Generalized Z-fuzzy soft β-covering based rough matrices and its application to MAGDM problem based on AHP method

Authors

  • Pavithra Sivaprakasam Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India
  • Manimaran Angamuthu Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India https://orcid.org/0000-0001-6717-1152

DOI:

https://doi.org/10.31181/dmame04012023p

Keywords:

β-level soft set, Fuzzy soft β-adhesion, Generalized Z-fuzzy soft β-covering based rough matrix, AHP

Abstract

Fuzzy, rough, and soft sets are different mathematical tools mainly developed to deal with uncertainty. Combining these theories has a wide range of applications in decision analysis. In this paper, we defined a generalized Z-fuzzy soft -covering-based rough matrices. Some algebraic properties are explored for this newly constructed matrix. The main aim of this paper is to propose a novel MAGDM model using generalized Z-fuzzy soft -covering-based rough matrices. A MAGDM algorithm based on the AHP method is created to recruit the best candidate for an assistant professor job in an institute, and a numerical example is presented to demonstrate the created method.

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References

Ali, M. I. (2011). A note on soft sets, rough soft sets and fuzzy soft sets. Applied Soft Computing, 11 (4), 3329-3332.

Cagman, N., & Enginoglu, S. (2010). Soft matrix theory and its decision making. Computers & Mathematics with Applications, 59 (10), 3308-3314.

Cagman, N., & Enginoglu, S. (2012). Fuzzy soft matrix theory and its application in decision making. Iranian Journal of Fuzzy Systems, 9 (1), 109-119.

Dubois, D., & Prade, H. (1990). Rough fuzzy sets and fuzzy rough sets. International Journal of General System, 17(2-3), 191-209.

Feng, F., Li, C., Davvaz, B., & Ali, M. I. (2010). Soft sets combined with fuzzy sets and rough sets: a tentative approach. Soft computing, 14, 899-911.

Greco, S., Matarazzo, B., & Slowinski, R. (2001). Rough sets theory for multicriteria decision analysis. European journal of operational research, 129 (1), 1-47.

Gurmani, S. H., Chen, H., & Bai, Y. (2022). Multi-attribute group decision-making model for selecting the most suitable construction company using the linguistic interval-valued T-spherical fuzzy TOPSIS method. Applied Intelligence. https://doi.org/10.1007/s10489-022-04103-0.

Maji, P. K., Roy, A. R., & Biswas, R. (2002). An application of soft sets in a decision making problem. Computers & Mathematics with Applications, 44 (8-9), 1077-1083.

Maji, P. K., Biswas, R., & Roy, A. R. (2003). Soft set theory. Computers & Mathematics with Applications, 45 (4-5), 555-562.

Molodtsov, D. (1999). Soft set theory - First results. Computers & Mathematics with Applications, 37 (4-5), 19-31.

Muthukumar, P., & Krishnan, G. (2018). Generalized Fuzzy Soft Rough Matrices and Their Applications in Decision-Making Problems. International Journal of Fuzzy Systems, 20(2), 500-514.

Pawlak, Z. (1982). Rough sets. International journal of computer & information sciences, 11 (5), 341-356.

Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill, New York.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1 (1), 83-98.

Sharma, H. K., Kumari, K., & Kar, S. (2018). Air passengers forecasting for Australian airline based on hybrid rough set approach. Journal of Applied Mathematics, Statistics and Informatics, 14(1), 5-18.

Sharma, H. K., Kumari, K., & Kar, S. (2021). Forecasting Sugarcane Yield of India based on rough set combination approach. Decision Making: Applications in Management and Engineering, 4(2), 163-177.

Sharma, H. K., Singh, A., Yadav, D., & Kar, S. (2022). Criteria selection and decision making of hotels using Dominance Based Rough Set Theory. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 41-55.

Tufail, F., Shabir, M., & Abo-Tabl, E. S. A. (2022). A Comparison of Promethee and TOPSIS Techniques Based on Bipolar Soft Covering-Based Rough Sets. IEEE Access, 10, 37586-37602.

Vijayabalaji, S. (2014). Multi-decision making in generalized soft-rough matrices. Mathematical Sciences-International Research Journal, 3 (1), 19-24.

Yang, B. (2022). Fuzzy covering-based rough set on two different universes and its application. Artificial Intelligence Review, 55, 4717-4753.

Yüksel, Ş., Ergül, Z. G., & Tozlu, N. (2014). Soft covering based rough sets and their application. The Scientific World Journal, Article ID 970893, 1-9.

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

Zhan, J., & Wang, Q. (2019). Certain types of soft coverings based rough sets with applications. International Journal of Machine Learning and Cybernetics, 10 (5), 1065-1076.

Zhan, J., & Sun, B. (2019). Covering-based soft fuzzy rough theory and its application to multiple criteria decision making. Computational and Applied Mathematics, 38 (4), 1-27.

Zhang, H., Liang, H., & Liu, D. (2004). Two new operators in rough set theory with applications to fuzzy sets. Information Sciences, 166 (1-4), 147-165.

Zhang, L., & Zhan, J. (2019). Fuzzy soft β-covering based fuzzy rough sets and corresponding decision-making applications. International Journal of Machine Learning and Cybernetics, 10 (6), 1487-1502.

Zhu, W., & Wang, F. Y. (2007). On three types of covering-based rough sets. IEEE transactions on knowledge and data engineering, 19 (8), 1131-1144.

Zhu, W., & Wang, F. Y. (2012). The fourth type of covering-based rough sets. Information Sciences, 201, 80-92.

Published

2023-04-08

How to Cite

Sivaprakasam, P., & Angamuthu, M. (2023). Generalized Z-fuzzy soft β-covering based rough matrices and its application to MAGDM problem based on AHP method. Decision Making: Applications in Management and Engineering, 6(1), 134–152. https://doi.org/10.31181/dmame04012023p