论文部分内容阅读
针对属性权重信息不完全确定、属性值为正态分布随机变量且数据信息来自于不同时期的动态随机多属性决策问题,给出了正态分布数的运算法则,定义了正态分布数加权算术平均(NDNWAA)算子和动态正态分布数加权算术平均(DNDNWAA)算子,进而提出了一种信息不完全确定的动态随机多属性决策方法.该方法利用DNDNWAA算子和NDNWAA算子对正态分布属性值进行集成;利用正态分布属性值的方差和属性权重的随机性,通过建立优化模型确定最优属性权重;利用正态分布3σ原则、区间数比较的可能度公式和互补判断矩阵的排序公式对决策方案进行排序和择优.最后,实例分析表明了该方法的可行性和有效性.
Aiming at the problem that the attribute weight information is not completely determined, the attribute value is a normal distribution random variable and the data information comes from the dynamic stochastic multiple attribute decision making problem in different periods, the algorithm of normal distribution number is given and the normal distribution number weighted arithmetic (NDNWAA) operator and the dynamic normal distribution number weighted arithmetic mean (DNDNWAA) operator, and then a dynamic stochastic multiple attribute decision making method with incomplete information is proposed. This method uses the DNDNWAA operator and the NDNWAA operator State distribution property value is integrated. By using the variance of the normal distribution property value and the randomness of the property weight, the optimal property weight is determined by establishing the optimization model. By using the 3σ principle of normal distribution, the probability formula of interval number comparison and the complementary judgment matrix The ranking formula is used to rank and select the optimal decision.Finally, case analysis shows the feasibility and effectiveness of the method.