群体决策支持系统中一种偏好集结模型

针对目前常用决策理论的不足，在计算机群体决策支持系统（GDSS）的偏好agent中用一种模糊数N^-模拟决策者的心理偏好，采用相关参数表达决策者的性格特征等个性因素。从决策人的角度出发，提出了一种偏好集结模型，使群体综合偏好照顾到个性因素，为群体决策提供了一个新的思路。     

决策信息为区间二元语义的双向专家权重确定方法

针对区间二元语义多属性群决策中的专家客观赋权问题,提出了一种区间二元语义群决策的双向专家权重确定方法。首先设置专家初始权重,通过专家个体与群体决策矩阵的偏差距离计算专家的偏离权重,再通过专家的直觉模糊熵计算专家的模糊熵权重,结合偏离权重和模糊熵权重,经过多次迭代后得到稳定的专家双向权重。该权重既反映了专家偏好信息与群体偏好信息的一致性,同时也反映了专家对决策问题的了解程度。最后,实例验证了该算法的可行性与有效性。     

面向问题的动态群体决策支持系统框架研究

在先进制造与管理模式下,企业经营过程管理的决策活动具有广泛的分布性和重要的协同性,个人决策逐步向群体决策发展,现代群体决策支持系统应该是能处理各类问题,具有开放、重构、事务处理能力的智能化群体决策。论文提出一种面向决策问题的动态群体决策支持系统框架,该框架通过决策个体内决策功能对象的重组,决策个体之间基于问题分解的组织合作,实现了群体决策要求的分布性、开放性、智能化特点,具有很强的灵活、开放、重构能力。     

基于属性多粒度的双层权重大群体决策方法

针对多属性大群体决策信息的模糊性以及属性差异性问题, 提出一种基于属性多粒度的区间二元语义大群体决策方法. 首先, 依据决策成员偏好将大群体进行聚类, 形成偏好聚集结构, 考虑到各聚集中决策成员对所处聚集一致性的贡献不同, 以及不同聚集的偏好对于群体偏好的贡献不同, 提出双层权重模型; 其次, 利用扩展的可能度公式对各聚集内决策成员的权重进行确定, 利用模糊熵确定各个聚集的权重; 再次, 结合模糊相对熵和优劣解距离法(TOPSIS) 得到决策方案的排序; 最后, 通过案例的分析与对比验证了所提出方法的有效性和适用性.

     

两种基于个体偏好集结的多属性群决策共识方法

针对多属性群决策中的共识问题, 提出两种使群体达成共识的方法. 假设群体决策的结果以从个体偏好通过集结得到的群体偏好为基础, 在使用算术加权集结算子和几何加权集结算子的条件下, 分别设计相应的共识达成算法, 并对算法的收敛性进行分析. 与已有方法相比, 所提出算法能够体现决策个体的差异和决策个体对群体的影响. 通过某市政图书馆空调系统安装方案的选择表明了所提出方法的合理性和可行性.

     

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.

     

基于改进遗传算法的一类多属性群决策方法

通过定义个体决策结果和群体决策结果的加权距离来反映决策结果的一致性,并通过定义对打分矩阵的调整程度来反映对决策者最初决策偏好的调整.在保留各决策者最初决策偏好的基础上,对原始专家打分矩阵进行调整,得到新的一致性较好的群决策结果.采用遗传算法对问题求解,以打分矩阵为基础设计了一种染色体编码格式,并对标准遗传算法进行了改进,提出了基于规则的遗传算法.最后,通过实例计算和结果的可靠度分析表明了该方法的有效性和合理性.     

