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Multi-Attribute Multi-Perception Decision-Making Based on Generalized T-Spherical Fuzzy Weighted Aggregation Operators on Neutrosophic Sets

Author

Listed:
  • Shio Gai Quek

    (A-Level Academy, UCSI College KL Campus, Lot 12734, Jalan Choo Lip Kung, Taman Taynton View, Cheras, Kuala Lumpur 56000, Malaysia)

  • Ganeshsree Selvachandran

    (Department of Actuarial Science and Applied Statistics, Faculty of Business and Information Science, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur 56000, Malaysia)

  • Muhammad Munir

    (Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Tahir Mahmood

    (Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Kifayat Ullah

    (Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Le Hoang Son

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    VNU Information Technology Institute, Vietnam National University, Hanoi 100000, Vietnam)

  • Pham Huy Thong

    (Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Raghvendra Kumar

    (Department of Computer Science and Engineering, LNCT College, Madhya Pradesh 462021, India)

  • Ishaani Priyadarshini

    (University of Delaware, Newark, DE 19716, USA)

Abstract

The framework of the T-spherical fuzzy set is a recent development in fuzzy set theory that can describe imprecise events using four types of membership grades with no restrictions. The purpose of this manuscript is to point out the limitations of the existing intuitionistic fuzzy Einstein averaging and geometric operators and to develop some improved Einstein aggregation operators. To do so, first some new operational laws were developed for T-spherical fuzzy sets and their properties were investigated. Based on these new operations, two types of Einstein aggregation operators are proposed namely the Einstein interactive averaging aggregation operators and the Einstein interactive geometric aggregation operators. The properties of the newly developed aggregation operators were then investigated and verified. The T-spherical fuzzy aggregation operators were then applied to a multi-attribute decision making (MADM) problem related to the degree of pollution of five major cities in China. Actual datasets sourced from the UCI Machine Learning Repository were used for this purpose. A detailed study was done to determine the most and least polluted city for different perceptions for different situations. Several compliance tests were then outlined to test and verify the accuracy of the results obtained via our proposed decision-making algorithm. It was proved that the results obtained via our proposed decision-making algorithm was fully compliant with all the tests that were outlined, thereby confirming the accuracy of the results obtained via our proposed method.

Suggested Citation

  • Shio Gai Quek & Ganeshsree Selvachandran & Muhammad Munir & Tahir Mahmood & Kifayat Ullah & Le Hoang Son & Pham Huy Thong & Raghvendra Kumar & Ishaani Priyadarshini, 2019. "Multi-Attribute Multi-Perception Decision-Making Based on Generalized T-Spherical Fuzzy Weighted Aggregation Operators on Neutrosophic Sets," Mathematics, MDPI, vol. 7(9), pages 1-34, August.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:9:p:780-:d:260451
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    Citations

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    Cited by:

    1. Muhammad Qiyas & Darjan Karabasevic & Neelam Khan & Srdjan Maričić, 2024. "Einstein Exponential Operational Laws Based on Fractional Orthotriple Fuzzy Sets and Their Applications in Decision Making Problems," Mathematics, MDPI, vol. 12(20), pages 1-21, October.
    2. Kou, Gang & Yüksel, Serhat & Dinçer, Hasan, 2022. "Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects," Applied Energy, Elsevier, vol. 311(C).

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