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Maclaurin symmetric means of dual hesitant fuzzy information and their use in multi-criteria decision making

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Abstract

This paper investigates the Maclaurin symmetric mean (MSM) within the context of dual hesitant fuzzy sets and develops the dual hesitant fuzzy Maclaurin symmetric mean (DHFMSM), which can address the issues in previous dual hesitant fuzzy aggregation operators. Moreover, we put forward the geometric Maclaurin symmetric mean considering both the MSM and the geometric mean and apply it to propose a dual hesitant fuzzy geometric Maclaurin symmetric mean (DHFGMSM), followed by its several properties and special cases. Subsequently, considering the importance of each argument, the weighted DHFMSM and the weighted DHFGMSM are presented and used to develop an algorithm for realistic multi-criteria decision-making problems. Finally, the practicality of the new results is illustrated by a case study, and the advantages of the new results are highlighted by a comparison with other existing methods.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 61672205), the Scientific Research Project of Department of Education of Hebei Province of China (No. QN2016235), and the Natural Science Foundation of Hebei University (Nos. 799207217073 and 799207217108).

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  1. College of Mathematics and Information Science, Hebei University, Baoding, 071002, China

    Zhiming Zhang

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  1. Zhiming Zhang
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Correspondence to Zhiming Zhang.

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Zhang, Z. Maclaurin symmetric means of dual hesitant fuzzy information and their use in multi-criteria decision making. Granul. Comput. 5, 251–275 (2020). https://doi.org/10.1007/s41066-018-00152-4

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