Fuzzy interval linguistic sets with applications in multi-attribute group decision making

Uncertain and hesitant information, widely existing in the real-world qualitative decision making problems, brings great challenges to decision makers. Hesitant fuzzy linguistic term sets(HFLTSs), an effective linguistic computational tool in modeling and eliciting such information, have hence aroused many scholars’ interests and some extensions have been introduced recently.However, these methods are based on the discrete linguistic term framework with the limited expression domain, which actually depict qualitative information using several single values. Therefore,it is hard to ensure the integrity of the semantics representation and the accuracy of the computation results. To deal with this problem, a semantics basis framework called complete linguistic term set(CLTS) is designed, which adopts a separation structure of linguistic scale and expression domain, enriching semantics representation of decision makers. On this basis the concept of fuzzy interval linguistic sets(FILSs) is put forward that employs the interval linguistic term with probability to increase the flexibility of eliciting and representing uncertain and hesitant qualitative information. For practical applications, a fuzzy interval linguistic technique for order preference by similarity to ideal solution(FILTOPSIS) method is developed to deal with multi-attribute group decision making(MAGDM) problems. Through the cases of movie and enterprise resource planning(ERP) system selection, the effectiveness and validity of the proposed method are illustrated.

Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective

A dynamic hesitant fuzzy linguistic group decisionmaking(DHFLGDM) problem is studied from the perspective of information reliability based on the theory of hesitant fuzzy linguistic term sets(HFLTSs). First, an approach is applied to transform the dynamic HFLTSs(DHFLTSs) into a set of proportional linguistic terms to eliminate the time dimension. Second, expert reliability is measured by considering both group similarity and degree of certainty, and an optimization method is employed to quantify the linguistic terms by maximizing the group similarity. Third, through computing the attribute stability as well as its reliability, a combination rule which considers both reliability and weight is proposed to aggregate the information, and then the aggregated grade values and degree of stability are used to make a selection. Finally,the application and feasibility of the proposed method are verified through a case study and method comparison.

An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green practices.Typically,hydrogen storage is associated with diverse sustainable and circular economy(SCE)criteria.As a result,the authors consider the situation a multi-criteria decision-making(MCDM)problem.Studies infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is unexplored.Driven by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking HSMs.The applicability of the developed framework is testified by using a case example of HSM selection in India.Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.

Warhead power assessment based on double hierarchy hesitant fuzzy linguistic term sets theory and gained and lost dominance score method

Warhead power assessment of the anti-ship missile plays a vital role in determining the optimal design of missile, thus having important strategic research significance. However, in the assessment process, expert’s judgement will directly affect the assessment accuracy. In addition,there are many criteria involved in the missile design alternatives. Some criteria with poor performance may be compensated by other criteria with excellent performance, and then it is impossible to find the truly optimal alternative. Aimed at solving these problems, this paper proposes a synthetical assessment process based on fuzzy hesitant linguistic term set and the Gained and Lost Dominance Score(GLDS) method. In order to improve the assessment accuracy of experts and solve the problem that experts generate different opinions, combined with the advantages of fuzzy hesitant sets and linguistic term sets, the double hierarchy hesitant fuzzy linguistic term sets are used in this paper to improve the accuracy of expert’s judgement. In order to effectively combine expert’s experience with the data of criteria, the evidence theory and entropy weight method are used to transfer the expert’s judgement to the weight. In order to avoid selecting defective alternative of missile design, the GLDS is used to fuse expert information and criteria information. Sensitivity analysis shows that the assessment process has sensitivity to some extent. However, when the fluctuation of expert’s assessment makes the fluctuation of θ in the range of-5% to 5%, the impact on the results is not quite conspicuous. The analysis of calculation result and comparative analysis show that the assessment process proposed in this paper is accurate enough, has great advantage in selecting the current and potential optimal alternative of missile design, and avoids the alternatives with low criteria performance that cannot be compensated by other criteria being selected.

