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.

Damage effectiveness assessment method for anti-ship missiles based on double hierarchy linguistic term sets and evidence theory

The research on the damage effectiveness assessment of anti-ship missiles involves system science and weapon science, and has essential strategic research significance. With comprehensive analysis of the specific process of the damage assessment process of anti-missile against ships, a synthetic damage effectiveness assessment process is proposed based on the double hierarchy linguistic term set and the evidence theory. In order to improve the accuracy of the expert ’s assessment information, double hierarchy linguistic terms are used to describe the assessment opinions of experts. In order to avoid the loss of experts ’ original information caused by information fusion rules, the evidence theory is used to fuse the assessment information of various experts on each case. Good stability of the assessment process can be reflected through sensitivity analysis, and the fluctuation of a certain parameter does not have an excessive influence on the assessment results. The assessment process is accurate enough to be reflected through comparative analysis and it has a good advantage in damage effectiveness assessment.

Warhead power assessment based on double hierarchy hesitant fuzzy linguistic term sets theory and gained and lost dominance score method

Warhead power assessment of the anti-ship missile plays a vital role in determining the optimal design of missile, thus having important strategic research significance. However, in the assessment process, expert’s judgement will directly affect the assessment accuracy. In addition,there are many criteria involved in the missile design alternatives. Some criteria with poor performance may be compensated by other criteria with excellent performance, and then it is impossible to find the truly optimal alternative. Aimed at solving these problems, this paper proposes a synthetical assessment process based on fuzzy hesitant linguistic term set and the Gained and Lost Dominance Score(GLDS) method. In order to improve the assessment accuracy of experts and solve the problem that experts generate different opinions, combined with the advantages of fuzzy hesitant sets and linguistic term sets, the double hierarchy hesitant fuzzy linguistic term sets are used in this paper to improve the accuracy of expert’s judgement. In order to effectively combine expert’s experience with the data of criteria, the evidence theory and entropy weight method are used to transfer the expert’s judgement to the weight. In order to avoid selecting defective alternative of missile design, the GLDS is used to fuse expert information and criteria information. Sensitivity analysis shows that the assessment process has sensitivity to some extent. However, when the fluctuation of expert’s assessment makes the fluctuation of θ in the range of-5% to 5%, the impact on the results is not quite conspicuous. The analysis of calculation result and comparative analysis show that the assessment process proposed in this paper is accurate enough, has great advantage in selecting the current and potential optimal alternative of missile design, and avoids the alternatives with low criteria performance that cannot be compensated by other criteria being selected.

An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green practices.Typically,hydrogen storage is associated with diverse sustainable and circular economy(SCE)criteria.As a result,the authors consider the situation a multi-criteria decision-making(MCDM)problem.Studies infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is unexplored.Driven by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking HSMs.The applicability of the developed framework is testified by using a case example of HSM selection in India.Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.

Fuzzy interval linguistic sets with applications in multi-attribute group decision making

Uncertain and hesitant information, widely existing in the real-world qualitative decision making problems, brings great challenges to decision makers. Hesitant fuzzy linguistic term sets(HFLTSs), an effective linguistic computational tool in modeling and eliciting such information, have hence aroused many scholars’ interests and some extensions have been introduced recently.However, these methods are based on the discrete linguistic term framework with the limited expression domain, which actually depict qualitative information using several single values. Therefore,it is hard to ensure the integrity of the semantics representation and the accuracy of the computation results. To deal with this problem, a semantics basis framework called complete linguistic term set(CLTS) is designed, which adopts a separation structure of linguistic scale and expression domain, enriching semantics representation of decision makers. On this basis the concept of fuzzy interval linguistic sets(FILSs) is put forward that employs the interval linguistic term with probability to increase the flexibility of eliciting and representing uncertain and hesitant qualitative information. For practical applications, a fuzzy interval linguistic technique for order preference by similarity to ideal solution(FILTOPSIS) method is developed to deal with multi-attribute group decision making(MAGDM) problems. Through the cases of movie and enterprise resource planning(ERP) system selection, the effectiveness and validity of the proposed method are illustrated.

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.

Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

The consensus scheme is an essential component in the real blockchain environment.The Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increase efficiency,respectively.However,how to study the knowledge representation of the collective voting information and then select delegates is a new open problem.To ensure the fairness and effectiveness of transactions in the blockchain,in this paper,we propose a novel fine-grained knowledge representation method,which improves the DPoS scheme based on the linguistic term set(LTS)and proportional hesitant fuzzy linguistic term set(PHFLTS).To this end,the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to evaluate the blockchain nodes.PHFLTS is used to model the collective voting information on the voted blockchain nodes by aggregating the voting information from other blockchain nodes.To rank the blockchain nodes and then choose the delegate,a novel delegate selection algorithm is proposed based on the cumulative possibility degree.Finally,the numerical examples are used to demonstrate the implementation process of the proposed DPoS consensus algorithm and also its rationality.Moreover,the superiority of the proposed DPoS consensus algorithm is verified.The results show that the proposed DPoS consensus algorithm shows better performance than the existing DPoS consensus algorithms.

Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective

A dynamic hesitant fuzzy linguistic group decisionmaking(DHFLGDM) problem is studied from the perspective of information reliability based on the theory of hesitant fuzzy linguistic term sets(HFLTSs). First, an approach is applied to transform the dynamic HFLTSs(DHFLTSs) into a set of proportional linguistic terms to eliminate the time dimension. Second, expert reliability is measured by considering both group similarity and degree of certainty, and an optimization method is employed to quantify the linguistic terms by maximizing the group similarity. Third, through computing the attribute stability as well as its reliability, a combination rule which considers both reliability and weight is proposed to aggregate the information, and then the aggregated grade values and degree of stability are used to make a selection. Finally,the application and feasibility of the proposed method are verified through a case study and method comparison.

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.

Prioritizing real estate enterprises based on credit risk assessment:an integrated multi‑criteria group decision support framework

Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects.Additionally,it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry.However,Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns,making subjective evalu-ation approaches inevitable,occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks.To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises,this study proposes an integrated multi-criteria group decision-making approach.First,a credit risk assessment index for Chinese real estate enterprises is established.Then,the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods.This approach is suitable for processing large amounts of data with high uncertainty,which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjec-tive evaluation information.Finally,the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility.This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise credit risk assessment.

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.

MCGDM model using uncertain linguistic consistency sets of single andinterval linguistic term multivalued sets

This paper presents hybrid linguistic expressions and operations for the hybridlinguistic multiple criteria group decision making (MCGDM) issue withidentical and/or different single and interval linguistic term values. First, wepropose a single and interval linguistic term multivalued set/element (SILTMS/SILTME) and develop a consistency measure of SILTMEs based on Shannonentropy to measure the consistency degree of single and interval linguistic termvalues in SILTME. Second, we converse SILTMS/SILTME into an uncertainlinguistic consistency set/element (ULCS/ULCE) in terms of the mean andconsistency measure of SILTMEs to reasonably perform operations betweendifferent information forms/sequence lengths in SILTMSs. Third, we definesome operations of ULCEs and the expected values and sorting rules ofULCEs. Fourth, we present the ULCE weighted mean and geometric operatorsand their characteristics. Finally, we develop a MCGDM model using theweighted mean operation of the two operators and apply it in the mine safetyassessment.

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.

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.

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.