犹豫模糊语言群决策的共识性模型

针对犹豫模糊语言群决策问题,研究其共识性调整方法.首先,定义犹豫模糊语言术语集的距离测度;然后,基于该距离测度定义犹豫模糊决策矩阵间的共识性水平及其相关概念,建立共识性调整模型,该模型采用反馈机制,并且尽可能提供给专家较多的信息,以方便专家进行信息修正,达到群体共识;最后,通过具体实例说明了所提出的共识性方法的可行性和实用性.     

基于群体共识算法的深度学习推荐系统评价

针对群决策过程中专家群体共识水平不高的问题,构建一种基于调整成本最小化的群决策算法。该算法在三个层面定义一致性-共识性测度,用于衡量模糊判断矩阵的一致性和共识性水平;建立基于最小调整成本的共识调整反馈机制来识别需要调整的决策者和偏好值;建立以共识调整总成本最小为目标函数的最优化模型计算每个决策者的最优调整参数和共识阈值上界;设计基于调整成本最小化的群决策算法,并验证其收敛性。通过深度学习推荐系统的优选实验表明,构建的群决策算法在成本和效率方面更有效。     

考虑个体累积共识贡献的犹豫模糊语言自适应共识模型

针对犹豫模糊语言信息下的多属性群决策问题,提出一种基于个体累积共识贡献的自适应共识决策模型.首先,利用犹豫模糊语言得分函数,基于经典的信息熵和相对熵理论,综合考虑同一属性下不同方案间的信息差异,以及各方案分别与正理想方案和负理想方案的信息差异,构建确定属性权重的优化模型;然后,提出个体累积共识贡献测度和全局共识测度,利用全局共识度进行共识控制,依据个体累积共识贡献度对专家权重进行自适应修正,构建一种新的犹豫模糊语言自适应共识过程.该过程的特点是对拥有较少合作的非全共识专家执行专家权重惩罚,而且专家权重的更新引起属性权重的自适应更新,反过来又影响个体共识贡献的累积.最后通过一个应急医疗设施选址的共识决策例子表明方法的可行性和有效性.     

基于概率信息不完备的群决策模型

针对犹豫模糊元中元素发生的概率信息不完备的群决策问题,提出一种基于最优化模型和一致性调整算法的群决策模型。该模型首先引入了概率不完备犹豫模糊偏好关系（PIHFPR）、概率不完备犹豫模糊偏好关系的期望一致性以及概率不完备犹豫模糊偏好关系的满意加性期望一致性等概念;其次,以PIHFPR和排序权重向量间的偏差最小化作为目标函数,构建线性最优化模型计算得到PIHFPR中不完备的概率信息;随后,通过提出的加权概率不完备犹豫模糊偏好关系集成算子确定综合的PIHFPR,同时设计一种群体一致性调整算法,不仅使得调整后的PIHFPR具有满意加性期望一致性,还可以计算方案的排序权重。最后,将群决策模型应用于区块链的选择实例中。实验结果表明,决策结果合理可靠,且更能反映实际决策情况。     

专家权威和共识在科研基金立项评估中的应用

针对科研基金项目立项评估问题,给出了一种研究方法。首先,把模糊多属性群决策引入到科研基金项目立项评估中,给出属性和属性权重的模糊语言评价,并把其转化为三角模糊数;然后,给出各个专家相对决策群体判断的共识性分析方法,对不共识情况综合考虑了专家权威性和专家群体意见共识性,给出了集结算法,并在此基础上利用模糊多属性群决策算法对科研基金立项进行排优;最后,利用实例说明了科研基金项目立项的具体评估过程。     

基于共识标准的语言标度颗粒优化模型

研究语言偏好信息下的群决策问题.定义了反映群体共识的两个测度指标,分别反映群体内所有专家的一致性水平及专家的个人观点与群体观点的分歧程度;基于共识测度指标构建一种语言标度的颗粒优化模型,提出了求解语言标度颗粒最佳分界点的改进PSO算法,并给出一种对方案排序进行择优的群决策方法. 最后,通过一个算例说明了所提出方法的可行性和有效性.     

基于群体共识性的高校教师教学质量评估

利用模糊多属性群决策方法对高校教师教学质量进行评优,首先讨论了专家群体评判水平共识性的判断方法;然后对群体不共识情况,给出了两种调整方法,即通过对专家评判水平逆判来加速专家群体达成共识;最后得到专家群体达成共识情况下的高校教师教学质量的优劣顺序。实例说明了高校教师教学质量评估的具体过程。     

研讨厅专家意见聚类分析及其可视化

综合集成研讨厅中群体共识达成依赖于专家之间有指导的互相探询和互相启发的研讨,因此需要一种反馈机制将专家个体意见和群体一致性状态实时展现给与会专家,从而激发专家思维,促进群体思维收敛.本文提出一种用于专家意见一致性分析的启发式聚类算法和基于该算法的群体一致性分析指标体系,并采用平行坐标法对聚类结果进行可视化展现,使专家群体及时了解群体意见聚类情况,以及每类意见受支持的程度.最后用一个实例说明该方法的有效性.     

