基于社交网络分析的区间二元语义群决策方法研究

通过基于Steiner点的区间二元语义集结方法,将评价信息通过相关转化规则量化为对应的二维坐标系中的坐标点集,运用植物模拟生长算法获取对应区间点集的Steiner点(专家群体最优结集点,即群体共识点),按照逆映射关系还原为二元语义集结信息,从而提出一种基于社交网络分析和专家自信偏好关系的区间二元语义群决策问题的新方法。结合专家自信程度系数和社交网络结构中的影响力调整专家主观权重,结合专家相对重要系数与群体相似度系数获取客观权重,最终确定专家综合权重,并对方案进行择优排序。通过算例分析说明方法的可行性和有效性。

Research on a Method of Interval Two-Tuple Linguistic Group Decision-Making Based on Social Network Analysis

Through the two-tuple linguistic aggregation method based on the Steiner point,the evaluation information is quantified into the coordinate point set in the corresponding two-dimensional coordinate system through the correlation transformation rules.The Steiner point(the optimal aggregation point of expert population,namely the consensus point of the population)of the corresponding interval point set is obtained by using the plant simulated growth algorithm.According to the inverse mapping relationship,this study presents a new approach to group decision making problems with interval two-tuple linguistic based on social network analysis and experts’confidence preference.The subjective weight of experts was adjusted by combining experts’confidence coefficient and influence in social network structure,and the objective weight was obtained by combining experts’relative importance coefficient and group similarity coefficient.Finally,the comprehensive weight of experts was determined,and the schemes were prioritized.An example is given to illustrate the feasibility and effectiveness of the method.