SOME NEW SIMILARITY MEASURES FOR INTUITIONISTIC FUZZY VALUES AND THEIR APPLICATION IN GROUP DECISION MAKING
SOME NEW SIMILARITY MEASURES FOR INTUITIONISTIC FUZZY VALUES AND THEIR APPLICATION IN GROUP DECISION MAKING作者机构:School of Economics and Management Southeast University Nanjing Jiangsu 211189 China Institute of Sciences PLA University of Science and Technology Nanjing Jiangsu 210007 China
出 版 物:《Journal of Systems Science and Systems Engineering》 (系统科学与系统工程学报(英文版))
年 卷 期:2010年第19卷第4期
页 面:430-452页
核心收录:
学科分类:0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 070105[理学-运筹学与控制论] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:supported in part by the National Science Fund for Distinguished Young Scholars of China (No.70625005) the National Natural Science Foundation of China (No.71071161) the Program Sponsored for Scientific Innovation Research of College Graduate in Jiangsu Province (No.CX10B_059Z)
主 题:Intuitionistic fuzzy value interval-valued intuitionistic fuzzy value similarity measure aggregation operator
摘 要:We first propose a series of similarity measures for intuitionistic fuzzy values (IFVs) based on the intuitionistic fuzzy operators (Atanassov 1995). The parameters in the proposed similarity measures can control the degree of membership and the degree of non-membership of an IFV, which can reflect the decision maker’s risk preference. Moreover, we can obtain some known similarity measures when some fixed values are assigned to the parameters. Furthermore, we apply the similarity measures to aggregate IFVs and develop some aggregation operators, such as the intuitionistic fuzzy dependent averaging operator and the intuitionistic fuzzy dependent geometric operator, whose prominent characteristic is that the associated weights only depend on the aggregated intuitionistic fuzzy arguments and can relieve the influence of unfair arguments on the aggregated results. Based on these aggregation operators, we develop some group decision making methods, and finally extend our results to interval-valued intuitionistic fuzzy environment.