Evidence map›Paper›PMID 40102532›Full record

ArticleScientific reports2025

A new approach to MADM problem using interval-valued hesitant Fermatean fuzzy Hamacher operators and statistical variance.

Xiuqin Ma, Xuli Niu, Hongwu Qin, Dong Ren, Siyue Lei, Kexin Tang

Abstract read
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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xiuqin MaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730020, Gansu, China.
Xuli NiuCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730020, Gansu, China.
Hongwu QinCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730020, Gansu, China. qinhongwu@nwnu.edu.cn.
Dong RenCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730020, Gansu, China.
Siyue LeiCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730020, Gansu, China.
Kexin TangCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730020, Gansu, China.

Funding

Gansu Provincial University Teacher Innovation Foundation 2024A-006National Natural Science Foundation of China 12461094National Natural Science Foundation of China 62162055The Key Research and Development Program Project of Gansu Province 24YFFA025
6 · The paper itself

Abstract

Water ecological civilization construction (WECC) is regarded as the core and cornerstone of ecological civilization construction. However, a lot of uncertainty is involved in assessing the WECC level, which presents serious and intricate difficulties for the related multiple- attribute decision-making (MADM) processes. The interval-valued hesitant Fermatean fuzzy set (IVHFFS) is a powerful tool for handling uncertainty in MADM issues. However, in the existing MADM approaches, attribute weight calculation involves high data redundancy and low computational efficiency. The existing aggregation operators ignore the importance of the attributes and their ordered positions. In order to solve these problems, in this paper, we propose a novel MADM model using interval-valued hesitant Fermatean fuzzy (IVHFF) Hamacher aggregation operator (AO) and statistical variance (SV) weight calculation. Firstly, the SV weight calculation method is given under IVHFFSs, aiming to computing objective weights of attributes. This greatly reduces data redundancy and improves the computational complexity. Secondly, we propose some IVHFF Hamacher AOs, such as IVHFF Hamacher (ordered) weighted averaging operator, IVHFF Hamacher (ordered) weighted geometric operator, IVHFF Hamacher hybrid averaging operator and geometric operator which consider the significance of the attributes and their ordered positions. Thirdly, a new MADM model based on the above information AOs and SV weight calculation is proposed. Finally, a comparative study on the real-world application for WECC and randomly generated data sets is also carried out to further demonstrate that our method outperforms the existing methods.

Indexed as

Hamacher aggregation operatorsInterval-valued hesitant Fermatean fuzzy setsMADMStatistical variance

Identifiers

PMID40102532
PMCPMC11920210

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