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.     

改进的犹豫模糊语言熵的一般公式与生成算法

由于现有的扩展犹豫模糊语言术语集的熵无法区分与补集相等的扩展犹豫模糊语言术语集的不确定性,并且对犹豫模糊语言信息中的犹豫性考虑得相对较少,无法全面地反映犹豫模糊语言信息的不确定性。改进了扩展犹豫模糊语言术语集的熵的定义,从模糊性和犹豫性两方面刻画了扩展犹豫模糊语言术语集的不确定性,分别定义了扩展犹豫模糊语言术语集的模糊熵和犹豫熵,给出了模糊熵和犹豫熵的一般公式与生成算法。定义了一个扩展犹豫模糊语言术语集的总熵,给出了总熵的一般公式。最后给出了一个基于犹豫模糊语言熵的高校教授晋升优选评估的多属性决策算例,通过比较分析说明了决策方法的可行性与有效性。     

Pythagorean犹豫模糊交叉熵的多属性决策方法

针对决策信息为Pythagorean犹豫模糊数的多属性群决策问题,提出一种基于Pythagorean犹豫模糊交叉熵的多属性群决策方法。引入Pythagorean犹豫模糊交叉熵的概念。以Pythagorean犹豫模糊交叉熵作为决策信息差异程度的度量,提出专家权重和属性权重的确定模型。提出一种基于Pythagorean犹豫模糊熵的TOPSIS方法,并通过光伏电站选址案例说明了该方法的可行性和有效性。     

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。     

犹豫模糊语言PROMETHEE方法在川酒品牌评价中的应用

基于犹豫模糊语言集理论,提出一种犹豫模糊语言信息环境下的PROMETHEE多属性决策方法,并应用于川酒品牌评价决策问题中.研究表明,犹豫模糊语言集能够很好地描述和处理复杂定性信息环境下的川酒品牌评价与决策问题;所提出的犹豫模糊语言PROMETHEE算法简便, 且改进的偏好函数允许决策者根据其对方案的严格优于偏好对参数进行选择,可保证决策过程的科学性和决策结果的准确性.     

犹豫语言信息集成算法及其数据库选择应用

针对犹豫模糊语言环境下的多属性群决策问题,建立了一种基于犹豫语言几何Bonferroni平均（HLGBM）算子的多属性群决策模型,该模型不仅充分考虑了每种属性的重要性,而且能够有效捕获属性间的内在联系。首先利用基于Archimedean T-范数和S-范数的犹豫语言运算法则,提出了一种新的HLGBM算子,并研究该算子的四种基本性质;其次,探讨了HLGBM算子的几类特殊形式,并提出了犹豫语言加权几何Bonferroni加权（HLWGBM）算子;最后基于HLWGBM算子构建了一种新的犹豫语言多属性群决策模型,并通过数据库选择实例验证决策模型是可行和有效的。     

参数化犹豫模糊熵及其应用

犹豫模糊熵是刻画犹豫模糊集不确定程度的重要工具。针对现有犹豫模糊熵的一些不足,首先基于犹豫模糊集提出犹豫模糊熵的公理化定义,并构造出参数化犹豫模糊熵;其次,通过一些具体数值算例,将新提出的参数化犹豫模糊熵与现有犹豫模糊熵进行对比分析,结果显示所研究的熵能够更加灵活有效地描述信息的未知程度;然后,探究了参数化犹豫模糊熵在多属性决策问题中的应用,使用该熵来确定属性的权重,并借助逼近于理想解排序法（TOPSIS）以及分数函数,提出了一种解决最优方案选取问题的方法;最后,通过具体实例,验证了参数化犹豫模糊熵与所给决策方法具有一定的实用性和可行性。     

面向犹豫模糊语言信息的大型群体分类集结模型

研究基于犹豫模糊语言信息的大型群体决策分类和集结问题.提出一种面向犹豫模糊语言信息的专家相似度构建方法,相似度测算基于犹豫相似度和距离相似度综合考虑;改进编网分类方法,借助基于相似矩阵的编网分类方法对大型群体专家进行初步分类,确定可接受范围,对专家进行再分类并通过分类精度指标对分类的有效性进行验证以确定最终类别;构建大规模群体信息集结的类内集结和类间集结框架,对类内专家信息进行集结以获得概率语言信息;提出一种基于语言概率分布的类可靠度计算方法,基于类可靠度和类专家数量占总体数量比例综合考虑确定类别权重以实现类间信息集结,进而根据集结的概率语言信息计算对象期望值并进行排序.最后,通过算例及方法比较验证所提出方法的有效性.     

