基于区间直觉梯形模糊数的改进TOPSIS多属性决策方法

研究了属性权重完全未知的区间直觉梯形模糊数的多属性决策问题,结合TOPSIS方法定义了相对贴近度及总贴近度公式.首先由区间直觉梯形模糊数的Hamming距离给出了每个方案的属性与正负理想解的距离,基于此,给出了相对贴近度矩阵,根据所有决策方案的综合贴近度最小化建立多目标规划模型,从而确定属性的权重值,然后根据区间直觉梯形模糊数的加权算数平均算子求出各决策方案的总贴近度,根据总贴近度的大小对方案进行排序;最后,通过实例分析说明该方法的可行性和有效性.

Improved TOPSIS of Multiple Attribute Decision Making Method Based on Interval-valued ITFN

The paper investigates the problem of multi-attribute decision making,in which the attribute values are the interval-valued intuitionistic trapezoidal fuzzy numbers and the weights of attribute weight information is complete unknown.Relative degree of approximation and approximation degree are defined based on the TOPSIS(technique for order preference by similarity to an ideal solution).Firstly,by using the hamming distances for interval-valued intuitionistic trapezoidal fuzzy numbers,the distance between attribute and ideal solution are given,thus formed a matrix of relative approximation,the model of multi-attribute decision making is constructed based on the max of the approximation degree,thereby the weights of attribute weight is determined.Furthermore,by using the weighted arithmetic average operator of the interval-valued intuitionistic trapezoidal fuzzy numbers,the approximation degree of each scheme is computed,and then scheme is ranked based on the approximation degree.Finally,the example analysis shows the effectiveness of the method.