基于正态分布区间数的信息不完全的群决策方法

针对属性值为正态分布区间数而属件权重信息不完全的多属性群决策问题,定义了一些新的集成算子,即正态分布区间数的加权算术平均(NDINWAA)算子、正态分布区间数的有序加权平均(NDINOWA)算子和正态分布区间数的混合加权平均(NDINHA)算子,进而提出一种幕于正态分布区间数的信息不完全的多属性群决策方法.该方法利用NDINWAA算了和NDINHA算子对正态分布区间数属性值进行集成,利用正态分布区间数属性值的方差,通过建立优化模型确定最优属性权重,利用期望-方差准则对方案进行排序并择优.实例分析表明了该方法的可行性和有效性.

Approach of group decision making based on normal distribution interval number with incomplete information

For multiple attribute group
decision making problems, in which the attribute values are normal
distribution interval numbers and the attribute weight information
is incomplete, some new aggregation operators are defined, such as the
normal distribution interval number weighted arithmetic averaging(NDINWAA) operator, the normal distribution interval number
ordered weighted averaging(NDINOWA) operator and the normal
distribution interval number hybrid weighted
averaging(NDINHA) operator. Then an approach is developed for
solving multiple attribute group decision making based on normal
distribution interval number with incomplete information. In this
method, normal distribution interval number attribute values are
aggregated by the NDINWAA operator and the NDINHA operator, some
optimal models are constructed to determine the optimal attribute
weights by using the variance of normal distribution interval
number attribute values, and ranking of alternatives is performed by
using expectation-variance principle. Finally, an example shows
the effectiveness of this method.