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Dual generalized Bonferroni mean operators based on 2-dimensional uncertain linguistic information and their applications in multi-attribute decision making

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Abstract

The dual generalized Bonferroni mean operator is a further extension of the generalized Bonferroni mean operator which can take the interrelationship of different numbers of attributes into account by changing the embedded parameter. The 2-dimensional uncertain linguistic variable (2DULV) adds a second dimensional uncertain linguistic variable (ULV) to express the reliability of the assessment information in first dimensional information, which is more rational and accurate than the ULV. In this paper, for combining the advantages of them, we propose the dual generalized weighted Bonferroni mean operator for 2DULVs (2DULDGWBM) and the dual generalized weighted Bonferroni geometric mean operator for 2DULVs (2DULDGWBGM). In addition, we explore several particular cases and some rational characters of them. Further, a new approach is introduced to handle multi-attribute decision making problems in the environment of 2DULVs by the proposed operators. Finally, we utilize several illustrative examples to testify the validity and superiority of this new method by comparing with several other methods.

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Funding

The Funding was provided by National Natural Science Foundation of China (Grant Nos. 71771140, 71471172) and Special Funds of Taishan Scholars Project of Shandong Province (Grant No. ts201511045).

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Authors and Affiliations

  1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, 250014, Shandong, China

    Peide Liu

  2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, People’s Republic of China

    Weiqiao Liu

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  1. Peide Liu
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  2. Weiqiao Liu
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Correspondence to Peide Liu.

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Liu, P., Liu, W. Dual generalized Bonferroni mean operators based on 2-dimensional uncertain linguistic information and their applications in multi-attribute decision making. Artif Intell Rev 54, 491–517 (2021). https://doi.org/10.1007/s10462-020-09857-y

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