Aczel–Alsina Hamy Mean Aggregation Operators in T-Spherical Fuzzy Multi-Criteria Decision-Making

Wang, Haolun; Xu, Tingjun; Feng, Liangqing; Mahmood, Tahir; Ullah, Kifayat

doi:10.3390/axioms12020224

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…Ali et al 41 introduced intuitionistic fuzzy soft Aczel–Alsina AOs. Wang et al 42 utilized Aczel–Alsina based Hamy-Mean AOs for T-spherical fuzzy MCDM. Some other contributions include 43 – 45 .…”

Section: Introductionmentioning

confidence: 99%

Identification of desalination and wind power plants sites using m-polar fuzzy Aczel–Alsina aggregation information

Rahman,

Ali,

Asif

et al. 2024

Sci Rep

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Real-world decision-making problems often include multi-polar uncertainties dependent on multi-dimensional attributes. The m-polar fuzzy (mF) sets can efficiently handle such multi-faceted complications with T-norm based weighted aggregation techniques. The Aczel–Alsina T-norms offer comparatively flexible and accurate aggregation than the other well-known T-norm families. Consequently, this work introduced novel mF Aczel–Alsina aggregation operators (AOs), including weighted averaging (mFAAWA, mFAAOWA, mFAAHWA) and weighted geometric (mFAAWG, mFAAOWG, mFAAHWG) AOs. The fundamental properties, including boundedness, idempotency, monotonicity, and commutativity are investigated. Based on the proposed AOs, a decision-making algorithm is developed and implemented to solve two detailed multi-polar site selection problems (for desalination plant and for wind-power plant). Finally, a comparison with mF Dombi and mF Yager AOs reveals that different T-norm based AOs may yeild different solutions for the same problem.

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Section: Introductionmentioning

confidence: 99%

Identification of desalination and wind power plants sites using m-polar fuzzy Aczel–Alsina aggregation information

Rahman,

Ali,

Asif

et al. 2024

Sci Rep

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“…Mahnaz et al [8] defined the T-spherical fuzzy frank aggregation operators and attribute weights determined by using the entropy measures. Wang et al [9] presented some T-spherical fuzzy Aczel-Alsina Hamy mean operators by integrating the Hamy mean with Aczel-Alsina operations. Ullah et al [10] studied the T-spherical fuzzy Hamacherweighted aggregation operators.…”

Section: Introductionmentioning

confidence: 99%

Some T-Spherical Hesitant Fuzzy Shapley Bonferroni Mean Operators and Their Applications

Pang,

Yang

2024

IEEE Access

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In this paper, some T-spherical hesitant fuzzy Bonferroni mean aggregation operators have been developed by extending the Bonferroni mean to the T-spherical hesitant fuzzy environment. The attribute weights are calculated by using the Shapley function to model interaction by considering the contribution of single attribute to the group attributes. The T-spherical hesitant fuzzy Shapley Bonferroni mean operator and the T-spherical hesitant fuzzy Shapley Dombi Bonferroni mean operator have been developed. Some properties of the new developed T-spherical hesitant fuzzy aggregation operators have been studied and some special cases of the new operators have been proposed. The new multiple attribute decision making method based on the new T-spherical hesitant fuzzy aggregation operators has been presented. The selection of the shared project investment is given to illustrate the new method. INDEX TERMS multiple attribute decision making, T-spherical hesitant fuzzy set, Bonferroni mean, Shapley value, Dombi.

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“…Ashraf et al [26] proposed single-valued neutrosophic Aczel-Alsina and utilized them in the decision-making process. Wang et al [27] worked on the Aczel-Alsina Hamy mean operators for T-spherical information. Other researchers have also explored the Aczel-Alsina aggregation operators in depth [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Selection of Investment Policy Using a Novel Three-Way Group Decision Model under Intuitionistic Hesitant Fuzzy Sets

et al. 2023

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In today’s fast-paced and dynamic business environment, investment decision making is becoming increasingly complex due to the inherent uncertainty and ambiguity of the financial data. Traditional decision-making models that rely on crisp and precise data are no longer sufficient to address these challenges. Fuzzy logic-based models that can handle uncertain and imprecise data have become popular in recent years. However, they still face limitations when dealing with complex, multi-criteria decision-making problems. To overcome these limitations, in this paper, we propose a novel three-way group decision model that incorporates decision-theoretic rough sets and intuitionistic hesitant fuzzy sets to provide a more robust and accurate decision-making approach for selecting an investment policy. The decision-theoretic rough set theory is used to reduce the information redundancy and inconsistency in the group decision-making process. The intuitionistic hesitant fuzzy sets allow the decision makers to express their degrees of hesitancy in making a decision, which is not possible in traditional fuzzy sets. To combine the group opinions, we introduce novel aggregation operators under intuitionistic hesitant fuzzy sets (IHFSs), including the IHF Aczel-Alsina average IHFAAA operator, the IHF Aczel-Alsina weighted average IHFAAWAϣ operator, the IHF Aczel-Alsina ordered weighted average IHFAAOWAϣ operator, and the IHF Aczel-Alsina hybrid average IHFAAHAϣ operator. These operators have desirable properties such as idempotency, boundedness, and monotonicity, which are essential for a reliable decision-making process. A mathematical model is presented as a case study to evaluate the effectiveness of the proposed model in selecting an investment policy. The results show that the proposed model is effective and provides more accurate investment policy recommendations compared to existing methods. This research can help investors and financial analysts in making better decisions and achieving their investment goals.

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Aczel–Alsina Hamy Mean Aggregation Operators in T-Spherical Fuzzy Multi-Criteria Decision-Making

Cited by 6 publications

References 47 publications

Identification of desalination and wind power plants sites using m-polar fuzzy Aczel–Alsina aggregation information

Identification of desalination and wind power plants sites using m-polar fuzzy Aczel–Alsina aggregation information

Some T-Spherical Hesitant Fuzzy Shapley Bonferroni Mean Operators and Their Applications

Selection of Investment Policy Using a Novel Three-Way Group Decision Model under Intuitionistic Hesitant Fuzzy Sets

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