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《Journal of Systems Engineering and Electronics》 2017年01期
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Double weight determination method for experts of complex multi-attribute large-group decision-making in interval-valued intuitionistic fuzzy environment

Bingsheng Liu  Sijia Guo  Kaijing Yan  Ling Li  Xueqing Wang  
【摘要】:The systematic clustering analysis based weight determination methods are suitable for the experts of complex multiattribute large-group decision-making(CMALGDM) in intervalvalued intuitionistic fuzzy environment. However, these methods mainly have two shortcomings: they do not consider the consistency of the experts in each aggregation; the aggregation weights are often determined simply by the "majority principle", and neglect the quantity of information provided by the holistic aggregation,leading to decision biases. Hence, a double weight determination method for experts is proposed to solve these problems. As for the first shortcoming, a mathematical programming model is used to solve for the optimal expert weights within each aggregation,ensuring consistency in the overall preferences of the aggregations. As for the second shortcoming, we propose a modification of the aggregation weights based on the information entropy, which fully considers both the number of experts and the amount of information provided by the holistic aggregation. With the proposed method, the final expert weights are determined more rigorously and objectively. The feasibility of the proposed method is investigated through an illustrative example.
【作者单位】College
【关键词】systematic clustering analysis double weight determination complex multi-attribute large-group decision-making(CMALGDM) interval-valued intuitionistic fuzzy environment
【基金】:supported by the National Natural Science Foundation of China(71572123;71172148)
【分类号】:O225
【正文快照】:
1.Introduction Since the french mathematician Borda introduced the ranking model of group alternatives in 1781,group decisionmaking problems have become an increasingly important focus of research,and numerous group decisionmaking methods have been prop

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