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Received:March 01, 2017 Revised:March 23, 2017
Received:March 01, 2017 Revised:March 23, 2017
中文摘要: 针对属性值为直觉模糊信息且属性权重完全未知的多属性决策问题,提出了一种基于粗糙集的直觉模糊TOPSIS多属性决策方法.首先给出了直觉模糊信息的正、负理想点的求法,根据属性值与理想点的贴近度和给定的阈值求得判断矩阵,再根据判断矩阵对属性约简,确定各属性的权重,最后依据TOPSIS思想计算各方案与理想点的加权贴近度,得到方案的排序,并通过算例的分析比较验证了此方法的有效性.
Abstract:Aiming at the problem of multiple attribute decision making with attribute values as intuitionistic fuzzy information and unknown attribute weights, a multiple attribute decision making method of intuitionistic fuzzy TOPSIS based on rough sets is proposed. Firstly, the positive and negative ideal points of the intuitionistic fuzzy information are given. The discernibility matrix is obtained according to the given threshold and the similarity degree between attribute values and the ideal points. Then, the attribute reduction is operated and the weight of attribute is determined by the discernibility matrix. Finally, according to the TOPSIS idea, the weighted similarity degree between each alternative and the ideal point is calculated. And the order of the alternatives is obtained. The validity of the method is verified with an example.
文章编号: 中图分类号: 文献标志码:
基金项目:国家自然科学基金(71501047)
Author Name | Affiliation |
LIU Wen-Qing | Decision Science Institute, Fuzhou University, Fuzhou 350108, China |
WANG Ying-Ming | Decision Science Institute, Fuzhou University, Fuzhou 350108, China |
Author Name | Affiliation |
LIU Wen-Qing | Decision Science Institute, Fuzhou University, Fuzhou 350108, China |
WANG Ying-Ming | Decision Science Institute, Fuzhou University, Fuzhou 350108, China |
引用文本:
刘文清,王应明.基于粗糙集的直觉模糊TOPSIS多属性决策方法.计算机系统应用,2017,26(11):277-281
LIU Wen-Qing,WANG Ying-Ming.Multiple Attribute Decision Making Method of Intuitionistic Fuzzy TOPSIS Based on Rough Sets.COMPUTER SYSTEMS APPLICATIONS,2017,26(11):277-281
刘文清,王应明.基于粗糙集的直觉模糊TOPSIS多属性决策方法.计算机系统应用,2017,26(11):277-281
LIU Wen-Qing,WANG Ying-Ming.Multiple Attribute Decision Making Method of Intuitionistic Fuzzy TOPSIS Based on Rough Sets.COMPUTER SYSTEMS APPLICATIONS,2017,26(11):277-281