Hesitant Fuzzy Linguistic Term Sets Research Articles

Evaluation of Supplier Sustainable Development Programs Using the HFLTS-QFD Method

Objective: This study proposes a decision model for evaluating sustainable supplier development programs (SSDPs) using the HFLTS-QFD technique, which is a combination of Hesitant Fuzzy Linguistic Term Sets (HFLTS) with the Quality Function Deployment (QFD) method. Theoretical Framework: The implementation of supplier development programs is important to improve supplier performance in aspects related to economic, environmental and social aspects. The evaluation of these programs is necessary to decide on their continuation or discontinuation, as well as to direct the allocation of resources. Method: The bibliographical research supported the definition of the requirements and criteria used for the evaluation of SDPs. The implementation of the model was done using MS Excel® software. The application in an illustrative case involving the evaluation of five SDPs allowed us to demonstrate the applicability of the model. Results and Discussion: The most relevant criteria were organization strategy, social responsibility, employee training and environmental management. The programs with the best classification were SDP4 and SDP5. Research Implications: The proposed model supports the selection of criteria considering their relative weights and the degree of difficulty in collecting data from suppliers when evaluating each PDFS. It also has the capacity to assist group decision-making, enabling the assignment of weights to decision-makers. Originality/Value: Unlike previous models for evaluating PDFS, which use economic or environmental criteria, the proposed model considers criteria related to the three dimensions of the Triple Bottom Line, in addition to allowing the use of linguistic expressions to deal with decisions under hesitation and uncertainty.

Enhancing group decision-making: Maximum consensus aggregation for fuzzy cross-efficiency under hesitant fuzzy linguistic information

Group decision-making (GDM) is essential as it recognizes the inherent complexity of many decision scenarios, which frequently require the collective wisdom and knowledge of multiple decision-makers (DMs) to be effectively resolved. The proposed method aims to develop fuzzy data envelopment analysis (DEA) cross-efficiency models tailored to address GDM challenges, wherein attribute values are provided by DMs using hesitant fuzzy linguistic term sets (HFLTSs). For this purpose, we initially transform HFLTSs into their corresponding fuzzy envelopes, defined as trapezoidal fuzzy numbers (TrFNs). This conversion strategy effectively minimizes the loss in assessments based on HFLTSs while retaining the inherent ambiguity of the original information. Building upon this foundation, we develop fuzzy cross-efficiency models by leveraging the α-level sets of fuzzy envelopes. These models are designed to handle fuzzy input and output variables under various α-level sets, which are capable of considering all possible attribute values for each alternative. Following this, we implement a maximum consensus model using fuzzy cross-efficiency to assign weights to DMs. These weights facilitate the aggregation of individual fuzzy cross-efficiency intervals obtained from DMs’ assessments into collective ones, which serve to rank alternatives. Finally, we showcase the effectiveness and superiority of our proposal through numerical validation and comparative analysis.

Entropy measures of multigranular unbalanced hesitant fuzzy linguistic term sets for multiple criteria decision making

The hesitant fuzzy linguistic term set (HFLTS) is an efficient tool for modeling linguistic information in multi-criteria decision making (MCDM), and the entropy measure of HFLTS, as a substantial representation of uncertainty, merits additional investigation. This article aims to exploit a general framework to facilitate the construction of entropy measure for multigranular unbalanced HFLTS. An axiomatic definition of the entropy for HFLTSs that considers both types of uncertainty (fuzziness and hesitation) is presented, with the entropy measure subsequently derived from distance-based mapping. From this definition, several deduced results have been developed for the mapping that depicts the entropy expression in order to get such functions with ease. Whereafter, a MCDM weight-determining model for multigranular unbalanced linguistic information without preset weights is devised, and an empirical application of the suggested model in MCDM is illustrated. Ultimately, comparisons and analyses with existing studies are conducted to demonstrate the advantages of the proposed work.

Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life scenarios. Most existing researches lack tools to fuse information quickly and interpret decision results for partially formed decisions. This limitation is particularly noticeable when there is a need to improve the efficiency of GDM. To address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) method is constructed from the perspective of granular computing. This method simultaneously considers the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) environment. First, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the concepts of expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are defined. Second, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function. Then, the granular structure of DHHFLTS based on the sequential three-way decision (S3WD) is defined to improve the decision-making efficiency, and the decision-making strategy and interpretation of each decision-level are proposed. Furthermore, the algorithm of S3W-GDM is given. Finally, an illustrative example of diagnosis is presented, and the comparative and sensitivity analysis with other methods are performed to verify the efficiency and rationality of the proposed method.

