区间犹豫模糊的改进层次TODIM在云制造资源中的应用研究

针对具有递阶层次准则结构的决策问题,考虑到决策者偏好表达的不确定和犹豫性以及认知的参照依赖和损失规避行为,提出一种区间犹豫模糊多层的TODIM多准则决策方法。首先在对传统JACCARD距离修正后提出一种新的区间犹豫模糊距离测度,接着结合层次分析法思想与TODIM方法建立一种能够解决递阶层次准则结构决策问题的区间犹豫模糊决策方法。最后将该方法运用于云制造资源选择问题,并通过对比分析证实了其有效性和可用性。     

基于层次分析法的犹豫模糊语言包络分析模型及其在边缘节点网络安全评估中的应用

针对传统的网络安全评估方法仅用单一语言术语作为偏好信息难以评估复杂网络环境的安全性的问题,利用犹豫模糊语言术语集作为评估网络安全的偏好信息,提出了一种基于层次分析法(analytic hierarchy process,AHP)的犹豫模糊语言包络分析模型,用以评估边缘节点的网络安全性。该模型通过求解目标规划模型获得最优的网络安全准则权重信息,进一步构建网络安全准则权重信息对应的AHP约束锥作为犹豫模糊语言包络分析模型的约束条件,获得移动用户安全评估的排序结果。实例证明该模型能够合理地融合网络安全准则偏好信息,并有效地处理边缘节点的网络安全评估问题。     

基于粒计算的犹豫模糊多准则决策方法*

提出基于粒计算的犹豫模糊多准则决策方法.给出各个准则下对应的犹豫模糊集中犹豫模糊元的大于可能度定义,并构造相应准则下的加性一致的模糊偏好矩阵.根据各准则的模糊偏好矩阵对应的预序熵及预序粒结构相似度确定属性的权重,对各个准则下模糊偏好矩阵的排序向量加权平均得到最终的排序向量.文中方法以评价数据序信息量及准则序与整体之间的关系确定准则权重,通过计算加权两两比较下的排序向量得到最终的排序决策结果.最后运用实例验证算法的有效性及可行性.     

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。     

考虑决策者心理行为的概率语义术语集多属性决策方法

概率语义术语集可以通过给语义赋予概率以表达不同程度偏好,采用概率语义术语集获取专家犹豫和不确定的评价信息。针对传统多属性决策中指标权重确定没有考虑指标间相互影响关系的问题,采用概率语义DEMATEL方法分析指标间的相互影响关系,其中采用二元语义将指标间关联关系概率语义术语集的得分函数转换为精确数值,得到指标的权重。考虑决策者的不同偏好和心理行为,采用改进的概率语义TODIM方法对决策对象进行排序。最后以某班轮公司的综合竞争力评估为例,验证了所提方法的有效性。     

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,采用解析的方法给出最优解;再次,在专家权重向量求解的基础上,基于TODIM方法实现群体专家视角下的概率犹豫模糊信息二次集结,并实现基于综合感知价值的多个备选方案优劣排序;最后,以信息化战争条件下陆军部队作战方案模糊优选为例,验证所提出交互式多属性群决策方法的可行性和有效性.     

基于可信度的基本不确定区间犹豫模糊广义TODIM应急群决策方法

针对应急决策环境所存在的信息不完备、认知不足等问题,区间犹豫模糊集能充分表达决策者在信息评价时的犹豫性和模糊性,但随着研究的深入,发现其存在无法保证信息质量的缺陷.为此,提出更符合实际决策需求、信息表达更加灵活的基本不确定区间犹豫模糊集,其为包含区间犹豫模糊集和确定度的二维信息集.在此基础上,定义基本不确定区间犹豫模糊加权平均算子、犹豫度及可信度,提出基于可信度的专家权重调整方法和属性权重确定方法,充分考虑了决策专家提供评价信息的可靠程度.最后,将广义TODIM方法拓展到基本不确定区间犹豫模糊环境,通过应急决策案例验证所提方法的可行性,并利用灵敏度分析和对比分析验证该方法的稳定性和有效性.     

