Application of Interval-valued T-spherical Fuzzy Dombi Hamy Mean Operators in the antiviral mask selection against COVID-19

Authors

  • Mehwish Sarfraz Department of Mathematics, Riphah International University Lahore, Lahore, Pakistan

DOI:

https://doi.org/10.31181/jdaic10030042024s

Keywords:

IVT-SPFDHM, IVT-SPFDDHM, IVT-SPFWDHM, IVT-SPFWDDHM, Multi-Attribute Decision-Making (MADM)

Abstract

This study introduces Interval-valued T-Spherical Fuzzy Dombi Hamy Mean (IVT-SPFDHM) Operators as a powerful tool for group decision-making. The IVT-SPFDHM operators allow for prioritization of fuzzy data effectively managing uncertainty. Its framework is applied in diverse group decision-making contexts, presenting its adaptability and robustness in addressing complex real-world problems. This study examines the Multi-Attribute Decision-Making (MADM) issue in IVT-SPFDHM environment where the qualifications and expertise are at varying levels of necessity. We regard the novel Aczel-Alsina aggregation operators (AOs) as the most recently created AOs, capable of handling considerable uncertainty. To propose some AOs we investigated the Hamy mean (HM) operator in following environment: Interval-valued T-Spherical Fuzzy weighted Dombi Hamy mean (IVT-SPWDHM) operator, stretch esteemed interval valued T-Spherical Dual Dombi Hamy Mean (IVT-SPFDDHM), and Interval-valued T-Spherical  Fuzzy weighted Dual Dombi Hamy Mean (IVT-SPFWDDHM). The weights for prioritization are derived from the knowledge of experts, and the proposed operators can capture the phenomenon of prioritization among the aggregated arguments. The MADM models are then planned using the IVPWDHM and IVPWDDHM operators. Finally, we provided a sample example within the prioritized ones to select the best antiviral mask for fighting COVID.

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References

Atanassov, K. T. (1999). Intuitionistic fuzzy sets: Theory and Applications. Studies in Fuzziness and Soft Computing, Vol. 35. Heidelberg: Physica.

Bao, T., Xie, X., Long, P., & Wei, Z. (2017). MADM method based on prospect theory and evidential reasoning approach with unknown attribute weights under intuitionistic fuzzy environment. Expert Systems with Applications, 88, 305–317.

Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069.

Chen, Z., Liu, P., & Pei, Z. (2015). An approach to multiple attribute group decision making based on linguistic intuitionistic fuzzy numbers. International Journal of Computational Intelligence Systems, 8(4), 747–760.

Cuong, B. C., Kreinovitch, V., & Ngan, R. T. (2016). A classification of representable t-norm operators for picture fuzzy sets. Eighth International Conference on Knowledge and Systems Engineering (KSE) (pp. 19–24). Hanoi: IEEE.

Dinh, N.V., Thao, N.X., & Chau, N.M. (2015). On the picture fuzzy database: Theories and application. Journal of Scientist and Development ,13(6), 1028-1035.

Dombi, J. (1982). A general class of fuzzy operators, the demorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Sets and Systems, 8(2), 149–163.

Garg, H. (2018). Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision-making process. International Journal of Intelligent Systems, 33(6), 1234–1263.

Garg, H., Ali, Z., Mahmood, T., Ali, M. R., & Alburaikan, A. (2023). Schweizer-Sklar prioritized aggregation operators for intuitionistic fuzzy information and their application in multi-attribute decision-making. Alexandria Engineering Journal, 67, 229–240.

Garg, H., Munir, M., Ullah, K., Mahmood, T., & Jan, N. (2018). Algorithm for T-Spherical Fuzzy Multi-Attribute Decision Making Based on Improved Interactive Aggregation Operators. Symmetry, 10(12), 670.

Gomes, L., & Lima, M. (1991). TODIM: Basics and apllication to multicriteria ranking of projects with environmental impacts. Foundations of Control Engineering, 16, 113-127.

Gope, D., Gope, A., & Gope, P. C. (2020). Mask material: Challenges and virucidal properties as an effective solution against coronavirus SARS-CoV-2. Open Health, 1(1), 37–50.