Chinese Family Farm Business Risk Assessment Using a Hierarchical Hesitant Fuzzy Linguistic Model

Chinese family farms are continuously expanding; they are also facing various business risks that lead to a shorter lifespan. This paper constructed a family farm business risk assessment model that combined a hesitant fuzzy linguistic term sets (HFLTS) model with a hesitant fuzzy weighted average (HFWA) operator. On the basis of the factor analysis, this study built a family farm indicator system that included the natural, technical, market, policy, society, and management risk. The HFLTS was used for the assessment of weights in pairwise comparison matrices, and the HFWA operator was used as an aggregation operator to calculate the business risk score of family farms. For our case study, a method comparison analysis was also performed to check the validity of the results obtained by our risk assessment model.

Security assessment and diagnosis for industrial water resources using TODIMSort considering Best–Worst Method with double hierarchy hesitant fuzzy linguistic term set

Motivated by the driving force to address global water scarcity, industrial water resources, as the second largest consumption of water resources, its security assessment plays a crucial role in improving the current situation. Hence, this paper proposes a novel methodology to conduct the industrial water resources security (IWRS) assessment. Firstly, a more targeted assessment system based on the framework of the Pressure–State–Response (P–S–R) on IWRS is established. Then, enhanced with a double hierarchy hesitant fuzzy linguistic term set (DHHFLTS), the Best–Worst Method (BWM) now determines subjective weights through DHHFLTS-BWM (DF-BWM). By introducing the Criteria Importance Through Intercriteria Correlation (CRITIC) method, which considers the indicator interactions, objective weights are obtained to modify the subjective weights. Furthermore, the global dominance of all alternatives is calculated by a TODIMSort method and grading them. Moreover, 16 cities in Anhui Province are taken as examples to assess IWRS in the decade from 2011 to 2020. Comparative analysis with original BWM, time series analysis, sensitivity analysis on loss attenuation coefficient, coupling and coordination analysis and obstacle analysis on all indicators are conducted to verify the rationality, effectiveness, and stability of the proposed assessment methodology. Simultaneously, explore the existing issues within IWRS. It can be seen from the results that the performance of Lu’an and Huainan cities is relatively better, while Ma’anshan city shows relatively poorer performance. In addition, per capita water resources and wastewater treatment facilities have a significant impact on the IWRS. Finally, some management suggestions are proposed to enhance the scientific and effective management of industrial water resources and ensure their sustainable utilization.

A Novel Correlation Measurement Method for Multi-Attribute Decision-Making Problems Based on Double Hierarchy Hesitant Fuzzy Linguistic Evaluation and Player Assignment Application in Football

Linguistic expressions are widely used to reflect the decision maker's evaluations more easily and clearly in the decision-making problems, The Double Hierarchy Hesitant Fuzzy Linguistic Term Set (DHHFLTS), an extension of linguistic expressions, helps the decision maker to reflect their hesitant evaluations in complex decision making problems using two different sets of linguistic terms. Correlation measurements are used as an important tool in making decisions by making comparative evaluations in complex decision making problems based on common criteria. In this study, a new method is proposed to improve the existing correlation measurement method using DHHFLTSs. The proposed method aims to increase the reflective power of hesitant thoughts in the evaluation process by including fuzzy linguistic expressions in the calculation process. In order to prove the validity of the proposed method, the original problem of choosing the most suitable player for the positions in football sport is considered as a Multi-Attribute Decision Making (MADM) problem. Correlation values and assignment results obtained from the proposed method are compared with the current method values. Consistency of results and values between methods reveals the validity of the proposed method.