基于犹豫模糊语言多属性群决策的VIKOR扩展方法

针对犹豫模糊语言多属性群决策问题,提出了一种基于可能度分布的VIKOR方法。该方法首先将基于犹豫模糊语言的评价信息转化成可能度分布值,定义了新的距离公式,避免了传统犹豫模糊语言评价信息在计算过程中造成的信息扭曲。然后,设计了基于最大群体效用与最小个体遗憾两个目标的群体信息集结优化模型,并给出多属性群决策的VIKOR扩展方法。运用一个交通建设方案选择的案例分析验证了方法的有效性和优越性。     

概率对偶犹豫模糊PROMETHEE多属性群决策算法

为解决犹豫模糊环境中由随机性和不确定性对实际决策造成偏差的多属性群决策问题,提出一种基于概率对偶犹豫模糊PROMETHEE的多属性群决策算法。构建各决策专家的概率对偶犹豫模糊信息矩阵;运用最大离差法与熵值法确定各决策专家与各指标属性的客观权重,结合改进的得分函数与偏离函数得到专家的综合决策评价信息矩阵;进而通过概率对偶犹豫模糊集与PROMETHEE结合的决策算法得到最终决策结果。将该算法运用于航空灾难事故应急响应方案评估的算例分析中,通过与TOPSIS、VIKOR及PDHFS决策算法的计算结果进行对比,验证了概率对偶犹豫模糊PROMETHEE多属性群决策算法的有效性与可靠性。     

A consensus model for hesitant fuzzy preference relations and its application in water allocation management

This paper investigates a consensus model for hesitant fuzzy preference relations (HFPRs). First, we present a revised definition of HFPRs, in which the values are not ordered for the hesitant fuzzy element. Second, we propose an additive consistency based estimation measure to normalize the HFPRs, based on which, a consensus model is developed. Here, two feedback mechanisms are proposed, namely, interactive mechanism and automatic mechanism, to obtain a solution with desired consistency and consensus levels. In the interactive mechanism, the experts are suggested to give their new preference values in a specific range. If the experts are unwilling to offer their updated preferences, the automatic mechanism could be adopted to carry out the consensus process. Induced ordered weighted averaging (IOWA) operator is used to aggregate the individual HFPRs into a collective one. A score HFPR is proposed for collective HFPR, and then the quantifier-guided dominance degrees of alternatives by using an OWA operator are obtained to rank the alternatives. Finally, both a case of study for water allocation management in Jiangxi Province of China and a comparison with the existing approaches are carried out to show the advantages of the proposed method.     

A dynamically weight adjustment in the consensus reaching process for group decision-making with hesitant fuzzy preference relations

The consensus reaching process is a dynamic and iterative process for improving group's consensus level before making a final decision in group decision-making (GDM). As the experts will express their opinions under their own intellectual level from different aspects, it is natural that the experts’ weights should reflect their judgment information. This paper proposes a dynamic way to adjust weights of decision-makers (DMs) automatically when they are asked to give original judgment information for GDM problems, in which the DMs express their judgment information by hesitant fuzzy preference relations (HFPRs). Two indices, an individual consensus index of hesitant fuzzy preference relation (ICIHFPR) and a group consensus index of hesitant fuzzy preference relation (GCIHFPR), are introduced. Normalisation of HFPRs with different numbers of possible values is taken into consideration for better computation and comparison. An iterative consensus reaching algorithm is presented with DMs’ weighting vector changing in each consensus reaching process and the process terminates until both the ICIHFPR and GCIHFPR are controlled within predefined thresholds. Finally, an example is illustrated and comparative analyses demonstrate the effectiveness of the proposed methods.     

Error Analysis Methods for Group Decision Making Based on Hesitant Fuzzy Preference Relation

Hesitant fuzzy preference relation (HFPR) is an effective way to depict the decision makers’ preferences over the objects (alternatives or attributes) in the process of group decision making. Each component of the HFPR is characterized by several possible values and can express the decision makers’ hesitant information comprehensively. To make a decision with the HFPR, it is very necessary to find a proper technique for deriving the priority weights from the HFPR. In this paper, we use the error analysis as a tool to develop several straightforward methods for the priorities of the HFPR. We first define the expected value and the average value of each hesitant fuzzy element in the HFPR. Then based on the error analysis, we come up with the interval midpoint method, the average value method, and the difference method to derive the priority weights from the HFPR. After that, we discuss the relations among these methods, and utilize them and the possibility degree formula to develop an approach to decision making with the HFPR. Finally, we demonstrate the effectiveness and practicality of our approach through a case study concerning the investment problem in liquor enterprise.     