Interval-Valued Dual Hesitant Fuzzy Hamacher Aggregation Operators for Multiple Attribute Decision Making

As an generalization of hesitant fuzzy set, interval-valued hesitant fuzzy set and dual hesitant fuzzy set, interval-valued dual hesitant fuzzy set has been proposed and applied in multiple attribute decision making. Hamacher t-norm and t-conorm is an generalization of algebraic and Einstein t-norms and t-conorms. In order to combine interval-valued dual hesitant fuzzy aggregation operators with Hamacher t-norm and t-conorm. We first introduced some new Hamacher operation rules for interval-valued dual hesitant fuzzy elements. Then, several interval-valued dual hesitant fuzzy Hamacher aggregation operators are presented, some desirable properties and their special cases are studied. Further, a new multiple attribute decision making method with these operators is given,and an numerical example is provided to demonstrate that the developed approach is both valid and practical.

基于FDHHFLTS-BN的海底管道泄漏失效风险定量分析

为预防海底油气管道泄漏失效事故,提出基于自由双层次犹豫模糊语言术语集(FDHHFLTS)和贝叶斯网络(BN)的FDHHFLTS-BN风险分析方法,用于分析海底油气管道泄漏失效事故概率及事故的关键风险因素。将故障树模型转换为BN结构,由专家根据FDHHFLTS评估基本事件发生可能性;采用最佳最差法(BWM)确定专家权重,结合相似性聚合方法(SAM)聚合专家意见;依据构建的BN模型,正向推理得到事故发生概率,反向推理得到后验概率,并进行敏感性分析。将该方法应用于实例分析,结果表明:分析段海底管道泄漏事故的概率值为P=6.20×10^(-3);焊缝施工缺陷、材料施工缺陷和渔具作用等为事故发生的关键因素;与传统方法对比分析结果证明,所提方法在确定海底管道风险方面具有一定的优势。

基于多粒度犹豫模糊语言术语集的TOPSIS决策方法研究

为了解决在实际决策时,由于知识背景不同决策者采用不同粒度语言术语集来表达而导致决策结果不准确的问题,本文提出了一种基于多粒度犹豫模糊语言术语集的逼近理想解排序(technique for order preference by similarity to ideal solution,TOPSIS)决策方法。首先选用各术语集中的最大粒度作为标准粒度,通过转换算法将每个决策者的语言术语集转换到同一标准粒度下进行集结,得出相应的隶属度语言术语集;然后结合TOPSIS方法,计算每个备选方案与正、负理想点距离,以相对贴近度的大小排序实现最优方案的选择;最后,通过一个实例,验证该方法的可行性和优越性。本文所提方法可应用于最优方案的选择问题中,提升决策结果准确度。

基于组合权重的犹豫模糊语言决策方法

犹豫模糊语言决策方法中的权重需要人为给出,具有较大主观性,导致决策结果不稳定。针对该问题,提出一种组合权重,用于解决犹豫模糊语言多属性决策中权重未知的问题,并克服因主观权重带来的不确定性。通过最大偏差方法分别获得犹豫模糊语言集中的犹豫模糊集与语言集的客观权重,对这2种客观权重进行组合并归一化,从而得到组合权重,并给出基于组合权重的逼近理想解排序法的决策方法与犹豫模糊语言组合加权几何算子决策方法。实例结果表明,2种方法的决策结果相同,验证了组合权重的可行性。

基于多目标属性权重优化的犹豫模糊语言TOPSIS决策方法

犹豫模糊语言术语集作为一种有效的信息表达形式,能够很好的反映出人们的定性且犹豫的决策信息。传统的距离测度会导致犹豫模糊语言信息的流失,因此,本文首先提出了一种新的犹豫模糊语言距离测度,并研究了该距离测度的性质。其次,针对属性权重完全未知的犹豫模糊语言多属性决策问题,考虑方案和属性两个层面,构建了多目标优化的属性权重确定模型。进而,基于多目标权重优化模型和犹豫模糊语言距离测度,提出了一种改进的犹豫模糊语言TOPSIS法。最后通过实例说明了所提出的TOPSIS法的实用性和有效性,并进行了灵敏度和比较分析。

