Hesitant Fuzzy Linguistic Term Sets Research Articles

A Large Scale Group Three-Way Decision-based consensus model for site selection of New Energy Vehicle charging stations

The development of New Energy Vehicles (NEVs) has contributed to the alleviation of environmental pollution in the transportation sector. Although NEVs have some advantages, such as energy saving, being environmentally friendly, and low noise, they are restricted by their cruising range or recharging-related problems. To minimize such disadvantages, an optimal plan for the charging station site is necessary. This paper proposes a Large-Scale Group Decision-Making (LSGDM) method to select the best location for such charging stations. This method involves a large group of experts providing their preferences according to their knowledge and background. To facilitate the elicitation, our method uses Hesitant Fuzzy Linguistic Term Sets (HFLTS) and Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) model to improve the elicitation process, guarantee precise computing processes and obtain interpretable results. A social network is then constructed based on experts’ preference similarity and trust relationships, which reflects both their relationship and its strength simultaneously. Afterwards, a social analysis-based clustering process groups the experts, and a Three-Way Decision (TWD)-based Consensus Reaching Process (CRP) is introduced to improve the group’s agreement. Finally, a selection of charging station site case studies is conducted, and a comparative analysis is carried out to illustrate the quality of the proposal.

A Novel General Data Envelopment Analysis Based Approach for MCDM Issues of Hydrogen Energy under a Fuzzy Environment

Multi-criteria decision-making (MCDM) issues related to hydrogen energy usually involve weight consideration of evaluation criteria, ambiguous evaluation information provided by experts, and lacking or incomplete evaluation information. Conventionally, the MCDM calculation method cannot effectively and simultaneously process evaluation information. To effectively address this issue, this study proposed a novel general data envelopment analysis (DEA)-based approach for MCDM issues of hydrogen energy under a fuzzy environment. The proposed general DEA-based approach integrates the typical DEA method, analytic hierarchy process (AHP) method, hesitant fuzzy linguistic term set (HFLTS), and the soft set to process the MCDM problems under a fuzzy environment. For the numerical verification, this study used the case of hydrogen energy key technologies ranking, as an important development reference for carbon reduction to further verify the correctness and reasonableness of the proposed approach. The calculation results were also compared with those from the typical DEA method, typical AHP/DEA method, and the fuzzy AHP/DEA method. The numerical verification results show that the proposed method can effectively process the MCDM problems under a fuzzy environment than listing different calculation methods.

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 consensus model for large scale group decision making with hesitant fuzzy linguistic information and hierarchical feedback mechanism

Large scale group decision making (LSGDM) has become a hot research topic in the field of decision science. Reaching a consensus in LSGDM is more difficult and complex than the traditional group decision making (GDM). In this paper, we propose a consensus model with hierarchical feedback mechanism for LSGDM dealing with hesitant fuzzy linguistic information. According to the majority rule, the sub-groups with more members are the ones playing a key role in the consensus reaching process. So the sub-groups are divided into three categories including dominant influencer, medium level of influencer and non-influencer. Then, to keep as much information as possible, we use possibility distributed extended hesitant fuzzy linguistic term sets to represent the sub-group opinion. After that, we take into account the sub-group size and the level of togetherness of the sub-group preferences to obtain the weight of each sub-group. Furthermore, a new hierarchical feedback mechanism is developed to provide different identification rules and recommendation strategies for the sub-groups performing different profiles. Finally, an application example of water resource management is utilized to show the effectiveness of the proposed model.

