犹豫模糊信息相似性测度与群一致性测度及群决策应用

针对目前关于犹豫模糊运算与测度的研究中存在的不足,首先给出犹豫模糊熵函数的定义,并将其作为犹豫模糊信息不确定性测度,进而提出犹豫模糊信息特征向量概念,以信息特征向量为出发点对犹豫模糊距离测度和相似性测度展开研究;为优化群决策过程,提出基于完全优先关系的群一致性测度概念并研究其性质;最后,提出基于相似性测度和群一致性测度的群决策方法并结合算例验证所提出方法的有效性.     

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.     

基于调和犹豫模糊信息的多属性决策方法

实践中发现,犹豫模糊信息和概率犹豫模糊信息在计算过程中存在着计算繁琐、与数量运算规则不相容等问题.对此,提出一套基于调和犹豫模糊元的解决方法.通过定义调和犹豫模糊元为一组概率分布相同的概率犹豫模糊元,在犹豫模糊信息和概率犹豫模糊信息之间架起一座桥梁,将它们纳入统一处理框架.在此基础上,定义调和犹豫模糊信息的基本运算规则、信息集成算子、距离测度和混合熵测度,构建基于调和犹豫模糊信息的多属性决策方法,并将其应用于陆军合成旅指挥控制能力评估.数值实验表明:调和犹豫模糊决策理论克服了已有理论的缺陷,具有计算量可控、易于编程实现、与数量运算规则相容等优势.     

基于新型距离测度的概率犹豫模糊多属性群决策方法

研究属性权重已知、专家权重未知条件下的概率犹豫模糊多属性群决策问题.首先,针对传统概率犹豫模糊距离测度的不足,提出改进的新型距离测度,并对其有效性和合理性进行数学证明;其次,在属性权重向量已知的前提下,通过加权算术平均的方式实现单个专家视角下的概率犹豫模糊信息初次集结,并基于分差最大化准则构建专家权重向量求解模型,采用...     

直觉对偶犹豫模糊集在多属性群决策中的应用

基于直觉对偶犹豫模糊集的定义,结合标准距离测度公式,给出了直觉对偶犹豫模糊集的Hamming距离测度公式、Euclidean距离测度公式等。给出了用以度量两个对偶模糊信息之间相关关系的相关系数、加权相关系数的公式及其相关性质。给出了用以度量直觉对偶犹豫模糊集模糊性的熵的定义,并给出了熵的计算公式。基于直觉对偶犹豫模糊集的距离测度、相关系数、熵给出了一种新的直觉对偶犹豫模糊集的多属性群决策方法,并通过实例说明了该方法的有效性。     

基于冲突分析的犹豫模糊多属性决策

在基于犹豫模糊距离的多属性决策问题中,定义距离测度和计算属性权重是两个关键点.在定义犹豫模糊数的距离测度时,针对添加相同元素,补齐较短犹豫模糊数,改变原有含义的问题,文中提出同时扩展两个犹豫模糊数使其达到相同长度的方法.再根据Pawlak的冲突分析理论,建立犹豫模糊信息系统的冲突分析模型,提出基于冲突度的计算属性权重的方法.最后给出求解犹豫模糊信息系统下多属性决策问题的具体方法.企业发展规划的实例说明文中方法的有效性和可行性.     

区间犹豫模糊熵应用于地方高等教育发展研究

构造了新的区间犹豫模糊熵、交叉熵公式,提出了一种新的区间犹豫模糊多属性群决策方法,并将其应用于地方高等教育发展研究的过程中。构建了一种新的区间犹豫模糊熵公式,并证明其满足区间犹豫模糊熵的公理化条件;给出了区间犹豫模糊距离测度的公理性定义,研究了区间犹豫模糊距离测度和区间犹豫模糊熵、交叉熵的关系,并构建了区间犹豫模糊加权交叉熵公式。在区间犹豫模糊环境下,基于区间犹豫模糊熵、交叉熵以及交叉熵贴近度,提出了一种新的属性权重未知的多属性决策方法,并将其应用于对地方高等教育发展研究的过程中,验证该方法的可行性和有效性。     

基于勾股模糊语言幂加权平均算子的多属性群体决策方法

针对多属性群决策问题,采用能够方便专家参考语言集信息进行评价并且取值灵活的勾股模糊语言集进行了处理。首先,基于语言集和勾股模糊集的距离测度给出了勾股模糊语言数距离测度的定义与相关性质;然后,以勾股模糊语言数的距离测度作为幂均（PA）算子的距离度量,提出了勾股模糊语言幂加权平均（PFLPWA）算子用以对群决策过程中不同专家评价矩阵进行融合,并同时在融合过程中考虑专家评价的差异性;最后,基于PFLPWA算子构建了勾股模糊语言环境下的群体决策新方法,并通过案例分析检验了PFLPWA算子应用于群决策中的有效性和适用性。     

基于新型符号距离的犹豫模糊多属性决策方法

研究属性权重完全未知的犹豫模糊决策问题.针对犹豫模糊元中人为添加元素导致的主观性过强问题,提出一种基于新型符号距离的犹豫模糊决策方法.首先,根据犹豫模糊集元素之间的方差以及元素个数定义一种含有对数函数的新型犹豫度,并基于新型犹豫度定义新型犹豫模糊符号距离;然后,基于新型符号距离给出一种属性权重完全未知的数学规划模型得出属性权重,并利用加权符号距离对方案进行排序;最后,通过数值案例表明所提出方法区分度明显并且合理、有效.     

