基于乘法偏好关系的群一致性偏差熵多属性群决策方法研究

在多属性群决策中, 针对每一个属性下决策者都有一个关于决策方案的乘法偏好关系的决策问题, 提出一种基于乘法偏好关系的群一致性偏差熵多属性群决策方法. 此方法考虑到不同属性下的决策者具有不同的权重, 并通过迭代运算可以达到群一致性水平, 从而得出最终的不同属性下决策者的权重; 同时, 可以利用偏差熵模型来求解属性权重, 利用这两个权重最终获得一个综合各方意见的群一致性乘法偏好关系. 最后通过算例分析验证了所提出方法的有效性.     

群决策中多阶段多元判断偏好的集结方法研究

研究群决策过程中决策者基干多个决策阶段、多种结构形式的判断偏好集结方法.基于互反判断偏好与互补判断偏好的转化公式,将多种类偏好的结构一致化;利用决策者判断偏好的一致性水平和与群体综合偏好偏差的距离,提出了确定决策者权重的方法;建立了基于决策先验信息的多阶段偏好集结的决策阶段赋权模型.根据各决策阶段的权重,将多阶段判断偏好集结成群体综合偏好.     

一种基于证据推理的混合型灰色多属性群决策方法

针对方案的属性值为区间灰数与确定语言等级,或在两个连续的语言等级之间权重完全已知的混合型灰色多属性群决策问题,提出一种新的决策方法．该方法可根据决策者的偏好给出定量属性的白化值和定性属性的信用结构,确定了每个决策者和群体的等级信用结构矩阵;提出了求解群体集成权重的新方法,并利用证据推理算法求出各方案在各等级的信任度;最后利用期望效用和区间数排序法对方案进行了排序．实例分析表明了该方法的合理性和有效性．     

群决策中多阶段多元判断偏好的集结方法研究

研究群决策过程中决策者基于多个决策阶段,多种结构形式的判断偏好集结方法.基于互反判断偏好与互补判断偏好的转化公式,将多种类偏好的结构一致化;利用决策者判断偏好的一致性水平和与群体综合偏好偏差的距离,提出了确定决策者权重的方法;建立了基于决策先验信息的多阶段偏好集结的决策阶段赋权模型.根据各决策阶段的权重,将多阶段判断偏好集结成群体综合偏好.

     

基于新型距离测度的概率语言多属性群决策方法

研究属性权重和专家权重均未知条件下的概率语言多属性群决策问题.首先,针对传统概率语言术语集距离测度的不足,提出改进的距离测度,并证明其性质和优越性.其次,基于新的距离公式,定义决策者的平均相似度,并结合专家之间的信任度矩阵计算每个属性下决策者的综合权重;构建基于相似-信任分析的群体共识调节模型,尽可能保留各属性下权威专家的意见;考虑到属性之间的相关性以及各个属性的重要程度,构建基于广义Choquet积分和离差最大化法的主客观综合赋权模型.随后,在新的距离测度的基础上,结合TODIM方法构建概率语言多属性群决策框架,实现对多个备选方案的排序.最后,以光伏电站的选址为例,验证所提出方法的有效性和合理性.     

考虑风险态度的区间值非加性BWM共识模型

针对属性权重未知,且考虑决策成员风险态度和属性关联关系的多属性决策问题,构建一种基于非加性最优最劣方法(BWM)的群体共识模型.首先,为处理不确定环境中的属性关联问题,提出区间值非加性BWM方法,该方法采用区间值刻画专家关于属性重要性的成对比较结果,以获取个体偏好下的属性重要性指数(权重),扩展了Shapley值的表示形式;其次,通过构建非加性BWM共识模型将个体偏好下的属性重要性指数转化为群体共识的属性重要性指数,在共识达成的过程中,利用隶属度函数度量决策成员的风险态度效用水平,并在一定预算限制下,探索决策成员的风险态度对群体共识的属性重要性指数及共识效用水平的影响;最后,通过案例分析验证模型的可行性和有效性.     

基于EDAS和共识模型的动态概率语言多属性群决策方法

针对评价信息为概率语言术语且属性权重未知的多属性群决策问题,提出一种新的、基于离平均解距离(EDAS)和共识模型的概率语言多属性后悔理论群决策方法.首先,考虑到现实决策中决策者面对方案选择时会呈现“有限理性”的行为,将后悔理论融入群决策方法的研究中,通过计算决策者的后悔-欣喜值来度量其感知效用;其次,将EDAS方法与概率语言信息处理相结合用于方案排序,并基于最大化偏差法思想,给出一种概率语言多属性决策问题属性权重的确定方法,以得到初始解决方案;然后,对初始解决方案进行共识测度,进一步根据个人属性集得到个人方案集,通过共识模型的动态反馈调整,得到符合大多数决策者意愿的最终解决方案;最后,以共享自行车设计方案的选择为例对所提出方法进行验证,并通过对比分析说明方法的可行性和有效性.     

