Hierarchy Hesitant Fuzzy Linguistic Term Research Articles

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 inteactions, 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.

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 inteactions, 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 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.

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.

Failure Evaluation of Electronic Products Based on Double Hierarchy Hesitant Fuzzy Linguistic Term Set and K-Means Clustering Algorithm

The extent of failure evaluation depends on the complexity and significance of electronic products. However, traditional failure mode and effect analysis (FMEA) has many shortcomings, which brings large difficulty to failure evaluating work. This paper uses the double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) and the K-means clustering algorithm to improve the shortcomings of traditional FMEA. The DHHFLTS is a set of language terms based on the symmetry method and uniform language granularity. Firstly, we determine the product failure mode and set up an evaluation team after formulating an evaluation symmetrical language set. The psychological changes of the evaluators can be truly expressed by using the DHHFLTS. Secondly, the entropy weight method is used to calculate the weight of the evaluation members. The evaluation information of the evaluation personnel on the failure mode is aggregated based on the weight of the evaluation members. Then, the K-means clustering algorithm is used to calculate the distance between failure modes and each cluster center point by using the normalized weight of influencing factors and the evaluation distance of each evaluator. Finally, the evaluation of an electromagnet failure mode is taken as an example to prove the objectivity and practicability of the new method.

A New Model for Failure Mode and Effects Analysis Based on k-Means Clustering Within Hesitant Linguistic Environment

Failure mode and effects analysis (FMEA) is a systematic and proactive risk prevention and management tool used to improve the reliability and safety of a system, process or service. However, many defects around the traditional FMEA affect the effectiveness of its practical applications. When dealing with the FMEA problems, the first and the most important step is to collect the risk assessment information of experts regarding failure modes. Moreover, clustering failure modes is more adaptable to actual needs, especially under a tight resource constraint. In this article, a new FMEA model using double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering is developed to evaluate and cluster the risk of failure modes. First, the DHHFLTSs are utilized to describe the complex linguistic assessments on failure modes given by FMEA team members. Next, an improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means algorithm is proposed and used to cluster failure modes into priority classes. Finally, a case study concerning the risk evaluation over floating offshore wind turbine is provided to illustrate the effectiveness and usefulness of our proposed FMEA. The results show that the new risk classification approach is more reasonable and practical for risk management decision making.

A fuzzy multicriteria group decision approach for classification of failure modes in a hospital’s operating room

The operating room (OR) is one of the most important departments of any hospital. Mismanagement can lead to severe problems with patient’s safety and satisfaction, costs, and profitability. The Failure Mode and Effects Analysis (FMEA) is a risk assessment tool used to identify potential failure modes (FM) which should be prioritized to be mitigated. To assess the risks of an OR, some important aspects cannot be neglected: it should be conducted by a team of experts from different functional areas; address the non-compensation between criteria; classify FM, instead of ranking it, since it is easier to understand and support decision making; address the subjectivity present on experts’ opinion; and consider the mitigation complexity, since hospital’s resources are limited. However, were not found studies in the literature which simultaneously covered all these aspects. Therefore, this paper proposes a new group decision model, which combines a consensus reaching process with ELECTRE TRI and double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS). A pilot application of the model was conducted in an OR of a Brazilian hospital. The results have shown that the consensus technique, the mitigation complexity, and the non-compensation can modify the failure modes classification. Also, the DHHFLTS allowed the experts to use more complex linguistic terms to express their opinion. In addition, the pilot application was carried out during the covid-19 pandemic and the results have shown that due to the high number of elective surgeries being rescheduled to give space to urgent surgeries, the patients' health problems become even worst and they turn to urgent surgeries.

Three-way investment decisions during the epidemic with Choquet-based bi-projection method

The outbreak of epidemic has had a big impact on the investment market of China. Facing the turbulence in the investment market, many enterprises find it difficult to judge the development prospects of investment projects and make the right investment decisions. The three-way decisions offer a novel study perspective to solve this problem. Then the developed model is applied to select the investment projects. Firstly, some relevant attributes of the project are described with the double hierarchy hesitant fuzzy linguistic term sets. And a double hierarchy hesitant fuzzy linguistic information system is constructed for each project. Secondly, the weights of attributes are determined with the Choquet integral method. And the closeness degree calculated by Choquet-based bi-projection method is taken as the conditional probability that the project will be profitable. Next, considering the influence of the bounded rationality of decision makers, the threshold parameters are calculated based on prospect theory. Finally, the decision results about investment projects during four stages are deduced based on the principle of maximum-utility, which demonstrates the practicability and effectiveness of the proposed model.

