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Multi-attribute Decision Making Based on the Choquet Integral Operator with Hesitant Fuzzy Linguistic Information

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Fuzzy Information and Engineering-2019

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1094))

Abstract

In this paper, we investigate the multiple attribute decision making (MADM) problems under hesitant fuzzy linguistic environment. Firstly, by using Choquet integral, the hesitant fuzzy linguistic Choquet integral operator is developed for aggregating hesitant fuzzy linguistic information. The properties of the proposed operator are also investigated, such as idempotence, monotonicity, boundedness and exchangeability. Next, we apply the proposed operator to deal with MADM problems under hesitant fuzzy linguistic environment. Finally, a numerical example of information security risk assessment brought by paperless office is given to demonstrate the proposed method. This aggregation method can not only better solve the interaction between attributes but also reflect the practicability and effectiveness of decision making.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (No. 11371130), the Natural Science Foundation of Fujian Province (No. 2017J01558), Key Laboratory of Applied Mathematics of Fujian Province University (Putian University) (No. SX201906), and Pre-Research Fund of Jimei University.

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Authors and Affiliations

  1. College of Science, Jimei University, Xiamen, 361021, Fujian, China

    Xiuqin Xu & Jialiang Xie

  2. Chengyi University College, Jimei University, Xiamen, 361021, Fujian, China

    Shuili Chen

Authors
  1. Xiuqin Xu
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  2. Jialiang Xie
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  3. Shuili Chen
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Correspondence to Shuili Chen .

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Editors and Affiliations

  1. Foshan University, Foshan, Guangdong, China

    Bing-yuan Cao

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Xu, X., Xie, J., Chen, S. (2020). Multi-attribute Decision Making Based on the Choquet Integral Operator with Hesitant Fuzzy Linguistic Information. In: Cao, By. (eds) Fuzzy Information and Engineering-2019. Advances in Intelligent Systems and Computing, vol 1094. Springer, Singapore. https://doi.org/10.1007/978-981-15-2459-2_8

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