Linear optimization modeling of consistency issues in group decision making based on fuzzy preference relations

The consistency measure is a vital basis for group decision making (GDM) based on fuzzy preference relations, and includes two subproblems: individual consistency and consensus consistency. This paper proposes linear optimization models for solving some issues on consistency of fuzzy preference relations, such as individual consistency construction, consensus model and management of incomplete fuzzy preference relations. Our proposal optimally preserves original preference information in constructing individual consistency and reaching consensus (in Manhattan distance sense), and maximizes the consistency level of fuzzy preference relations in calculating the missing values of incomplete fuzzy preference relations. Linear optimization models can be solved in very little computational time using readily available softwares. Therefore, the results in this paper are also of simplicity and convenience for the application of consistent fuzzy preference relations in GDM problems.     

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus-based perspective

Hesitant fuzzy linguistic preference relations (HFLPRs) can efficiently denote the hesitant qualitative judgments of decision makers. Consistency and consensus are two critical topics in group decision making (GDM) with preference relations. This paper uses the additively consistent concept for linguistic fuzzy preference relations (LFPRs) to give an additive consistency definition for HFLPRs. To judge the additive consistency of HFLPRs, 0-1 mixed programming models (0-1-MPMs) are constructed. Meanwhile, additive-consistency-based 0-1-MPMs to ascertain missing values in incomplete HFLPRs are established. Following the consistent probability of LFPRs, an algorithm to calculate the linguistic priority weighting vector is presented. In consideration of the consensus of GDM, a consistency-probability-distance-measure-based consensus index is defined, and an interactive improving consensus method is provided. Finally, a method for GDM with HFLPRs is offered that can address incomplete and inconsistent cases. Meanwhile, numerical examples are offered, and comparative analysis is made.     

Adaptive consensus model with multiplicative linguistic preferences based on fuzzy information granulation

An adaptive consensus model based on fuzzy information granulation (fuzzy IG) is presented for group consensus decision-making problems with multiplicative linguistic preference relations (MLPRs). Firstly, a granular representation of linguistic terms is concerned with the triangular fuzzy formation of a family of information granules over given Analytical Hierarchy Process (AHP) numerical scales. On this basis, the individual consistency and group consensus measure indices using fuzzy granulation technique are constructed, respectively. Then, the optimal cut-off points of fuzzy information granules are obtained by establishing a multi-objective optimization model together with a multi-objective particle swarm optimization (MOPSO) algorithm. A novel group consensus decision-making approach where consensus reaching process (CRP) is achieved by adaptively adjusting individual preferences through the optimization of the cut-off points is proposed. After conflict elimination, the obtained group preference gives the ranking of the alternatives. Finally, a real emergency decision-making case for liquid ammonia leak is given to illustrate the application steps of the proposed method and comparative analysis with the existing GDM methods. Comparative results demonstrate that the proposed method has some advantages in aspects of avoiding information loss or distortion and improving consensus performance.     

基于犹豫模糊决策算法的云制造系统选择研究

云制造系统的选择评价是云制造管理中的重要研究课题。针对复杂的云制造系统评价问题,构建了基于信息集成算子和一致性提升算法的犹豫模糊互补判断矩阵（HFCJM）决策模型。由于现有的犹豫模糊加权平均算子存在不能满足幂等性的不足,提出了一种改进的犹豫模糊加权平均（I-HFWA）算子,并验证其满足幂等性这一重要性质;研究了完全乘性一致HFCJM的构造方法,并引入一致性指数概念来衡量HFCJM的一致性水平;建立了基于犹豫模糊决策算法的云制造系统选择模型,并分析了决策模型在每次迭代过程中的信息损失量、一致性水平提高程度、计算复杂度以及模型的收敛性。采用案例进行验证分析。实验结果表明,相对于对比模型,犹豫模糊决策算法在可靠性和有效性上更优。     

Clustering-based method for large group decision making with hesitant fuzzy linguistic information: Integrating correlation and consensus

Aiming at the large-scale experts and the lower consensus in large group decision making, a novel clustering-based method integrating correlation and consensus of hesitant fuzzy linguistic information is proposed. Firstly, develop a new hesitant degree function for hesitant fuzzy linguistic element considering its scale. Secondly, put forward the correlation measure and consensus measure models combining the hesitant degree. And then present a clustering method integrating the correlation and consensus to divide the large-scale experts into several clusters. The clustering method simultaneously ensures the cohesion of clusters and the gradual increasing of the collective consensus level. After clustering, activate the selection process to update the weights of clusters combining the number of experts in clusters and the consensus level of clusters and use the score function considering the hesitant degree to rank the alternatives. Finally, a case and some comparisons are studied and analyzed to verify the rationality and effectiveness of the method.