A new method for quality function deployment using double hierarchy hesitant fuzzy linguistic term sets and axiomatic design approach

As a useful product development technique, quality function deployment (QFD) can convert customer requirements into suitable engineering characteristics for maximum customer satisfaction. Nonetheless, the traditional QFD has been extensively criticized as having many defects in practical applications, which include the crisp evaluation of relationships between customer requirements and engineering characteristics, and the non-robust ranking of engineering characteristics. In this paper, we put forward a new method using double hierarchy hesitant linguistic term sets (DHHLTSs) and axiomatic design approach to enhance the performance of the classical QFD. Firs, the DHHLTSs are employed to evaluate the relationships between customer requirements and engineering characteristics. Then, the axiomatic design theory is extended to derive the prioritization of engineering characteristics in QFD. Additionally, a maximizing consensus method is used to determine the weights of experts considering inconsistency among their opinions. Finally, an empirical case of a shared car manufacturer is provided with a comparison analysis to demonstrate the feasibility and effectiveness of our proposed QFD approach.

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.

Risk Assessment of Logistics Enterprises Using FMEA Under Free Double Hierarchy Hesitant Fuzzy Linguistic Environments

Failure Mode and Effects Analysis (FMEA) is an effective tool for prioritization of the failures by identifying and eliminating potential risks, free double hierarchy hesitant fuzzy linguistic term set (FDHHFLTS) can describe the hesitation more accurately and reasonably by adding the free second hierarchy hesitant fuzzy linguistic terms, and multiattribute decision-making (MADM) methods have advantages in ranking and selecting different alternatives. By considering the advantages of MADM methods and FMEA model, in this paper, we propose a novel model to evaluate the risk failure modes (FMs) under FDHHFLTS context. First, an extended Complex Proportional Assessment (COPRAS) method is proposed to address the risk assessment problems. The proposed risk assessment model is utilized to assess and rank the FMs to address the limitations of the traditional FMEA. Second, a weight determination model is constructed based on the Kemeny Median (KEM) method and Stepwise Weight Assessment Ratio Analysis (SWARA) method, which can make the weight more practical and effective under the situation of completely unknown weights. Finally, the proposed model is used to the case of logistics enterprises’ risk assessment. The application of the case shows the feasibility of the proposed model, and the comparisons with some existing methods illustrate the advantages of the proposed risk assessment model.

A generalized TODIM-ELECTRE II based integrated decision-making framework for technology selection of energy conservation and emission reduction with unknown weight information

Energy conservation and emission reduction (ECER) has become an indispensable trend of green life. How to choose a suitable technology for ECER under complex decision-making scenarios has become a key issue faced by traditional industrial enterprises. In this paper, a new integrated decision-making framework is established for technology-selection problems. Firstly, to describe complex linguistic information more accurately and reduce information loss, this framework employs the double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) to describe the performance of alternatives. To avoid data redundancy and simplify the computation, we improve the operations of double hierarchy hesitant fuzzy linguistic elements (DHHFLEs) and propose a new comparison method for DHHFLEs. Secondly, based on the proposed normalized projection-based difference measurement, a novel method is proposed to derive the weights of experts regarding each criterion, the group decision-making method based on the best-worst method is also utilized to derive the criteria’s weights via consistent pairwise comparisons. Further, an integrated generalized TODIM (an acronym in Portuguese of interactive and multiple attribute decision making)-ELECTRE II (Elimination and Choice Translating Reality II) method is established to rank alternatives, which has the advantages of dealing with the non-compensatory problem of criteria and considering risk-aversion behavior of experts simultaneously. Subsequently, a case study of emission-reduction technology investment is provided to manifest the practicality and reliability of the given framework. Finally, the preponderance and effectiveness of the proposed method are illuminated through a comparative analysis with several existing representative methods.