指数型犹豫模糊熵在多属性决策中的应用

提出一种指数型犹豫模糊熵,并基于熵权法给出犹豫模糊多属性决策模型.首先,给出犹豫模糊元熵的公理化定义,构造犹豫模糊元的指数型犹豫模糊熵测度公式,并证明指数型犹豫模糊熵测度公式满足犹豫模糊元熵的公理化定义基本准则.在此基础上,引入犹豫模糊集的熵定义和熵测度公式,并证明犹豫模糊集的指数型犹豫模糊熵测度公式同样满足犹豫模糊集...     

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。     

Consensus-reaching methods for hesitant fuzzy multiple criteria group decision making with hesitant fuzzy decision making matrices

Group decision making plays an important role in various fields of management decision and economics. In this paper, we develop two methods for hesitant fuzzy multiple criteria group decision making with group consensus in which all the experts use hesitant fuzzy decision matrices (HFDMs) to express their preferences. The aim of this paper is to present two novel consensus models applied in different group decision making situations, which are composed of consensus checking processes, consensus-reaching processes, and selection processes. All the experts make their own judgments on each alternative over multiple criteria by hesitant fuzzy sets, and then the aggregation of each hesitant fuzzy set under each criterion is calculated by the aggregation operators. Furthermore, we can calculate the distance between any two aggregations of hesitant fuzzy sets, based on which the deviation between any two experts is yielded. After introducing the consensus measure, we develop two kinds of consensus-reaching procedures and then propose two step-by-step algorithms for hesitant fuzzy multiple criteria group decision making. A numerical example concerning the selection of selling ways about ‘Trade-Ins’ for Apple Inc. is provided to illustrate and verify the developed approaches. In this example, the methods which aim to reach a high consensus of all the experts before the selection process can avoid some experts’ preference values being too high or too low. After modifying the previous preference information by using our consensus measures, the result of the selection process is much more reasonable.     

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.     

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.     

Signed distance‐based ORESTE for multicriteria group decision‐making with multigranular unbalanced hesitant fuzzy linguistic information

The objective of this study is to develop an integrated approach for solving multicriteria group decision‐making problems with multigranular unbalanced hesitant fuzzy linguistic term sets (HFLTSs). Firstly, a signed distance‐based transformation function is proposed to unify multigranular unbalanced hesitant fuzzy linguistic (HFL) assessments. Secondly, a mathematical programming model based on the maximum consensus is constructed to allocate decision‐makers (DMs)' weights objectively. Thirdly, a new signed distance‐based preference score function is defined to aggregate HFL assessments and determine the weak ranking of alternatives, and a novel preference, indifference, and incomparability test framework is constructed to identify the subtle relations among alternatives. On these bases, a signed distance‐based ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) method, in which knowledge regarding criterion values and weights are expressed as multigranular unbalanced HFLTSs, is developed to obtain the ranking of alternatives. Finally, an illustrative example, followed by sensitivity and comparative analyses, is presented to verify the feasibility and effectiveness of the proposed approach.     

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.

     

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.     

A Multigranularity Linguistic Group Decision‐Making Method Based on Hesitant 2‐Tuple Sets

Hesitant fuzzy linguistic term set (HFLTS) is a very useful technology in dealing with decision‐making problems where people have hesitancy in providing their linguistic assessments. Distinct methods have been developed to aid decision making with HFLTSs, yet there is little research involving the issue that how to deal with the multigranularity hesitant fuzzy linguistic information. The aim of this paper is to develop the aggregation method for multigranularity hesitant fuzzy linguistic information and solve the linguistic group decision problem with different linguistic term sets. To do so, we first modify the translation functions and aggregation operators in the existing 2‐tuple linguistic representation models so as to aggregate linguistic terms from different linguistic term sets. Then, we introduce the notion of hesitant 2‐tuple sets to make computation of HFLTSs without loss of information, and develop some new operators to aggregate HFLTSs from different linguistic term sets. Using these operators, we propose a method to deal with multigranularity linguistic group decision‐making problems with different situations where importance weights of either criteria or experts are known or unknown. Finally, the multigranularity linguistic group decision‐making model is implemented to the healthcare waste treatment in West China Hospital to validate its effectiveness and efficiency in aiding decision‐making process.