Hesitant Fuzzy Linguistic Term Sets Research Articles

A global optimization feedback model with PSO for large scale group decision making in hesitant fuzzy linguistic environments

In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to “COVID-19” and some comparisons are provided to verify the feasibility and advantages of the proposed method.

Limited budget-based consensus model for large group decision making with hesitant fuzzy linguistic information

Considering the uncertainty of large group decision-making and the limitation of resources, this paper constructs a large group decision-making method with hesitant fuzzy linguistic term set (HFLTS) based on limited budget. Firstly, by studying the basic theory of HFLTS, a weighted aggregation model of HFLTS is proposed, which can ensure the minimum weighted distance between aggregated HFLTS and HFLTSs before aggregation. Secondly, a decision makers’ weight determination algorithm is proposed by considering the quality of trust sources in the social trust network, which is calculated by the LeaderRank centrality measure. Furthermore, a maximum group consensus level model (MGCM) based on limited budget is constructed, in which the adjustment cost of decision makers is determined by the hesitancy degree in the evaluation and trust out-degree. For the situation of insufficient budget, the order correlation between the alternatives’ ranking before and after the budget increase is introduced to judge whether to increase the budget to achieve a higher group consensus level. Then, a limited budget-based large group decision-making method with hesitant fuzzy linguistic information is proposed. And this method is applied to the selection of equipment suppliers for hydrogen refueling station. Finally, the effectiveness of the proposed method is verified by comparison with existing research methods and sensitivity analysis.

An integrated FMEA framework considering expert reliability for classification and its application in aircraft power supply system

The risk assessment of the integrated drive generator (IDG) is a significant problem in flight safety that directly affects the operation of the aircraft power system However, the current IDG risk assessment methods still have many limitations, leading to risk identification and classification that is not accurate and scientific. Therefore, this paper constructs a failure mode and effect analysis (FMEA) framework for IDG risk identification and classification considering expert reliability in unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) environment. First, since the UHFLTSs can effectively describe uncertain and imprecise linguistic information of decision makers (DMs) and adapt it to practical applications, it is specially chosen to depict the evaluation of DMs. Second, the importance of experts is fully assessed by combining expert reliability and expert weight since the variation in expert reliabilities may have a substantial impact on the final sorting outcomes. In addition, the group best–worst method (GBWM) is combined with the entropy method to determine the risk factor weight. Third, to build up the accuracy and stability of classification, a new integrated TOPSIS-ELECTRE TRI decision framework is proposed to classify failure modes more rationally. The sensitivity analysis of parameters demonstrates the robustness of the proposed method. Meanwhile, the effectiveness and superiority of the method are verified both qualitatively and quantitatively. By comparing with several typical sorting methods, the proposed method has more realistic and stable classification results.

A Telemedicine Decision-Making Model for Teleconsultation Decision Support System Based on Intuitionistic Hesitant Fuzzy Linguistic Term Sets

Teleconsultation is conducive to the rational use of health resources and plays a key role in the realization of hierarchical diagnosis and treatment. Previous research on telemedicine multicriteria decision making methods rarely consider the difference in responsibilities between remote experts and attending physicians. To show the difference precisely, this article first defines the intuitionistic hesitant fuzzy linguistic term set (IHFLTS) as an extended mathematical expression of the fuzzy set. We propose two kinds of correlation measuring methods and related hesitancy degrees based on information energy and statistics with different application scenarios. Based on information entropy and distance measurement, two criterion weight determination methods are studied. To formally implement the joint decision-making process of remote experts and attending physicians, a telemedicine decision-making model is established for designing the teleconsultation decision support system. We apply the proposed methods to a sinusitis teleconsultation case to demonstrate their effectiveness. The weight determination methods are applied, and the influence of parameter variation in the distance-based method is analyzed. Finally, the rationality and applicability of the proposed methods are verified by comparisons. The results show that by considering the importance and differences between the two roles of teleconsultation, IHFLTS can obtain a more realistic result for the referral problems.

Supplier selection in the industry 4.0 era by using a fuzzy cognitive map and hesitant fuzzy linguistic VIKOR methodology.

