Performance Evaluation of Healthcare Supply Chain in Industry 4.0 with Linear Diophantine Fuzzy Sine-Trigonometric Aggregation Operations

Anam Habib, Zareen A. Khan (), Muhammad Riaz and Dragan Marinkovic
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Anam Habib: Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan
Zareen A. Khan: Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Muhammad Riaz: Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan
Dragan Marinkovic: Faculty of Mechanical Engineering, University of Nis, 18000 Nis, Serbia

Mathematics, 2023, vol. 11, issue 12, 1-29

Abstract: The concept of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong model for machine learning and data-driven multi-criteria decision making (MCDM). The sine-trigonometric function (STF) has two significant features, periodicity and symmetry about the origin that are very useful tools for information analysis. Keeping in view the characteristics of both STF and LDFS theory, this article introduces the sine-trigonometric operations for linear Diophantine fuzzy numbers (LDFNs). These operational laws lay a foundation for developing new linear Diophantine fuzzy sine-trigonometric aggregation operators (LDFSTAOs). The integration of Industry 4.0 technology into healthcare has the potential to revolutionize patient care. One of the most challenging tasks is the selection of efficient suppliers for the healthcare supply chain (HSC). The traditional suppliers are not efficient in accordance with Industry 4.0, with particular uncertainties. A new MCDM framework is presented based on LDFSTAOs to examine the HSC performance in industry 4.0. A credibility test, sensitivity analysis and comparative analysis are performed to express the novelty, reliability, and efficiency of the proposed methodology.

Keywords: linear Diophantine fuzzy sets; sine-trigonometric operational laws; Industry 4.0; healthcare supply chain; aggregation operators; MCDM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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