Hesitant Fuzzy Linguistic Term Sets Research Articles

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.

A Systematic Decision-Making Approach for Quality Function Deployment Based on Hesitant Fuzzy Linguistic Term Sets

As a powerful tool for improving customer satisfaction, quality function deployment (QFD) can convert customer requirements (CRs) into engineering characteristics (ECs) during product development and design. Aiming to address the deficiencies of traditional QFD in expert evaluation, CRs’ weight determination and ECs’ importance ranking, this paper proposes an enhanced QFD model that integrates hesitant fuzzy binary semantic variables, the Best–Worst Method (BWM), and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The objective is to determine the prioritization of product engineering characteristics. Indeed, hesitant fuzzy linguistic term sets (HFLTS) have found extensive application in decision-making problems. Compared to other fuzzy language methods, HFLTS offers greater convenience and flexibility in addressing decision-makers’ hesitations and uncertainties. Initially, the combination of hesitant fuzzy linguistic term sets with interval binary tuple language variables is employed to articulate the uncertainty in the assessment information provided by QFD team members. Subsequently, the improved BWM and TOPSIS methods based on HFLTS are used to improve the accuracy of the importance ranking of engineering characteristics by determining the weights of CRs and prioritizing ECs in two stages. Finally, the feasibility and effectiveness of the proposed method are validated through an illustrative example.

A consumers’ Kansei needs mining and purchase intention evaluation method based on fuzzy linguistic theory and multi-attribute decision making method

Numerous previous researches have demonstrated that consumers are increasingly prioritizing their Kansei needs, with the development of technology and social economy. Moreover, whether consumers' Kansei needs (CKN) can be satisfied greatly affects their purchase intention (PI). Despite the substantial impact of CKN on PI, there remains a paucity of research on this subject. To bridge this gap, this paper proposes a CKN mining and PI evaluation method. Firstly, the double hierarchy hesitant fuzzy linguistic term set is employed to enhance the semantic differential model, facilitating the acquisition of product Kansei features evaluation and PI information. Subsequently, grey correlation analysis is applied to calculate the correlation coefficient between them, enabling quantitative CKN mining. Then, cluster consumers based on the correlation coefficient, and analyze the difference of CKN. Thirdly, construct the PI evaluation model based on the multi-attribute decision-making method, taking CKN into consideration, to rank the alternative product. Finally, the proposed method is implemented in the armchair evaluation problem. Comparative experimental results affirm the improved semantic differential model has higher reliability, which can be used to deal with the subjectivity and complexity of product evaluation information. Additionally, the feasibility and effectiveness of the PI evaluation model is validated through eye movement experiments, which can predect the consumers’ Kansei perference and rank the alternative products.

Sustainability assessment of small hydropower from an ESG perspective: A case study of the Qin-Ba Mountains, China

Small hydropower (SHP) has made significant contributions to economic and social development in rural and remote mountainous regions. However, the adverse ecological-environmental impacts resulting from the SHP sector and challenges in hydropower management have become major areas of concern. From an Environmental, Social, and Governance (ESG) perspective and using three SHP stations (GXD, WZL, and SJB) in the Qin-Ba Mountains as case studies, we constructed a sustainability assessment system comprising 18 indicators across three dimensions. The hesitant fuzzy linguistic term sets (HFLTSs) and cloud models were employed to determine the sustainability level of SHP by characterizing the hesitancy of the evaluator and the uncertainty of the evaluated data. (1) The ecological-environmental protection (E) dimension was assigned the greatest weight, followed by the dimensions of social responsibility contribution (S) and corporate governance management (G). The weights of certain indicators, including the water qualification rate, river morphology maintenance, guaranteed rate of instream flow, comprehensive utilization, and production safety standardization grade were relatively high, conforming to the current context of green development prioritization in which ecological-environmental protection is of the utmost importance. (2) The overall sustainability levels of all three SHP stations were “good”, with the E-dimension contributing the most and the G-dimension contributing the least to the sustainability goal. (3) The GXD, WZL, and SJB stations were ranked first, second, and third, respectively, in terms of their sustainability scores. This study provides an innovative perspective for the sustainability assessment of SHP. The evaluation method can be generalized to encompass multi-attribute decision-making problems. The findings of this study can aid in addressing the shortcomings associated with SHP development and promote sustainability within the SHP industry.