A multi-attribute decision-making framework for Chinese medicine medical diagnosis with correlation measures under double hierarchy hesitant fuzzy linguistic environment

In order to meet the increasingly complex and changeable decision-making needs, the double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) expresses linguistic information through two independent linguistic term sets, which is a powerful tool for expressing complex linguistic information. Therefore, the research on the DHHFLTS has great theoretical and practical significance. Considering that correlation measures are not only the theoretical basis for solving linguistic decision-making problems, but also the cornerstone of many multi-attribute decision-making (MADM) methods. Therefore, this paper is devoted to the research on the correlation measure of DHHFLTSs. Based on the equivalent transformation functions of the double hierarchy hesitant fuzzy linguistic element (DHHFLE), we propose the mean and hesitancy degree of DHHFLEs and the mean and variance of DHHFLTSs. Then, this paper proposes the hesitancy degree-based correlation and correlation coefficient of DHHFLTSs. In addition, on the one hand, considering that hesitation is a key feature of the DHHFLTS, this paper gives the upper and lower bounds of the above correlation coefficient. On the other hand, taking into account the weighting factors in the actual problem, the weighted correlation coefficient and the ordered weighted correlation measure are proposed. Then, a general framework for applying the correlation measures proposed in this paper to practical MADM problems is given. Finally, taking the diagnosis of traditional Chinese medicine as an example, it proves that this method is scientific and feasible, and compares and analyzes this method with the existing related correlation measure methods.

Work Resumption After Epidemic Using Three-Way Decisions

After the epidemic, all enterprises are faced with the difficult decision about whether the enterprise should resume work and production immediately, which is related to the safety and development of enterprises. The three-way decisions offer a novel study perspective to solve this problem. Firstly, we describe some relevant attributes of the enterprise with double hierarchy hesitant fuzzy linguistic term sets, and construct double hierarchy hesitant fuzzy linguistic information systems for each enterprise. Secondly, we get the weights of attributes with the entropy weight method, and take the weighted aggregation of attributes information as the conditional probability that the enterprise is in a safe state. Next, we classify each enterprise according to its size. Considering the influence of different sizes of enterprises, we put forward the corresponding loss function matrix. Then we get the decision results about work resumption based on the principle of minimizing the loss, which demonstrates the practicability and effectiveness of our method. Finally, we compare the method proposed by us with the other method and discuss the advantages and limitations of our proposed method.

A Multiple Attribute Decision-Making Method Based On Free Double Hierarchy Hesitant Fuzzy Linguistic Information Considering the Prioritized and Interactive Attributes

As the development and extension of hesitant fuzzy linguistic term sets (HFLTSs), free double hierarchy hesitant fuzzy linguistic term sets (FDHHFLTSs) can describe the evaluation information in more detail. In addition, in practical multiple attribute decision-making (MADM) problems, priority relations and interaction relations usually exist among attributes, and the prioritized interactive Choquet (PIC) operator based on generalized prioritized measure-guided aggregation (GPMGA) operator and Choquet integral (CI) operator is an effective tool for dealing with decision-making problems with priority relations and interaction relations. Under the above contexts, we improve the PIC operator and propose the FDHHFLPIC operator which can aggregate the evaluation information under free double hesitant fuzzy linguistic environment and take both priority and interaction relations into account. Furthermore, we propose an approach based on the FDHHFLPIC operators to address the real MADM problem, and the proposed method considering the different relations between attributes in decision-making process. Finally, two numerical examples are given to demonstrate the effectiveness and advantages of the proposed method which can consider priority relations and interaction relations under the FDHHFLTSs.

Probabilistic double hierarchy linguistic term set and its use in designing an improved VIKOR method: The application in smart healthcare

Double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) can be used to express complex linguistic information by combining two hierarchy linguistic term sets with 2-tuple linguistic structures. In decision-making processes, experts’ assessment information may often be represented by some possible double hierarchy hesitant fuzzy linguistic elements (DHHFLEs) or some DHHFLEs with probability information, and we cannot ignore these probabilities when they are directly provided or aggregated by the experts’ assessments. As we are aware that representing probability information is a new improvement and challenge for DHHFLTSs, this paper defines a novel and more general concept named probabilistic double hierarchy linguistic term set (PDHLTS). Then, to propose some more reasonable operations and a distance measure of PDHLTSs, we develop an adjustment method to ensure that two PDHLTSs have same probability distribution. Additionally, this paper develops an extended probabilistic double hierarchy linguistic VIKOR method by improving the traditional VIKOR method. Moreover, the advantages and practicality of the proposed method are demonstrated by applying it to solve a practical multiple criteria decision-making problem involving smart healthcare. Finally, we make some comparative analyses, as well as discussing possible directions for future studies.