Organizations will be increasingly concerned about maintaining their positions in today's changing world, the high-tech era, and the emergence of innovative technologies because of the industrial revolutions. Everyone has come to believe that to survive and continue their constructive roles, they must achieve competitive advantages by working based on the trends. It is undeniable that the introduction of Industry 4.0 has had a significant impact on enterprises, organizations, and, of course, supply chains. In the meantime, selecting a supplier is one of the main strategic decisions of the organization because choosing the right supplier leads to increasing profitability, improving market competition, better accountability, enhancing product quality, and reducing costs. While the issue of supplier evaluation has been one of the interesting topics for researchers in recent decades, its development in the fourth supply chain generation needs further consideration. In this regard, current technologies in the fourth-generation industrial revolution, methods, and criteria used in previous studies based on industry 4.0 and before that are reviewed separately. By reviewing previous articles and experts' opinions, thirteen sub-criteria considering industry 4.0 have been identified for selecting suppliers in three categories, economic, environmental, and social. The weight of each criterion has been determined using a set of fuzzy cognitive maps (FCMs) and considering the centrality of criteria in the concept of communication networks. To prioritize the suppliers, the hesitant fuzzy linguistic term sets (HFLTS) VIKOR method has been used in hesitant fuzzy linguistic terms. Finally, a case study is introduced to illustrate the effectiveness and usefulness of our integrated methodology and prioritize its four suppliers.

Vector Similarity Measures of Dual Hesitant Fuzzy Linguistic Term Sets and Their Applications

The dual hesitant fuzzy linguistic term set (DHFLTS) is defined by two functions that express the grade of membership and the grade of non-membership using a set of linguistic terms. In the present work, we first quote an example to point out that the existing complement operation of DHFLTS is on the wrong track. Meanwhile, we redefine this operation to fill the holes in the existing ones. Next, the notion of information energy under a dual hesitant fuzzy linguistic background is provided in order to build the criteria weight determination model. To further facilitate the theory of DHFLTS, we propose two vector similarity measures, i.e., Jaccard and Dice similarity measures, and their weighted forms for DHFLTS. In addition, we pioneer some generalized similarity measures of DHFLTSs and indicate that the Dice similarity measures are particular instances of the generalized similarity measures for some parameter values. Afterward, the similarity measures-based model with unknown weight information under the background of dual hesitant fuzzy linguistic environment is constructed. Lastly, an illustrated example is included to validate the method’s application, along with sensitivity analysis and comparative analysis, demonstrating the practicality and validity of its results.

Consensus improvement model in group decision making with hesitant fuzzy linguistic term sets or hesitant fuzzy linguistic preference relations

Hesitant fuzzy linguistic term sets (HFLTSs) and hesitant fuzzy linguistic preference relations (HFLPRs) are widely used where decision makers are permitted to provide qualitative preferences with several linguistic terms over two alternatives. There are several issues deserving further investigation in the existing consensus models of HFLTSs and HFLPRs: the extension process of HFLTSs which is always performed in distance measure and aggregation of HFLTSs distorts the original information, the modified HFLTSs does not belong to standardized HFLTSs, and most of the existing models don’t discussed the number of optimal solutions. In order to solve the above problems, this paper introduces new distance and entropy measures for HFLTSs, by which information aggregation method and two consensus improvement models in group decision making (GDM) with HFLTSs and HFLPRs are proposed. The first model is a four-stage optimization model with four objectives, based on which an equivalent linear weighted optimization model is derived by assigning appropriate weights to four objectives with different priorities. According to either of these two models, the revised individual and collective opinions can be obtained. In addition, the prominent properties of the collective opinion obtained from optimization models are further investigated. As last, some examples are provided to demonstrate the applicability of the optimization models proposed, and the results demonstrate the models proposed can better deal with the abovementioned issues.

Dynamic evaluation of a post-pandemic agricultural traceability system, based on the HFLTS-DEMATEL method

