2TLCGPOWA算子及其在多属性群决策中的应用

基于集对分析理论中二元联系数的不确定性,将联系变量引入到区间二元语义环境中,定义了二元语义联系变量,给出了二元语义联系变量的运算法则,并提出了几种新的算术集结算子。针对决策矩阵元素为区间二元语义变量和属性权重完全未知的不确定多属性群决策问题,提出了一种基于2TLCGPOWA算子的不确定多属性群决策方法。最后通过对某大学教师的任职和晋升考核来说明该方法的可行性和有效性。     

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.     

Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT

Modern systems for information retrieval,fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels.In this paper,we propose and use two new 2-Tuple linguistic representation models(i.e.,a distribution function model(DFM) and an improved Herrera-Martínez's model) jointly with the fusion rules developed in Dezert-Smarandache Theory(DSmT),in order to combine efficiently qualitative information expres...     

A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process

The fuzzy analytic hierarchy process (FAHP) has been used to solve various multi-criteria decision-making problems where trapezoidal type-1 fuzzy sets are utilized in defining decision-makers’ linguistic judgment. Previous theories have suggested that interval type-2 fuzzy sets (IT2 FS) can offer an alternative that can handle vagueness and uncertainty. This paper proposes a new FAHP characterized by IT2 FS for linguistic variables. Differently from the typical FAHP, which directly utilizes trapezoidal type-1 fuzzy numbers, this method introduces IT2 FS to enhance judgment in the fuzzy decision-making environment. This new model includes linguistic variables in IT2 FS and a rank value method for normalizing upper and lower memberships of IT2 FS. The proposed model is illustrated by a numerical example of work safety evaluation. Comparable results are also presented to check the feasibility of the proposed method. It is shown that the ranking order of the proposed method is consistent with the other two methods despite difference in weight priorities.     

基于累积前景理论的动态风险灰靶决策方法

研究信息值为区间灰数, 指标权重未知的动态风险决策问题, 提出一种基于累积前景理论和灰靶思想的决策方法. 该方法定义了区间灰数的距离测度和排序方法; 以各指标值的平均值作为参照点计算各时段的前景矩阵; 通过WAA算子将动态前景矩阵集结为静态前景矩阵; 在此基础上求解基于极大熵思想的规划模型得出各指标权重. 构造正负椭球灰靶模型, 根据各方案的正负靶心综合距对方案进行排序. 最后, 通过算例分析结果验证了该方法更加符合决策者的心理行为.

     

二元语义信息下的动态激励群体评价方法及应用

针对目前关于动态群体评价问题的研究相对较少,且主要是基于点值信息评价的不足,提出一种新的二元语义信息下的动态激励群体评价方法,将动态激励评价及语言评价信息引入到群体评价中。首先探讨了能够较好地集结群体语言评价信息的二元语义一致算子;然后在各阶段的动态评价中引入激励区间,并运用时间诱导的二元语义时序激励加权平均算子集结得到受“奖惩”的动态群体综合评价值,从而进行最终评价。最后,通过算例给出了所提方法的步骤,并进行了结果分析,证明了该方法的可行性和有效性。     

区间直觉语言Frank集结算子及其在决策中的应用

基于区间直觉语言变量和Frank算子的概念,首先提出区间直觉语言环境下Frank算子的运算规则;然后介绍几种区间直觉语言Frank信息集成算子,如:区间直觉语言Frank加权算术平均算子、区间直觉语言Frank加权几何平均算子、区间直觉语言广义Frank加权算术平均算子等,并介绍各算子具有的性质,同时,基于上述算子提出两种属性权重为实数且属性值为区间直觉语言变量的多属性决策方法;最后,结合示例表明所提出方法的有效性和实用性.     

区间模糊语言多属性群决策方法及应用研究

基于我国银行业所面临的数据匮乏、信息披露制度不完善等问题,国际上先进的统计量化方法并不能完全适用于对我国操作风险的评价,从定性和定量相结合的角度出发,针对操作风险评价中的多属性群决策问题,提出一种在区间模糊语言信息下基于不确定的拓展有序加权平均算子（UEOWA）和不确定语言混合集结算子（ULHA）的多专家多属性商业银行操作风险评价方法,构建基于区间模糊语言的评价集来描述专家不精确、难量化的意见,梳理评价思路,设计评价步骤,通过实例验证了该方法的可操作性和有效性。     

