多重属性不确定性模型的分层拆解及应用

文章利用梯形模糊数构建多重属性不确定性模型,发现传统多重属性不确定性模型利用欧几里得距离判断决策正确程度,仅适用于二维复杂决策问题,对存在多种方案的复杂决策问题需进一步拆解与分层。运用最小化系数、向量距离公式等方法测度最优解与理想决策的差异值,通过比较差异值大小判断最优解的理想程度;同时基于满意度最大化理论确立缺失原始数据复杂决策问题的权重,为多重属性不确定性模型提供有效的运算数据。研究显示,多重属性不确定性模型排序方法具有较强的可操作性与适用性。

Layered Disassembly and Application of Multi-attribute Uncertainty Model

By using trapezoid fuzzy number to build the multi-attribute uncertainty model,this paper finds that the traditional multi-attribute uncertainty model using Euclidean distance to judge the degree of decision accuracy is only applicable to two-dimensional complex decision-making.The complex decision-making with multiple schemes need to be further disassembled and stratified.This paper uses minimization coefficient,vector distance formula and other methods to measure the difference value between the optimal solution and the ideal decision-making,and then makes a comparison of the difference value to determine the ideal degree of the optimal solution.At the same time,based on the theory of satisfaction maximization,the weight of complex decision-making with missing raw data is established to provide effective operational data for the multi-attribute uncertainty model.The research shows that the sorting method of multi-attribute uncertainty model has relatively strong operability and applicability.