基于犹豫模糊语言的多阶段多属性决策方法

针对不确定语言条件下的决策问题,考虑专家偏好的阶段性差异,提出基于犹豫模糊语言的多阶段多属性决策方法。首先为了更准确地表达专家在决策过程中的犹豫性,采用犹豫模糊语言来表达专家的评估信息;其次考虑阶段权重的波动性,运用基于专家阶段性偏好的信息熵方法构建阶段权重优化模型确定阶段权重;然后利用最大化偏差法来求解属性权重,并采用基于平均解距离评价(EDAS)方法对备选方案进行排序;最后以某企业选取物流商问题为例,验证该方法的有效性和可行性。     

对偶犹豫模糊集的相关系数及其应用

对偶犹豫模糊集因其可以给决策者提供更多的决策信息成为模糊决策的热点研究问题,相关性指标可以用来度量两个模糊信息之间的相关关系,熵可以用来度量模糊信息的不确定程度。提出了一种基于对偶犹豫模糊集相关系数和熵的模糊多属性群决策方法。定义了对偶犹豫模糊集相关系数的概念,讨论了其基本性质;提出了两种对偶犹豫模糊集的熵,在此基础上,给出了模糊多属性群决策的权重确定方法;基于对偶犹豫模糊集相关系数和熵,提出了一种属性权重完全未知条件下的模糊多属性群决策方法;通过案例分析说明了该方法的有效性和可行性。     

广义犹豫正态模糊信息集成及其多属性群决策

定义了犹豫正态模糊元及其运算法则、得分函数、Euclidean距离等概念;提出了广义犹豫正态模糊有序加权平均算子,并研究其性质,该算子不仅尽可能多地保留决策者的偏好信息,还可依据决策者的主观意愿选择不同的参数和属性权重,使得决策结果达到决策者的期望值;紧接着对属性权重和算子参数赋予不同的数值,获取广义犹豫正态模糊有序加权平均算子的若干种特殊算子,并探讨两个常用算子的大小关系;针对属性权重完全未知的多属性群决策问题,构建一种基于广义犹豫正态模糊有序加权平均算子的群决策方法。该方法利用同一属性下所有方案属性值间的距离求得最优权重,然后将同一方案下各属性值集结成为综合属性值,进而得到方案优劣排序。通过实例分析说明该方法的可行性和有效性。     

Multi-criteria decision-making using interval-valued hesitant fuzzy QUALIFLEX methods based on a likelihood-based comparison approach

QUALIFLEX is a very efficient outranking method to handle multi-criteria decision-making (MCDM) involving cardinal and ordinal preference information. Based on a likelihood-based comparison approach, this paper develops two interval-valued hesitant fuzzy QUALIFLEX outranking methods to handle MCDM problems within the interval-valued hesitant fuzzy context. First, we define the likelihoods of interval-valued hesitant fuzzy preference relations that compare two interval-valued hesitant fuzzy elements (IVHFEs). Then, we propose the concepts of the concordance/discordance index, the weighted concordance/discordance index and the comprehensive concordance/discordance index. Moreover, an interval-valued hesitant fuzzy QUALIFLEX model is developed to solve MCDM problems where the evaluative ratings of the alternatives and the weights of the criteria take the form of IVHFEs. Additionally, this paper propounds another likelihood-based interval-valued hesitant fuzzy QUALIFLEX method to accommodate the IVHFEs’ evaluative ratings of alternatives and non-fuzzy criterion weights with incomplete information. Finally, a numerical example concerning the selection of green suppliers is provided to demonstrate the practicability of the proposed methods, and a comparison analysis is given to illustrate the advantages of the proposed methods.

     

Linguistic hesitant intuitionistic fuzzy decision-making method based on VIKOR

Linguistic hesitant intuitionistic fuzzy set, which allows an element having several linguistic evaluation values and each linguistic argument having several intuitionistic fuzzy memberships, is a power tool to model uncertain information existing in multiple attribute decision-making problems. In this paper, we propose new methods by using TOPSIS and VIKOR for multiple attribute decision-making problems, in which evaluation values are in the form of linguistic hesitant intuitionistic fuzzy elements. Different situations of attribute weight information are considered. If attribute weights are partly known, a linear programming model is set up based on the idea that reasonable weights should make the relative closeness of each alternative evaluation value to the linguistic hesitant intuitionistic fuzzy positive ideal solution as large as possible. If attribute weights are unknown completely, an optimization model is set up based on the maximum deviation method. A numerical example is presented to illustrate feasibility and practical advantages of the proposed method. We compare the alternatives’ rankings derived from the linguistic hesitant intuitionistic fuzzy TOPSIS method with those derived from the hesitant fuzzy linguistic TOPSIS and the hesitant intuitionistic fuzzy TOPSIS approach to further illustrate their advantages.