犹豫模糊语言Heronian平均算子在多属性决策中的应用

针对输入变量之间的相互影响以及评价值为犹豫模糊语言信息的多属性决策问题,提出一种基于犹豫模糊语言Heronian平均算子的多属性决策方法。由于Heronian平均(HM)算子具有能够反映输入变量之间相互关联的良好特性,在犹豫模糊语言信息环境下,提出了两种新的集成算子,即犹豫模糊语言Heronian平均(HFLHM)算子和犹豫模糊语言几何Heronian平均(HFLGHM)算子,同时研究了它们的一些特性。考虑到输入变量具有不同的重要程度,还定义了犹豫模糊语言加权Heronian平均(HFLWHM)算子和犹豫模糊语言加权几何Heronian平均(HFLWGHM)算子。最后提出了基于HFLWHM算子和HFLWGHM算子的犹豫模糊语言多属性决策方法,并通过实例验证了这些算子的合理性和可行性。

基于语言值有序对三元组的多类型数据标准化方法

为了标准化处理多类型数据,基于语言值二元组,提出语言值有序对三元组表示模型,研究语言值有序对三元组及其性质,给出语言值有序对三元组之间的相似度.讨论语言值有序对三元组加权平均算子,构造模糊集、直觉模糊集、犹豫模糊集、语言真值直觉模糊集与语言值有序对三元组的标准化转化模型.结合语言值有序对三元组之间的相似度,将模糊集、直觉模糊集、犹豫模糊集、语言真值直觉模糊集的转化模型应用于模式识别中.最后,通过医院智能分诊的实例说明文中方法的合理性和有效性.

基于双重犹豫模糊语言的多属性决策方法

结合语言术语集和双重犹豫模糊集给出了双重犹豫模糊语言集的定义,并给出双重犹豫模糊语言数的运算法则和比较方法。针对属性值为双重犹豫模糊语言数的多属性决策问题,提出集结双重犹豫模糊语言信息的双重犹豫模糊语言加权平均算子以及基于此算子的多属性决策方法,并将所提的决策方法应用于选址建厂问题以说明其可行性和有效性。

混合型广义犹豫模糊语言多准则群决策方法

针对各专家决策矩阵中方案赋值为不同犹豫模糊语言集的多准则群决策问题,提出基于广义犹豫模糊语言集、得分函数与排序得分的新方法。运用新得分函数,将各专家的广义犹豫模糊语言决策矩阵转化为得分决策矩阵;根据各专家给出的准则权重向量的不同情况,计算各方案在准则集下的加权得分函数值或得分函数值;依据这些函数值的特征,得到各专家认为各方案的排序位置及其得分;按照专家权重向量的不同情况,求得各方案的综合排序得分,进而确定各方案的排序。以新能源汽车零配件小微企业的信用评估为例,验证了所提方法的可行性和有效性。

基于区间犹豫模糊语言评价的多属性决策方法及其应用

对于多属性决策问题,文章结合现实群决策过程,定义了标准化区间犹豫模糊语言数以及区间犹豫模糊语言数向量的概念,修正了已有文献中区间犹豫模糊语言数的得分函数和精确函数计算公式。在此基础上,构建了区间犹豫模糊语言数向量之间的距离测度,并运用多属性决策中常用的TOPSIS方法思想,建立了基于区间犹豫模糊语言评价的多属性决策模型,简化了现有文献中构建PA聚合算子的决策计算过程。通过对现有文献中同一问题及其数据的计算,验证了方法的有效性和可行性。

基于QFD与PD-HFLTS的应急预案评估研究

为在应急预案评估中,能充分考虑预案实施人员的诉求,并有效表征预案评估人员给出评价时的犹豫性,建立基于质量功能展开(QFD)与可能性分布-犹豫模糊语言术语集(PD-HFLTS)的预案评估模型。首先,由预案实施人员给出需求信息,借助预案需求项与内容项的关联信息,确定内容项基本重要度;然后,依据预案内容制定或改进的困难程度修正内容项重要度,确定内容项综合重要度,据之判定预案的关键内容与不足之处。以某铁路局防洪应急预案为对象的案例分析结果表明,该模型具有较强的可行性与可操作性;现有预案存在重应急响应、轻应急准备的问题。