PurposeDue to consumption changes in the post-pandemic era, the production safety of agricultural products is affecting global consumers. This paper constructs an evaluation index of the agricultural Internet of things (IOT) traceability system and evaluates it using the dynamic hesitant-fuzzy linguistic term sets (HFLTS)-based DEMATEL method to improve agricultural supply-chain links and improve production quality.Design/methodology/approachThe agricultural IOT traceability index system is constructed using the literature and expert interviews; it comprises 6 first-level indices and 20 second-level indices. The agricultural IOT traceability system is evaluated using the dynamic HFLTS-DEMATEL method.FindingsProducers' awareness of agricultural-production safety (A11) has the most significant impact on production and processing links, while warehouse location and storage capacity (A31) have the largest impact on the circulation link. Inspection authenticity and transparency and quarantine information (A41) have the largest impact on the detection-consumption link. The extent to which the traceability-platform construction is complete (A62) has the largest impact on technical support.Research limitations/implicationsThe present paper may be limited to the era of post-pandemic, and it is hard to consider all the indices. Further research can broaden the research context and establish a more comprehensive index system.Practical implicationsThe index system constructed in this study will surely help relevant regulatory authorities in China to promote the construction of agricultural IOT traceability system and establish a unified standard, so as to provide a basis for future developers to enter the field. Accordingly, it also can help every subject to identify the key indices of each process in the agricultural-product supply chain and guide relevant departments to conduct targeted information tracking and management. The consumers could also understand the standards of traceable agricultural products and effectively protect their own rights and interests.Originality/valueThe existing literature does not provide an objective, unified standard for measuring a decentralized traceability system or identifying key processes. This study therefore proposes a new evaluation index system and uses a dynamic evaluation method to determine the importance of key indices. This study identifies the most important indices in each process, making it possible to discover, improve, and enhance the quality of agricultural products at a practical level.

Assessing risks in international investments using hesitant fuzzy linguistic term sets

The world economy, global markets and competition for international investments have undergone great change in recent years. Changes in the economy of a society affect many countries as a result of international commerce and agreements with other countries. This has given rise to many risks that governments have to deal with; hence, the importance of risk management has increased for all countries, especially in the finance sector. Global economics means that financial institutions need to make decisions about investment in other countries (eg opening a new bank branch or a participation company), so they need to measure the financial risks of candidate countries in fuzzy conditions. This study presents a novel method to meet that need. First, the most common financial risks are determined by means of a literature survey and consulting expert opinion. Secondly, the relative weight of each main and sub-risk is determined by expert opinion and hesitant fuzzy linguistic term sets (HFLTS). These weights are used to measure the financial risks of 30 countries to select the best four and the results are approved by the experts. Finally, the proposed method is implemented to aid the investment decision of a large financial institution. This paper proposes a new method to assess financial risks in fuzzy conditions.

Assessment on High-quality Development of Guizhou's Agricultural Economy Based on Hesitant Fuzzy Linguistic Term Sets

In order to scientifically evaluate the quality of agricultural economic development in Guizhou's prefecture-level cities, the article constructs an index system for evaluating the quality development level of agricultural economy from four aspects, including optimization of agricultural economy structure, mechanization level of agricultural economy, green development of agricultural economy, and openness and sharing of agricultural economy, with reference to the current situation of agricultural economy development in nine cities and municipalities of Guizhou Province; and selects the original data of indicators from 2017 to 2021, and uses the hesitant fuzzy linguistic PROMETHEE method to the empirical evaluation of the quality of agricultural economic development was conducted. The empirical results show that Zunyi, Bijie and Guiyang have a high level of agricultural economic development, ranking among the top three in the province, and have a greater advantage in development level compared with other cities and municipalities, while Qianxinan Autonomous Prefecture has the lowest level of agricultural economic development. The level of high-quality development of the agricultural economy in Guizhou province shows overall growth but uneven regional development. In response to these problems, this paper gives policy advice.

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

Probabilistic linguistic multi-attribute decision making approach based upon novel GMSM operators

<abstract><p>Probabilistic linguistic terms set (PLTS), a new tool for expressing uncertain decision information, is composed of all possible linguistic terms (LTs) and their related probabilities. It also increases the corresponding probability of LTs in hesitant fuzzy linguistic term set (HFLTS). On the other hand, aggregation operator is an important information fusion tool, the Maclaurin symmetric mean (MSM) operator can provide more flexibility and robustness in information fusion, and make it more suitable for solving MADM problems with independent attributes. This current study adopts the merits of PLTS and MSM operator, and then a novel probabilistic linguistic decision making approach are targeted. Firstly, the operations of two PLTSs are redefined based upon Archimedean t-norm (ATN) and Archimedean t-conorm (ATC); Secondly, the probabilistic linguistic generalized MSM operator (PLGMSM) is proposed based on ATN and ATC, some properties of PLGMSM are investigated, then some special PLGMSM operators have been studied in detail when the parameters take different values and the generator of ATN takes different functions. Thirdly, the weighted probabilistic linguistic generalized MSM operator (WPLGMSM) is studied along with some properties of PLGMSM, some special WPLGMSM operators have been also investigated in detail when the parameters take different values and the generator of ATN takes different functions. Finally, on the basis of our proposed aggregation operators, the aggregated-based decision making approach is designed and an example is supplied to manifest the effectiveness of the proposed approach. Furthermore, some comparison analyses with extant decision approaches are carried out to illustrate the validity and feasibility of the proposed approach.</p></abstract>

