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Hesitant Intuitionistic Fuzzy Aggregation Operators Based on the Archimedean t-Norms and t-Conorms

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Abstract

There have always been problems associated with managing vague or imprecise information, and therefore, various tools have recently been investigated to handle this uncertainty. In this study, hesitant intuitionistic fuzzy sets are introduced to resolve situations where decision-makers hesitate in selecting between several intuitionistic fuzzy values when assessing alternatives. Furthermore, new operations, based on the Archimedean t-norms and t-conorms, are developed and corresponding properties and the ranking method of HIFNs are also investigated. Additionally, the hesitant intuitionistic fuzzy weighted averaging operator and the hesitant intuitionistic fuzzy power weighted averaging operator based on the Archimedean t-norms and t-conorms are proposed to aggregate decision-makers’ information in multi-criteria decision-making (MCDM) problems, and an approach to MCDM problems that employ hesitant intuitionistic fuzzy information is constructed based on the proposed aggregation operators. Lastly, an example of selecting project is investigated to verify the applicability and validity of the proposed approach and the study is supported by comparative analyses.

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Acknowledgements

The authors appreciate the editors and anonymous reviewers for their helpful comments and suggestions. This paper is supported by the National Natural Science Foundation of China (No. 71571193) and the Humanities and Social Sciences Foundation of Ministry of Education of China (No. 15YJCZH127).

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Authors and Affiliations

  1. School of Business, Central South University, Changsha, 410083, China

    Juan-juan Peng, Jian-qiang Wang & Xiao-hui Wu

  2. School of Economics and Management, Hubei University of Automotive Technology, Shiyan, 442002, China

    Juan-juan Peng, Xiao-hui Wu & Chao Tian

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  1. Juan-juan Peng
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Correspondence to Juan-juan Peng.

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Peng, Jj., Wang, Jq., Wu, Xh. et al. Hesitant Intuitionistic Fuzzy Aggregation Operators Based on the Archimedean t-Norms and t-Conorms. Int. J. Fuzzy Syst. 19, 702–714 (2017). https://doi.org/10.1007/s40815-017-0303-4

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