Gurmani, S. H., Chen, H., & Bai, Y. (2021). The operational properties of linguistic interval valued q-Rung orthopair fuzzy information and its VIKOR model for multi-attribute group decision making. Journal of Intelligent & Fuzzy Systems, 41(6), 7063–7079.

Hara, T., Uchiyama, M., & Takahasi, S.E. (1998). A refinement of various mean inequalities. Journal of Inequalities and Applications, 2(4), 387–395

Heidary Dahooie, J., Razavi Hajiagha, S. H., Farazmehr, S., Zavadskas, E. K., & Antucheviciene, J. (2021). A novel dynamic credit risk evaluation method using data envelopment analysis with common weights and combination of multi-attribute decision-making methods. Computers & Operations Research, 129, 105223.

Hwang, C.-L., & Youn, K. (1981). Multiple Attribute Decision Making - Methods and Application: A State of the Art Survey. New York: Springer.

Jin, H., Ashraf, S., Abdullah, S., Qiyas, M., Bano, M., & Zeng, S. (2019). Linguistic Spherical Fuzzy Aggregation Operators and Their Applications in Multi-Attribute Decision Making Problems. Mathematics, 7(5), 413.

Jin, H., Jah Rizvi, S. K., Mahmood, T., Jan, N., Ullah, K., & Saleem, S. (2020). An Intelligent and Robust Framework towards Anomaly Detection, Medical Diagnosis, and Shortest Path Problems Based on Interval-Valued T-Spherical Fuzzy Information. Mathematical Problems in Engineering, 2020, 1–23.

Jin, Y., Kousar, Z., Ullah, K., Mahmood, T., Yapici Pehlivan, N., & Ali, Z. (2021). Approach to multi-attribute decision-making methods for performance evaluation process using interval-valued T-spherical fuzzy Hamacher aggregation information. Axioms, 10(3), 145.

Khalil, A. M., Li, S.-G., Garg, H., Li, H., & Ma, S. (2019). New operations on interval-valued picture fuzzy set, interval-valued picture fuzzy soft set and their applications. Ieee Access, 7, 51236–51253.

Liao, M., Liu, H., Wang, X., Hu, X., Huang, Y., Liu, X., Brenan, K., Mecha, J, Nirmalan, M., & Lu, J. R. (2021). A technical review of face mask wearing in preventing respiratory COVID-19 transmission. Current Opinion in Colloid & Interface Science, 52, 101417.

Lin, M., Li, X., & Chen, L. (2020). Linguistic q-rung orthopair fuzzy sets and their interactional partitioned Heronian mean aggregation operators. International Journal of Intelligent Systems, 35(2), 217–249.

Liu, D., Luo, Y., & Liu, Z. (2020). The Linguistic Picture Fuzzy Set and Its Application in Multi-Criteria Decision-Making: An Illustration to the TOPSIS and TODIM Methods Based on Entropy Weight. Symmetry, 12, 1170.

Mahmood, T., Ullah, K., Khan, Q., & Jan, N. (2019). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 31(11), 7041–7053.

Mongia, A., Saha, S. K., Chouzenoux, E., & Majumdar, A. (2021). A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials. Scientific Reports, 11(1), 9047.

Munir, M., Kalsoom, H., Ullah, K., Mahmood, T., & Chu, Y.-M. (2020). T-spherical fuzzy Einstein hybrid aggregation operators and their applications in multi-attribute decision making problems. Symmetry, 12(3), 365.

O’Dowd, K., Nair, K. M., Forouzandeh, P., Mathew, S., Grant, J., Moran, R., Bartlett, J., Bird, J., & Pillai, S.C. (2020). Face Masks and Respirators in the Fight Against the COVID-19 Pandemic: A Review of Current Materials, Advances and Future Perspectives. Materials, 13(15), 3363.

Opricović, S. (1998). Multicriteria optimization of civil engineering systems (in Serbian). Belgrade: Faculty of Civil Engineering.

Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications. 42, 3016–3028.

Pemmada, R., Zhu, X., Dash, M., Zhou, Y., Ramakrishna, S., Peng, X., Thomas, V., Jain, S., & Nanda, H.S. (2020). Science-Based Strategies of Antiviral Coatings with Viricidal Properties for the COVID-19 Like Pandemics. Materials, 13(18), 4041.

