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New Similarity Measures for Dual Hesitant Fuzzy Sets and Their Application

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Abstract

Dual hesitant fuzzy sets (DHFSs) are powerful and efficient to express hesitant preferred and non-preferred information simultaneously. This paper focuses on similarity measures for DHFSs. To do this, it first analyzes the limitations of previous similarity measures for DHFSs. Then, several new dual hesitant fuzzy similarity measures are defined that can avoid the issues of previous ones. To discriminate the importance of decision-making criteria, several weighted similarity measures are further defined in views of additive and 2-additive measures. When the weighting information is not exactly known, optimization methods for determining additive and 2-additive measures are built, respectively. Furthermore, a method for multi-criteria decision-making based on new weighted similarity measures is developed. Finally, two numerical examples are provided to show the utilization of the new method and compare with previous methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 71571192), the Beijing Intelligent Logistics System Collaborative Innovation Center (No. 2019KF-09), and the Major Project for National Natural Science Foundation of China (No. 71790615).

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Authors and Affiliations

  1. School of Information, Beijing Wuzi University, Beijing, 101149, China

    Ruiping Yuan & Fanyong Meng

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

    Fanyong Meng

Authors
  1. Ruiping Yuan
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  2. Fanyong Meng
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Correspondence to Fanyong Meng.

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Yuan, R., Meng, F. New Similarity Measures for Dual Hesitant Fuzzy Sets and Their Application. Int. J. Fuzzy Syst. 22, 1851–1867 (2020). https://doi.org/10.1007/s40815-020-00910-0

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  • DOI: https://doi.org/10.1007/s40815-020-00910-0

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