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勾股模糊偏好关系及其在群体决策中的应用
引用本文:杨艺,钱桂生,丁恒,李延来,吕红霞.勾股模糊偏好关系及其在群体决策中的应用[J].控制与决策,2019,34(2):287-297.
作者姓名:杨艺  钱桂生  丁恒  李延来  吕红霞
作者单位:西南交通大学交通运输与物流学院,成都610031;湖南商学院大数据与互联网创新研究院,长沙410205;香港城市大学系统工程与工程管理系,香港,999077;西南交通大学交通运输与物流学院,成都,610031
基金项目:国家自然科学基金项目(71373222,71371156);湖南省重点实验室开放研究基金项目(2017TP1026).
摘    要:以区间模糊偏好关系(IVFPR)和直觉模糊偏好关系(IFPR)的理论框架为依据,将勾股模糊数(PFN)引入偏好关系中,定义勾股模糊偏好关系(PFPR)和加性一致性PFPR.然后,提出标准化勾股模糊权重向量(PFWV)的概念,并给出构造加性一致性PFPR的转换公式.为获取任意给定的PFPR的权重向量,建立以给定的PFPR与构造的加性一致性PFPR偏差最小为目标的优化模型.针对多个勾股模糊偏好关系的集结,利用能够有效处理极端值并满足关于序关系单调的勾股模糊加权二次(PFWQ)算子作为集结工具.进一步,联合PFWQ算子和目标优化模型提出一种群体决策方法.最后,通过案例分析表明所提出方法的实用性和可行性.

关 键 词:勾股模糊偏好关系  目标优化模型  勾股模糊加权二次算子  群体决策

Pythagorean fuzzy preference relations and its application to group decision making
YANG Yi,QIAN Gui-sheng,DING Heng,LI Yan-lai and LYU Hong-xia.Pythagorean fuzzy preference relations and its application to group decision making[J].Control and Decision,2019,34(2):287-297.
Authors:YANG Yi  QIAN Gui-sheng  DING Heng  LI Yan-lai and LYU Hong-xia
Affiliation:School of Transportation and Logistics,Southwest Jiaotong University,Chengdu610031,China;Institute of Big Data and Internet Innovation,Hu''nan University of Commerce,Changsha410205,China,Department of Systems Engineering and Engineering Management,City University of Hong Kong,Hong Kong 999077,China,School of Transportation and Logistics,Southwest Jiaotong University,Chengdu610031,China,School of Transportation and Logistics,Southwest Jiaotong University,Chengdu610031,China and School of Transportation and Logistics,Southwest Jiaotong University,Chengdu610031,China
Abstract:Based on the theoretical framework of interval-valued fuzzy preference relation(IVFPR) and intuitionistic fuzzy preference relation(IFPR), the Pythagorean fuzzy number(PFN) is introduced into the preference relation, and the concepts of Pythagorean fuzzy preference relation(PFPR) and additive consistent PFPR are proposed. Then, the definition of normalized Pythagorean fuzzy weight vector(PFWV) is proposed, and a conversion formula is provided to convert this PFWV into the additive consistent PFPR. For any given PFPR, a goal programming model is developed to obtain its Pythagorean fuzzy weights by minimizing its deviation from the constructed additive consistent PFPR. Because the Pythagorean fuzzy weighted quadratic(PFWQ) operator can effectively deal with extremes and satisfy the monotonicity with respect to the order relation, it is used for the aggregation of multiple PFPRs. A group decision making approach is proposed by using the PFWQ operator and the goal programming model, and a practice example is given to illustrate the practicability and feasibility of the proposed approach.
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