Prospects and barriers analysis framework for the development of energy storage sharing

Energy storage is a key technology to support large-scale development of new energy and ensure energy security. However, high initial investment and low utilization rate hinder its widespread application. The success of the sharing economy provides new ideas. Energy storage sharing (ESS) has the advantages of efficient operation, safety, controllability and economic saving. Hence, this paper aims to promote the development of ESS by analyzing its barriers and solutions. First, twelve barriers to ESS from economics, technology, policy, and business models are identified. The application scenarios are divided into power supply side, power grid side and load side. Then, triangular fuzzy numbers and hesitant fuzzy linguistic term set are used to collect evaluation information. An integration of methods including decision-making trial and evaluation laboratory (DEMATEL), interpretative structural modeling (ISM), and Matrix impacts cross-reference multiplication applied to a classification (MICMAC) is used to analyze the interaction between barriers and identify significant barriers. Finally, strategic solutions and policy recommendations are proposed to remove or mitigate major barriers. Through model analysis, the establishment of policies, regulations and industry standards is the basis for the development of ESS.

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.

Expertise-Structure and Risk-Appetite-Integrated Two-Tiered Collective Opinion Generation Framework for Large-Scale Group Decision Making

The generation of collective preference assessments occupies a critical position in deriving accurate and reliable alternative rankings in the context of large-scale group decision making (LSGDM). In general, the collective opinion generation framework entails the following three phases, which are clustering analysis, weighting clusters, and preference aggregation. However, the clustering of experts has been frequently based on preference similarities among them without taking into account individual opinions in which knowledge elicitation plays a crucial role. The traditional collective opinion generation framework suffering from this drawback may result in unreliable decision outcomes. To this end, we propose an expertise-structure and risk-appetite-integrated two-tiered collective opinion generation framework to address this concern. The first tier of the two-tiered collective opinion generation framework divides the entire expert group into several subgroups based on individual expertise structures, which are extracted from hesitant fuzzy linguistic term set (HFLTS)-based preference assessments, and it then weighs the resulting clusters in accordance with the overall expertise levels. The second-tier clusters the first-tier subgroups conditioned on the indicator of individual assessment similarities and gathers the generated subgroup preference constructs in the use of the risk appetite-oriented power average operator. In addition, the notion of proportional HFLTSs was introduced to manifest collective evaluations in second-tier subgroups to eliminate information loss and distortion. The effectiveness and flexibility of the proposed collective opinion generation algorithm are eventually illustrated by a case study and a comparison analysis.

A Large Group Decision Making Method Considering Experts’ Non-cooperative Behavior for Investment Selection of Renewable Energy Projects

The rapid expansion of renewable energy has attracted the attention of investors, which makes the evaluation of renewable energy projects a momentous issue. As the investment selection of renewable energy projects requires the joint discussion of experts from different professional backgrounds (such as energy, transportation, construction, economy, environment, etc.), it belongs to the category of large group decision-making (LGDM). Therefore, this paper is devoted to propose a novel LGDM method considering experts’ non-cooperative behavior for investment selection of renewable energy projects. First, considering that the complexity of renewable energy projects makes it difficult for experts to express their views in a single linguistic word, the hesitant fuzzy linguistic term set is used as the tool for expert evaluation in this paper. Second, since the assessment information provided by experts from different fields are often heterogeneous, a consensus-reaching process with a feedback mechanism is introduced which comprehensively considers three reliable sources: the experts’ trust relationship in the social trust network, the consensus contribution in the subgroup and the opinions’ similarity among experts. Further, to improve the efficiency and rationality of decision-making, an experts’ historical adjustment data-based non-cooperative behavior management method is proposed. Finally, the effectiveness and innovation of the proposed method are verified by a case of renewable energy power generation project investment selection in Qingdao, China and a series of comparative analysis.