Large-scale group decision-making for prioritizing engineering characteristics in quality function deployment under comparative linguistic environment

Quality function deployment (QFD) is a well-known tool for quality management. With its systematic operation mechanism for translating customer requirements (CRs) into corresponding engineering characteristics (ECs), QFD highly facilitates product/service design and improvement. However, the evolution of the decision-making environment has made the quantification within the traditional QFD operation could hardly able to meet the demanded efficiency as well as the accuracy of the generated results. To cope with the problem, an enhanced large-scale group decision-making method that integrates proportional hesitant fuzzy linguistic term sets (PHFLTSs) and the cumulative prospect theory (CPT) is put forward by this study to determine the ranking priority of ECs in doing QFD. To facilitate QFD team members carry out an easier and more accurate evaluation, they would use comparative linguistic expressions in making judgments, and the comparative linguistic expressions are subsequently transformed into hesitant fuzzy linguistic term sets (HFLTSs). The obtained HFLTSs are transformed into PHFLTSs based on the statistical method under a group decision-making environment for preventing information loss, and, at the same time, improving calculation accuracy. Taking decision-makers’ heterogeneity and risk attitudes into consideration, the CPT method is adopted and extended under the PHFL environment to prioritize the ECs in QFD operation. To illustrate how the proposed large-scale group decision-making-based QFD method is to be applied, a case study about solar photovoltaic cell development is presented. Furthermore, a comparative analysis is conducted to exhibit the effectiveness of the proposed QFD method.

Interval Type-2 Fuzzy Envelope of Proportional Hesitant Fuzzy Linguistic Term Set: Application to Large-Scale Group Decision Making

Large-scale group decision-making (LS-GDM) problems are common in the daily life of human beings. Both information fusion and computing with words (CWW) technologies in LS-GDM suffer from challenges. In the current research, a proportional hesitant fuzzy linguistic term set (PHFLTS) will be applied to capture the preferences of sub-groups in LS-GDM, which decreases the information lost in information fusion processes. Novel fuzzy semantic representation models of PHFLTS, such as type-1 fuzzy envelope and interval type-2 fuzzy envelope, are respectively studied. The application of the proposed fuzzy entropies facilitates the CWW process with the PHFLTS under the framework of a fuzzy linguistic approach. In particular, linguistic uncertainties contained in the PHFLTS can be reflected in a comprehensive way when the type-2 fuzzy envelope is applied, which contributes to the decrease in the information lost during the CWW process. A novel LS-GDM method cooperating with the fuzzy semantic models of PHFLTS is proposed, in which weights for the sub-groups are determined by size, cohesion, and degree of reliability among the sub-groups. Finally, the proposed decision method as well as CWW tools are applied to the process of urban renewal plan selection.

An integrated hesitant fuzzy linguistic model for multiple attribute group decision-making for health management center selection

Eliciting information in multiple attribute group decision-making (MAGDM) is a key for obtaining more reliable results in decisions defined under uncertain domains. In this case, the use of a hesitant fuzzy linguistic term set (HFLTS) as a flexible model to express decision-makers’ (DMs’) qualitative and hesitant decision-making information has been successfully applied to multiple decision situations. However, in many decision situations, DMs’ and attributes’ weights are still subjectively provided. These actions may cause randomness of decision results. Therefore, to solve the two issues, this paper aims to develop a group consensus optimization model and DEA (data envelopment analysis) model to derive DMs’ weights and attributes’ weights, respectively, based on HFLTS. Hence, a new hesitant fuzzy linguistic distance measure is introduced first, and some properties are discussed. The individual consensus measure and the group consensus measure are then defined based on this hesitant fuzzy linguistic distance measure. Subsequently, a new group consensus optimization model is introduced and included in the DEA-based TODIM method for MAGDM with HFLTSs. This integrated approach is applied in health management center site selection. Finally, a sensitivity and a comparative analysis with other methods such as hesitant fuzzy linguistic VIKOR and hesitant fuzzy linguistic TOPSIS are performed to show the validity of our proposed approach.

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.

Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means

Government-invested construction project (GICP) has a great significance to social and economic development but suffered many risks due to its large scale, huge investment, and long construction period. The risks in GICP are complex so as to lead the project to failure; it is extremely urgent to take the risk management of GICP. This study establishes a risk early-warning framework to help the managers to understand the risk threat in advance, which supports them to make proper management strategies for the risk control. The whole framework can be concluded as three parts: information collection, data processing, and result prediction. Firstly, the 16 risk factors of GICP are identified. To express the hesitance of human decision and reduce the information loss in quantification, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are used to collect the experts’ linguistic term and transform them into numerical value. And then, these inputs are simplified into five factors based on principal component analysis (PCA), decreasing the impact of redundancy to risk early-warning. Meanwhile, the warning level is divided based on K-means, which avoids the subjectivity of experience decision. Further, the backpropagation neural network optimized by the genetic algorithm (GA-BP) is used to complete the simulation of risk value. The 75 groups of questionnaire data are used to train the network and the 10 groups are used as test set. The validation of the proposed framework has been verified with an average relative error in 7.2% and the average absolute error in 3.91. Finally, corresponding suggestions to prevent and control the different risks in GICP are put forward.

Lanchester equation for cognitive domain using hesitant fuzzy linguistic terms sets

Intelligent wars can take place not only in the physical domain and information domain but also in the cognitive domain. The cognitive domain will become the key domain to win in the future intelligent war. A Lanchester equation considering cognitive domain is proposed to fit the development tendency intelligent wars in this paper. One party is considered to obtain the exponential enhancement advantage on combat forces in combat if it can gain an advantage in the cognitive domain over the other party according to the systemic advantage function. The operational effectiveness of the cognitive domain in war is considered to consist of a series of indicators. Hesitant fuzzy sets and linguistic term sets are powerful tools when evaluating indicators, hence the indicators are scored by experts using hesitant fuzzy linguistic terms sets here. A unique hesitant fuzzy hybrid arithmetical averaging operator is used to aggregate the evaluation.

A hesitant fuzzy SMART method based on a new score function for information literacy assessment of teachers

As two powerful and flexible tools for decision-makers (DMs) to model the complex cognition, the hesitant fuzzy set (HFS) and hesitant fuzzy linguistic term set (HFLTS) allow DMs to express their opinions with several possible membership values or linguistic terms on the objects over each criterion. The aim of this article is to develop a novel score function of the HFS and HFLTS including hesitant degree and fuzzy degree information. For this purpose, the notion of fuzzy degree of the hesitant fuzzy element (HFE) and hesitant fuzzy linguistic element (HFLE) is introduced first. Then, considering both the hesitant degree and fuzzy degree information in expressions, the new score function, namely the Score-H&FD, is designed. Based on which, we extend the classical SMART (simple multi-attribute rating technique) method to the hesitant fuzzy environment. As a result, the hesitant fuzzy SMART (HF-SMART) method is developed in this article. Afterwards, we apply our proposed approach to assess and rank several teachers concerning information literacy. Finally, sensitive analysis and comparative analysis are carried out. The results show that the proposed method in this article has substantial advantages and applicability.

Type-2 fuzzy envelope of extended hesitant fuzzy linguistic term set: Application to multi-criteria group decision making

Multi-criteria group decision making (MCGDM) is a common activity in human-beings’ daily life. In real-world problems is common that decision makers prefer to use linguistic values instead of numerical values to express their opinions/preferences on the alternatives. Solving MCGDM problems under linguistic environment suffers difficulties such as loss of information during the computing with words (CWW) processes. Existing fuzzy encoding technology is still limited to single word, thus it is difficult to apply previous fuzzy encoding methodologies to carry out CWW processes when more complex linguistic information appears in MCGDM. To overcome these limitations, a novel MCGDM method has been proposed, in which extended hesitant fuzzy linguistic term sets (EHFLTS) is applied to decrease information loss in preference aggregation process. Novel type-1 and type-2 fuzzy envelopes of EHFLTS are proposed to decrease information loss in CWW processes. A new context-free grammar and the notion of rough comparative linguistic expression (RCLE) are introduced to supplement the computation with fuzzy envelopes of EHFLTS, and to increase the flexibility of preference elicitation. The proposed fuzzy encoding models and syntax extend the fuzzy perceptual computing technology from single words to EHFLTS and RCLE. Finally, the proposed MCGDM method which adopts fuzzy envelopes of EHFLTS is applied in a cross-border e-commerce selection situation.

