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A Novel Approach to Hesitant Fuzzy Soft Set Based Decision Making

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1074))

Abstract

Babitha et al. defined a hybrid model called hesitant fuzzy soft sets by combining the hesitant fuzzy set with the soft set. The aim of this paper is to give deeper insights into decision making based on hesitant fuzzy soft sets. We point out that the algorithm designed by Babitha et al. based decision making is not fit to solve some decision making problems and a counterexample is given. By means of reduct fuzzy soft sets and level hesitant fuzzy soft sets, we present an adjustable approach to hesitant fuzzy soft sets based decision making and some numerical examples are provided to illustrate the developed approach.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61966032), the Fundamental Research Funds for the Central Universities of Northwest MinZu University (No. 31920170010) and the Research Project Funds for Higher Education Institutions of Gansu Province (No. 2016B-005).

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Authors and Affiliations

  1. School of Electrical Engineering, Northwest MinZu University, Lanzhou, 730030, People’s Republic of China

    Yanping He

Authors
  1. Yanping He
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Correspondence to Yanping He .

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Editors and Affiliations

  1. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

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He, Y. (2020). A Novel Approach to Hesitant Fuzzy Soft Set Based Decision Making. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_86

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