Fuzzy Linguistic Group Research Articles

Retraction Note: Interval-valued dual hesitant fuzzy linguistic group recommendation method by considering the double relevance

Retraction Note: Interval-valued dual hesitant fuzzy linguistic group recommendation method by considering the double relevance

Developing and evaluating cybersecurity competencies for students in computing programs.

Changes due to technological development in the workplace are putting pressure on academia to keep pace with the changing nature of work. Due to the growing need for cybersecurity professionals, universities improve their cybersecurity programs to develop qualified cybersecurity competencies. The purpose of this study is to validate the cybersecurity knowledge, skills, and abilities (KSAs) competencies of cybersecurity degree programs using a fuzzy linguistic group decision-making method. This study shows that cybersecurity knowledge is essential, along with technical skills and human abilities for cybersecurity professionals.

Evaluation of small and medium-sized enterprises’ sustainable development with hesitant fuzzy linguistic group decision-making method

Evaluation of small and medium-sized enterprises’ sustainable development with hesitant fuzzy linguistic group decision-making method

A consensus model for hesitant fuzzy linguistic group decision-making in the framework of Dempster–Shafer evidence theory

In group decision-making (GDM), a consensus building process (CRP) is necessary to improve the level of consensus within the group. This paper develops a new CRP model for GDM problems with hesitant fuzzy linguistic term sets (HFLTSs), which also considers the reliability of experts. Firstly, we present the calculation of expert reliability by measuring the hesitancy degree and similarity degree, simultaneously. Next, we propose an approach to generate group opinions based on the reliability of experts within the framework of the Dempster–Shafer evidence theory (DSET). In this way, the original individual opinions can be retained well and the discourse power of experts with higher reliability but lower weights can be strengthened. Further, the reliability is introduced to build a new feedback mechanism that provides experts with explicit advice during the consensus reaching process. Subsequently, a new CRP algorithm for hesitant fuzzy linguistic GDM (HFLGDM) problems is proposed to help the group achieve consensus. A practical case study is utilized to illustrate the application of the proposed algorithm. Finally, we conduct a comparison experiment to verify the advantages of the proposed CRP algorithm.

A Revised Picture Fuzzy Linguistic Aggregation Operator and Its Application to Group Decision-Making

The complexity of decision environments poses challenges for individuals engaged in decision-making proceedings. Picture fuzzy linguistic sets (PFLSs) are an effective tool for depicting the inherent subjective nature of human cognition. Aiming for the promotion of a general theory on PFLSs, we conduct a critical research to identify some limitations of a recent publication in Cognitive Computation [2018, 10(2), 242–259]. This published article is a meaningful and interesting study that initiates the PFLS as well as introduces the corresponding operations and the Archimedean picture fuzzy linguistic weighted arithmetic averaging operator. Unfortunately, we have carefully analyzed these operations and found that they may not be appropriate to all situations within picture fuzzy linguistic environment, which would result in the violation of human cognition. To eliminate this limitation, we explore and suggest novel operational laws and an aggregation operator for PFLSs from a modified version, so as to support further study on picture fuzzy linguistic group decision-making. Furthermore, a comparative and illustrative example is presented to validate the advantages of our modified operations. Towardly , the research outcome will be of significant benefit to promoting the development of picture fuzzy linguistic decision-making theory. Accordingly, our contribution to improving the current research on PFLSs makes their application in solving realistic problems feasible and practical.

Identifying and prioritizing factors affecting in-cabin passenger comfort on high-speed rail in China: A fuzzy-based linguistic approach

Factors affecting customer comfort are crucial for the success of many services such as public transportation, health facilities, and so on. Therefore, the identification and prioritization of such factors is an important demand from stakeholders. This research aims to identify and prioritize the factors that affect in-cabin passenger comfort on high-speed rail (HSR) based on empirical evidence collected from China. For the identification process, the quality-management tool known as quality function deployment (QFD) to capture the voice of the customer is used to discover the most important demands as the driving influencing factors of in-cabin passenger comfort, by utilizing passengers’ feedback regarding their experiences with HSR posted in social media. Such factors will be prioritized by a fuzzy linguistic group decision-making approach based on the adoption of generalized comparative linguistic expressions obtained from a questionnaire investigation on randomly selected HSR passengers, and accomplishing a decision-solving procedure that includes consistency-checking and consensus-reaching processes to achieve an effective, reliable, and agreed prioritization of the factors. The research outputs will suggest the factors of greatest concern among all HSR passenger demands. This study gives HSR operators and designers not only insights and tools for managing HSR passenger demands but also advice for refining the design and service quality of HSR in China.

