一种改进的模糊软集多参数决策方法

An improved multi-parameter decision making algorithm based on fuzzy soft set

模糊软集多参数决策方法中经常将Zadeh交与代数积使用在数据融合方法中,在一些实际应用中会产生信息缺失,导致决策者无法做出准确的选择。针对这一问题,结合Einstein运算法则提出一种新的数据融合方法,用于解决信息缺失和对象无法排序的问题。所提出的基于模糊软集的多参数决策方法是通过Einstein积运算进行多个参数集合的整合,从而得到一个合成模糊软集,再由合成模糊软集计算得到对照矩阵与得分表,最终得到对象的全排序,为决策者提供判断依据。通过实例结果,可以验证新方法在决策问题中的正确性和有效性。

The current information fusion method usually adopts Zadeh intersection or product in fuzzy soft sets based on multi-parameter decision making. In some practical applications, this method suffers a lack of information, which makes decision makers fail to make a right choice. In order to solve this problem, we propose a new algorithm based on the Einstein algorithm which can deal with the problems of information loss and improper object sorting. We obtain the resultant fuzzy soft set by the Einstein product operation, and get the comparison table and score table by the resultant fuzzy soft set. Finally, objects can be sorted totally and a basis is provided for decision makers. The correctness and effectiveness of the method in decision making are verified by an example.