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AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation

Author

Listed:
  • Muhammad Riaz

    (Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan)

  • Freeha Qamar

    (Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan)

  • Sehrish Tariq

    (Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan)

  • Kholood Alsager

    (Department of Mathematics, College of Science, Qasim University, Buraydah 51452, Saudi Arabia)

Abstract

Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply chain (HSC) and managing medical needs. Medical organizations can boost the logistics of their tasks, reduce pharmaceutical trash, and strengthen revenue projections through the adoption of AI tools. This study aims to provide a structured evaluation of AI-driven solutions for enhancing healthcare supply chain robustness, especially under conditions of uncertainty and complex logistics demands. To determine the investment value of AI applications in HSC management, the current research adopted a revolutionary multi-criteria decision-making (MCDM) methodology tailored to the healthcare sector’s unique demands, including six critical factors. In light of these criteria, six highly technologically advanced AI-based solutions are examined. The implementation of a circular intuitionistic fuzzy set (CIFS) in the instance discussed provides a versatile and expressive way to describe vague and uncertain information. This study leverages the CIF topology to address data complexities and uncover the underlying structural features of a large dataset. At the outset, we adopted the LOPCOW approach, which includes logarithmic variation to assign weights to criteria, whereas the AROMAN method utilizes a powerful two-step normalization technique to rank alternatives, hence guaranteeing a trustworthy and accurate appraisal. A substantial degree of robustness was confirmed by the technique following a comparison of the operators as well as sensitivity testing.

Suggested Citation

  • Muhammad Riaz & Freeha Qamar & Sehrish Tariq & Kholood Alsager, 2024. "AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation," Mathematics, MDPI, vol. 12(22), pages 1-32, November.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3593-:d:1522293
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    References listed on IDEAS

    as
    1. Ivana Nikolić & Jelena Milutinović & Darko Božanić & Momčilo Dobrodolac, 2023. "Using an Interval Type-2 Fuzzy AROMAN Decision-Making Method to Improve the Sustainability of the Postal Network in Rural Areas," Mathematics, MDPI, vol. 11(14), pages 1-26, July.
    2. Muhammad Riaz & Shaista Tanveer & Dragan Pamucar & Dong-Sheng Qin, 2022. "Topological Data Analysis with Spherical Fuzzy Soft AHP-TOPSIS for Environmental Mitigation System," Mathematics, MDPI, vol. 10(11), pages 1-36, May.
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