Attitude quantifier based possibility distribution generation method for hesitant fuzzy linguistic group decision making

The possibility distribution-based approach is one of the powerful tools available to manage hesitant fuzzy linguistic term set (HFLTS) information. However, existing possibility distribution studies have not considered the experts’ satisfied preference for HFLTSs in the process of generating the possibility distribution. This paper aims at filling this research gap. To achieve this goal, a novel possibility distribution generation method based on the concept of linguistic quantifier is proposed. This is accomplished by defining a new attitude linguistic quantifier, which is supported with theoretical results to analyze the relationship between the proposed attitude linguistic quantifier with the original linguistic quantifier, attitude indices and the expected linguistic term. The new possibility distribution generation method is proved to be (1) more general than the two main existing approaches, which are particular cases for specific linguistic quantifiers; and (2) useful to implement the concept of soft majority in the resolution process of the decision making situation. Additionally, a new two stages feedback mechanism of attitude adjustment and assessment adjustment is devised to guarantee the convergence of the consensus reaching process. Finally, a framework of group decision making with HFLTSs information is presented and an illustrative example is conducted to verify the proposed method.

Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective

Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective

Hesitant Fuzzy Linguistic VIKOR Method and Its Application in Qualitative Multiple Criteria Decision Making

The hesitant fuzzy linguistic term set (HFLTS) has turned out to be a powerful and flexible technique in representing decision makers’ qualitative assessments in the processes of decision making. The aim of this paper is to develop a method to solve the multicriteria decision making (MCDM) problem within the context of HFLTS in which the criteria conflict with each other. To do so, the concepts of ideal solutions for a HFL-MCDM problem have been introduced. In addition, in order to represent the closeness of one solution to the ideal one, we propose a sort of hesitant fuzzy linguistic measures, such as the hesitant fuzzy linguistic group utility measure, the hesitant fuzzy linguistic individual regret measure, and the hesitant fuzzy linguistic compromise measure. Based on these measures, we develop a hesitant fuzzy linguistic VIKOR (HFL-VIKOR) method, which is motivated by the traditional VIKOR method. The general procedures for the HFL-VIKOR method are given. Some numerical examples are provided to demonstrate the advantages and practicality of our method. Finally, we make some discussions on the advantages of the HFL-VIKOR method, as well as future work.

Using mutually validated memories of experts for case-based knowledge systems

Organizing a reliable case base, which serves as a repository of experience, is crucial for the success of a case-based reasoning (CBR) system. To ensure that such repositories contain high-quality cases, this paper proposes a framework employing the methodology of fuzzy linguistic group decision-making (GDM) in the context of multiple attributes. The overall process of MAGDM could be analogous to the memory-related behaviors of the human brain, in which knowledge is elicited and validated, as in the short-term memory, and then eventually integrated into the long-term memory to serve as solutions to build-up the number of high-quality cases. Moreover, the proposed approach is flexible, as it enables experts to define the set of the parameters of the membership functions associated with labels, thus improving the quality of the linguistic term sets and leading to better assessments. Furthermore, the proposed KC index, characterized by measures of both individual and group consistencies, can provide a more effective assessment to assign suitable experts’ weights than most existing GDM models. This is supported by the experimental results presented in this work, indicating that the KC index can indeed lead to a more satisfactory overall level of consensus. In addition, the mutual validation between the set of the parameters of the membership functions associated with labels by experts and the evaluation of the experts’ weights can be manifested in terms of the KC index.The extended collective decision matrix derived from the process of MAGDM that is used to construct case bases is more practical and effective than other approaches, as its elements are meaningful and interpretable. The proposed framework, integrating fuzzy linguistic GDM and CBR, thus enhances the efficiency and effectiveness of a CBR system. This is further evidenced in the results of an experiment, which show that this hybrid framework is very effective in implementing a case-based knowledge system and provides a powerful methodology for performance ranking.