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.

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.

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.

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.

Evaluation of green building suppliers based on IVPLTS-CBR decision-making method

Purpose-The purpose of this paper is to propose a multi-criteria decision-making model based on the casebased reasoning(CBR)method for interval-valued probabilistic linguistic term set(IVPLTS),which can cluster different categories of building suppliers for targeted management.Design/methodology/approach-First,a new score function and distance measure for IVPLTS are proposed.Second,a green building supplier evaluation criterion system is constructed from five aspects:operation management,green management,cooperation potential,service level and product information.Finally,the IVPLTS-CBR model is used to evaluate the green building suppliers and groups them into three preset categories.Findings-The feasibility and validity of the proposed method are verified by comparing with the advanced TOPSIS method and the IVPLTS-based VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)method.The compared results show that the proposed method is more consistent with the actual situation and has strong theoretical significance and practical value.Research limitations/implications-This paper presents a new method for clustering construction suppliers.Decision makers can use this method to classify construction suppliers into different categories,so that they can be targeted management.In this way,suppliers can be better guided and motivated to accelerate the green transformation and contribute their share to achieve the strategic goal of carbon neutral and carbon peak as soon as possible.Originality/value-A new score function and distance measure for IVPLTS are proposed.Besides,a novel IVPLTS-CBR method is applied to rank and cluster building suppliers.