An integrated method for multi-criteria decision-making based on the best-worst method and Dempster-Shafer evidence theory under double hierarchy hesitant fuzzy linguistic environment

Double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is a newly developed complex linguistic expression model, and has been well applied to multi-criteria decision-making (MCDM) problems. However, the determination of criteria weights and the innovation of decision-making methods are still two issues that worth exploring in this field. At present, conventional weight-determination methods sometimes have the disadvantages of complicated calculation and low consistency of the obtained results. On the other hand, the existing methods for linguistic information sometimes cannot consider the uncertainty of information caused by ignorance. Considering that the best-worst method (BWM) is a weight-determination method, which can not only greatly simplify the calculation process, but also improve the consistency degree of the results. Dempster-Shafer evidence theory (DSET) can better deal with information uncertainty caused by ignorance. Therefore, this paper extends the BWM and DSET to double hierarchy hesitant fuzzy linguistic environment to solve the above two problems respectively, and the DHHFL-BWM-DSET method is proposed. First, the weight of each criterion is derived based on the BWM-based weight-determination method. Then, inspired by DSET, we propose a DSET-based MCDM method which can not only obtain the decision results of a single decision maker, but also integrate the decision information of multiple decision makers to obtain more rational results. Therefore, decision makers can choose the method based on the specific situation. Finally, taking the selection of financial products as an example, it shows that the method proposed in this paper has some breakthroughs and advantages.

Double hierarchy hesitant fuzzy linguistic entropy-based TODIM approach using evidential theory

Double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) can describe hesitation more accurately and reasonably than other linguistic representation models by adding a second hierarchy hesitant fuzzy linguistic term set. The Dempster–Shafer evidence theory (DSET) can minimize the loss of evaluation information in denoting and fusing uncertain information, and TODIM (an acronym in Portuguese for interactive and multi-criteria decision making) can consider the loss aversion behavior of decision makers (DMs). Considering their unique advantages, in this study, we propose a novel multiple criteria group decision-making method based on DSET and TODIM under the DHHFLTSs. First, we improve the cumulative functions and distance measure of DHHFLTSs. Next, we apply DSET to fuse double hierarchy hesitant fuzzy linguistic (DHHFL) information provided by a group of experts and obtain an evidence matrix consisting of belief degrees and DHHFLTSs. Then, we use the TODIM method to address the evidence matrix based on improved cumulative functions and distance measures to obtain the final ranking results. Finally, a weight determination model is constructed based on information entropy in the scenario of completely unknown weights. The newly proposed method reduces the loss of evaluation information and also considers the loss aversion behavior of DMs. Furthermore, an application example of a postgraduate course evaluation is used to demonstrate the effectiveness and rationality of the proposed method. Additionally, we compare the proposed method with other existing methods to further demonstrate the advantages of the proposed method.

Multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic preference information

Double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is one of the successful extensions of the hesitant fuzzy linguistic term set used for describing the uncertain information in a more prominent manner for solving the group decision-making problems. In DHHFLTS, the membership functions are represented in terms of linguistic membership degrees which are a flexible structure for preference elicitation and enrich the ability for rational decision-making with complex linguistic expressions. Driven by the flexibility of DHHFLTS, in this paper, a new decision framework is developed for solving decision-making problems, which provides scientific and rational decisions based on the preference information. For it, initially, a new aggregation operator is proposed for aggregating decision-makers’ preferences. Later, the importance of the attribute weights in the problems is determined by formulating a mathematical model and the COPRAS method is extended to the DHHFLTS context for prioritizing alternatives. The applicability of the presented approach is demonstrated through a numeric example related to green supplier selection. A comparative analysis with existing studies is also administered to test the effectiveness and verify the method.

Identification of Key Performance Indicators for Hospital Management Using an Extended Hesitant Linguistic DEMATEL Approach.

Performance analysis is of great significance to increase the operational efficiency of healthcare organizations. Healthcare performance is influenced by numerous indicators, but it is unrealistic for administrators to improve all of them due to the restriction of resources. To solve this problem, we integrated double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) with the decision-making trial and evaluation laboratory (DEMATEL) and proposed a DHHFL– DEMATEL method to identify key performance indicators (KPIs) in healthcare management. For the developed approach, the judgments of experts on the inter-relationships among indicators were represented by DHHFLTSs, and a novel combination weighting approach was proposed to obtain experts’ weights in line with hesitant degree and consensus degree. Then, the normal DEMATEL method was extended and used for examining the cause and effect relationships between indicators; the technique for the order of preference by similarity to the ideal solution (TOPSIS) method was utilized to generate the ranking of performance indicators. Finally, the feasibility and effectiveness of the proposed DHHFL–DEMATEL approach were illustrated by a practical example in a rehabilitation hospital.