基于云模型和前景理论的语言评价双边匹配决策研究

Decision Method for Two-sided Matching with Linguistic Evaluation Based on Cloud Model and Prospect Theory

针对主体偏好为语言评价信息的双边匹配问题,提出了一种基于云模型和前景理论相结合的决策方法。该方法以云模型实现定性语言信息转换为定量数值的过程,通过主体偏好和正负理想方案的灰关联系数建立前景价值矩阵,进而在规范化前景矩阵的基础上构建双边匹配问题的多目标优化决策模型。研究表明:该方法考虑了语言评价信息包含的模糊性和不确定性,纳入了主体对风险偏好的心理考量,准确地实现了双边匹配,实例分析表明其具有可行性和有效性。

For the two-sided matching problem considering agents’preference attitude with linguistic evaluation information,a decision-making method is used based on cloud model and prospect theory.Use the cloud model to transform qualitative language information into quantitative data,establish the prospect value matrix by gray correlation coefficient between the preference and positive-negative ideal schemes,then set up the multi-objective optimization decision model for two-sided matching problem based on the normalized prospect matrix.The results reveal that the method can achieve the two-sided matching accurately and practically through a case study which is feasible and effective.