语言分布评估信息下的群决策方法及其群体一致性分析

本文提出了基于语言分布评估加权平均(DAWA)算子的多属性群决策方法;定义了个体决策者评价结果与决策群体评价结果的次序一致性和数值一致性测度,以此分析决策群体评价结果的可靠性;最后,通过具体实例验证了群决策方法的有效性和实用性,分析了个体决策者评价结果与决策群体评价结果的次序一致性和数值一致性。     

群决策中成员意见的分歧特征及判断准则研究

群决策过程中各成员的评价在决策初始存在分歧,对这些分歧进行一致性分析是群决策过程的一个关键步骤,不剔除与群体意见偏离较远的决策者的评价信息就进行群体意见集结,可能会得出不符合客观实际的结论;讨论了群决策中成员意见的分歧特征,提出了成员评价的集中性和相似性概念;定义了群决策过程中判断个人意见是否与群体意见一致的判断准则,即集中性指标和相似性指标.     

解决一类群决策问题的改进德尔菲法

专家综合评价属于委员会形式的群决策问题,提出了基于一致性分析的群决策机制和基于OWA算子的群决策机制,用来弥补常用机制的缺陷;介绍了判断群体意见分歧特征和定量判断标准;提出了基于一致性分析的改进德尔菲法,介绍了这种方法的思想、特点和应用步骤;分析了这类群决策的人员组成和工作程序图.     

基于隶属度分布函数的多粒度语言群决策一致性测度

研究多粒度语言群决策中群体意见一致性问题.根据群体决策中多粒度语言的基本特征,提出一种基于隶属度分布函数的一致性测度计算方法.并且与基于二元语义的决策的一致性测度计算方法进行了比较,通过算例计算分析,说明了该方法的可行性和有效性.     

基于投影的单值中智集MAGDM一致性合成方法

针对投影测度下的属性值和属性权重均为单值中智集的多属性群决策问题,提出了一种基于投影的群决策一致性合成方法。该方法以群体评价均值矩阵和群体评价正、负理想值矩阵为群体评价的参考基准,根据投影测度,构建了衡量决策者个体评价与群体评价一致性程度的投影贴近度公式;进而以决策者的相对一致性程度和决策者重要性合成得到策者权重,并构造加权规范化的群体最终决策矩阵;然后以单值中智集得分函数求解各方案的最终得分并排序;并给出详细的决策步骤,最后通过算例同其他方法进行了对比分析,表明本文方法的可行、有效。     

层次分析法中基于SVDD群体信息提取方法

针对层次分析法中群决策问题提出一种基于支持向量域描述(SVDD)的集结方法.首先利用生成树的方法把判断矩阵进行一致性剖分;然后利用支持向量域描述的方法排除干扰信息,找出群体公共信息,引入群体相容性,最优解等概念,提出并证明了关于群体信息球的特性;根据最大特征值法把群体信息球中的向量合成为群决策最优解,即关于方案的排序向量.并通过一个具体示例给出该方法的算法步骤同时显示了该方法的可行性和有效性.     

基于不同粒度语言判断矩阵的群体一致性分析方法

针对群决策中基于不同粒度语言判断矩阵形式偏好信息的群体一致性问题,提出了一种分析方法。首先,给出有关不同粒度语言判断矩阵和二元语义等若干定义,通过转换函数将不同粒度语言判断矩阵一致化为由二元语义表示的判断矩阵;然后,通过定义专家与群偏好的偏差矩阵以及各专家的总体偏差指标,给出了专家群体一致性的判别方法及专家群体判断不一致的调整方法;最后,通过一个算例说明了该方法的有效性。     

互反判断矩阵一致性指标研究

首先给出了互反判断矩阵与一致性互反判断矩阵集之间距离的定义,基于此定义,提出了一个新的互反判断矩阵一致性指标,并给出了此一致性指标的度量方法。对于不满足此一致性指标的互反判断矩阵,提出了一个迭代算法来提高其一致性程度。得出了群体互反判断矩阵一致性指标的下界,为提出的一致性指标应用于群决策问题提供了理论基础。最后用数值例子说明了该迭代算法的可行性和有效性以及群决策中的相关结论。     

改进的多属性群决策共识方法及其在图书供应商选择中的应用

针对多属性群决策问题,提出了一种改进的加型集结共识方法.在每轮决策中,共识一致性指标较小的决策个体都修改其偏差较大的偏好,能快速使参与决策的个体对各属性形成满意的一致性意见.数值实验也表明该共识方法是合理有效的.     

