A q-rung orthopair fuzzy multi-attribute group decision making model based on attribute reduction and evidential reasoning methodology

Wu, Meiqin; Song, Jiawen; Fan, Jianping

doi:10.1016/j.eswa.2023.122558

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…In recent years, the research method of MAGDM 40 , 41 combined with fuzzy set theory has developed by leaps and bounds in various fields. At present, the extensive application of this method in the medical field is especially outstanding.…”

Section: Introductionmentioning

confidence: 99%

A new multi-attribute group decision-making method based on Einstein Bonferroni operators under interval-valued Fermatean hesitant fuzzy environment

Lei,

Ma,

Qin

et al. 2024

Sci Rep

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Faced with the increasing complexity and uncertainty of decision-making information, interval-valued Fermatean hesitant fuzzy sets (IVFHFSs) were presented as a novel mathematical model that handled uncertain data more effectively. However, existing multi-attribute group decision-making (MAGDM) methods based on IVFHFSs do not thoroughly investigate the operational laws. Also, these existing MAGDM methods do not take into account the connections between attributes and are less flexible. To address these issues, this paper proposes a new MAGDM method based on Einstein Bonferroni operators under IVFHFSs. First, we thoroughly examine the operational laws of Einstein t-norms under the IVFHFSs to further extend the study of the operational laws. Then, we introduce the interval-valued Fermatean hesitant fuzzy Einstein Bonferroni mean operator and the interval-valued Fermatean hesitant fuzzy Einstein weighted Bonferroni mean operator under Einstein t-norms. Our suggested aggregation operators consider the relationship between attributes and are far more flexible in comparison to the current approaches. Later, a novel MAGDM method based on Einstein Bonferroni operators under the IVFHFSs is given. Finally, the practicality and validity of the proposed method are demonstrated by a cardiovascular disease diagnosis application.

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Section: Introductionmentioning

confidence: 99%