为了解决在实际决策时,由于知识背景不同决策者采用不同粒度语言术语集来表达而导致决策结果不准确的问题,本文提出了一种基于多粒度犹豫模糊语言术语集的逼近理想解排序(technique for order preference by similarity to ideal solution,TOPSIS)决策方法.首先选用各术语集中的最大粒度作为标准粒度,通过转换算法将每个决策者的语言术语集转换到同一标准粒度下进行集结,得出相应的隶属度语言术语集;然后结合TOPSIS方法,计算每个备选方案与正、负理想点距离,以相对贴近度的大小排序实现最优方案的选择;最后,通过一个实例,验证该方法的可行性和优越性.本文所提方法可应用于最优方案的选择问题中,提升决策结果准确度.
Abstract
In order to solve the problem that decision makers adopt different granularity linguistic term sets for expres-sion and thus lead to inaccurate decision results due to different knowledge backgrounds in practical decision making,this paper proposes a technique for order preference by similarity to ideal solution(TOPSIS)decision-making method based on multi-granularity hesitant fuzzy linguistic term sets.Firstly,the maximum granularity of each term set is selec-ted as the standard granularity,and the linguistic term set of each decision maker is converted to the same standard gran-ularity for clustering through the conversion algorithm,which results in corresponding subordination linguistic term set;Then,combining with TOPSIS,the distance between each alternative and the positive and negative ideal points is calcu-lated,and the selection of the optimal solution is realized by the ordering of the magnitude of relative closeness;Finally,the feasibility and superiority of the method are verified by an example.The method proposed in this paper can be ap-plied to the problem of choosing the optimal solution to improve the accuracy of decision-making results.
关键词
多粒度/多属性决策/犹豫模糊集/语言术语集/模糊语言/决策模型/逼近理想解排序法/最优方案选择
Key words
multi-granularity/multi-attribute decision/hesitant and fuzzy set/linguistic term set/ambiguous linguistic/decision model/TOPSIS method/optimal solution selection
维普
万方数据