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Enhanced dissimilarity measurement for pythagorean fuzzy sets in real-world scenarios

Author

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  • Yograj Singh

    (Jaypee Institute of Information Technology, Uttar Pradesh)

  • Dinesh C. S. Bisht

    (Jaypee Institute of Information Technology, Uttar Pradesh)

Abstract

Researchers often encounter significant challenges when dealing with the inherent unpredictability of real-world data. Fuzzy sets are widely recognized for their ability to address ambiguity, yet a persistent challenge remains: accurately determining the dissimilarity or similarity between two fuzzy sets. This paper introduces a novel divergence measure specifically designed to assess the dissimilarity between Pythagorean fuzzy sets. A comparative analysis demonstrates that the proposed measure provides a clearer differentiation between Pythagorean fuzzy sets compared to other established popular measures. The suggested measure can be used in various applications, this work explains the use of the measure in three main real-world problems, namely multi-attribute decision-making problems, medical diagnosis problems, and pattern recognition problems. The proposed divergence measure has been integrated with the VIKOR approach, followed by a sensitivity analysis, affirming the stability of this novel methodology. A comparative analysis is conducted to demonstrate the feasibility and practicality of the proposed research.

Suggested Citation

  • Yograj Singh & Dinesh C. S. Bisht, 2025. "Enhanced dissimilarity measurement for pythagorean fuzzy sets in real-world scenarios," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(1), pages 402-424, January.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:1:d:10.1007_s13198-024-02628-w
    DOI: 10.1007/s13198-024-02628-w
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    References listed on IDEAS

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    1. Debesh Mishra & Suchismita Satapathy, 2023. "Reliability and maintenance of agricultural machinery by MCDM approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 135-146, February.
    2. Meenu Singh & Millie Pant, 2021. "A review of selected weighing methods in MCDM with a case study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 126-144, February.
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    4. Zaoli Yang & Xin Li & Harish Garg & Meng Qi, 2020. "Decision Support Algorithm for Selecting an Antivirus Mask over COVID-19 Pandemic under Spherical Normal Fuzzy Environment," IJERPH, MDPI, vol. 17(10), pages 1-27, May.
    5. Qianli Zhou & Hongming Mo & Yong Deng, 2020. "A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis," Mathematics, MDPI, vol. 8(1), pages 1-20, January.
    6. Priyanka Nagar & Pankaj Kumar Srivastava & Amit Srivastava, 2022. "A new dynamic score function approach to optimize a special class of Pythagorean fuzzy transportation problem," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 904-913, June.
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