     

An outranking method for multicriteria decision making with probabilistic hesitant information

Defects of hesitant fuzzy set (HFS) manifest in actual decision-making process, so adding probabilities to the values in HFS is necessary. The probabilistic HFS (PHFS) is a useful tool to describe the uncertainty of elements in HFS by introducing occurrence probabilities. However, some important issues in PHFS utilization remain to be addressed. In this study, an outranking method for multicriteria decision making (MCDM) with probabilistic hesitant information is presented. First, the binary relations between two probabilistic hesitant fuzzy elements (PHFEs) are defined on the basis of the elimination and choice translating reality method. Some outranking relations between the alternatives are then introduced. Second, we provide a Hausdorff distance between two PHFEs. The main characteristic of the proposed Hausdorff distance is that it does not require the same length and arrangement of the PHFEs. Third, a maximizing Hausdorff distance deviation method is developed to obtain the evaluation criteria weights under a probabilistic hesitant fuzzy environment. Finally, an illustrative example in conjunction with comparative analysis is used to demonstrate that the proposed method is feasible for practical MCDM problems.     

Multi-criteria group decision making with incomplete hesitant fuzzy preference relations

In order to simulate the hesitancy and uncertainty associated with impression or vagueness, a decision maker may give her/his judgments by means of hesitant fuzzy preference relations in the process of decision making. The study of their consistency becomes a very important aspect to avoid a misleading solution. This paper defines the concept of additive consistent hesitant fuzzy preference relations. The characterizations of additive consistent hesitant fuzzy preference relations are studied in detail. Owing to the limitations of the experts’ professional knowledge and experience, the provided preferences in a hesitant fuzzy preference relation are usually incomplete. Consequently, this paper introduces the concepts of incomplete hesitant fuzzy preference relation, acceptable incomplete hesitant fuzzy preference relation, and additive consistent incomplete hesitant fuzzy preference relation. Then, two estimation procedures are developed to estimate the missing information in an expert's incomplete hesitant fuzzy preference relation. The first procedure is used to construct an additive consistent hesitant fuzzy preference relation from the lowest possible number, (n − 1), of pairwise comparisons. The second one is designed for the estimation of missing elements of the acceptable incomplete hesitant fuzzy preference relations with more known judgments. Moreover, an algorithm is given to solve the multi-criteria group decision making problem with incomplete hesitant fuzzy preference relations. Finally, a numerical example is provided to illustrate the solution processes of the developed algorithm and to verify its effectiveness and practicality.     

基于模糊偏序关系评估决策的智能信息融合

针对信息融合系统应综合考虑获取信息所要求的资源、计算复杂度和时间所需要的最小成本的问题,提出了基于模糊偏序关系评估决策的智能信息融合系统,利用模糊偏序关系的排序方法,来对传感器进行评估决策和智能选择,并与证据理论相组合,应用于目标识别,仿真结果表明了该方法的有效性。     

A novel weight‐derived method and its application in graduate students' physical health assessment

In multiattribute decision making, the analytic network process (ANP) is an important methodology to derive the subjective weights of attributes when the dependence and feedback relations exist between attributes, and the number of attributes should be no more than seven in a comparison matrix. To reduce the dimensions of attributes, we propose a hybrid hesitant fuzzy linguistic factor analysis method to cluster the attributes into main factors. The method takes multiple forms of decision‐making information into consideration, such as single linguistic terms, hesitant fuzzy linguistic terms, and numeric values. Meanwhile, the objective weights of the main factors are obtained as well. As for the subjective weights of main factors, the incomplete probabilistic linguistic ANP is developed after improving the incomplete probabilistic linguistic preference relation with multiplicative consistency. At last, the final weights of the main factors are calculated by combining the objective and subjective weights. A questionnaire survey about assessing the weights of the main factors influencing graduate students' physical health is designed to explain the application of the proposed methodology. To sum up, the main importance and contributions of this study are as following: (1) developing a hybrid hesitant fuzzy linguistic factor analysis method and incomplete probabilistic linguistic ANP, (2) proposing a novel weight‐derived method from both objective and subjective perspectives, and (3) applying it to graduate students' physical health assessment.