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.     

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.

     

基于云模型具有语言评价信息的多属性群决策研究

针对多属性群决策中具有语言评价信息偏好的表示与集结的关键问题．研究了基于云模型的决策专家个体偏好表示、偏好集结和方案优选方法．首先采用云模型表示决策者给出的自然语言评价信息,而属性和决策者权重大小则用云的语气运算表示;然后用浮动云进行偏好集结,根据云模型的相对距离进行方案的排序和优选．此方法可充分表达评价语言的模糊性和随机性,具有较大的客观性．     

基于属性不完全判断的语言群决策方法

研究多个决策者对属性有不完全类别偏好的语言案例决策方法.基于案例学习框架定义属性公共提及因子,提出考虑多重类别偏好的信息增益系数;基于灰靶决策框架建立综合靶心距最小的属性权重优化模型;基于决策者个体和群体的关联度确定决策者权重,进而给出方案排序.案例分析表明了所提出方法的应用步骤和可行性.     

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.

     

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。     

动态模糊形式化关系群体决策方法

群体决策问题是决策科学的核心问题之一。基于动态模糊理论,从动态角度研究群体决策问题,提出了一种动态模糊形式化关系决策方法。从个体偏好信息表达、个体偏好数据分析、个体偏好集结、方案选择和意见反馈五个阶段探讨了动态模糊群体决策模型,并通过实例验证了该模型的可行性和合理性。     

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.     

基于累积前景理论的大群体风险型动态应急决策方法

针对复杂环境下决策者对于应急事件作出的决策往往会面对偏好转移的问题,提出一种新的大群体风险型动态应急决策方法.首先利用偏好判断矩阵对全体决策者偏好进行聚类分析和偏好集结;其次,利用累积前景理论计算决策大群体的总体前景值;再次,考虑未来状态转移链,经过多轮调整得出决策者偏好转移矩阵,结合偏好转移矩阵和大群体总体前景值可得到当前突发事件状态下的最优方案;最后,通过案例分析与对比表明所提出方法的有效性和可行性.     

广义区间梯形模糊软集在群偏好集结中的应用

针对群推荐中个体偏好信息如何集成群体偏好信息,不同用户对商品关注的属性不完全相同,以及用户对商品属性权重分配不同的复杂问题,提出区间梯形模糊软集的群偏好集结模型。首先,结合广义区间梯形模糊集和软集的概念建立广义区间梯形模糊软集的概念,定义其基本运算性质;其次,将模糊语言映射成广义区间梯形模糊数,利用广义区间梯形模糊软集的并、交运算,以及广义区间梯形模糊数的重心算法进行排序,将个体用户的偏好信息集结成群偏好信息;最后,以汽车的推荐为例介绍群偏好集结算法。算例结果显示,用该模型解决群体偏好集结是合理和有效的。     

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.     

A novel approach for combination of individual and group decisions based on fuzzy best-worst method

The process of decision-making in an enterprise may either keep the business on track or derail it. Thus, a senior decision maker often use a group of experts as the supportive team to ensure appropriate decisions. The experts often have different expertise level regarding their knowledge, talent, proficiency, and experience. In this study, we first extend the best-worst method based on the linguistic preferences of decision-makers about importance of attributes. These preferences are converted into triangular fuzzy numbers to be utilized in the linear programming model. That is, in contrast with the original best-worst method in which the preferences towards the attributes are crisp, fuzzy preferences are considered in the proposed method to reflect the imprecise comments of experts. Second, we propose a novel group decision making approach based on the fuzzy best-worst method to combine the opinion of senior decision-maker and the opinions of the experts. Indeed, our model helps the senior decision-maker to make a significant trade-off between democratic and autocratic decision-making styles. From sensitivity analyses on two numerical examples, we show that, when there is conflict between senior decision-maker and group of decision-makers, the consistency of group decision-making (democracy) will increase as it tends to individual decision-making (autocracy).     

Emphasizing the rank positions in a distance-based aggregation procedure

This paper deals with the problem of aggregating individual preferences in order to obtain a social order. In particular, a preference aggregation procedure is proposed for those cases in which the decision-makers express their preferences by means of a ranking of alternatives. Among the most commonly applied methods for this purpose are those based on distance measures between individual and collective preferences, which look for the solution that minimizes the disagreement across decision-makers. This class of procedures may include weighting factors in order to emphasize the relative importance of the individuals. In the model proposed here, a weighted disagreement function that computes the differences between alternatives differentiating the rank positions of the alternatives is developed. The proposed disagreement function weighs the differences between orders depending on the ordinal position that the alternative occupies.     