多属性群决策中一种基于主观偏好确定属性权重的方法

提出一种多属性群决策中同时考虑专家群体对属性主观赋权的偏好和决策者对决策重要性认识的偏好来确定属性权重的方法,能够兼容专家实数型、区间型和语言型等类型的属性权重赋值.首先建立标准属性重要差异矩阵以实现专家对属性赋权的优劣比较和差异规范,定义统一的决策者偏好映射对其进行调整;然后求解各矩阵的排序向量以量化属性的相对重要程度,并与专家权重聚合得到属性权重向量;最后给出了方法的具体步骤,并通过算例说明了该方法的具体应用.     

基于梯形模糊数期望值的多维偏好群决策模型

提出一种基于梯形模糊数距离期望值的多维偏好群决策模型,以解决偏好和属性值均为梯形模糊数的群决策问题．其算法为：首先定义在β 截集下主／客观偏好之间的偏差函数,通过构造目标规划模型,求解属性的权重向量;然后集结不同β 截集下所有决策者的加权规范化模糊决策矩阵,形成总加权规范化模糊决策矩阵;最后求出各备选方案与模糊理想解的相对贴近度δi,按大小排序确定最优方案．     

基于决策者信任度的风险型混合多属性群决策方法

考虑决策者对风险型混合多属性评价结果的信任程度不同, 提出基于前景理论和改进投影理论的群决策方法. 建立一个数组以描述在不同信任度下群决策专家的评价结果, 并将数组中混合数据类型转化为三角模糊数. 在考虑决策者信任度的前提下集结群信息、确定属性权重. 引入综合前景价值和考虑权重的投影相对接近度两种方法对方案进行排序. 最后通过实例表明了所提出方法的合理性和有效性.

     

面向偏好冲突的直觉模糊多属性群体决策方法

针对偏好具有冲突性且权重信息完全未知的直觉模糊多属性群体决策问题, 提出一种基于多目标决策的求解方法. 首先, 建立以决策方案差异程度和决策成员偏好冲突程度为目标函数的多目标决策模型; 然后, 利用极小极大方法求解该模型, 得到各方案的属性权重和决策成员权重, 据此确立最优方案; 最后, 通过数值算例表明了该方法的有效性.

     

A dynamic hybrid trust network-based dual-path feedback consensus model for multi-attribute group decision-making in intuitionistic fuzzy environment

This paper proposes a dual-path feedback consensus model based on dynamic hybrid trust relationships to solve multi-attribute group decision-making problems in intuitionistic fuzzy environment. This model comprises two main parts: (a) the construction of a dynamic hybrid trust network among decision makers (DMs) and (b) the formation of a dual-path feedback mechanism to improve the group consensus. In the first part, a hybrid trust network is constructed by combining DMs’ prior knowledge of each other and the preference similarities between them. Then, the hybrid trust network is dynamically updated iteratively to reflect the changes in the trust relationships in the process of joint decision-making. In the second part, DMs with low consensus degrees are identified and provided with either a preference or weight adjustment path to improve the group consensus. The preference adjustment path is activated for DMs who agree to modify their preferences, and a nonlinear programming model is proposed to help DMs improve consensus degrees while minimizing adjustment cost. The weight adjustment path is activated for DMs who stick to their own opinions and refuse to make changes, and their weights is adjusted accordingly. An illustrative example along with the related sensitivity analysis and comparative study are used to verify the effectiveness of the proposed model.     

An approach to group decision making with heterogeneous incomplete uncertain preference relations

For practical group decision making problems, decision makers tend to provide heterogeneous uncertain preference relations due to the uncertainty of the decision environment and the difference of cultures and education backgrounds. Sometimes, decision makers may not have an in-depth knowledge of the problem to be solved and provide incomplete preference relations. In this paper, we focus on group decision making (GDM) problems with heterogeneous incomplete uncertain preference relations, including uncertain multiplicative preference relations, uncertain fuzzy preference relations, uncertain linguistic preference relations and intuitionistic fuzzy preference relations. To deal with such GDM problems, a decision analysis method is proposed. Based on the multiplicative consistency of uncertain preference relations, a bi-objective optimization model which aims to maximize both the group consensus and the individual consistency of each decision maker is established. By solving the optimization model, the priority weights of alternatives can be obtained. Finally, some illustrative examples are used to show the feasibility and effectiveness of the proposed method.     