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 hesitant fuzzy linguistic bidirectional projection-regret decision making model

Real-world decision-making problems are usually defined under uncertain contexts due to their complexity. In such situations, the psychological behavior of decision makers is often ignored, and it is assumed that they can provide accurate knowledge despite of being in a context defined as ‘uncertain’. Such assumptions are not realistic and may bias final decisions. To overcome such problems, this paper aims to consider the psychological behavior of decision makers and facilitate preference modeling under uncertainty in the decision-making process. It will do so by introducing a novel decision-making model that includes new bidirectional projection-based regret theory (RT) measures under a hesitant fuzzy linguistic term set (HFLTS) environment, thus establishing novel utility and new regret-rejoice functions to handle decision-making situations. Eventually, a supplier selection case study is used to show the performance of the proposed method, and a fair comparison with two existing methods is carried out to illustrate the advantages of our method.

Distance-Based Knowledge Measure of Hesitant Fuzzy Linguistic Term Set With Its Application in Multi-Criteria Decision Making

Motivated by the structural aspect of the probabilistic entropy, the concept of fuzzy entropy enabled the researchers to investigate the uncertainty due to vague information. Fuzzy entropy measures the ambiguity/vagueness entailed in a fuzzy set. Hesitant fuzzy entropy and hesitant fuzzy linguistic term set based entropy presents a more comprehensive evaluation of vague information. In the vague situations of multiple-criteria decision-making, entropy measure is utilized to compute the objective weights of attributes. The weights obtained due to entropy measures are not reasonable in all the situations. To model such situation, a knowledge measure is very significant, which is a structural dual to entropy. A fuzzy knowledge measure determines the level of precision in a fuzzy set. This article introduces the concept of a knowledge measure for hesitant fuzzy linguistic term sets (HFLTS) and show how it may be derived from HFLTS distance measures. Authors also investigate its application in determining the weights of criteria in multi-criteria decision-making (MCDM).

Obstacle analysis of application of blockchain technology in power data trading based on improved DEMATEL–ISM method under fuzzy environment

Power data transaction is significant to the benefit increment of power enterprises and revitalization of social resources. Blockchain has a wide application prospect in data trading, but its concrete implementation faces many difficulties. This article proposes an obstacle analysis framework to understand the barriers of application of blockchain technology in power data trading, aiming to provide a decision-making guidance and management suggestions to relevant personnel. Firstly, an obstacle index system with 11 barriers is determined from three aspects of application, technology and legal policy. Secondly, a quantitative analysis model is constructed with (IT2TrFN), improved hesitant fuzzy linguistic term set (HFLTS), decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM). And the K-means method is employed to optimize the discrimination of influence relations among indicators. Then, the model is applied in a case study to testify its applicability. The result shows that the difficulty in data maintenance is fundamental obstacle, the barriers of smart contract and supervision in transaction are important factors. Finally, a series of optimization measures and suggestions are put forward for different obstacles.

FLOC: Hesitant Fuzzy Linguistic Term Set Analysis in Energy Harvesting Opportunistic Clustering Using Relative Thermal Entropy and RF Energy Transfer

Limited energy resources and sensor nodes’ adaptability with the surrounding environment play a significant role in the sustainable Wireless Sensor Networks. This paper proposes a novel, dynamic, selforganizing opportunistic clustering using Hesitant Fuzzy Linguistic Term Analysis- based Multi-Criteria Decision Modeling methodology in order to overcome the CH decision-making problems and network lifetime bottlenecks. The asynchronous sleep/awake cycle strategy could be exploited to make an opportunistic connection between sensor nodes using opportunistic connection random graph. Every node in the network observe the node gain degree, energy welfare, relative thermal entropy, link connectivity, expected optimal hop, link quality factor etc. to form the criteria for Hesitant Fuzzy Linguistic Term Set. It makes the node to evaluate its current state and make the decision about the required action (‘CH’, ‘CM’ or ‘relay’). Our proposed scheme leads to an improvement in network lifetime, packet delivery ratio and overall energy consumption against existing benchmarks.