A reliability analysis method for electromagnet performance degradation based on FMEA and fuzzy inference system

AbstractElectromagnets are often used in indirect control for industrial applications. The ability of an electromagnet to control objects should decrease with performance degradation. And electromagnets product poses a danger to people and objects in the working environment. So, it is very difficult to analyze the reliability of electromagnetic performance degradation because of the complicated working condition. Failure Mode and Effect Analysis (FMEA) is the most commonly used tool for product reliability analysis. The new version of FMEA uses integer as evaluation value, which cannot represent the hesitation psychology of the evaluator. The Action Priority (AP) table of the FMEA describes the relationship between the evaluation of influencing factors and the risk level of the failure mode, which provides rules for determining the risk level of the failure mode. However, the AP table may result in multiple failure modes having the same ranking, which does not align with the intention of FMEA to prevent failures. Therefore, this paper proposes a reliability analysis method for electromagnetic performance degradation based on FMEA and FIS. Firstly, the Double Hierarchy Hesitant Fuzzy Linguistic Term Set (DHHFLTS) is used as the evaluation language to describe the hesitation psychology of evaluators. Secondly, the AP table of FMEA is used as FIS fuzzy inference rule. In this way, the idea of FMEA to determine the risk level of failure mode is retained and the problem of FIS fuzzy rule making is overcome. Then, FIS defuzzification AP table inference results to determine the risk ranking of failure modes. This avoids situations where the order of failure modes is equal. Finally, a performance degradation model of the electromagnet is constructed based on the Wiener process, and the calculation results of the new method are verified.

Engineering characteristics prioritization for quality function deployment by using CRITIC and cumulative prospect theory with extended hesitant fuzzy linguistic term sets

Quality Function Deployment (QFD) is a powerful approach for improving product quality that can transform customer requirements (CRs) into engineering characteristics (ECs) during product manufacturing. The limitations of traditional QFD methods lead to imprecise quantification of CRs and difficulty in accurately mapping customer needs. To address these issues, this paper introduces an innovative QFD approach that integrates extended hesitant fuzzy linguistic term sets (EHFLTSs), CRITIC, and cumulative prospect theory. The method expresses the subjectivity and hesitancy of decision makers when evaluating the relationship between ECs and CRs using EHFLTSs, considering the conflicts among CRs. The CRITIC is used to comprehensively evaluate the comparison strength and conflict between indicators, and the cumulative prospect theory is utilized to derive the prioritization of ECs. A case study is presented to demonstrate the effectiveness of the proposed approach.

A 2-order additive fuzzy measure identification method based on hesitant fuzzy linguistic interaction degree and its application in credit assessment

To reflect both fuzziness and hesitation in the evaluation of interactivity between attributes in the identification process of 2-order additive fuzzy measure, this work uses the hesitant fuzzy linguistic term set (HFLTS) to describe and depict the interactivity between attributes. Firstly, the interactivity between attributes is defined by the supermodular game theory. According to this definition, a linguistic term set is established to characterize the interactivity between attributes. Under the linguistic term set, the experts employ linguistic expressions generated by context-free grammar to qualitatively describe the interactivity between attributes. Secondly, through the conversion function, the linguistic expressions are transformed into the hesitant fuzzy linguistic term sets (HFLTSs). The individual evaluation results of all experts were further aggregated with the defined hesitant fuzzy linguistic weighted power average operator (HFLWPA). Thirdly, based on the standard Euclidean distance formula of the hesitant fuzzy linguistic elements (HFLEs), the hesitant fuzzy linguistic interaction degree (HFLID) between attributes is defined and calculated by constructing a piecewise function. As a result, a 2-order additive fuzzy measure identification method based on HFLID is proposed. Based on the proposed method, using the Choquet fuzzy integral as nonlinear integration operator, a multi-attribute decision making (MADM) process is then presented. Taking the credit assessment of the big data listed companies in China as an application example, the analysis results of application example prove the feasibility and effectiveness of the proposed method. This work successfully reflects both the fuzziness and hesitation in evaluating the interactivity between attributes in the identification process of 2-order additive fuzzy measure, enriches the theoretical framework of 2-order additive fuzzy measure, and expands the applicability and methodology of 2-order additive fuzzy measure in multi-attribute decision making.

Novel Distance Measures of Multigranular Unbalanced Hesitant Fuzzy Linguistic Term Sets Based on Semantics Intervals.