一种基于AC-IOWA算子的方案优选群决策方法

针对方案优选群决策中专家给出的关于方案两两比较的评价矩阵,给出了一种基于AC-IOWA算子的群决策方法.在该方法中,首先计算评估专家评价矩阵的一致性水平,并以此作为AC-IOWA算子的诱导分量完成对专家评价信息的集结,得到评估专家的群体综合评价矩阵,在此基础上采用基于量化优势度的方法对方案进行优选.最后给出了一个算例验证了该方法的有效性和可行性.     

群决策中基于判断相似度的专家聚类及群体意见集结方法

针对大规模群决策问题,提出了一种基于专家意见相似度的群体判断信息逐步集结规划的方法。首先利用备选方案序关系向量的灰色关联度和夹角余弦构造两两专家判断信息的组合相似度;其次以判断相似度为标准,采用一种广度邻居搜索算法对专家进行聚类;然后以判断偏差最小为目标,构造非线性的约束规划模型对每一类专家意见进行集结,从而获得类内专家的集结信息;最后从专家数量最多的类别开始,依次对每类专家集结后的判断信息进行再次集结,从而获得最终的评判结果。该方法将大规模的复杂群决 策转化为低复杂度的多阶段专家信息集结问题,并保证了群体结果的一致性。算例分析验证了方法的可行性和有效性。     

社会网络环境下保护少数意见的风险性大群体应急决策方法

大群体决策过程中,少数意见十分重要,但考虑少数意见又有一定风险性,本文提出了一种社会网络环境下保护少数意见的风险性大群体应急决策方法。首先,利用自然语言处理技术对社交媒体中公众对突发事件的传播信息进行关键词提取,再采用TF-IDF技术提取公众关心的事件属性并确定属性权重;其次,将专家之间的联系考虑到决策当中,构建决策专家之间的社会网络关系;然后,建立少数意见的识别和风险测度机制,并定义了两个风险系数:信任风险系数和偏好风险系数,通过少数意见聚集的信任风险系数调节少数意见聚集的权重,通过偏好风险系数调节群体共识,以得到共识水平较高的大群体决策方案;最后,通过“7.1”宁乡洪灾案例分析,验证了所提方法的可行性和有效性。     

基于决策者风险偏好大数据分析的大群体应急决策方法

针对重大突发事件应急决策大群体成员的风险偏好复杂难测问题,提出了一种新的基于决策者风险偏好大数据分析的大群体应急决策方法。首先专家群体对突发事件进行快速响应,生成若干应急预案及其风险属性信息;其次,社会公众通过网络等渠道参与到应急决策中来并形成决策大群体,给出不同预案的偏好值;然后,利用证据推理算法得出公众对各预案的风险效用值,将预案风险效用值与预案偏好值加权组合,得到各个预案的大群体决策者的风险偏好值;最后,基于风险偏好值,利用大数据分析技术对大群体的风险偏好进行聚类识别,从中筛选出风险中立者组成新的应急决策群体,再次聚类得出应急决策群体的成员组成结构,以此为基础计算决策者权重和应急预案的最终效用值,得应急预案排序结果。最后通过算例分析验证了方法的有效性和可行性。     

决策偏好不确定条件下群体决策收敛一致性研究

决策个体之间交互影响是达成群体一致意见的重要因素。本文引入决策个体决策偏好的不确定性以及个体间不对称的影响权重,以此为基础建立群体决策动态观点演化模型,并进行matlab仿真。文中取决策个体的偏好不确定性为常数,作为决策个体对自身观点确定性的大致考量,并把群体中所有决策者的决策偏好不确定性分为相同和不相同两种情况来讨论。仿真实例证明:决策个体观点的不确定性是最终影响群体决策一致性的重要因素,当决策个体偏好不确定性较大时,决策群体更容易得到一致意见;当偏好不确定性较小时,决策群体难以达成一致意见,最终形成几个观点簇;在初始观点分布均匀的情况下,群体观点最终会演化出怎样的观点形态,是由群体中观点不确定性较小的群体所持有的观点决定的。研究内容有助于理解群体决策中一致观点的演化规律,能够为群体决策的引导策略提供参考和借鉴。     

