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Overview of Hesitant Linguistic Preference Relations for Representing Cognitive Complex Information: Where We Stand and What Is Next

  • Huchang Liao
  • Ming TangEmail author
  • Rui Qin
  • Xiaomei Mi
  • Abdulrahman Altalhi
  • Saleh Alshomrani
  • Francisco Herrera
Article

Abstract

Hesitant fuzzy linguistic preference relations (HFLPRs) can be used to represent cognitive complex information in a situation in which people hesitate among several possible linguistic terms for the preference degrees of pairwise comparisons over alternatives. HFLPRs have attracted growing attention owing to their efficiency in dealing with increasingly cognitive complex decision-making problems. Due to the emergence of various studies on HFLPRs, it is necessary to make a comprehensive overview of the theory of HFLPRs and their applications. In this paper, we first review different types of linguistic representation models, including the hesitant fuzzy linguistic term set, hesitant 2-tuple fuzzy linguistic term set, probabilistic linguistic term set, and double-hierarchy hesitant fuzzy linguistic term set. The reasons for proposing these models are discussed in detail. Then, the hesitant linguistic preference relation models associated with the aforementioned linguistic representation models are addressed one by one. An overview is then provided in terms of their consistency properties, inconsistency-repairing processes, priority vector derivation methods, consensus measures, applications, and future directions. Basically, we try to answer to two questions: where we stand and what is next? The preference relations and consistency properties are discussed in detail. The inconsistency-repairing processes for those preference relations that are not acceptably consistent are summarized. Methods to derive the priorities from the HFLPRs and their extensions are further reviewed. The consensus measures and consensus-reaching processes for group decision making with HFLPRs and their extensions are discussed. The applications of HFLPRs and their extensions in different areas are highlighted. The future research directions regarding HFLPRs are given from different perspectives. This paper provides a comprehensive overview of the development and research status of HFLPRs for representing cognitive complex information. It can help researchers to identify the frontier of cognitive complex preference relation theory in the realm of decision analysis. Since the research on HFLPRs is still at its initial stage, this review has guiding significance for the later stage of study on this topic. Furthermore, this paper can engage further research or extend the research interests of scholars.

Keywords

Hesitant fuzzy linguistic preference relations Group decision making Hesitant fuzzy linguistic term set Cognitive complex information Consistency Prioritization Consensus 

Notes

Funding Information

The work was supported by the National Natural Science Foundation of China (71771156), the 2018 Key Project of the Key Research Institute of Humanities and Social Sciences in Sichuan Province (No. LYC18-02, No. DSWL18-2), and the Spark Project of Innovation at Sichuan University (No. 2018hhs-43).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

As this article does not contain any studies with human participants or animals performed by any of the authors, the informed consent is not applicable.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Huchang Liao
    • 1
    • 2
    • 3
  • Ming Tang
    • 1
    Email author
  • Rui Qin
    • 1
  • Xiaomei Mi
    • 1
  • Abdulrahman Altalhi
    • 3
  • Saleh Alshomrani
    • 4
  • Francisco Herrera
    • 2
    • 3
  1. 1.Business SchoolSichuan UniversityChengduChina
  2. 2.Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)University of GranadaGranadaSpain
  3. 3.Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia
  4. 4.Faculty of Computing and Information TechnologyUniversity of JeddahJeddahSaudi Arabia

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