In the field of qualitative decision making with hesitant fuzzy linguistic term sets (HFLTSs), distance measure (DM) is a significant concept that reflects the degree of difference between HFLTSs. Various DMs among HFLTSs have been proposed, which enhance the applicability of HFLTSs in multicriteria decision making (MCDM) under qualitative hesitation information. However, existing strategies not only have their own pros and cons but also fail to measure linguistic assessments from multiple linguistic term sets with different distributions (i.e., multigranular unbalanced linguistic information). In this article, we first propose a new strict DM between two hesitant fuzzy linguistic elements (HFLEs) based on the Wasserstein distance of their semantics intervals. Then, two novel kinds of DMs (Euclidean and Chebyshev forms) for HFLTSs are proposed and their strictness is proved. Weighted and ordered weighted versions of HFLTSs based on Euclidean form are derived. Afterward, illustrative examples, simulations, and related analyses are given to demonstrate the rationality of the proposals. Finally, two cases are applied to verify the efficiency and practicality of the novel measures in real life.

Risk assessment of deep-sea floating offshore wind power projects based on hesitant fuzzy linguistic term set considering trust relationship among experts.

The development of deep-sea floating offshore wind power (FOWP) is the key to fully utilizing water resources to enhance wind resources in the years ahead, and then the project is still in its initial stage, and identifying risks is a crucial step before promoting a significant undertaking. This paper proposes a framework for identifying risks in deep-sea FOWP projects. First, this paper identifies 16 risk criteria and divides them into 5 groups to establish a criteria system. Second, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are utilized to gather and describe the criterion data to ensure the robustness and completeness of the criterion data. Third, extending the method for removal effects of criteria (MEREC) to the HFLTS environment through the conversion of TFNs, under the influence of subjective preference and objective fairness, a weighting method combining analytic network process (ANP) and MEREC is utilized to calculate criteria weights, and the trust relationship and consistency between experts are used to calculate the expert weights to avoid the subjective weighting given by experts arbitrariness. Fourth, the study's findings indicated that the overall risk level of the deep-sea FOWP projects is "medium." Fifth, sensitivity and comparative analyses were conducted to test the reliability of the assessment outcomes. lastly, this research proposes risk management measures for the deep-sea FOWP project's establishment from economic, policy, technology, environment, and management aspects.

Resilience evaluation of low-carbon supply chain based on improved matter-element extension model.

How to evaluate the resilience level and change trend of supply chain is an important research direction in current supply chain management practice. This paper proposes a new method of supply chain resilience assessment based on hesitant fuzzy linguistic term set (HFLTS) and matter element extension theory. Firstly, based on the research status quo at home and abroad, a low-carbon enterprise supply chain resilience assessment index system is established, which includes six first-level indicators and corresponding 21 second-level indicators of product supply resilience, resource resilience, partner resilience, information response resilience, financial resilience and knowledge resilience. Secondly, HFLTS was used to collect expert opinions and Ordered Weighted Arithmetic (OWA) to calculate the expert composite language, by which the fuzzy evaluation matrix of supply chain resilience assessment indicators was obtained. Once again, the resilience indicator weights are determined based on a game-theoretic portfolio assignment method combining the best-worst method (BWM) and the CRITIC method. Finally, the nearness degree function is combined with the extension comprehensive evaluation method to improve the matter element extension model, and the supply chain resilience assessment model of low-carbon enterprises based on the game theory combination assignment-improved matter element extension is established. Taking X low-carbon enterprise as an example, the evaluation results show that the supply chain resilience level of this enterprise is II, and the eigenvalue of the grade variable is 2.69, and the supply chain resilience is shifting to III, and the supply chain resilience is shifting to III, which indicates that the supply chain resilience of this enterprise is being enhanced. Therefore, the improved matter element extension not only ensures the accuracy of the evaluation results, but also has higher prediction accuracy.

A novel approach to hazard evaluation for MASS operation based on a hesitant fuzzy linguistic term set

Expert judgment has been widely utilized in ship risk assessment. The traditional method is believed to be capable of processing only designated linguistic variables, which fails to capture the degree of expert hesitation in assessing a complex system. A novel model, the bi-independent hesitant fuzzy linguistic term set (BHFLTS), based on the hesitant fuzzy linguistic term set (HFLTS), is proposed to incorporate this hesitation. This paper also identifies ten navigational hazards to maritime autonomous surface ship (MASS) operation based on a literature review. In addition, the BHFLTS approach combined with the technique for order preference by similarity to an ideal solution (TOPSIS) is utilized for hazard assessment and ranking, and with this method, “Collision avoidance decision algorithm malfunction”, “Ship situation awareness system failure” and “Command transmission system failure” are rated as the top three navigational hazards that threaten MASS operation.

