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.     

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。     

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。     

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。     

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。     

基于优序关系的犹豫模糊语言多准则决策方法

犹豫模糊语言集是语言集和犹豫模糊集的扩展,受传统Electre方法的启发,构建基于优序关系的犹豫模糊语言多准则决策方法. 首先,给出犹豫模糊语言数的Hausdorff距离公式;然后,基于每一准则下方案评价的对比,建立犹豫模糊语言数的优序关系,并在此基础上,提出一种基于优序关系的犹豫模糊语言多准则决策方法;最后,通过算例表明了所提出方法的有效性和可行性.     

基于证据理论和前景理论的犹豫-直觉模糊语言多准则决策方法

在犹豫模糊语言和直觉模糊语言的基础上,提出一种新的犹豫-直觉模糊语言集,并定义得分函数、精确函数和Hamming距离公式.针对准则值为犹豫-直觉模糊语言的不确定多准则决策问题,构建基于证据理论和前景理论的拓展型VIKOR方法.该决策方法利用证据理论处理自然状态发生的概率未知的不确定状况,应用前景理论刻画人在做决策时的有限理性行为,使决策结果更能反映实际情况.最后,通过算例表明了所提出方法的有效性和可行性.     

犹豫模糊图及其在多属性决策中的应用*

犹豫模糊集作为定量描述犹豫不决的有效工具,通过同时处理不确定性信息中的犹豫性与模糊性,解决犹豫不决背景下的多属性决策问题.文中针对属性值为犹豫模糊数的多属性决策问题,研究基于模糊图论的相关模型与多属性决策方法.首先,提出犹豫模糊图的概念和部分常见运算规则.然后,构建基于犹豫模糊图的一般性多属性决策方法.最后,通过具体算例和对比性分析验证文中方法的合理性.     

基于前景理论的犹豫模糊TOPSIS多属性决策方法

针对属性权重未知、属性值为犹豫模糊集的决策问题,提出一种前景理论和逼近理想解(TOPSIS)相结合的多属性决策方法.考虑到决策者对指标集的不同偏好,利用犹豫模糊熵的相关理论,提出一种基于犹豫模糊熵的熵权法确定属性权重.将决策者的风险心理因素引入犹豫模糊多属性决策中,定义了犹豫模糊数的前景价值函数,并以此将犹豫模糊决策矩阵转化为价值矩阵,计算出各方案的收益损失比值.最终应用TOPSIS的基本思路,确定备选方案的优劣排序,并通过算例分析验证了所提出方法的有效性.     

一种基于前景理论的毕达哥拉斯犹豫模糊不确定语言ELECTRE多属性决策方法

针对属性权重已知、属性值为毕达哥拉斯犹豫模糊不确定语言变量的多属性决策问题,提出一种前景理论和消去与选择转换法(ELECTRE)相结合的多属性决策方法.首先给出毕达哥拉斯犹豫模糊不确定语言集的定义,包括得分函数、距离测度和运算法则;然后将前景理论引入该决策环境下,构建前景价值矩阵,运用传统ELECTRE法求得一致矩阵和矛盾矩阵,并对其进行转化构建前景优/劣势判断矩阵,进而求得总优/劣势值以确定备选方案的优劣排序;最后通过算例分析验证所提出方法的有效性和可行性,并与该决策环境下的传统ELECTRE法进行对比,表明了该方法的优越性和实用性.     

基于MACBETH方法的犹豫模糊语言多准则决策方法

犹豫模糊语言集是语言术语集的拓展.受传统的MACBETH(measuring attractiveness by a categorical-based evaluation technique)方法的启发,构建基于MACBETH方法的犹豫模糊语言多准则决策方法.首先将语言表达式表示的决策信息通过转化函数转化为犹豫模糊语言数,进而得出犹豫模糊语言判断矩阵;然后将此结果应用于改进的MACBETH决策支持系统;最后,通过毕业生选择就职企业这一实例说明该方法的有效性和可行性.     

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.

     

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.

     

A normal wiggly hesitant fuzzy linguistic projection-based multiattributive border approximation area comparison method

As a useful information representation tool, hesitant fuzzy linguistic term set (HFLTS) allows decision makers (DMs) to express their cognitive preferences in terms of several ordered and continuous linguistic terms. Considering the fact that much valuable information related to the cognitive behavior of DMs is hidden in the original evaluation information, this paper studies how to comprehensively mine uncertain information from original hesitant fuzzy linguistic evaluation information given by DMs. To address this objective, we present a new representation tool, normal wiggly hesitant fuzzy linguistic term set (NWHFLTS), which not only retains the original evaluation information, but also delivers and quantifies potential uncertain information, and can also help DMs express their evaluation information in a more complete manner. First, we develop the basic operations, score function, and comparison rule of NWHFLTS based on linguistic scale functions (LSFs), and propose the projection measure, the normal projection measure, and the normalized projection-based distance measure to describe the degree of deviation between two NWHFLTSs. Furthermore, for the case when the attribute weight is completely unknown, we combine the multiattributive border approximation area comparison (MABAC) method and develop a new method called as normal wiggly hesitant fuzzy linguistic projection-based MABAC to solve the multiattribute decision-making problems where attribute values are expressed in the form of NWHFLTS. Finally, through a practical example of marine ecological security situation, the specific calculation steps of this method are exemplified, the feasibility and advancement of the proposed method are demonstrated via a comprehensive comparative study.     

Assessment of traffic congestion with ORESTE method under double hierarchy hesitant fuzzy linguistic environment

With the new generation of information technology development and the promotion of the Internet, local governments turn their attention to the construction of intelligent transportation systems. More and more cities began building intelligent transportation which has been widely used to monitor urban traffic. Experts can evaluate urban traffic congestion based on the information collected from the big data of intelligent transportation. In recent two years, double hierarchy hesitant fuzzy linguistic term set has been widely used to depict explicit evaluation information, which is straightforward and broad-spectrum. When evaluating traffic congestion in a city, decision makers can utilize double hierarchy hesitant fuzzy linguistic term sets to express vague information. Moreover, the ORESTE method is an applicative method which can select a reliable alternative by subdividing alternatives and reduce the loss of information in the conversion process. In this paper, we propose a double hierarchy hesitant fuzzy linguistic ORESTE method and a new score function of double hierarchy hesitant fuzzy linguistic term set. The method raises a new perspective to reduce the error from other methods and the new score function derives a robust decision-making result. Then, we apply the double hierarchy hesitant fuzzy linguistic ORESTE method to solve a practical case involving choosing the congested city by evaluating the 5S traffic congestion model. Finally, we compare the double hierarchy hesitant fuzzy linguistic ORESTE method with other methods such as the classical ORESTE method and the double hierarchy hesitant fuzzy linguistic MULTIMOORA to illustrate the advantages of our method.     

TOPSIS for Hesitant Fuzzy Linguistic Term Sets

We propose a new method to aggregate the opinion of experts or decision makers on different criteria, regarding a set of alternatives, where the opinion of the experts is represented by hesitant fuzzy linguistic term sets. An illustrative example is provided to elaborate the proposed method for selection of the best alternative.