属性权重信息不完全区间数多属性决策方法

依据传统的TOPSIS方法的基本思路,基于相对隶属度法,给出了解决属性权重信息不完全的区间数多属性决策问题的计算步骤。其核心是借助每一方案的综合加权距离求得每个方案相对于优等方案的隶属度,再按隶属度的大小进行决策。该方法克服了以往研究此类问题时所遇到的区间数难以排序的困难,表明相对隶属度法的择优与排序能力要比传统的逼近理想解法强。最后,用实例说明了模型的可行性和有效性。

A New Method for Interval Multiple Attribute Decision-Making Problem with Incomplete Attribute Weights

Based on the conventional TOPSIS and in the light of the relative membership degree method,the calculation steps are given to solve the problem of multiple attribute decision-making with incomplete information on attribute weights.The key is the relative membership degree,which is obtained by means of the synthetically weighted distance.Then,the decision is made according to the relative membership degrees.By using this new method the difficulties that occur in ranking intervals in the traditional analysis methods are overcome.This shows that the relative membership degree method is better in selecting the best and ranking alternatives than the traditional TOPSIS method.Finally,practical calculations are given,which shows that the models are feasible and practical.