Evaluation of optimal waste lithium-ion battery recycling technology driven by multiple factors

As energy and environmental issues become increasingly prominent, the process of global carbon emission reduction is now underway. In the transportation sector, the application of electric vehicles is an effective way to achieve energy saving and emission reduction. Lithium-ion battery, an important component of electric vehicles, has received widespread attention for its advantages including small size, high energy density and smooth discharge voltage. However, their potential safety and pollution issues are becoming prominent and must be properly handled. Moreover, the technical route and future direction of LIB recycling are still unclear at this stage. Herein, this paper evaluates different waste lithium-ion battery recycling technologies in a multi-criteria decision framework to determine the best technology. A criteria system driven by multiple factors is established, including environmental impact (C1), technical risk (C2), comprehensive resource utilization (C3), resource consumption (C4) and economic performance (C5). Subsequently, since the optimal technology is novel and difficult to select, the hesitant fuzzy linguistic term sets are used to express the degree of hesitation in expert decision making. Finally, from the aspects of the choice of sorting method, the Tomada de decisao interativa multicriterio method based on the expectation effect is used to evaluate the optimal waste lithium-ion battery recycling technology, and the validity of the framework is illustrated by data analysis.

A multi-granularity hesitant fuzzy linguistic decision making VIKOR method based on entropy weight and information transformation

Multi-granularity hesitant fuzzy linguistic terms set is an effective expression of linguistic information, which can utilize some fuzzy linguistic terms to evaluate various common qualitative information and plays an important role when experts provide linguistic information to express hesitancy. Since the alternative description in the decision-making information system is characterized by multi-granularity, uncertainty, and vagueness, this paper proposes a multi-granularity hesitant fuzzy linguistic decision-making VIKOR method based on entropy weight and information transformation. Specifically, this paper firstly adopts fuzzy information entropy to obtain the weights of different attributes and introduces a multi-granularity hesitant fuzzy linguistic term set conversion method to realize the semantic information conversion between different granularities. Then for the converted affiliation linguistic decision matrix, the entropy weighting method is used to obtain the weights of different affiliation granularity layers, and a weight optimization VIKOR method based on the affiliation linguistic decision matrix is further proposed to rank the alternatives. Finally, the feasibility of the proposed method verified by arithmetic examples, experimental analysis is carried out in terms of parameter sensitivity analysis and comparison with other methods. The experimental results prove the rationality and effectiveness of the proposed method.

A hybrid heterogeneous framework for medical waste disposal evaluation by fusing group BWM and regret-rejoice MABAC

This article attempts to develop a hybrid heterogeneous framework to evaluate the disposal modes for emergency medical waste (EMW) by fusing GBWM (group best-worst method), RT (regret theory), and MABAC (multi-attributive border approximation area comparison). This hybrid framework tries to address three key challenges: (i) determining criteria weights, (ii) computing the regret-rejoice evaluation matrix, and (iii) identifying the optimal alternative. To address the first challenge, this paper innovates an innovative interval-valued intuitionistic fuzzy (IVIF) GBWM, which considers multiplicatively consistent IVIF preference relation, to efficiently determine global criteria weights (GCWs). For the second challenge, RT is incorporated to compute the regret-rejoice evaluation matrix using experts' heterogeneous evaluations which consist of intervals, triangular fuzzy numbers, hesitant fuzzy linguistic term sets and IVIF values. Regarding the third challenge, MABAC is applied to sort alternatives based on the obtained regret-rejoice evaluation matrix. Afterwards, the validity of the hybrid framework is verified by an illustrative example, and its advantages are demonstrated with some worthy sensitivity and comparative analyses. Some innovations of the proposed framework are highlighted below: (i) The IVIF GBWM streamlines calculation load, since it derives GCWs by solving the group weight assignment model only once; (ii) The introduction of RT into MABAC assists decision-makers in mitigating regret psychology, enhancing the quality of decision outcomes; (iii) The proposed framework’s practicality is enhanced by leveraging heterogeneous evaluations which capture the inherent complexities of actual decision-making problems.