New hybrid AHP-QFD-PROMETHEE decision-making support method in the hesitant fuzzy environment: an application in packaging design selection

This paper presents a new hybrid decision-making support method (New Hesitant Fuzzy AHP-QFD-PROMETHEE II Method), which jointly uses the Analytic Hierarchy Process (AHP), the Quality Function Deployment (QFD) and the Preference Ranking Method for Enrichment Evaluation (PROMETHEE II), as well as the Hesitant Fuzzy Linguistic Term Sets (HFLTS) to capture hesitation and aggregate divergent opinions from different experts. A real application of the new method to a packaging design selection problem for an automotive company is described, finding that AHP assisted in determining the importance of QFD’s customer requirements (CRs) and PROMETHEE II was used to select the best packaging design. With this same problem, for the purpose of validating the proposed method, a comparative analysis was made with the use of the Hesitant Fuzzy AHP-QFD-TOPSIS method and also with the traditional AHP-QFD-PROMETHEE method, which makes it impossible to capture the hesitation of decision makers. The result showed similarity in the rankings of design alternatives found in the three methods application. The proposed method proved advantageous for solving problems that can generally be solved with the QFD House of Quality but have serious difficulties when decision makers have divergent opinions and hesitate in evaluating criteria and alternatives.

Clustering and compatibility-based approach for large-scale group decision making with hesitant fuzzy linguistic preference relations: An application in e-waste recycling

Considering that large-scale group decision making (LSGDM) has been widely concentrated on clustering and lower compatibility problems, this paper focuses on clustering and consensus reaching of hesitant fuzzy linguistic preference relations (HFLPRs) in LSGDM. Firstly, we define the hesitant degree and the fuzzy degree functions for hesitant fuzzy linguistic term sets (HFLTSs) according to their scale and deviation of elements. To normalize HFLTSs with different length, a minimum deviation model based on the hesitant and the fuzzy degrees is developed. After that, we propose a new similarity measure and compatibility measure via integrating the hesitant and fuzzy degrees. Then a clustering process combining similarity and compatibility is used to assign large-scale experts into several clusters. The clustering method not only strengths the cohesion of subgroups, but also guarantees that HFLPRs in subgroups are acceptable compatibility. Moreover, a compatibility adjusting process with self-selected feedback mechanism is presented to improve the compatibility level of clusters until they reach a predefined threshold in selection process. And we combine the number of members in subgroups and the compatibility level of subgroups to determine weights of subgroups. Finally, a case of e-waste recycling and comparison are analyzed to show the availability of the proposed method.

Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model

Design decisions occur in all phases of product design and largely affect the merits of the final solution, which will ultimately determine the success or failure of the product in the market. Product design is a continuous process, and a large number of existing studies have proposed decision methods and decision indicators for the characteristics of different stages of design. These methods and indicators can meet the requirements of one of the phases: demand analysis, conceptual design, or detailed design. However, further research can still be conducted on the integration of methods throughout the design phase, using intelligent design methods, and improving the design continuity and efficiency. To address this problem, a TOPSIS-MOGA-based multi-indicators decision model for product design solutions is proposed, including its product design process, decision algorithm, and selection method. First, a TOPSIS-MOGA integrated model for conceptual design and detailed design process is established, the continuity of decision-making methods is achieved by integrating decision indicators. Second, conceptual design solutions are selected through the technique for order of preference by similarity to ideal solution (TOPSIS), based on hesitant fuzzy linguistic term sets and entropy weight method. Finally, detailed design solutions are selected through a multiobjective genetic algorithm (MOGA), based on a polynomial-based response surface model and central combination experimental design method. A case study of the decision-making in the design of high-voltage electric power fittings is presented, the conceptual design phase and the detailed design phase are connected through the indicators, which demonstrates that the proposed approach is helpful in the decision-making of the product design solutions.