基于记分函数的Vague集方案排序局限性及改进策略

分析了Vague集中记分函数的特性，对比了现有几种记分函数的优缺点，得到利用记分函数进行决策方案排序的局限性。通过挖掘Vague值中未知信息与不确定信息的内涵，结合Vague值期望的概念，设计一种新的记分函数，将不同的Vague值对应于Fuzzy集中的某个值；当记分函数值相等时，采用确定性信息进行二次排序。实例验证，该方法可以得到与现实认知匹配的结果，对应不同记分函数值的Vague值可以给出一个合理的评价值。

Ranking limitation and improvement strategy of Vague sets based on score function

This thesis exposes the ranking limitation of score functions by exploring the connotation of score functions of Vague sets and appraising the strength and weakness of various existing score functions. Through an exhaustive enquiry into the connotation of the undefined and unascertained information of Vague value as well as an expected definition of Vague value, a new category of score function is to be contrived, which correlates various Vague value with a certain value of the Fuzzy set. When there is an equal score function value, a second ranking is to be executed with definite information. It has been confirmed by examples that this approach renders it feasible to produce an outcome which matches reality cognition and the Vague value which corresponds various score function values can present a reasonable evaluation.