一种改进的非常规突发事件应急决策集结算法

针对非常规突发事件应急决策集结问题,分析了集结过程中的冲突问题,构建了非常规突发事件应急决策集结框架,在集结过程中通过群体冲突程度指标修正决策偏好信息并提出一种改进的非常规突发事件应急决策集结算法,构建应急决策群体冲突程度指标,并通过该指标来修正决策组成员的偏好信息,改善偏好修正时的主观性和随意性问题,最终获得冲突程度能够接受的应急群体决策方案。最后通过算例验证了该算法的有效性。     

考虑偏好与合作的供应链网络设计与优化

提出了考虑核心企业决策偏好与成员企业合作意愿的双目标供应链网络设计与优化问题。应用模糊多目标规划方法,将决策者对各个目标的不精确期望水平考虑到供应链网络的设计与优化模型中。研究了集中决策模式下核心决策者的不同目标偏好与分散决策模式下各组织成员群体决策的合作意愿,对供应链的各个目标及网络优化配置的影响。结果表明,"极大极小"算子体现了核心决策者对各个目标的偏好,但大权重目标以牺牲小权重目标的值为代价的。新的"模糊与"补偿算子能使供应链的各个目标值获得理想的均衡效果,体现了供应网络中核心企业与其他成员企业群体协商决策的管理思想与行为特征。     

基于群体距离的多目标粒子群优化算法

为实现偏好与群体决策的结合应用,提出基于群体距离的多目标粒子群优化算法。通过调整解与参考点的群体距离引导粒子靠近偏好区域,运用格栅方法和改进的剪枝策略实现解在Pareto边界的均匀分布,求出与群体成员偏好相关的部分Pareto最优集,从而减少计算成本、加快收敛速度。实验结果表明,该算法得到的解更靠近真实Pareto前沿,且对不同个体决策成员都有效。     

基于方案修订的群决策一致性形成的研究

层次分析法(AHP)是群决策中经常使用的一种方法,利用AHP进行群决策的过程实质上也是决策者个体偏好集结的过程。针对如何将不同形式的偏好信息进行有效集结以形成群决策一致性方案的问题,提出一种通过活用AHP修订决策方案达到决策者群体的一致性偏好最终得以形成的方法,在利用决策者的决策信息进行群组划分的基础上明确各个划分的决策偏好差异,提出了活用AHP进行群决策一致性形成的方向,并形成了有效的动态群决策过程。     

基于信任关系和互补判断矩阵的群决策方法

随着信息和网络技术的不断发展,基于社会网络的群决策问题受到越来越多研究者的关注.针对社会网络环境下模糊互补判断矩阵的群决策问题,研究群体共识调整过程和方案选择方法.首先,融合决策者之间的社会关系、身份地位、知识能力3个方面信息来构建决策者两两之间的信任关系;其次,提出一种尽可能减少元素间共识补偿的共识度度量方法,在此基础上建立基于信任关系的共识调整模型,并从理论上证明该模型的有效性;最后通过信任关系矩阵的特征向量中心度分别求出专家的重要性权重,用以集结专家的偏好信息和对方案进行排序选择,算例分析表明了所提出方法的有效性.     

基于重要度引导偏好识别修正的多粒度语言共识模型

研究多粒度语言偏好信息下的群体共识决策问题. 首先, 从个体和群体两个角度充分挖掘偏好信息下隐含的专家重要度信息, 基于个体一致度及个体与群体的相似度构建确定专家重要度的优化模型; 其次, 以专家重要度引导非共识偏好的识别和修正过程, 提出一种自适应的语言共识模型; 然后, 给出一种群决策方法, 确保在集结专家意见前群体达成一定程度的共识; 最后, 通过算例验证所提出方法的可行性和有效性.

     

Consensus building in multiperson decision making with heterogeneous preference representation structures: A perspective based on prospect theory

Multiperson decision making (MPDM) models with heterogeneous preference representation structures have initiated by Chiclana et al. In this study, we propose a novel framework for MPDM problems with heterogeneous preference representation structures. This framework takes the decision makers’ psychological behaviors into consideration and is based on the prospect theory, which is one of the most influential psychological behavior decision theories. In this framework, the heterogeneous preference representation structures are all transformed into preference orderings. The preference-approval structures are introduced to determine the reference points in prospect theory, and then the prospect value function is used to obtain individual and collective prospect values. Finally, based on the prospect values and preference-approval structures, we propose the two-step feedback adjustment rules, which will adjust both the preference evaluations and the preference-approval information, to help the decision makers reach higher consensus degree. Further, we find that there are two significant observations in this framework: (1) the uses of different reference points will yield different outputs of the selection process in MPDM; (2) compared with only adjusting the preference evaluations in the existing studies, the pace of consensus reaching will be accelerated in adjusting both the preference evaluations and the preference-approval information.     