Pradhan, D., Biswasroy, P., Kumar Naik, P., Ghosh, G., & Rath, G. (2020). A Review of Current Interventions for COVID-19 Prevention. Archives of Medical Research, 51(5), 363–374.

Sarfraz, M., Ullah, K., Akram, M., Pamucar, D., & Božanić, D. (2022). Prioritized Aggregation Operators for Intuitionistic Fuzzy Information Based on Aczel–Alsina T-Norm and T-Conorm and Their Applications in Group Decision-Making. Symmetry, 14(12), 2655.

Singh, P. (2015). Correlation coefficients for picture fuzzy sets. Journal of Intelligent and Fuzzy Systems, 28(2), 591-604.

Son, L. H., & Thong, P. H. (2017). Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences. Applied Intelligence, 46(1), 1–15.

Ullah, K., Garg, H., Gul, Z., Mahmood, T., Khan, Q., & Ali, Z. (2021). Interval Valued T-Spherical Fuzzy Information Aggregation Based on Dombi t-Norm and Dombi t-Conorm for Multi-Attribute Decision Making Problems. Symmetry, 13(6), 1053.

Ullah, K., Garg, H., Mahmood, T., Jan, N., & Ali, Z. (2020). Correlation coefficients for T-spherical fuzzy sets and their applications in clustering and multi-attribute decision making. Soft Computing, 24(3), 1647–1659.

Ullah, K., Mahmood, T., & Jan, N. (2018). Similarity measures for T-spherical fuzzy sets with applications in pattern recognition. Symmetry, 10(6), 193.

Wei, G., Wang, J., Lu, M., Wu, J., & Wei, C. (2019). Similarity measures of spherical fuzzy sets based on cosine function and their applications. IEEE Access, 7, 159069–159080.

Wu, M.-Q., Chen, T.-Y., & Fan, J.-P. (2019). Divergence measure of T-spherical fuzzy sets and its applications in pattern recognition. IEEE Access, 8, 10208–10221.

Wu, S., Wang, J., Wei, G., & Wei, Y. (2018). Research on construction engineering project risk assessment with some 2-tuple linguistic neutrosophic Hamy mean operators. Sustainability, 10(5), 1536.

Xu, Z., & Yager, R. R. (2008). Dynamic intuitionistic fuzzy multi-attribute decision making. International Journal of Approximate Reasoning, 48(1), 246–262.

Yager, R. R. (2008). Prioritized aggregation operators. International Journal of Approximate Reasoning, 48(1), 263–274.

Yager, R. R. (2017). Generalized Orthopair Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230.

Yeh, C.-H. (2002). A Problem-based Selection of Multi-attribute Decision-making Methods. International Transactions in Operational Research, 9(2), 169–181.

Zadeh, L. A. (1983) A Computational Approach to Fuzzy Quantifiers in Natural Languages. Computers & Mathematics with Applications, 9, 149-184.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.

Zeng, S., Garg, H., Munir, M., Mahmood, T., & Hussain, A. (2019). A Multi-Attribute Decision Making Process with Immediate Probabilistic Interactive Averaging Aggregation Operators of T-Spherical Fuzzy Sets and Its Application in the Selection of Solar Cells. Energies, 12, 4436.

Zhou, M., Chen, Y.-W., Liu, X.-B., Cheng, B.-Y., & Yang, J.-B. (2020a). Weight assignment method for multiple attribute decision making with dissimilarity and conflict of belief distributions. Computers & Industrial Engineering, 147, 106648.

Zhou, M., Liu, X.-B., Chen, Y.-W., Qian, X.-F., Yang, J.-B., & Wu, J. (2020b). Assignment of attribute weights with belief distributions for MADM under uncertainties. Knowledge-Based Systems, 189, 105110.

Zhou, M., Liu, X.-B., Yang, J.-B., Chen, Y.-W., & Wu, J. (2019). Evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment. Knowledge-Based Systems, 163, 358–375.

Published

30.04.2024

How to Cite

Sarfraz, M. (2024). Application of Interval-valued T-spherical Fuzzy Dombi Hamy Mean Operators in the antiviral mask selection against COVID-19. Journal of Decision Analytics and Intelligent Computing, 4(1), 67–98. https://doi.org/10.31181/jdaic10030042024s