Consistency and consensus models for group decision-making with uncertain 2-tuple linguistic preference relations

Due to the uncertainty of the decision environment and the lack of knowledge, decision-makers may use uncertain linguistic preference relations to express their preferences over alternatives and criteria. For group decision-making problems with preference relations, it is important to consider the individual consistency and the group consensus before aggregating the preference information. In this paper, consistency and consensus models for group decision-making with uncertain 2-tuple linguistic preference relations (U2TLPRs) are investigated. First of all, a formula which can construct a consistent U2TLPR from the original preference relation is presented. Based on the consistent preference relation, the individual consistency index for a U2TLPR is defined. An iterative algorithm is then developed to improve the individual consistency of a U2TLPR. To help decision-makers reach consensus in group decision-making under uncertain linguistic environment, the individual consensus and group consensus indices for group decision-making with U2TLPRs are defined. Based on the two indices, an algorithm for consensus reaching in group decision-making with U2TLPRs is also developed. Finally, two examples are provided to illustrate the effectiveness of the proposed algorithms.     

属性权重未知情形下的q阶orthopair犹豫模糊TOPSIS法

在多属性决策问题中,q阶orthopair犹豫模糊集可以有效地处理不确定信息.基于Theil不等系数,提出一种新的q阶orthopair犹豫模糊距离测度,并研究该距离测度的性质.针对属性权重完全未知的q阶orthopair犹豫模糊多属性决策问题,构建离差最大化的属性权重模型.基于该权重确定模型和q阶orthopair犹...     

Existence and construction of weight-set for satisfying preferenceorders of alternatives based on additive multi-attribute valuemodel

Based on the additive multi-attribute value model for multiple attribute decision making (MADM) problems, the paper investigates how the set of attribute weights (or weight-set thereafter) is determined according to the preference orders of alternatives given by decision makers. The weight-set is a bounded convex polyhedron and can be written as a convex combination of the extreme points. We give the sufficient and necessary conditions for the weight-set to be not empty and present the structures of the weight-set for satisfying the preference orders of alternatives. A method is also proposed to determine the weight-set. The structure of the weight-set is used to determine the interval of weights for every attribute in the decision analysis and to judge whether there exists a positive weight in the weight-set. The research results are applied to several MADM problems such as the geometric additive multi-attribute value model and the MADM problem with cone structure     

On consensus reaching process based on social network analysis in uncertain linguistic group decision making: Exploring limited trust propagation and preference modification attitudes

This paper explores a limited trust propagation-based consensus model considering individual attitude for preference modification in a social networked setting with uncertain preference information. To examine the construction of complete linkages, and the status of decision makers in group decision making, it is assumed that the group size and network density will affect the scale of mediators in the propagation process, then a definition of limited trust propagation is proposed and the propagation efficiency can be introduced. On this basis, we obtain missing trust relationships and individual centrality in network. In the process of consensus reaching, both the decision maker’s original preference and recommendation advice are considered for flexibly modeling the preference modification process: the individual attitude toward modification is determined by a newly introduced measure of comprehensive relative out-degree centrality, showing the degree of willingness to adjust assessments. When the willingness is too low to reach the preset consensus level, a multi-objective programming model is designed to improve the consensus as much as possible. Moreover, the proposed feedback mechanism narrows the individual acceptable modification range based on the previous adjustment rule, so as to simulate the personalized and targeted decision behavior. To guarantee obtaining a collective aggregated preference in a logical and precise manner, a two-stage optimization model composing of comprehensive relative in-degree centrality-based information aggregation and best consistency-based uncertainty elimination, is proposed. A numerical example and comparative analyses are performed to show the validity and feasibility of the proposed model.     

属性权重不确定条件下的区间直觉模糊多属性决策

在区间直觉模糊集(Interval-valued intuitionistic fuzzy set, IVIFS)的框架内,重点研究了属性权重在一定约束条件下和属性权重完全未知的 多属性群决策问题.首先利用区间直觉模糊集成算子获得方案在属性上的综合区间直觉模糊决策矩阵,进一步依据逼近理想解排序法(Technique for order preference by similarity to an ideal solution, TOPSIS) 的思想计算候选方案和理想方案的加权距离,最后确定方案排序.其中针对属性权重在一定约束条件下的决策问题,提出了基于 区间直觉模糊集精确度函数的线性规划方法,用以解决属性权重求解问题.针对属性权重完全未知的决策问题,首先定义了区间直觉 模糊熵,其次通过熵衡量每一属性所含的信息量来求解属性权重.实验结果验证了决策方法的有效性和可行性.