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.     

An optimization approach to multiperson decision making based on different formats of preference information

Multiperson decision making (MPDM) problems with different formats of preference information are one of the emerging research areas in decision analysis. Existing approaches for dealing with different preference formats tend to be unwieldy. This paper proposes a new method to solve the problem, in which the preference information on alternatives provided by experts can be represented in four different formats, namely: 1) utility values; 2) preference orderings; 3) multiplicative preference relations; and 4) fuzzy preference relations. An optimization model is constructed to integrate the four formats of preference and to assess ranking values of alternatives. The model is shown to be theoretically sound and complete via a series of theorems, and then a corresponding algorithm is developed. A numerical example is given to illustrate the procedure. The proposed approach is more efficient and simpler than existing approaches because it does not need to unify different formats of preferences or to aggregate individual preferences into a collective one. Therefore, it overcomes a major shortcoming of existing approaches that lose or distort the original preference information in the process of unifying the formats.     

新型偏好敏感决策树算法*

针对现有决策树模型在分类过程中没有考虑决策者对结果的偏好行为,因而不能很好的预测具有明显偏好倾向问题的不足,提出了一种偏好敏感决策树(Preference Sensitive Decision Tree, PSDT)分类算法。该算法引入了偏好度和偏好代价的概念,并通过综合考虑属性信息和有效偏好,构建新型属性选择因子和基于有效偏好的结点类标号分配准则。通过自适应调整偏好度,可生成最佳偏好敏感决策树。实验结果证明该算法既能实现对偏好类的高精度预测,同时能够保证决策树拥有良好的整体精度,且具有较高的有效性和实用性,能够很好的解决偏好敏感环境下的决策问题。     

Possibilistic programming approach for fuzzy multidimensional analysis of preference in group decision making

Multiple attribute decision making (MADM) problems are the most encountered problems in decision making. Fuzziness is inherent in decision making process and linguistic variables are well suited to assessing an alternative on qualitative attributes using fuzzy rating. A few techniques in MADM assess the weights of attributes based on preference information on alternatives. But they are not practical any more when the set of all paired comparison judgments from decision makers (DMs) on attributes are not crisp and also we have to deal with fuzzy decision matrix. This paper investigates the generation of a possibilistic model for multidimensional analysis of preference (LINMAP). The model assesses the fuzzy weights as well as locating the ideal solution with fuzzy decision making preference on attributes and fuzzy decision matrix. All of the information is assumed as triangular fuzzy numbers (TFNs). This method is developed in group decision making environments and formulates the problem as a possibilistic programming with multiple objectives.     

Penalizing strategic preference manipulation in multi-agentdecision making

We consider the problem of group decision making where the selection process is based upon a group preference function, obtained by an aggregation of the participating agents individual preference functions. We describe some methods for formulating the group preference from the individual preference functions. We note the possibility of the individual agents strategically manipulating the information they provide, so as to further their own goal of getting their most preferred alternative selected by the group. With this in mind, we suggest ways of modifying the formulation of the group decision functions to discourage strategic manipulation by the participating agents     

Group decision making with linguistic preference relations with application to supplier selection

Linguistic preference relation is a useful tool for expressing preferences of decision makers in group decision making according to linguistic scales. But in the real decision problems, there usually exist interactive phenomena among the preference of decision makers, which makes it difficult to aggregate preference information by conventional additive aggregation operators. Thus, to approximate the human subjective preference evaluation process, it would be more suitable to apply non-additive measures tool without assuming additivity and independence. In this paper, based on λ-fuzzy measure, we consider dependence among subjective preference of decision makers to develop some new linguistic aggregation operators such as linguistic ordered geometric averaging operator and extended linguistic Choquet integral operator to aggregate the multiplicative linguistic preference relations and additive linguistic preference relations, respectively. Further, the procedure and algorithm of group decision making based on these new linguistic aggregation operators and linguistic preference relations are given. Finally, a supplier selection example is provided to illustrate the developed approaches.