基于混合多属性信息的复杂多阶段决策方法

Complex multi-stage decision making method based on mixed multi-attribute information

针对属性权重和阶段权重完全未知且属性信息表示为混合形式的多阶段决策问题,提出一种新的决策方法。首先,将混合型属性信息统一成区间数形式,在此基础上根据属性信息的熵权区间和离差水平确定属性权重。然后,借助有序聚类法将决策对象各个阶段的矢量信息划分为若干个聚集,再以聚集中决策矢量的距离最小化为目标,构建优化模型求得聚集内各矢量的阶段权重,进而得到所有决策对象的综合阶段权重。最后,利用TOPSIS法对决策对象进行排序,并通过算例对该方法的可行性和实用性进行证明。

For the multi-stage decision-making problem that attribute weights and stage weights are com-pletely unknown and attribute information is expressed as different forms,a new decision-making method is pro-posed.Firstly,mixed attribute information is normalized into the form of interval numbers,and on this basis, the attribute weights are obtained by attribute entropy weight intervals and deviation degree of attribute infor-mation.Secondly,the decision information in different stages for each decision object is divided into several ag-gregations by a sequential clustering method.Thirdly,an optimization model which aims minimizing the sum of squares over the decision vector distance in aggregation is proposed,the stage weights over different aggrega-tions are obtained,and the comprehensive stage weights over all decision objects are achieved.Finally,TOPSIS is introduced to rank the decision objects.A numerical example is introduced to illustrate the feasibility and va-lidity of this approach.