A Framework for Multi-Attribute Group Decision-Making Using Double Hierarchy Hesitant Fuzzy Linguistic Term Set

  • R. Krishankumar
  • L. S. Subrajaa
  • K. S. Ravichandran
  • Samarjit KarEmail author
  • Arsham Borumand Saeid


As a generalization to hesitant fuzzy linguistic term set (HFLTS), double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is presented which circumvents the weakness of HFLTS in representing complex linguistic terms. DHHFLTS has two linguistic hierarchies with the second hierarchy supplementing the primary which enables decision-makers (DMs) to represent complex linguistic terms better. Motivated by the power of DHHFLTS, in this paper, a new decision framework is presented under DHHFLTS context. Initially, a new aggregation operator called double hierarchy hesitant fuzzy hybrid aggregation (DHHFHA) operator is proposed for sensible aggregation of DMs’ preference information. Further, weights of attributes are calculated by extending statistical variance (SV) method under DHHFLTS context. Objects are prioritized by extending the popular WASPAS (weighted aggregated sum product assessment) method to DHHFLTS context. The applicability and usefulness of the proposed framework are realized by demonstrating a risk management technique (RMT) selection problem for a construction project. Finally, the superiority and weakness of the proposed framework are discussed by comparison with other methods.


Double hierarchy hesitant fuzzy linguistic term set Group decision-making Hybrid aggregation Statistical variance and WASPAS method 



Authors dedicate their earnest gratitude to the editor and the anonymous reviewers for their valuable comments that helped us improve the quality of the paper.


Authors thank University Grants Commission (UGC), India, and Department of Science & Technology (DST), India, for their financial support from Grant Nos. F./2015-17/RGNF-2015-17-TAM-83 and SR/FST/ETI-349/2013.

Compliance with Ethical Standards

Conflict of interest

All authors of this research paper declare that there is no 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

Informed consent was obtained from all participants included in the study.


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Copyright information

© Taiwan Fuzzy Systems Association 2019

Authors and Affiliations

  • R. Krishankumar
    • 1
  • L. S. Subrajaa
    • 1
  • K. S. Ravichandran
    • 1
  • Samarjit Kar
    • 2
    Email author
  • Arsham Borumand Saeid
    • 3
  1. 1.School of ComputingSASTRA UniversityThanjavurIndia
  2. 2.Department of MathematicsNational Institute of TechnologyDurgapurIndia
  3. 3.Department of Pure Mathematics, Faculty of Mathematics and ComputerShahid Bahonar University of KermanKermanIran

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