Document Type : Research Paper

Authors

1 Department of Mathematics, Karnatak University, Dharwad-580003, Karnataka, India.

2 Department of Computer Science, Karnatak University, Dharwad-580003, Karnataka, India.

Abstract

Many real-world problems face strenuous in making decisions. Many theories have evolved for dealing with such problems. The present paper deals with Fuzzy Binary Soft Sets and their applications to Multi Criteria Decision Making (MCDM) problems. Then introduced an expanded matrix representation of Fuzzy Binary Soft Sets, an extended resultant matrix, and operator, and an algorithm to solve a proposed problem.

Keywords

Main Subjects

[1]     Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353.
[2]     Alamin, A., Rahaman, M., Mondal, S. P., Chatterjee, B., & Alam, S. (2022). Discrete system insights of logistic quota harvesting model: A fuzzy difference equation approach. Journal of uncertain systems, 15(02), 2250007. https://www.worldscientific.com/doi/abs/10.1142/S1752890922500076
[3]     Das, S. K., & Mandal, T. (2017). A MOLFP method for solving linear fractional programming under fuzzy environment. International journal of research in industrial engineering, 6(3), 202–213.
[4]     Halder, P. K., Karmarker, C. L., Kundu, B., & Daniel, T. (2018). Evaluation of factors affecting the productivity of RMG in Bangladesh: A fuzzy AHP approach. International journal of research in industrial engineering, 7(1), 51–60.
[5]     Mahmoudi, F., & Nasseri, S. H. (2019). A new approach to solve fully fuzzy linear programming problem. Journal of applied research on industrial engineering, 6(2), 139–149.
[6]     Rahaman, M., Maity, S., De, S. K., Mondal, S. P., & Alam, S. (2021). Solution of an Economic production quantity model using the generalized Hukuhara derivative approach. Scientia Iranica, 1-37. https://scientiairanica.sharif.edu/article_22582.html
[7]     Rahaman, M., Mondal, S. P., Chatterjee, B., & Alam, S. (2022). The solution techniques for linear and quadratic equations with coefficients as Cauchy neutrosphic numbers. Granular computing, 1–19. https://link.springer.com/article/10.1007/s41066-021-00276-0
[8]     Rahaman, M., Mondal, S. P., Alam, S., De, S. K., & Ahmadian, A. (2022). Study of a fuzzy production inventory model with deterioration under Marxian principle. International journal of fuzzy systems, 24(4), 2092–2106.
[9]     Tudu, S., Gazi, K. H., Rahaman, M., Mondal, S. P., Chatterjee, B., & Alam, S. (2023). Type-2 fuzzy differential inclusion for solving type-2 fuzzy differential equation. Annals of fuzzy mathematics and informatics, 25(1), 33–53.
[10]   Alzahrani, F. A., Ghorui, N., Gazi, K. H., Giri, B. C., Ghosh, A., & Mondal, S. P. (2023). Optimal site selection for women university using neutrosophic multi-criteria decision making approach. Buildings, 13(1), 152. https://doi.org/10.3390/buildings13010152
[11]   Gazi, K. H., Mondal, S. P., Chatterjee, B., Ghorui, N., Ghosh, A., & De, D. (2023). A new synergistic strategy for ranking restaurant locations: A decision-making approach based on the hexagonal fuzzy numbers. RAIRO-operations research, 57(2), 571–608.
[12]   Ghorui, N., Mondal, S. P., Chatterjee, B., Ghosh, A., Pal, A., De, D., & Giri, B. C. (2023). Selection of cloud service providers using MCDM methodology under intuitionistic fuzzy uncertainty. Soft computing, 27(5), 2403–2423.
[13]   Momena, A. F., Mandal, S., Gazi, K. H., Giri, B. C., & Mondal, S. P. (2023). Prediagnosis of disease based on symptoms by generalized dual hesitant hexagonal fuzzy multi-criteria decision-making techniques. Systems, 11(5), 231. https://www.mdpi.com/2079-8954/11/5/231
[14]   Nandi, S., Granata, G., Jana, S., Ghorui, N., Mondal, S. P., & Bhaumik, M. (2023). Evaluation of the treatment options for COVID-19 patients using generalized hesitant fuzzy-multi criteria decision making techniques. Socio-economic planning sciences, 88, 101614. https://www.sciencedirect.com/science/article/pii/S0038012123001143
[15]   Molodtsov, D. (1999). Soft set theory—first results. Computers & mathematics with applications, 37(4–5), 19–31.
[16]   Roy, A. R., & Maji, P. K. (2007). A fuzzy soft set theoretic approach to decision making problems. Journal of computational and applied mathematics, 203(2), 412–418.
[17]   Kong, Z., Gao, L., & Wang, L. (2009). Comment on “A fuzzy soft set theoretic approach to decision making problems”. Journal of computational and applied mathematics, 223(2), 540–542.
[18]   Snekaa, B., Dorathy, C., & Porchelvi, R. S. (2020). A combination of FAHP and fuzzy soft set theory method for solving MCDM problem in sports application. Malaya journal of matematik (MJM), 8(4), 1577–1579.
[19]   Acikgoz, A., & Tas, N. (2016). Binary soft set theory. European journal of pure and applied mathematics, 9(4), 452–463.
[20]   Metilda, P. G., & Subhashini, J. (2021). An application of fuzzy binary soft set in decision making problems. Webology (ISSN: 1735-188x), 18(6), 3672-3680.
[21]   Cagman, N., Enginoglu, S., & Citak, F. (2011). Fuzzy soft set theory and its applications. Iranian journal of fuzzy systems, 8(3), 137–147.
[22]   Kharal, A., & Ahmad, B. (2009). On fuzzy soft sets. Advances in fuzzy systems, 586507. https://ksascholar.dri.sa/en/publications/on-fuzzy-soft-sets-2