Consistency and Consensus Models with Local Adjustment Strategy for Hesitant Fuzzy Linguistic Preference Relations

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Abstract

This paper studies the consistency and consensus models with local adjustment strategy for hesitant fuzzy linguistic preference relations (HFLPRs). A consistency and consensus reaching model with local adjustment strategy is devised. In the consistency improving process, only the most inconsistent preferences are suggested to be modified toward the preferences in the consistent HFLPR. In the consensus reaching process, an individual to group consensus index and a group consensus index are introduced to measure the consensus degree for the individual and the group. Meanwhile, only those HFLPRs whose consensus degrees are not within the predefined threshold are suggested to be modified, and only the preferences in these HFLPRs which have the largest distances from the preferences in group HFLPR are suggested to be revised by the group’s preference values. For both the consistency and consensus reaching processes, the improved preference values are still using the original linguistic terms. Finally, an example is illustrated to show the effectiveness of the proposed method, and comparative analyses with the existing methods are offered to show the advantages of the proposed method.

Keywords

Hesitant fuzzy linguistic preference relation (HFLPR) Consistency Consensus Local adjustment Group decision making 

Notes

Acknowledgements

This work was partly supported by the Key Project of National Natural Science Foundation of China (No. 71633002), sponsored by Qing Lan Project of Jiangsu Province.

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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Business SchoolHohai UniversityNanjingPeople’s Republic of China
  2. 2.College of Economic and ManagementSouth China Agricultural UniversityGuangzhouPeople’s Republic of China
  3. 3.School of BusinessQingdao UniversityQingdaoPeople’s Republic of China

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