Extension of TOPSIS for fuzzy multi-attribute decision making problem based on experimental analysis

This paper is concerned with a technique for order performance by similarity to ideal solution（TOPSIS） method for fuzzy multi-attribute decision making,in which the information about attribute weights is partly known and the attribute values take form of triangular fuzzy numbers.Considering the fact that the triangular fuzzy TOPSIS results yielded by different distance measures are different from others,a comparative analysis of triangular fuzzy TOPSIS ranking from each distance measure is illustrated with discussion on standard deviation.By applying the most reasonable distance,the deviation degrees between attribute values are measured.A linear programming model based on the maximal deviation of weighted attribute values is established to obtain the attribute weights.Therefore,alternatives are ranked by using TOPSIS method.Finally,a numerical example is given to show the feasibility and effectiveness of the method.

基于群一致性检验的多属性TOPSIS方法

针对现有TOPSIS方法中未考虑决策者的一致性所造成的选取结果不合理以至于决策失误的问题,提出了一种基于群一致性检验的多属性TOPSIS方法。首先,在分析数据特点的基础上,利用三角可能性分布对备选方案的属性值进行不确定性描述;其次,利用基于相似性度量方法检验决策者的一致性程度;最后,利用满足一致度的、改进的TOPSIS方法对备选方案进行排序,得到最优的决策结果。通过尾矿坝综合评价实例,对方法进行验证。结果表明可有效避免因使用不一致信息导致的决策失误问题。

基于联系数贴近度的区间数多属性决策方法

利用集对分析和联系数学理论对区间数多属性决策方法进行了研究.首先给出了联系数贴近度的定义和性质;然后在将区间数决策矩阵转化为联系数决策矩阵的基础上,依据传统的逼近理想解的排序方法(TOPSIS)的基本思路,基于联系数贴近度提出了一种区间数多属性决策新方法.该方法简洁直观,易于计算,无需对区间数进行排序;最后,通过算例表明了它的有效性和实用性.