Lean Manufacturing Assessment: Dimensional Analysis with Hesitant Fuzzy Linguistic Term Sets

Lean Manufacturing has become, in recent years, one of the most important philosophies for improving production and organizational systems. The literature shows that Hesitant Fuzzy Linguistic Terms Sets (HFLTSs) are highly capable of manipulating the uncertainty that the judgments made by evaluators carry and that they are subject to their perception, especially when used in combination with multicriteria decision making (MCDM) for the measurement of indicators in this type of system, as well as their general performance. However, it is still of interest to researchers to develop techniques and instruments that facilitate the measurement of the results obtained after applying this philosophy in organizations. This article proposes a model for the evaluation of the Lean Manufacturing performance through the Analytic Hierarchy Process (AHP) and Dimensional Analysis (DA) using HFLTSs. The results obtained show that the proposed model is a solid tool for the evaluation of Lean Manufacturing systems from a different perspective and that it can be integrated into the issuance of evaluations in a better way by considering human subjectivity. At the same time, it offers a strategy to create priorities in the action plans that Lean system managers propose after evaluating. However, it is important to apply the proposed model to multiple organizations and analyze the results obtained to maximize its benefits.

An integrated hesitant 2-tuple linguistic Pythagorean fuzzy decision-making method for single-pilot operations mechanism evaluation

Single-pilot operations (SPO), i.e., one pilot on board the flight in charge of all the operations, has received extensive attention in recent years. SPO involves several participants including the pilot, the autopilot, and the ground operator, and it is of great significance to determine the roles of these participants under the SPO mechanism. However, the evaluation of SPO mechanism has received little attention. Aiming at providing a reliable approach for SPO mechanism evaluation under uncertainty, this paper proposes a novel hesitant 2-tuple linguistic Pythagorean fuzzy decision-making method based on hesitant fuzzy linguistic term sets (HFLTS), 2-tuple linguistic information, Pythagorean fuzzy sets (PFS), and VIKOR method to handle the SPO mechanism evaluation problem. Firstly, based on the analysis of SPO, the evaluation criteria system for SPO mechanism is established. Then, considering the uncertain information in the SPO mechanism evaluation process, the HFLTS is used to represent the linguistic judgments of experts under uncertainty. Next, the HFLTSs of different experts are converted into 2-tuple linguistic information and PFSs to model the overall judgments of different criteria. Finally, the extended VIKOR method is adopted to evaluate and rank different SPO mechanisms. A case study of SPO mechanism evaluation for the ground proximity warning system (GPWS) is used to demonstrate the effectiveness and feasibility of the proposed method, and the SPO mechanism that the autopilot being responsible for GPWS is evaluated by the proposed method to be optimal. Sensitivity and comparison analyses further confirm that the proposed method could provide reliable and reasonable SPO mechanism evaluation results. In conclusion, the proposed method presents a novel and effective way to evaluate and select appropriate SPO mechanisms.

Three-way group decisions using evidence theory under hesitant fuzzy linguistic environment

In the actual decision-making process, there will be situations where decision-makers with hesitant attitudes have difficulties in evaluating alternatives numerically, and hesitant fuzzy linguistic term sets can provide decision-makers with an effective way to describe hesitancy in linguistic terms. In multi-attribute group decision-making, each decision maker typically holds different preferences. If the variation in decision makers’ assessment weights across evaluations of each attribute for every alternative is not adequately accounted for, it can result in a problem of coarse-grained calculations, leading to information loss. Additionally, the three-way decision model faces significant challenges in information fusion within the context of the hesitant fuzzy linguistic environment. Therefore, we propose a new three-way decision-making model under the hesitant fuzzy linguistic environment. The model obtains the confidence of different decision makers in attribute evaluations through the fusion of D-S evidence theory, and can perform more fine-grained fusion calculations on the evaluation information of different decision makers. In addition, the model considers the cost function of each alternative in different decision-making actions under hesitant fuzzy linguistic environment, calculates the two thresholds of each alternative in the three-way decision model, and derives the decision rules. The effectiveness of the model is verified through a numerical example and two comparative experiments, therefore, the model can be applied in intelligent classification or recommendation systems of hesitant fuzzy linguistic information systems.