Group decision making with hesitant fuzzy linguistic preference relations based on multiplicative DEA cross-efficiency and stochastic acceptability analysis

The aim of this paper is to investigate a novel approach to group decision making (GDM) based on multiplicative DEA cross-efficiency and stochastic acceptability analysis with hesitant fuzzy linguistic preference relations (HFLPRs), which can avoid information distortion and obtain more credible decision-making results. First, a transform function is defined, which can extract effective information of the hesitant fuzzy linguistic term set (HFLTS) sufficiently. Then, an optimization model is proposed to derive the occurring probabilities of the elements in the HFLTS. It can avoid the normalization process of HFLPRs and simulate the element-selection process of decision makers (DMs). Moreover, a Maximum Log DEA Cross Efficiency model is proposed to evaluate the relative efficiency of each alternative from HFLPR. We further develop the stochastic weight space acceptability analysis method to solve the GDM problem and a step-by-step procedure is presented. Finally, numerical examples are given to illustrate the validity and applicability of the proposed method. This is the first attempt of employing the multiplicative DEA cross-efficiency to the GDM with HFLPRs.

Lifecycle risk assessment on the sustainable development of upgrading energy projects using abandoned mines: An ISM-BWM method

Mining industries have left countless opencast and underground mines in Northwest China without careful disposal for the past decades. The authorities encourage rehabilitation of abandoned mines under the green development strategy, while the path for relevant enterprises remains unclear so far. This paper thus studies the risk distribution of energy alternatives for abandoned mine upgrading. 5 types of upgrading alternatives are studied, including both generation and storage ones. A lifecycle risk system for the alternatives is established for the first time. 12 risk factors extracted through literature and expert involvement are classified as decision-making, implementation, and operation stages. Then, subjective judgements are converted into hesitant fuzzy linguistic term sets under Pythagorean fuzzy environment. And the best worst method combined with the interpretive structure model is firstly introduced to calculate relative importance based on mutual influence. Results indicate that photovoltaic and heat storage stations are generally the safest alternatives, with the lowest risk level ranging from 0.7166 to 0.8073 and hesitant degree from 0.3473 to 0.4248. Moreover, risk response measures for each alternative and policy implications for local governments are first raised to put forward AMUEP development. The analytical framework could also be applied elsewhere for smooth rehabilitation of abandoned mines.

Sustainable evaluation of energy storage technologies for wind power generation: A multistage decision support framework under multi-granular unbalanced hesitant fuzzy linguistic environment

Energy storage technology (EST) plays a foundational role for dealing with the intermittency of wind power and accelerating the structural revolution of renewable energy systems. Generally, EST selection is treated as a multiple-criteria group decision-making problem. However, stakeholders are not allowed to express multiple preferences via personalized linguistic distribution assessment and their risk appetites have received less attention in the existing approaches. This study aims at prioritizing ESTs by developing a novel multistage support framework where multi-granular unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) are adopted to depict and quantify stakeholders’ opinions based on personalized semantics and granularities. A sustainable index system is devised in four dimensions (economic, technical, environmental and social) and the extended best-worst method (BWM) under multi-granular UHFLTSs environment is combined with maximum deviation method to determine the hybrid weights of criteria. A novel approach linking multi-granular UHFLTSs with double parameters TOPSIS method integrating risk appetite and optimism preference of stakeholders is further proposed by constructing optimization model, which can simultaneously yield the credible experts’ weights and prioritize the most desirable technology. The application of the proposed framework is demonstrated through an empirical case. Eventually, sensitivity analysis and comparative analysis are implemented to verify the effectiveness and validity of our proposal.

An Additive Consistency and Consensus Approach for Group Decision Making With Probabilistic Hesitant Fuzzy Linguistic Preference Relations and Its Application in Failure Criticality Analysis.

In this article, probabilistic hesitant fuzzy linguistic preference relations (PHFLPRs) are proposed to present the qualitative pairwise preference information of decision makers (DMs) with hesitation and probability uncertainty assessments. The measurements and improvements of additive consistency and consensus of PHFLPRs are investigated in group decision making (GDM). First, a new concept of probabilistic hesitant fuzzy linguistic term sets is defined. Second, the consistency and consensus measurements are established to survey the additive consistency and consensus levels of PHFLPRs. Subsequently, an optimization model is developed to improve the unacceptably additive consistent PHFLPR. By optimizing the unacceptable consensual PHFLPRs with repeating additive consistency improvement, the acceptably additive consistent and consensual PHFLPRs are obtained, based on which DMs' weights are determined objectively and then, the collective PHFLPR is aggregated from individual PHFLPRs. Alternatives' priority weights are derived from the collective PHFLPR as GDM. Finally, an example about failure criticality analysis is given, and a comparison analysis is presented.