Hesitant Fuzzy Linguistic Consensus Model Based on Trust-Recommendation Mechanism for Hospital Expert Consultation

Wu, Hangyao; Ren, Peijia; Zhang, Xu

doi:10.1109/tfuzz.2019.2896836

Cited by 23 publications

(9 citation statements)

References 39 publications

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“…In addition, the extension of PLTSs was proposed to represent evaluation information [ 113 ]. As for hesitant fuzzy linguistic term sets (HFLTSs), [ 114 – 118 ] took full advantage of classic HFLTSs to quantify linguistic evaluation information in the healthcare industry, while [ 119 , 120 ] enriched the content of hesitant fuzzy linguistic preference relation. Gou et al [ 121 ] expressed the assessment information of experts in the form of double hierarchy hesitant fuzzy linguistic term set.…”

Section: The Implementation Of Gdm Methods In Healthcare Industry 40mentioning

confidence: 99%

“…Subsequently, confronting the failure of consensus reaching, scholars have put forward diverse methods to promote agreement among experts, such as allowing experts to adjust their opinions or modifying the decision matrices of experts automatically based on some rules. Based on similarity and distance measures, some scholars proposed original consensus reaching methods which are presented as follows: Wu, Ren, and Xu [ 120 ] proposed a consensus measure tool named hesitant fuzzy linguistic preference relation (HFLPR) satisfaction degree. First, they defined some operations of linguistic terms to overcome defects in measuring the consistency of linguistic preference relations, which contributed to constructing a perfectly consistent HFLPR.…”

Section: The Implementation Of Gdm Methods In Healthcare Industry 40mentioning

confidence: 99%

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RETRACTED ARTICLE: A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Liao

2022

Appl Intell

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Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0.

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Section: The Implementation Of Gdm Methods In Healthcare Industry 40mentioning

confidence: 99%

Section: The Implementation Of Gdm Methods In Healthcare Industry 40mentioning

confidence: 99%

RETRACTED ARTICLE: A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Liao

2022

Appl Intell

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“…In contrast, some scholars developed another popular tool named as optimization model to help experts achieve consensus (Ben-Arieh & Easton, 2007;Fan et al, 2006;Meng et al, 2019;Wan et al, 2018;Wu et al, 2019aWu et al, , 2019bXu et al, 2018;Yu & Xu, 2020;Zhang et al, 2018aZhang & Pedrycz, 2018). As far as we know, the most important advantage of optimization models is that they can provide the adjusted preference solutions directly by setting goals and solving the established models.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang, Liang, Gao and Zhang (2018a) proposed a consensusoriented aggregation model to obtain a collective opinion with maximum consensus degree by minimizing the information deviation between individual and collective opinions. Wu, Ren and Xu (2019b) constructed an optimization model to directly identify hesitant fuzzy linguistic preference values, which greatly improves the efficiency of the consensus reaching process. developed a multi-stage optimization-based consensus reaching processes, which have better comprehensive consensus efficiency in different group decision making settings.…”

Section: Introductionmentioning

confidence: 99%

Managing Consensus by Multi-Stage Optimization Models With Linguistic Preference Orderings and Double Hierarchy Linguistic Preferences

Gou

Zhang

Zhou

2020

Technological and Economic Development of Economy

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Preference ordering structures are useful and popular tools to represent experts’ preferences in the decision making process. In the existing preference orderings, they lack the research on the precise relationship between any two adjacent alternatives in the preference orderings, and the decision making methods are unreasonable. To overcome these issues, this paper establishes a novel concept of linguistic preference ordering (LPO) in which the ordering of alternatives and the relationships between two adjacent alternatives should be fused well, and develops two transformation models to transform each LPO into the corresponding double hierarchy linguistic preference relation with complete consistency. Additionally, to fully respect the experts’ expression habits and provide more refined solutions to experts, this paper establishes a multi-stage consensus optimization model by considering the suggested preferences represented in both the continuous scale and the discrete scale, and develops a multi-stage interactive consensus reaching algorithm to deal with multi-expert decision making problem with LPOs. Furthermore, some numerical examples are presented to illustrate the developed methods and models. Finally, some comparative analyses between the proposed methods and models and some existing methods have been made to show the advantages of the proposed methods and models.

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“…Decision-makers can apply these DLTSs based on the characteristics of MAGDM problems and linguistic evaluation information from experts. The DLTSs show its wide applicability in the fields of medical treatment [12,13], product evaluation [14,15], engineering construction [16], and public emergency [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

The Linguistic Interval-Valued Intuitionistic Fuzzy Aggregation Operators Based on Extended Hamacher T-Norm and S-Norm and Their Application

2020

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Linguistic interval-valued intuitionistic fuzzy sets, as an extension of interval-valued intuitionistic fuzzy sets, have strong practical value in the management of complex uncertainty system with qualitative evaluation information. This study focuses on the development of several linguistic interval-valued intuitionistic fuzzy Hamacher (LIVIFH) aggregation operators based on the extended Hamacher t-norm and s-norm. First, the extended Hamacher t-norm and s-norm, which are applicable to linguistic information environment, are applied to define the linguistic interval-valued intuitionistic fuzzy Hamacher operational laws. Second, based on the proposed operational laws, this study defines the linguistic interval-valued intuitionistic fuzzy Hamacher weighted average (LIVIFHWA) operator and the linguistic interval-valued intuitionistic fuzzy Hamacher weighted geometric (LIVIFHWG) operator, and then investigates their properties. Furthermore, the degeneracy and monotonicity of the proposed operators with respect to the adjustable parameter are explored. Finally, a multiple attribute group decision-making (MAGDM) approach is developed based on the proposed LIVIFH aggregation operators, and then this approach is applied to a supplier selection problem. Parameter analysis indicates that the adjustable parameter in the proposed LIVIFH aggregation operators could reflect the attitudes of decision makers. The LIVIFHWA operator would be more appropriate to optimistic decision makers, and the LIVIFHWG operator to pessimistic decision makers. In addition, as the adjustable parameter increasing, both attitudes tend to be neutral. The proposed method is also compared with two other approaches to show its feasibility and efficiency.Keywords: extended Hamacher t-norm and s-norm; linguistic interval-valued intuitionistic fuzzy set; linguistic interval-valued intuitionistic fuzzy Hamacher operators; multi-attribute group decision-making

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Hesitant Fuzzy Linguistic Consensus Model Based on Trust-Recommendation Mechanism for Hospital Expert Consultation

Cited by 23 publications

References 39 publications

RETRACTED ARTICLE: A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

RETRACTED ARTICLE: A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Managing Consensus by Multi-Stage Optimization Models With Linguistic Preference Orderings and Double Hierarchy Linguistic Preferences

The Linguistic Interval-Valued Intuitionistic Fuzzy Aggregation Operators Based on Extended Hamacher T-Norm and S-Norm and Their Application

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