A multiattribute GDSS for aiding problem-solving

This study proposes a group decision support system (GDSS) with multiattribute to help solve problems in the real world. The problems are usually characterized as a multiattribute decision making (MADM) for selections, and shall be the responsibility of an expert group. On a regular basis, experts within that group will meet and conduct discussions on the web. After each individual make efforts of judgments, comparisons, and rankings, they shall determine, collectively as a group, the final rankings of all possible alternatives. Furthermore, aimed at insuring the decision quality of the collective decisions, an integrated procedure will be applied to make any modifications as necessary. Based on the geometric aspects of decision quality, the disparity of each individual member's preferences on attribute can be filtered out by the suggested bounded indicators. And then the outliers related to attributes' weights will be identified through a different set of consensus indicators, thus, further improving the decision quality while maintaining a quantitative level of consensus. Finally, using a car-selection problem herein, the proposed integrated procedure is implemented on a network-based PC system with web interfaces.     

A consistency and consensus-based method to group decision making with interval linguistic preference relations

Preference relations are a powerful tool to address decision-making problems. In some situations, because of the complexity of decision-making problems and the inherent uncertainty, the decision makers cannot express their preferences by using numerical values. Interval linguistic preference relations, which are more reliable and informative for the decision-makers’ preferences, are a good choice to cope with this issue. Just as with the other types of preference relations, the consistency and consensus analysis is very importance to ensure the reasonable ranking order by using interval linguistic preference relations. Considering this situation, this paper introduces a consistency concept for interval linguistic preference relations. To measure the consistency of interval linguistic preference relations, a consistency measure is defined. Then, a consistency-based programming model is built, by which the consistent linguistic preference relations with respect to each object can be obtained. To cope with the inconsistency case, two models for deriving the adjusted consistent linguistic preference relations are constructed. Then, a consistency-based programming model to estimate the missing values is built. After that, we present a group consensus index and present some of its desirable properties. Furthermore, a group consensus-based model to determine the weights of the decision makers with respect to each object is established. Finally, an approach to group decision making with interval linguistic preference relations is developed, which is based on the consistency and consensus analysis. Meanwhile, the associated numerical examples are offered to illustrate the application of the procedure.     

Monte Carlo simulation techniques for group decision making with incomplete information

In this paper we deal with group decision-making problems where several decision makers elicit their own preferences separately. The decision makers’ preferences are quantified using a decision support system, which admits incomplete information concerning the decision makers’ responses to the questions they are asked. Consequently, each decision maker proposes classes of utility functions and attribute weight intervals for the different attributes. We introduce an approach based on Monte Carlo simulation techniques for aggregating decision maker preferences that could be the starting point for a negotiation process, if necessary. The negotiation process would basically involve the decision maker tightening the imprecise component utilities and weights to output more meaningful results and achieve a consensus alternative. We focus on how attribute weights and the component utilities associated with a consequence are randomly generated in the aggregation process taking into account the decision-makers’ preferences, i.e., their respective attribute weight intervals and classes of utility functions. Finally, an application to the evaluation of intervention strategies for restoring a radionuclide contaminated lake illustrates the usefulness and flexibility of this iterative process.     

Inferring consensus weights from pairwise comparison matrices without suitable properties

Pairwise comparison is a popular method for establishing the relative importance of n objects. Its main purpose is to get a set of weights (priority vector) associated with the objects. When the information gathered from the decision maker does not verify some rational properties, it is not easy to search the priority vector. Goal programming is a flexible tool for addressing this type of problem. In this paper, we focus on a group decision-making scenario. Thus, we analyze different methodologies for getting a collective priority vector. The first method is to aggregate general pairwise comparison matrices (i.e., matrices without suitable properties) and then get the priority vector from the consensus matrix. The second method proposes to get the collective priority vector by formulating an optimization problem without determining the consensus pairwise comparison matrix beforehand.     

学术论文评审的同行评议专家共识问题研究

根据多属性群决策原则,本文提出了一种提高学术论文同行评议的共识度方法——同行专家相互评判的评审方法。即在每位同行评议专家单独评审的基础上,由编辑部将每位同行评议的评审意见和结果反馈到其他同行评议专家的手中,并进行相互逆判,以此得到同行评议专家的群体认可度(据此,可以计算专家权重)。本文将同行评议专家的评判偏好以区间数形式表示,并将其映射成二维坐标平面上的点集,通过引入Weber点对同行评议专家的评判偏好点进行加权集结。本文采用模拟植物生长算法(PGSA),求取集结点,并将其作为评判的依据。最后,本文通过学术论文评审的实例分析以及与其他方法精度比较,说明了该方法的有效性和可行性。