基于相似度的多粒度不确定语言群决策方法

针对具有多粒度不确定语言评价信息的多属性群决策问题,提出了一种基于区间二元语义信息处理和矢量相似度的群决策方法,弥补了基于距离测度的决策方法易造成信息混淆的不足。该方法首先使用二元语义转换函数对多粒度区间语言评价信息进行一致化处理;然后通过建立使备选方案对正理想解相似度最大、负理想解相似度最小的最优化模型来获得相应的属性权重;最后利用区间二元语义的集结算子对评价信息进行加权集成,并通过优序数排序法实现对各方案的排序。实例分析说明了该方法的可行性和有效性。     

基于概率语言COPRAS的多属性风险型应急决策方法

针对多属性应急决策过程中的风险性和不确定性,提出一种结合累积前景理论和概率语言COPRAS的应急决策方法。首先考虑到专家在评价过程中的犹豫性和偏好程度,采用概率语言表达专家的评估信息;其次将概率语言形式的评价信息纳入到累积前景理论的决策框架中,通过计算方案在不同前景状态下的前景价值矩阵和概率权重矩阵得到综合前景价值矩阵;然后在综合前景矩阵的基础上,以所有备选方案的整体效用度最大化为目标函数建优化模型来求解指标权重,并利用COPRAS方法对备选应急方案进行排序;最后通过一个算例说明了所提方法的有效性和可行性。     

INGIOWGA算子的多属性决策方法及其应用

研究了方案的属性评估信息以模糊语言形式给出的多属性群决策问题，在导出的有序加权几何平均（IOWGA）算子理论的基础上，给出了一种区间数广义导出有序加权几何平均（INGIOWGA）算子，利用广义的导出有序加权平均（GIOWA）算子，对专家所给出的对应于各方案的属性评估信息进行了集结，并提出了一种基于模糊语言评估和GIOWA算子的多属性群决策方法。利用该算法对X射线实时成像检测系统方案选择中的判断信息进行集结，并且通过算例说明了该方法的有效性和实用性。     

Multiple-attribute group decision-making based on power Bonferroni operators of linguistic q-rung orthopair fuzzy numbers

In this paper, a new conception of linguistic q-rung orthopair fuzzy number (Lq-ROFN) is proposed where the membership and nonmembership of the q-rung orthopair fuzzy numbers ( q-ROFNs) are represented as linguistic variables. Compared with linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers, the Lq-ROFNs can more fully describe the linguistic assessment information by considering the parameter q to adjust the range of fuzzy information. To deal with the multiple-attribute group decision-making (MAGDM) problems with Lq-ROFNs, we proposed the linguistic score and accuracy functions of the Lq-ROFNs. Further, we introduce and prove the operational rules and the related properties characters of Lq-ROFNs. For aggregating the Lq-ROFN assessment information, some aggregation operators are developed, involving the linguistic q-rung orthopair fuzzy power Bonferroni mean (BM) operator, linguistic q-rung orthopair fuzzy weighted power BM operator, linguistic q-rung orthopair fuzzy power geometric BM (GBM) operator, and linguistic q-rung orthopair fuzzy weighted power GBM operator, and then presents their rational properties and particular cases, which cannot only reduce the influences of some unreasonable data caused by the biased decision-makers, but also can take the interrelationship between any two different attributes into account. Finally, we propose a method to handle the MAGDM under the environment of Lq-ROFNs by using the new proposed operators. Further, several examples are given to show the validity and superiority of the proposed method by comparing with other existing MAGDM methods.     

零模的直觉模糊不确定语言集成算子研究

基于零模与共轭零模算子,探讨了直觉模糊不确定语言变量运算法则,得到了基于零模与共轭零模的直觉模糊不确定语言加权几何算子,并给出了一种使用直觉不确定语言变量的集成算子的多属性群决策方法,最后通过Matlab软件分析了直觉模糊不确定语言加权几何算子的K值与语言术语下标间关系。为多属性群决策提供了有价值的参考,有效地解决了一类具有直觉模糊不确定语言评估信息的多属性群决策问题。     

Research on the multi-attribute decision-making under risk with interval probability based on prospect theory and the uncertain linguistic variables

With respect to risk decision making problems with interval probability in which the attribute values take the form of the uncertain linguistic variables, a multi-attribute decision making method based on prospect theory is proposed. To begin with, the uncertain linguistic variables can be transformed into the trapezoidal fuzzy number, and the prospect value function of the trapezoidal fuzzy number based on the decision-making reference point of each attribute and the weight function of interval probability can be constructed; then the prospect value of attribute for every alternative is calculated through prospect value function of the trapezoidal fuzzy number and the weight function of interval probability, and the weighted prospect value of alternative is acquired by using weighted average method according to attribute weights, and all the alternatives are sorted according to the expected values of the weighted prospect values; Finally, an illustrate example is given to show the decision-making steps, the influence on decision making for different parameters of value function and different decision-making reference point, and the feasibility of the method.     

基于语言型混合算子的模糊信息聚合方法

在多属性决策过程中经常会用到聚合算子,有序加权平均聚合(OWA)算子是最常用的聚合算子之一,通常用于聚合确切的数值.然而,现实世界部分信息的不确定性以及决策者对一些信息的模糊性,使得部分信息不能用确切的数值表示,从而导致OWA算子及其扩展算子向着多元化发展.对此,给出一种语言型混合有序加权平均聚合(LHOWA)算子,同时研究该算子所应具备的一些基本性质,并给出一种基于该算子的语言型信息聚合方法,用于多属性决策过程中模糊信息的聚合.最后,通过一个煤矿安全评价的算例对所提出方法的优越性进行了验证.     

HNLOWA集成算法应用于推荐系统评估

针对复杂决策环境下的决策者倾向于使用定性评价数据和现实环境中数据信息通常存在正态分布规律的问题,构建了基于犹豫正态语言有序加权平均（hesitant normal linguistic ordered weighted average,HNLOWA）算子的决策模型。将正态模糊数和犹豫模糊语言元相结合,引入了犹豫正态语言元（hesitant normal linguistic element,HNLE）的概念,其不仅能够运用语言变量来描述决策信息,还能传递出语言决策信息的分布情况;定义了HNLE之间的基本运算法则和大小判别准则,并设计HNLOWA算子用于对HNLE进行信息融合,同时探究了HNLOWA算子的相关性质;构建了基于HNLOWA集成算子的多属性决策方法,并运用推荐系统选择评估案例进行了模型的验证分析。     

基于累积前景理论的多指标灰关联决策方法

考虑决策者风险态度对多指标决策的影响,提出一种基于累积前景理论的多指标灰关联决策方法.该方法利用奖优罚劣的[-1,1]线性变换算子对原始决策信息进行规范化处理,由此得到正负理想方案.根据累积前景理论和灰色关联分析定义了前景价值函数,以此构建方案综合前景值最大化的优化模型.求解该模型得出最优权向量,并最终确定出方案的排序.最后通过一个投资决策实例说明了该模型的有效性和可行性.     

基于相对熵和二元熵的多粒度直觉语言TODIM决策方法

针对评估信息为多粒度直觉语言集的决策问题,提出一种基于相对熵和二元熵的TODIM方法。该方法首先定义了直觉语言数的相对熵和二元熵,以度量决策信息的差异和不确定性;其次,构建了基于相对熵和二元熵的专家赋权模型,并建立了主观权重完全已知、部分已知和完全未知场景下的属性赋权模型;最后,为集结多粒度群体决策信息,提出了多粒度直觉语言加权算术平均(MIL-WAA)算子。算例分析表明,该方法能够较好地度量决策信息的不确定性和差异性,并考虑了决策者的有限理性行为,具有一定的合理性和有效性。     

Multi-period multi-attribute group decision-making under linguistic assessments

In fuzzy environments, decision information is more suitable to be expressed in linguistic labels than exact numerical values. Group decision-making with linguistic assessments has received more and more attention over the last decades. Most research on this topic has focused on situations where all the original decision information is provided at the same time and refers to one and same period. However, in many decision areas, such as multi-period investment decision-making, medical diagnosis, personnel dynamic examination, military system efficiency dynamic evaluation, etc., the original decision information is usually collected at different periods and/or refers to different moments in time. This paper investigates the multi-period multi-attribute group decision-making problems where all decision information is expressed by decision-makers in multiplicative linguistic labels at different periods. The paper first introduces a new operator called a dynamic linguistic weighted geometric (DLWG) operator and uses the minimum variability model to derive the time series weights associated with the DLWG operator, and then utilises, respectively, the linguistic weighted geometric (LWG) operator and the DLWG operator to aggregate the given linguistic labels. Moreover, the paper develops an approach to multi-period multiple attribute group decision-making under linguistic assessments so as to derive the final ranking of alternatives, and finally, gives an illustrative example and extends the above results to uncertain linguistic environments.     

基于决策者信任度的风险型混合多属性群决策方法

考虑决策者对风险型混合多属性评价结果的信任程度不同, 提出基于前景理论和改进投影理论的群决策方法. 建立一个数组以描述在不同信任度下群决策专家的评价结果, 并将数组中混合数据类型转化为三角模糊数. 在考虑决策者信任度的前提下集结群信息、确定属性权重. 引入综合前景价值和考虑权重的投影相对接近度两种方法对方案进行排序. 最后通过实例表明了所提出方法的合理性和有效性.