闭世界框架下灰色模糊多属性决策方法

针对D-S 证据理论在融合高度冲突的证据时可能导致与直观结果相悖的问题, 已有的基于修改数据模型的方法更多地考虑如何提高冲突证据结果的聚焦程度, 而没有考虑冲突成因以及如何通过冲突原因判断来削减证据之间的冲突. 对此, 提出一种闭世界框架下的灰色模糊多属性决策方法. 根据一般的灰色关联分析方法的基本思路, 将灰色关联度与欧氏距离有机结合, 构造出一种平均相似度对方案进行评价, 进而得到方案的排序结果. 最后, 通过实例分析验证了所提出方法的可行性和有效性.

Approach for grey fuzzy MADA in closed world

For dealing with seriously conflicting evidence when the D-S evidence theory can not identify the actual conditions, revising mass fusion based methods consider improving focusing degree of combination results more, but they don’t consider the reason for conflict between evidences and how to effectively reduce the conflict between evidences by revision according to the real reason for conflict. Therefore, the grey fuzzy multi-attribute decision-making(MADA) method is presented in the closed world. Firstly, a kind of similar degree between two interval grey numbers is established by combining the Euclidean distance with grey correlation degree to appraise the scheme. Then, the average similar degree is constructed. Furthermore, the alternatives are ranked by using the average similar degree. Illustrative numerical examples show the feasibility and effectiveness of the proposed method.