基于最大团的条件偏好挖掘

针对在数据库的个性化查询中条件约束（或上下文约束）没有被充分考虑的问题,首先提出了条件约束模型i⁺≻i^-|X,它表示在上下文X的约束下,相对于i^-,用户更偏好i⁺。在此模型的基础上,采用最大团（MaxClique）关联规则算法挖掘获得用户偏好;随后又提出了条件偏好挖掘（CPM）算法,该算法结合上下文用于挖掘偏好规则,从而得出用户的偏好。实验结果表明,基于CPM算法的偏好挖掘模型具有较强的偏好表达能力,将CPM算法与基于Apriori的算法以及CONTENUM算法进行了实验对比,实验的主要参数为最小支持度、最小可信度、数据规模等,实验结果进一步表明所提出的CPM算法可明显提高用户偏好规则的产生效率。     

Participatory decision-support model in the context of building structural design embedding BIM with QFD

The design and optimisation of building structures is a complex undertaking that requires the effective collaboration of various stakeholders and involves technical and non-technical expertise. The paper investigated an integrated decision-support framework using Quality Function Deployment (QFD) in structural design optimisation. The aim of the study was to develop and test a systematic participatory model that utilises Building Information Modelling (BIM)-enabled technologies for data collection and group decision-making theory. The uncertainties associated with the decision-makers’ preferences were computed using Evidential Reasoning (ER) algorithms in the QFD house of quality. An actual decision scenario was used to test the proposed framework and investigate its capabilities in the context of reinforced concrete buildings. The study demonstrated how the proposed QFD model could effectively enhance decision-making by managing the diversity of stakeholders’ preferences via design integration, enhanced communication and shared domain knowledge.     

供应商选择的组合多属性群决策

为解决图书供应商选择问题,建立同时考虑多个属性,由多人进行决策的组合多属性群决策模型。先用TOPSIS法求出单个决策者对各供应商的综合评价值,再用三种不同的社会选择函数分别对个体偏好进行集结,然后将三种结果进行组合,得出决策群体对供应商的最终排序结果。最后以一个实例说明模型的应用。该方法可提高供应商选择的有效性。     

社交网络群决策环境下基于量子干涉的信任测度

针对已有的信任测度模型中信任源出现的冗余以及之间的干扰未被考虑的缺点,提出一种基于干扰效应的社会网络信任测度的决策模型.该模型在计算决策者之间的信任值时,通过简化信任源类型可避免部分信任源的重复出现,同时增加了信任源之间的干扰项.首先,根据社会关系与背景对信任的决定性,将决策者之间信任关系的来源划分为“决策者之间的亲近关系”和“决策者的客观背景”;其次,提出“亲疏度”和“专业度”的概念作为这两个信任源的测度,在保证信任源完整的前提下,克服信任关系冗余的缺陷;然后,进一步设计考虑干扰效应的信任值量化方法,所求的信任值能够综合体现两个信任源的个体决定程度和整体影响程度;最后,将决策者的信任值转化为权重聚集决策者偏好,以解决实际群决策问题.数值和理论结果表明,所提出考虑干扰效应的信任测度方法能够作为提高信任关系的准确性以及解决社交网络群决策问题的工具,具有广泛的应用前景.     

On consensus models with utility preferences and limited budget

This paper mainly focuses on the consensus problem with utility preferences denoted by simple trapezoidal membership function. In group decision-making (GDM), for acquiring the best consensus opinion, not only the total cost required by achieving the consensus, but also the utility of all the decision-makers (DMs) should be considered. Ben-Arieh et al. propose a consensus model from the view of the minimized cost. Based on their models, a kind of optimization consensus model has been put forward under the constraints of limited budget and different kinds of utility, whose objective function aims to obtain the maximum utility level of the whole GDM process. From an economic point of view, results show that different utility preferences of all the individual DMs have impacts on the final optimal consensus opinion. Besides, the moderator has a dominant role in the development trends throughout the whole decision-making process to some extent. Numerical examples are given to deeply explain the proposed models.