Correlation Measure for Neutrosophic Hesitant Fantastic Filter for Supply Chain Management

Authors

P. Susithabanu

Department of Mathematics, Research Scholar, Karpagam Academy of Higher Education, Echanari, Coimbatore – 624021 (India)

V. Nirmala

Faculty of Engineering, Karpagam Academy of Higher Education, Echanari, Coimbatore - 624021 (India)

Article Information

DOI: 10.51584/IJRIAS.2025.100900037

Subject Category: Mathematics

Volume/Issue: 10/9 | Page No: 378-387

Publication Timeline

Submitted: 2025-09-26

Accepted: 2025-10-02

Published: 2025-10-12

Abstract

In order to address the complexity of decision-making processes that incorporate uncertainty, hesitation, probabilistic aspects, and the Fantastic Filter method, we present the Correlation Measure (CM) for Neutrosophic Hesitant Fantastic Filter (NHFF). By taking into account the Truth Membership Hesitancy Degree (TMHD), Indeterminacy-Membership Hesitancy Degree (IMHD), and Falsity-Membership Hesitancy Degree (FMHD), the suggested measures provide a methodical way to compute the CM between probability neutrosophic hesitant fuzzy set and neutrosophic hesitant fantastic filter. The study also proposed the Weighted Correlation Measure (WCM) approach, which enables varied weighting according to the relative importance of truth, indeterminacy, and falsity degrees, as well as the risk preferences of decision-makers (DMs).

Keywords

Probability neutrosophic hesitant set, Neutrosophic hesitant fantastic filter, correlation measure

Downloads

References

1. Abbott. J. C (1967) Implication Algebra, Bulletin Mathematique de la soc sci math de roumanie nouvelle series, vol 11, 3-23. [Google Scholar] [Crossref]

2. Ahadpanath. A, Torkzadeth. L and Borumand. S. A (2024) Filter Theory of Smarandache Residuated Lattice, UP. B Scientific Bulletin Series, Vol 76, 87-98. [Google Scholar] [Crossref]

3. Atanassov. K. (1986) Intuitionistic Fuzzy Set, Fuzzy Sets and System, Vol 20, 87-96. [Google Scholar] [Crossref]

4. Atanassov.K and Gargov (1989) Interval-Solved Intuitionistic Fuzzy Set, Fuzzy Sets and System, vol 31, 343-349. [Google Scholar] [Crossref]

5. Banihashemi, S. A., Khalilzadeh, M., Antucheviciene, J., & Edalatpanah, S. A. (2023). Identifying and prioritizing the challenges and obstacles of the green supply chain management in the construction industry using the fuzzy BWM method. Buildings, 13(1), 38. https://doi.org/10.3390/buildings13010038 [Google Scholar] [Crossref]

6. Bhat, S. A. (2023). An enhanced AHP group decision-making model employing neutrosophic trapezoidal numbers. J. oper. strateg anal, 1(2), 81–89. https://doi.org/10.56578/josa010205 [Google Scholar] [Crossref]

7. Faisal Al-Sharqi, Yousef Al-Qudah and Naif Alotaibi (2023) Decision Making Techniques Based on Similarity Measures Soft Expert Set, Journal of Intelligent andFuzzy Systems, vol 55, 357-382. [Google Scholar] [Crossref]

8. Henafi. I. M, Salama. A. A and Mahfowz. K (2012) Correlation of Neutrosophic Data, International Refereed Journal of Engineering and Science, vol 1, 39-43. [Google Scholar] [Crossref]

9. Hesami, F. (2025). A hybrid ANP-TOPSIS method for strategic supplier selection in reverse logistics under rough uncertainty: a case study in the electronics industry. Decision making advances, 3(1), 70–95. https://doi.org/10.31181/dma31202545 [Google Scholar] [Crossref]

10. Ibrahim. A, Nirmala. V (2018) Fuzzy Implicative Filters of Residuated Lattice Wajsberg Algebras, Journal of Computer and Mathematical Science, vol 9, 1201-1209. [Google Scholar] [Crossref]

11. Ibrahim. A, Nirmala. V (2018) Implicative Filters of Residuated Lattice Wajsberg Algebras, Global Journal of Pure and Applied Mathematics, vol 14, 625-634. [Google Scholar] [Crossref]

12. Malik, S. C., Raj, M., & Thakur, R. (2023). Weighted correlation coefficient measure for intuitionistic fuzzy set based on cosine entropy measure. International journal of information technology, 15(7), 3449 3461. https://doi.org/10.1007/s41870-023-01384-7 [Google Scholar] [Crossref]

13. Mehmood, A., Ahmad, A., Nawaz, M., Saeed, M. M., & Nordo, G. (2024). Discussion on Entropy and similarity measures and their few applications because of vague soft sets.Systemic analytics, 2(1), 157–173. https://doi.org/10.31181/sa21202423 [Google Scholar] [Crossref]

14. Ning, B., Wei, C., & Wei, G. (2024). Some novel correlation coefficients of probabilistic dual hesitant fuzzy sets and their application to multi-attribute decision-making. International journal of fuzzy systems, 26(4), 1–16. https://doi.org/10.1007/s40815-024- 01762-8 [Google Scholar] [Crossref]

15. Rahul Thakur1, Masum Raj2, Suresh Chander Malik1, (2025), Correlation Coefficient Measures for Probabilistic Single Valued Neutrosophic Hesitant Fuzzy Sets and Its Application in Supply Chain Management, Journal of Fuzzy Extension and Applications, Vol. 6, No. 2, 391–409. [Google Scholar] [Crossref]

16. Smarandache. F Neutrosophic set-a generalization of the intuitionistic fuzzy set, Infinite Study, 2010. [Google Scholar] [Crossref]

17. Zadeh. L. A (1965) Fuzzy Sets, Information and Control, vol 8, 338-353. [Google Scholar] [Crossref]

18. Zhang. X, Mao. X, Wu. Y and Zhai. X (2018) Neutrosophic Filters in Pseudo BCI Algebra, International Journal for Uncertainty Quantification, vol 8, 511-526. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles