A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.

Multidimensional Quality Evaluation of Graduate Thesis:Based on the Probabilistic Linguistic MABAC Method

Graduate education is the main way to train high-level innovative talents,the basic layout to cope with the global talent competition,and the important cornerstone for implementing the innovation-driven development strategy and building an innovation-driven country.Therefore,graduate education is of great remarkably to the development of national education.As an important manifestation of graduate education,the quality of a graduate thesis should receive more attention.It is conducive to promoting the quality of graduates by supervising and examining the quality of the graduate thesis.For this purpose,this work is based on textmining,expert interviews,and questionnaire surveys to obtain the factors influencing the quality of a graduate thesis first.Then,through three rounds of expert consultation,a multidimensional evaluation indicator system for the graduate thesis quality is built.Furthermore,probabilistic linguistic termsets(PLTSs)are utilized to obtain the initial evaluation information and apply the stepwise weight assessment ratio analysis method to determine the weights of attributes.In the ensuing step,the novel multi-attribute border approximation area comparison based on the PLTS method is established.Finally,the proposed method is employed in a case study concerning the quality evaluation of a graduate thesis and the effectiveness of this approach is further illustrated.

Information Security Evaluation of Industrial Control Systems Using Probabilistic Linguistic MCDM Method

Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.

Research on Recommendation Algorithms Based on Cloud Models in Probabilistic Linguistic Environments

To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information,and proposes a recommendation algorithm based on cloud model in probabilistic language environment.Initially,this paper quantifies the attributes in the review text based on the probabilistic linguistic term set.Subsequently,the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended,and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud generator.Finally,the products are recommended and sorted based on the digital characteristic value of the cloud model.The algorithm is applied to the recommendation of 10 hotels,and the results show that the method is effective and practical,enriching the application of cloud models in the recommendation field.

基于调和概率语言术语集的Power平均算子

实践发现现存概率语言Power平均算子在应用中存在概率信息丢失、计算过于繁琐等问题,针对这些问题,提出一种基于调和概率语言术语集的Power平均算子。结合新的基本运算法则与新的支撑度,给出新的概率语言Power平均算子、概率语言Power几何平均算子、概率语言Power加权平均算子、概率语言Power加权几何平均算子,同时论证分析了这几类新型算子的性质。最后通过实验数据,将提出的算子与现存算子进行对比分析,验证了所提算子的有效性与可行性。

Probabilistic linguistic term ordered weighted distance operator and its applications in public Eco-environment satisfaction evaluation

This thesis aims to propose a novel distance operator,the probabilistic linguistic term ordered weighted distance(PLTOWD)operator,which enriches the distance theory in probabilistic linguistic term circumstances.The PLTOWD operator is an efficient tool to deal with qualitative evaluation information and their corresponding probabilities or importance degrees.Moreover,some of its desired properties and different families of thePLTOWDoperator are discussed.Meanwhile,the extensions of the PLTOWD operator are also investigated.Then,a method of multiple attribute group decision making(MAGDM)in probabilistic linguistic term information is proposed on the basis of the PLTOWD operator.Finally,a numerical evaluation example in public Eco-environment satisfaction is developed to illustrate the practicability and effectiveness of the given method.Some discussions and comparisons are carried out according to the case results.

考虑期望强度与属性补偿关系的新能源汽车在线推荐方法

基于消费者属性期望,结合柔性决策和补偿原则理论,定义期望强度与属性补偿关系,明确属性期望与期望强度融合方法以及产品属性补偿规则,提出考虑期望强度和属性补偿关系的新能源汽车在线筛选方法。结合证据理论与消费者风险偏好提出新能源汽车在线排序方法,实现在线推荐满足消费者期望的新能源汽车。研究发现:与考虑属性期望严格约束的推荐方法相比,同时考虑期望强度与属性补偿关系能够避免综合高性价比车型因部分消费者属性期望严格约束而被忽略;采用证据推理结合消费者风险偏好确定车型优先级,能够更加精准地为消费者推荐满足个性化期望的新能源汽车。

社会网络下基于在线评论的旅游供应商优选研究

在线旅游平台上评论规模庞大,信息量浩如烟海,平台如何依据他人评论信息为游客提供最优旅游供应商,从而使游客得到满意的服务体验是亟待解决的问题。在社会网络视角下,基于在线评论和概率语言提出一种旅游供应商的优选方法。首先,采集旅游平台上的游客评论数据,运用TF-IDF方法提取各供应商相关评论的关键词,通过筛选整理形成主题并计算其权重;然后,分析每条评论的情感值,并将其转换成概率语言术语集的形式;最后,构建游客群体社会网络,将游客群体意见与在线评论信息相结合,运用PROMETHEE-Ⅱ方法对旅游供应商进行优选排序。结果表明,本文方法推荐结果与旅游平台数据基本一致,但能在相似评分的供应商中做出优劣比较,精准有效地提供最优供应商,且简单可靠。

The Aviation Technology Two-Sided Matching with the Expected Time Based on the Probabilistic Linguistic Preference Relations

The two-sided matching has been widely applied to the decision-making problems in the field of management.With the limited working experience,the two-sided agents usually cannot provide the preference order directly for the opposite agent,but rather to provide the preference relations in the form of linguistic information.The preference relations based on probabilistic linguistic term sets(PLTSs)not only allowagents to provide the evaluation with multiple linguistic terms,but also present the different preference degrees for linguistic terms.Considering the diversities of the agents,they may provide their preference relations in the form of the probabilistic linguistic preference relation(PLPR)or the probabilistic linguistic multiplicative preference relation(PLMPR).For two-sided matching with the expected time,we first provide the concept of the time satisfaction degree(TSD).Then,we transform the preference relations in different forms into the unified preference relations(u-PRs).The consistency index to measure the consistency of u-PRs is introduced.Besides,the acceptable consistent u-PRs are constructed,and an algorithm is proposed to modify the unacceptable consistent u-PRs.Furthermore,we present the whole two-sided matching decisionmaking process with the acceptable consistent u-PRs.Finally,a case about aviation technology suppliers and demanders matching is presented to exhibit the rationality and practicality of the proposed method.Some analyses and discussions are provided to further demonstrate the feasibility and effectiveness of the proposed method.

Probabilistic uncertain linguistic TODIM method based on the generalized Choquet integral and its application

Purpose-The purpose of this paper is to develop a probabilistic uncertain linguistic(PUL)TODIM method based on the generalized Choquet integral,with respect to the interdependencies between criteria,for the selection of the best alternate in the context of multiple criteria group decision-making(MCGDM).Design/methodology/approach-Owing to decision makers(DMs)do not always show completely rational and may have the preference of bounded rational behavior,this may affect the result of the MCGDM.At the same time,criteria interaction is a focused issue in MCGDM.Hence,a novel TODIM method based on the generalized Choquet integral selects the best alternate using PUL evaluation,where the generalized Choquet integral is used to calculate the weight of criterion.The generalized PUL distance measure between two probabilistic uncertain linguistic elements(PULEs)is calculated and the perceived dominance degree matrices for each alternate relative to other alternates are obtained.Furthermore,the comprehensive perceived dominance degree of each alternate can be calculated to get the ranking.Findings-Potential application of the PUL-TODIM method is demonstrated through an evaluation example with sensitivity and comparative analysis.Originality/value-As per author’s concern,there are no TODIM methods with probabilistic uncertain linguistic sets(PULTSs)to solve MCGDM problems under uncertainty.Compared with the result of existing methods,the final judgment value of alternates using the extended TODIM methodology is highly corroborated,which proves its potential in solving MCGDM problems under qualitative and quantitative environments.

基于前景理论和LOWA算子的概率语言LEC风险评估方法

考虑到作业条件危险性评价法(LEC)存在的评价信息遗漏、风险量化分析能力弱、忽略决策者有限理性特征等缺陷,提出一种基于前景理论和语言有序加权平均(LOWA)算子的概率语言LEC风险评估方法。首先,该方法采用概率语言术语集(PLTS)代替精确的非连续性数值来刻画决策者评价信息的不确定性;其次,利用概率语言连乘层次分析法(PL-MAHP)和正、负理想点双向投影贴近度最小偏差模型确定指标的主、客观权重,并基于博弈论的思想对主、客观权重进行组合优化;然后,考虑到风险评估过程中决策者的损失规避和参照依赖等心理行为,在前景理论决策视角下,引入LOWA算子确定前景参照点以提升参照点选取的科学性,进而实现安全隐患的风险排序;最后,通过危化品运输安全隐患的风险评估算例,验证了该方法的合理性和有效性。

基于概率语言术语集和TODIM法的健康社区评价研究

为提升健康社区水平,提出基于概率语言术语集改进TODIM法的健康社区综合评价方法。首先,基于社会生态学理论,从社区治理、人际、设施、环境4个维度构建健康社区水平评价指标体系;其次,根据概率语言术语集确定初始指标权重,采用KANO模型修正确定最终指标权重,并将概率语言术语集中的距离测度与TODIM法相结合,通过计算各方案的全局优势度进行评价排序;最后,将此模型用于健康社区进行综合评价。结果表明,利用所构建模型对社区进行实证分析是可行的,概率语言术语集将模糊性、不确定性、难以量化的评价信息灵活准确地描述出来,使吸纳的评价信息更加全面。基于概率语言术语集和TODIM法对健康社区进行评价,在模糊环境下也可解决群决策问题,对提升地方政府对社区健康认知、推动健康社区建设有一定的参考价值。

WPLTS多属性群决策问题的点坐标解法

为了解决加权概率语言术语集(WPLTS)多属性群决策问题,将该问题转换为加权概率犹豫模糊集(WPHFS)多属性群决策问题,并从点坐标的角度出发建立了解决WPHFS多属性群决策问题的一种算法.首先,将WPHFS中的隶属度、概率、专家权重以点坐标形式刻画,在此基础上提出了加权概率犹豫模糊元(WPHFE)的相关运算;其次,分别建立了计算决策专家综合权重以及属性综合权重的计算模型;最后,采用Maclaurin对称平均算子集结WPHFE以获取每个方案的综合属性值.决策结果表明,WPLTS多属性群决策算法不仅可行,而且可以从多个角度来客观地分析方案的排序问题,为精准地确定最优方案提供了一种有效路径.

基于群决策的带决策时间窗竞争匹配问题的决策组织优化

针对共享资源匹配中个体决策方案未考虑个体决策相互影响且不稳定,导致决策竞争和产生决策失效的问题,基于决策时间窗内相互影响的个体决策实质为群决策的特征,引入群决策理论,将各个体决策竞争者视为有效决策的群决策成员,构建了资源匹配中关于决策时间点选择的群决策模型。以各个体决策的组合为群决策备选方案,考虑到决策过程时间因素对决策者心理行为的影响,建立了心理偏好系数描述不同时间点的决策者对属性偏好的程度,以此修正决策信息、优化决策。基于概率语言术语集(PLTSs)以概率形式融合语言评价信息和数值评价信息,并结合心理偏好系数给出了评价值的概率计算公式。最后将评价值概率集合与逼近理想解排序法(TOPSIS)结合,对各决策方案排序,求出群体共识下的最优决策方案,该决策方案为所有决策者的最佳决策时间点集合。以物流信息平台中的车找货为例验证了模型的有效性。所提出的方法可为平台解决匹配竞争而实施决策控制以及资源调度提供方向。

考虑中介平台收益的企业-投资商双边匹配决策方法

针对概率不确定语言环境下的企业-投资商双边匹配问题,提出了一种考虑中介平台收益的双边匹配模型.首先,定义了可信度,对匹配主体偏好信息的可靠度进行测量,进而根据可信度给出了概率不确定语言术语集得分值的计算公式;其次,通过改进的TODIM方法得到满意度矩阵;然后,给出了中介平台费用函数并规范化其函数,基于此,将企业与投资商匹配满意度最大化以及中介平台收益最大化作为目标,构建双边匹配优化模型并求解;最后,通过某投资理财平台的案例,证明了该方法的可行性及有效性.

Consensus model of social network group decision-making based on trust relationship among experts and expert reliability

Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

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,验证了该方法的有效性。

基于概率语言术语集的服务外包风险评估方法

考虑服务外包风险评估的不确定性,采用概率语言术语集获取FMEA方法所需的专家评价信息,通过给语言集赋予不同的概率以表达偏好的程度。针对传统FMEA方法没有考虑风险间的相互影响关系,风险严重度评估不够准确的问题,采用广义Choquet积分分析风险间的相互影响关系,得到修正后的风险严重度。考虑严重度、发生度和难检度直接相乘准确性低的问题,采用概率语言VIKOR方法对风险的三个因子进行综合评估。最后以公共体育场馆服务外包风险评估为例,验证了所提方法的有效性。

基于概率语言相关系数的多属性群决策模型

针对评价信息为概率语言术语集,专家权重和属性权重完全未知的多属性群决策问题,提出基于概率语言相关系数的多属性群决策模型.首先定义了概率语言的相关系数并且拓展到概率语言矩阵间的相关系数;然后基于每个专家的决策矩阵,通过最小化加权相关系数总和求得每个专家的属性权重向量,并且通过决策矩阵的相关系数客观地确定专家权重;最后将经典的ELECTRE方法加以改进用于方案排序,并且通过算例分析与比较分析证明了该决策方法的有效性与优越性.

多粒度概率语言环境下基于PROMETHEE的改进FMEA方法

针对传统故障模式和影响分析(FMEA)方法中存在关于故障模式评估、风险因子权重和风险优先级排序等方面的固有缺陷,提出了一种多粒度概率语言环境下基于偏好顺序结构评估法(PROMETHEE)的改进FMEA方法。该方法运用多粒度概率语言术语集(PLTS)刻画了专家评估信息的多样性和不确定性,并基于二元语义转换函数为引入工具的语言计算模型统一各专家多粒度风险评估信息,运用最优最劣法(BWM)和熵权法相结合的综合赋权法确定风险因子权重,将PROMETHEE拓展到概率语言环境中用于确定故障模式风险优先序。最后,运用托盘交换架故障风险评估案例来验证该方法的适用性和有效性,并进一步通过敏感度与对比分析以显示该方法的优越性。