犹豫Pythagorean模糊语言优先级集结算子及应用

基于犹豫Pythagorean模糊集、直觉模糊语言集和优先级算子研究了属性存在优先级关系的犹豫Pythagorean模糊语言多属性决策问题。定义犹豫Pythagorean模糊语言集,提出犹豫Pythagorean模糊语言数的运算法则、得分函数及其精确函数;根据犹豫Pythagorean模糊语言集和优先级算子提出犹豫Pythagorean模糊语言优先级集结算子;根据犹豫Pythagorean模糊语言优先级集结算子构建能够解决属性存在优先级关系的多属性决策模型,并通过案例验证了该模型的有效性。

基于犹豫模糊语言的大学生素质测评研究

本文针对大学生素质测评中出现的犹豫性和模糊性问题,介绍了犹豫模糊语言术语集的概念和相关计算方法。分析了比较犹豫模糊术语间的可能性关系,通过构造可能性关系矩阵,采用信息熵计算各属性的权重,根据计算加权几何算子和有序加权平均算子的方法,得到决策者对不同方案的偏好顺序。将其应用到大学生素质测评模型中,通过实例,论证该方法具有可行性。

基于多目标规划的产品延伸服务规划

为解决顾客/专家常在多个评价值间犹豫不决的问题,结合犹豫模糊术语集和质量功能展开将顾客需求及重要度转化为产品延伸服务工程特性及重要度。基于min-upper和max-lower算子解决犹豫模糊术语集的多样化粒度问题,利用二元语义词计算方法将犹豫模糊术语集转化为区间数形式,采用区间数偏好度计算方法计算顾客需求的重要度和产品延伸服务工程特性的重要度。通过对定性候选服务规格的量化,建立以顾客满意度最大化、服务实现成本和服务响应时间最小化的多目标多约束优化模型,利用灰色速率关联度将其转化为单目标规划模型进行求解。最后,以某数控机床服务工程特性方案规划为例,验证了所提方法的有效性。

基于PD-HFLTS与群决策理论的可持续供应商选择

为构建不确定环境下的可持续供应商选择模型,依据可持续供应链管理的三重底线原则,从社会绩效、环境绩效与经济绩效3方面构建可持续供应商评估指标体系。考虑可能性分布—犹豫模糊语言集(PD-HFLTS)在刻画不确定信息的优势,以及群决策理论在提升选择结果可信度与可靠性的优势,提出基于PD-HFLTS与群决策理论的层次分析法确定评估指标权重,进而提出基于PD-HFLTS与群决策理论的三阶段决策方法,以降低可持续供应商选择的复杂度。此外,针对群决策中专家权重确定问题,提出综合考虑专家先验信息、所给评估信息模糊程度和群体意见一致性水平的组合赋权法;针对群决策中的意见冲突问题,提出基于PD-HFLTS的群体意见一致性检验与调整方法。最后,以某汽车制造企业选择可持续零部件供应商为例,验证了本模型的科学性与有效性。

基于犹豫语言信息集结算法的数据库选择

为了提高数据库选择效率,针对多属性群决策问题提出了一种基于犹豫语言多属性群决策算法的数据库选择方法。首先,构建一种基于广义犹豫语言Heronian平均(GHLHM)算子的数据库选择模型。其次,将Archimedean范数引入到犹豫语言环境下,定义了新的犹豫语言运算法则;其次,基于新的运算法则和Heronian平均,提出了GHLHM算子,并探讨了GHLHM算子的一些基本性质,研究了GHLHM算子的几类常用算子形式,提出了广义犹豫语言加权Heronian平均(GHLWHM)算子;最后,基于GHLWHM算子构建了一种新的犹豫语言多属性群决策方法并将其应用于数据库选择中,实验证明,该方法可以实现对数据库性能的综合优选排序,在其他领域也有着广泛的应用前景。