Configuration optimization of product-service system design requirements based on hesitant information axiom

Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solution-providing, which is a marketable set of products and services. The existing researches emphasize the fulfillment of individualized customer requirements through different PSS configurations. The PSS planning phase is of high importance in generating conceptual schemes, which translates customer requirements (CRs) to design requirements (DRs). In this paper, a systematic decision-making approach based on QFD is put forward aiming to configure the PSS design requirements (DRs). To address the uncertainty and hesitancy in QFD modeling, a hesitant fuzzy linguistic term sets (HFLTSs) is applied to elicit the experts’ linguistic preferences in evaluating the importance of CRs and the relationships between CRs and DRs. To dealing with the group decision-making problems concerning the HFLTSs, the min-upper operator and the max-lower operator assemble the experts’ evaluation results into a linguistic interval, and then the numerical results can be obtained by using the 2-tuple linguistic representation model and the interval preference degree computation. A non-linear 0-1 programming model is proposed to select the target DRs’ specifications for maximizing customer satisfaction under cost constraint. In order to objectively determine the satisfaction degree of each optional specification of DR, the information axiom is introduced to construct the objective function via information content computation. To deal with the qualitative DRs, HFLTSs and information axiom are combined and hesitant information axiom (HIA) is proposed. Finally, a DRs optimization model is established using HIA and the imprecision method. A case study is carried out to demonstrate the effectiveness of the optimal PSS planning approach developed.

Personalized fuzzy semantic model of PHFLTS: Application to linguistic group decision making

The proportional hesitant fuzzy linguistic term set (PHFLTS) has been effectively employed in analyzing the group’s hesitancy in linguistic group decision making (LGDM). The application of PHFLTS assists in capturing the individual’s hesitancy across diverse time periods. It is acknowledged that a single word could potentially convey various meanings to different decision makers, such differences can be proficiently managed by utilizing personalized individual semantic (PIS) models. Previous approaches for calculating PIS failed to incorporate the individual’s updating preference information over time, which increases the risk that the computation of PIS is affected by random factors in a specific moment. In our current research, individual linguistic preference gathered over a time period are leveraged to form the PHFLTS. Additionally, a consistency driven optimization model based on PHFLTS is formulated to obtain PIS of linguistic terms. Subsequently, a fuzzy representation model termed as the fuzzy envelope of PHFLTS is introduced to facilitate the computation with words processes, integrating PHFLTS in LGDM. The practicality and legitimacy of these proposed models are evaluated through a comparative analysis. Lastly, these proposed models are tested and applied in a dedicated case study to further prove their usefulness and efficacy.

Retracted: A Novel Group Decision-Making Approach for Hesitant Fuzzy Linguistic Term Sets and Its Application to VIKOR

Retracted: A Novel Group Decision-Making Approach for Hesitant Fuzzy Linguistic Term Sets and Its Application to VIKOR

A comparison of hesitant fuzzy VIKOR methods for supplier selection

Supplier selection is a complex decision-making process which is influenced by uncertainty and DM hesitation. To deal with this problem, various studies have proposed the use of methods based on Fuzzy Set Theory and its extensions. There are several varieties of Hesitant Fuzzy Linguistic VIKOR (HFLVIKOR) methods in the literature which can use two or more linguistic terms in each evaluation and can generate solutions based on the group’s utility or individual regret. Despite these benefits, we have not found comparative studies involving HFLVIKOR methods to supplier selection. Given this gap, this study applies and compares Extended Hesitant Fuzzy Linguistic VIKOR (EHFLVIKOR) methods and Hesitant Fuzzy Linguistic Term Set VIKOR (PDHFLVIKOR) methods with Possibility Distributions within the supplier selection context. This application was realized using a service company. We also performed 8 scenario simulations to analyze the effect of including other criteria and alternatives. Despite the identified limitations of each method, we conclude that both are appropriate for dealing with group decision making under uncertainty and require little effort in their data collection. The PDHFLVIKOR results were more consistent in maintaining the ranking and the compromise solution in the real application as well as simulated scenarios. Unlike PDHFLVIKOR, EHFLVIKOR loses information when there is a consensus. On the other hand, because it makes it possible to create subgroups of DMs, EHFLVIKOR appears to be more appropriate for companies in which there is more than one DM involved in supplier selection for each functional area. This study’s results will help managers and academics in selecting the most appropriate method for supplier selection.

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 evaluation 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 subjective 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.