguistic trust social matrix are defined, and a method to determine the weight of experts in the evaluation network is proposed. Furthermore, in view of the highly incomplete evaluation information in the decision matrix, the K-nearest neighbor algorithm is used to evaluate the incomplete evaluation information, and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method is proposed based on the distance measure of distributed linguistic trust function. Finally, the feasibility and the effectiveness of the group decision making method are verified by the comprehensive evaluation of the cost performance of the takeout service platform.

Key words: distributed linguistic trust network, trust propagation model, highly incomplete ness, K-nearest neighbor algorithm, technique for order preference by similarity to an ideal solution (TOPSIS) method

CLC Number:

C934

Shouzhen ZENG, Yingjie HU. Group decision making method for distributed linguistic trust network based on highly incomplete information[J]. Systems Engineering and Electronics, 2022, 44(6): 1907-1919.

Figures/Tables 13

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References 26

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e₁	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.1), (D₂, 0.7), (D₃, 0.2)}	{(D₁, 0.2), (D₂, 0.5), (D₃, 0.3)}	{(D₁, 0.2), (D₂, 0.5), (D₃, 0.3)}	{(D₁, 0.2), (D₂, 0.1), (D₃, 0.7)}
X₂	-	-	-	-
X₃	{(D₁, 0.5), (D₂, 0.2), (D₃, 0.3)}	{(D₁, 0.6), (D₂, 0.3), (D₃, 0.1)}	{(D₁, 0.4), (D₂, 0.5), (D₃, 0.1)}	{(D₁, 0.1), (D₂, 0.8), (D₃, 0.1)}

e₂	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.1), (D₂, 0.0), (D₃, 0.9)}	{(D₁, 0.3), (D₂, 0.6), (D₃, 0.1)}	{(D₁, 0.0), (D₂, 0.2), (D₃, 0.8)}	{(D₁, 0.7), (D₂, 0.3), (D₃, 0.0)}
X₂	{(D₁, 0.5), (D₂, 0.3), (D₃, 0.2)}	{(D₁, 0.3), (D₂, 0.6), (D₃, 0.1)}	{(D₁, 0.8), (D₂, 0.2), (D₃, 0.0)}	{(D₁, 0.3), (D₂, 0.7), (D₃, 0.0)}
X₃	{(D₁, 0.4), (D₂, 0.6), (D₃, 0.0)}	{(D₁, 0.2), (D₂, 0.4), (D₃, 0.4)}	{(D₁, 0.6), (D₂, 0.4), (D₃, 0.0)}	{(D₁, 0.0), (D₂, 0.5), (D₃, 0.5)}

e₃	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.0), (D₂, 0.7), (D₃, 0.3)}	{(D₁, 0.2), (D₂, 0.8), (D₃, 0.0)}	{(D₁, 0.2), (D₂, 0.4), (D₃, 0.4)}	{(D₁, 0.1), (D₂, 0.1), (D₃, 0.8)}
X₂	{(D₁, 0.5), (D₂, 0.3), (D₃, 0.2)}	{(D₁, 0.7), (D₂, 0.2), (D₃, 0.1)}	{(D₁, 0.6), (D₂, 0.4), (D₃, 0.0)}	{(D₁, 0.4), (D₂, 0.4), (D₃, 0.2)}
X₃	{(D₁, 0.2), (D₂, 0.3), (D₃, 0.5)}	{(D₁, 0.2), (D₂, 0.4), (D₃, 0.4)}	{(D₁, 0.0), (D₂, 0.9), (D₃, 0.1)}	{(D₁, 0.4), (D₂, 0.5), (D₃, 0.1)}

e₄	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.0), (D₂, 0.4), (D₃, 0.6)}	{(D₁, 0.2), (D₂, 0.3), (D₃, 0.5)}	{(D₁, 0.0), (D₂, 0.7), (D₃, 0.3)}	{(D₁, 0.3), (D₂, 0.4), (D₃, 0.3)}
X₂	{(D₁, 0.8), (D₂, 0.2), (D₃, 0.0)}	{(D₁, 0.5), (D₂, 0.4), (D₃, 0.1)}	{(D₁, 0.6), (D₂, 0.3), (D₃, 0.1)}	{(D₁, 0.2), (D₂, 0.7), (D₃, 0.1)}
X₃	{(D₁, 0.2), (D₂, 0.6), (D₃, 0.2)}	{(D₁, 0.2), (D₂, 0.3), (D₃, 0.5)}	{(D₁, 0.2), (D₂, 0.7), (D₃, 0.1)}	{(D₁, 0.1), (D₂, 0.9), (D₃, 0.0)}

e₅	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.4), (D₂, 0.5), (D₃, 0.1)}	{(D₁, 0.3), (D₂, 0.4), (D₃, 0.3)}	{(D₁, 0.5), (D₂, 0.3), (D₃, 0.2)}	{(D₁, 0.5), (D₂, 0.0), (D₃, 0.5)}
X₂	{(D₁, 0.5), (D₂, 0.4), (D₃, 0.1)}	{(D₁, 0.3), (D₂, 0.5), (D₃, 0.2)}	{(D₁, 0.3), (D₂, 0.6), (D₃, 0.1)}	{(D₁, 0.6), (D₂, 0.4), (D₃, 0.0)}
X₃	{(D₁, 0.3), (D₂, 0.7), (D₃, 0.0)}	{(D₁, 0.0), (D₂, 0.8), (D₃, 0.2)}	{(D₁, 0.5), (D₂, 0.4), (D₃, 0.1)}	{(D₁, 0.4), (D₂, 0.6), (D₃, 0.0)}

Group decision making method for distributed linguistic trust network based on highly incomplete information

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a	ICD(X_i)	方案排序
0.1	ICD(X₁)=0, ICD(X₂)=－13.40, ICD(X₃)=－7.353	X₁＞X₃＞X₂
0.3	ICD(X₁)=0, ICD(X₂)=－13.44, ICD(X₃)=－7.382	X₁＞X₃＞X₂
0.5	ICD(X₁)=0, ICD(X₂)=－13.54, ICD(X₃)=－7.441	X₁＞X₃＞X₂
0.7	ICD(X₁)=0, ICD(X₂)=－13.54, ICD(X₃)=－7.442	X₁＞X₃＞X₂
0.9	ICD(X₁)=0, ICD(X₂)=－13.61, ICD(X₃)=－7.493	X₁＞X₃＞X₂

特性	方法1	方法2	本文方法
分布式语言信任社会矩阵	残缺	残缺	完整
专家权重获取	基于残缺的分布式语言信任关系	基于量词Q	基于完整的分布式语言信任关系
专家e₁权重	0.197	0.143	0.197
专家e₂权重	0.219	0.185	0.200
专家e₃权重	0.180	0.105	0.201
专家e₄权重	0.180	0.120	0.212
专家e₅权重	0.223	0.447	0.190
残缺信息的评估	基于其余专家的相对信任度	基于其余专家的重要度	基于K-近邻算法

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