[1]PANG Jifang,HOU Zhiguo,SONG Peng,et al.Adaptive group consensus decision-making method with dynamic update of attribute weights[J].CAAI Transactions on Intelligent Systems,2024,19(4):941-951.[doi:10.11992/tis.202208039]
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Adaptive group consensus decision-making method with dynamic update of attribute weights

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 941-951 Column: 学术论文—智能系统 Public date: 2024-07-05
Title:
Adaptive group consensus decision-making method with dynamic update of attribute weights
Author(s):
PANG Jifang1;  HOU Zhiguo1;  SONG Peng2;  ZHANG Chao1
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006;
2. School of Economics and Management, Shanxi University, Taiyuan 030006, China
Keywords:
heterogeneous multi-attribute group decision-making; uncertain linguistic variable; conversion function; multi-level consensus measure; attribute weight; bi-objective optimization; adaptive consensus model; interpretability
CLC:
TP18
DOI:
10.11992/tis.202208039
Abstract:
To improve the quality, efficiency, and interpretability of heterogeneous multi-attribute group decision making in an uncertain linguistic environment, an adaptive group consensus decision-making method with dynamic update of attribute weights is proposed. It first defines the conversion functions between uncertain linguistic variables and median values, and develops the multi-level consensus measures, then calculates the initial weights of attributes by establishing a bi-objective optimization model, and further establishes the group consensus automatic reaching rules and attribute weight dynamic update mechanism to achieve accurate positioning and automatic modification of the values to be adjusted, further improving the level of group consensus while optimizing attribute weights. Furthermore, for the group decision matrix that has reached consensus, the median values in the matrix are first converted into corresponding uncertain linguistic variables by using the conversion function, and then the optimized attribute weight and the uncertain linguistic weighted average operator (ULWA) are used to calculate the overall evaluation results, and the alternatives are ranked and selected by using the dominance index. Finally, the effectiveness and feasibility of the proposed method are verified through the case of supplier selection and experimental comparative analysis. The proposed method has the characteristics of wide application scope, high consensus efficiency, flexibility, practicality, and strong interpretation, which provides an effective approach for solving multi-attribute group decision-making problems in complex environments.
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