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An Innovative Decision Model Utilizing Intuitionistic Hesitant Fuzzy Aczel-Alsina Aggregation Operators and Its Application

Author

Listed:
  • Wajid Ali

    (Department of Mathematics, Air University, E-9, Islamabad 44000, Pakistan)

  • Tanzeela Shaheen

    (Department of Mathematics, Air University, E-9, Islamabad 44000, Pakistan)

  • Hamza Ghazanfar Toor

    (Biomedical Engineering Department, Riphah International University, Islamabad 46000, Pakistan)

  • Faraz Akram

    (Biomedical Engineering Department, Riphah International University, Islamabad 46000, Pakistan)

  • Md. Zia Uddin

    (Software and Service Innovation, SINTEF Digital, 0373 Oslo, Norway)

  • Mohammad Mehedi Hassan

    (Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia)

Abstract

The intuitionistic hesitant fuzzy set is a significant extension of the intuitionistic fuzzy set, specifically designed to address uncertain information in decision-making challenges. Aggregation operators play a fundamental role in combining intuitionistic hesitant fuzzy numbers into a unified component. This study aims to introduce two novel approaches. Firstly, we propose a three-way model for investors in the business domain, which utilizes interval-valued equivalence classes under the framework of intuitionistic hesitant fuzzy information. Secondly, we present a multiple-attribute decision-making (MADM) method using various aggregation operators for intuitionistic hesitant fuzzy sets (IHFSs). These operators include the IHF Aczel–Alsina average ( I H F A A A ) operator, the IHF Aczel–Alsina weighted average ( I H F A A W A ϣ ) operator, and the IHF Aczel–Alsina ordered weighted average ( I H F A A O W A ϣ ) operator and the IHF Aczel–Alsina hybrid average ( I H F A A H A ϣ ) operators. We demonstrate the properties of idempotency, boundedness, and monotonicity for these newly established aggregation operators. Additionally, we provide a detailed technique for three-way decision-making using intuitionistic hesitant fuzzy Aczel–Alsina aggregation operators. Furthermore, we present a numerical case analysis to illustrate the pertinency and authority of the esteblished model for investment in business. In conclusion, we highlight that the developed approach is highly suitable for investment selection policies, and we anticipate its extension to other fuzzy information domains.

Suggested Citation

  • Wajid Ali & Tanzeela Shaheen & Hamza Ghazanfar Toor & Faraz Akram & Md. Zia Uddin & Mohammad Mehedi Hassan, 2023. "An Innovative Decision Model Utilizing Intuitionistic Hesitant Fuzzy Aczel-Alsina Aggregation Operators and Its Application," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2768-:d:1174533
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    References listed on IDEAS

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    1. Jayanta Bera & Kinkar Chandra Das & Sovan Samanta & Jeong-Gon Lee, 2023. "Connectivity Status of Intuitionistic Fuzzy Graph and Its Application to Merging of Banks," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    2. Zeeshan Ali & Tahir Mahmood & Florentin Smarandache, 2021. "Three-Way Decisions with Single-Valued Neutrosophic Uncertain Linguistic Decision-Theoretic Rough Sets Based on Generalized Maclaurin Symmetric Mean Operators," Springer Books, in: Florentin Smarandache & Mohamed Abdel-Basset (ed.), Neutrosophic Operational Research, pages 71-101, Springer.
    3. Zixue Guo & Sijia Liu, 2023. "Study on the Selection of Pharmaceutical E-Commerce Platform Considering Bounded Rationality under Probabilistic Hesitant Fuzzy Environment," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
    4. Majed Albaity & Tahir Mahmood & Zeeshan Ali, 2023. "Impact of Machine Learning and Artificial Intelligence in Business Based on Intuitionistic Fuzzy Soft WASPAS Method," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    5. Şuara Onbaşıoğlu & Banu Pazar Varol, 2023. "Intuitionistic Fuzzy Metric-like Spaces and Fixed-Point Results," Mathematics, MDPI, vol. 11(8), pages 1-15, April.
    6. Yulia Resti & Chandra Irsan & Adinda Neardiaty & Choirunnisa Annabila & Irsyadi Yani, 2023. "Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
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    Cited by:

    1. Wajid Ali & Tanzeela Shaheen & Iftikhar Ul Haq & Hamza Ghazanfar Toor & Tmader Alballa & Hamiden Abd El-Wahed Khalifa, 2023. "A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis," Mathematics, MDPI, vol. 11(19), pages 1-18, October.

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