双论域上的犹豫模糊语言多粒度粗糙集及其应用

犹豫模糊语言术语集结合了模糊语言方法与犹豫模糊集的优势, 常应用于定性环境下的群决策中. 基于犹豫模糊语言关系, 提出双论域上的犹豫模糊语言多粒度粗糙集. 在该粗糙集中, 定义了双论域上的乐观和悲观犹豫模糊语言多粒度粗糙集, 并讨论了其相关性质. 在此基础上提出以人岗匹配为背景的决策模型, 并用算例阐述了所提出模型的有效性. 结果表明, 该模型不仅可以处理定性环境下的语言信息, 而且可以结合不同专家的意见给出最终决策结果, 为人岗匹配提供一种新思路.     

Expertise-based consensus building for MCGDM with hesitant fuzzy linguistic information

The integration of a consensus reaching process (CRP) becomes paramount to make highly accepted group decisions in complex real-life multi-criteria group decision making (MCGDM) problems. Notwithstanding, existing CRPs for MCGDM do neither exhaustively analyse the diversity in decision makers’ expertise levels, nor they consider that (because of such diversity) individuals might exhibit distinct perceptions on the relative importance of evaluation criteria. In this study, we present a novel expertise-based consensus building model for MCGDM under a hesitant fuzzy linguistic setting. Firstly, an expertise identification approach is devised to objectively determine the expertise degree of each decision maker based on multiple features. The proposed approach allows to dynamically assigning importance weights to the decision makers’ opinions based on their expertise, as well as intelligently combining their individually elicited subjective and objective criteria weights into meaningful expertise-dependent combinative weights. Then, a CRP for MCGDM problems is introduced based on an improved consensus measurement process and an expertise-based feedback mechanism that provides a highly tailored, personalised means of direction rules to guide decision makers during the consensus building process. A numerical example is provided to illustrate the application of the CRP, and a detailed comparison analysis is presented to verify the validity and accuracy of this study’s proposal.     

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.     

Some interesting properties of the fuzzy linguistic model based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information

The management of hesitant fuzzy information is a topic of special interest in fuzzy decision making. In this paper, we focus on the use and properties of the fuzzy linguistic modelling based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information. Among these properties, we can highlight the existence of aggregation functions with no need of transformations or the possibility of a greater flexibilization of the opinions of the experts, even using different linguistic chains (multigranularity). Furthermore, based on these properties we perform a comparison between this model and the one based on hesitant fuzzy linguistic term sets, showing the advantages of the former with respect to the latter. Finally, a fuzzy decision making model based on discrete fuzzy numbers is proposed.     

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.     

Interval-valued dual hesitant fuzzy linguistic group recommendation method by considering the double relevance

Applied Intelligence - Traditional group recommendation method cannot reflect the interactive relationships among group users or items attribute. We propose a new method,and the new method can...     

Double-hierarchy hesitant fuzzy linguistic information-based framework for green supplier selection with partial weight information

Neural Computing and Applications - Green supplier selection (GSS) is a crucial issue in green supply chain management. CAPS indicate that industries spend yearly USD 25 million per procurement,...     

Extension of the VIKOR method for group decision making with extended hesitant fuzzy linguistic information

Group decision-making approaches are very important due to the complexity and uncertainty of many real-world decision-making problems. Some of the decision-making problems are defined in qualitative frameworks. Extended hesitant fuzzy linguistic term set (EHFLTS) is proposed as a new and powerful tool for elicitation of hesitant qualitative information in group decision-making process. In this paper, we first introduced the comparison laws and a family of distance and similarity measures for extended hesitant fuzzy linguistic terms (EHFLTs) and EHFLTSs, respectively. Next, we developed the extended hesitant fuzzy linguistic (EHFL)-VIKOR method as a qualitative multi-attributes group decision-making approach based on the EHFLTS distance measures to deal with the qualitative hesitancy in group decision making. Finally, we presented an application example about selection of suitable telecommunications service provider of small- and medium-sized enterprises to verify applicability and validation of proposed method in the process of qualitative group decision making.