Adjustable approaches to multi-criteria group decision making based on inverse fuzzy soft matrices

Document Type : Article

Authors

1 Department of Mathematics, University College of Engineering, Nagercoil-629004, Tamilnadu, India

2 Department of Mathematics, Faculty of Science and Arts, Yozgat Bozok University, Yozgat, Turkey

Abstract

In this paper, we focus on the matrices representing the inverse fuzzy soft sets over both the universal object set and the universal parameter set. Some basic operations and properties of these inverse fuzzy soft matrices are investigated. Moreover, two adjustable approaches to multi-criteria group decision making (MCGDM), namely inverse fuzzy soft sum-product decision making (IFSSPDM) and inverse fuzzy soft distributive If-difference decision making (IFSDIf-dDM), are proposed. The IFSSPDM approach achieves the optimal choice for the MCGDM problem based on the inverse fuzzy soft structures consisting of multiple-discrete parameter sets and common universal object sets. The objective of IFSDIf-dDM approach is to present a solution for the MCGDM problem based on the inverse fuzzy soft structures consisting of a common universal parameter set and two
discrete universal object sets. Thus, the solutions can be obtained using the practicality of inverse fuzzy soft matrices for two different types of decision making problems. Besides, the comparisons are presented showing that the proposed approaches produce more convincing outputs than the current fuzzy soft approaches.

Keywords


References:
1. Zadeh. L.A. "Fuzzy sets", Information and Control, 8(3), pp. 338-353 (1965).
2. Molodtsov, D. "Soft set theory-first results", Computers and Mathematics with Applications, 37(4-5), pp. 19-31 (1999).
3. Maji, P.K., Biswas. R., and Roy, A.R. "Soft set theory", Computers and Mathematics with Applications, 45(4-5), pp. 555-562 (2003). 
4. Ali, M.I., Feng, F., Liu, X., et al. "On some new operations in soft set theory", Computers and Mathematics with Applications, 57(9), pp. 1547-1553 (2009).
5. Alkhazaleh, S., Salleh, A.R., and Hassan, N. "Soft multisets theory", Applied Mathematical Sciences, 5(72), pp. 3561-3573 (2011).
6. Han, B.H. and Geng, S.L. "Pruning method for optimal solutions of intm  intn decision making scheme", European Journal of Operational Research, 231(3), pp. 779-783 (2013).
7. C agman, N. and Enginoglu, S. "Soft set theory and uni-int decision making", European Journal of Operational Research, 207(2), pp. 848-855 (2010).
8. Cagman, N. and Enginoglu, S. "Soft matrix theory and its decision making", Computers and Mathematics with Applications, 59(10), pp. 3308-3314 (2010).
9. Atagun, A.O., Kamaci, H., and Oktay, O. "Reduced soft matrices and generalized products with applications in decision making", Neural Computing and Applications, 29(9), pp. 445-456 (2018).
10. Basu, T.M., Mahapatra, N.M., and Mondal, S.K. "Matrices in soft set theory and their applications in decision making problems", South Asian Journal of Mathematics, 2(2), pp. 126-143 (2012).
11. Kamaci, H., Atagun, A.O., and Sonmezoglu, A. "Rowproducts of soft matrices with applications in multipledisjoint decision making", Applied Soft Computing, 62, pp. 892-914 (2018).
12. Kamaci, H., Atagun, A.O., and Toktas, E. "Bijective soft matrix theory and multi-bijective linguistic soft decision system", Filomat, 32(11), pp. 3799-3814 (2018).
13. Kamaci, H., Saltik, K., Akiz, H.F., et al. "Cardinality inverse soft matrix theory and its applications in multicriteria group decision making", Journal of Intelligent and Fuzzy Systems, 34(3), pp. 2031-2049 (2018).
14. Kamaci, H., Atagun, A.O., and Aygun, E. "Difference operations of soft matrices with applications in decision making", Punjab University Journal of Mathematics, 51(3), pp. 1-21 (2019).
15. Maji, P.K., Biswas, R., and Roy, A.R. "Fuzzy soft sets", Journal of Fuzzy Mathematics 9(3), pp. 589-602 (2001).
16. Roy, A.R. and Maji, P.K. "A fuzzy soft set theoretic approach to decision making problems", Journal of Computational and Applied Mathematics, 203(2), pp. 412-418 (2007).
17. Kong, Z., Gao, L., and Wang, L. "Comment on a fuzzy soft set theoretic approach to decision making problems", Journal of Computational and Applied Mathematics, 223(2), pp. 540-542 (2009).
18. Das, P.K. and Borgohain, R. "An application of fuzzy soft set in multicriteria decision making problem", International Journal of Computer Applications, 38(12), pp. 33-37 (2012).
19. Gogoi, K., Dutta, A.Kr., and Chutia, C. "Application of fuzzy soft set theory in day to day problems", International Journal of Computer Applications, 85(7), pp. 27-31 (2014).
20. Eraslan, S. "A decision making method via TOPSIS on fuzzy soft sets", Journal of New Results in Science, 4(8), pp. 57-70 (2015).
21. Khameneh, A.Z., Kilicman, A., and Salleh, A.R. "An adjustable approach to multi-criteria group decisionmaking based on a preference relationship under fuzzy soft information", International Journal of Fuzzy Systems, 19(6), pp. 1840-1865 (2017).
22. Liu, Z., Qin, K., and Pei, Z. "A method for fuzzy soft sets in decision-making based on an ideal solution", Symmetry, 9(10), p. 246 (2017).
23. Basu, T.M., Mahapatra, N.K., and Mondal, S.K. "A balanced solution of a fuzzy soft set based decision making problem in medical science", Applied Soft Computing, 12(10), pp. 3260-3275 (2012).
24. Li, Z., Wen, G., and Xie, N. "An approach to fuzzy soft sets in decision making based on grey relational analysis and Dempster-Shafer theory of evidence: An application in medical diagnosis", Artificial Intelligence in Medicine, 64(3), pp. 161-171 (2015).
25. Wang, J., Hu, Y., Xiao, F., et al. "A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: an application in medical diagnosis", Artificial Intelligence in Medicine, 69, pp. 1-11 (2016).
26. Xiao, F. "A hybrid fuzzy soft sets decision making method in medical diagnosis", IEEE Access, 6, pp. 25300-25312 (2018).
27. Sandhiya, S. and Selvakumari, K. "Application of fuzzy soft sets in job requirement problem", Journal of Computer and Mathematical Sciences, 10(1), pp. 184-189 (2019).
28. Cagman, N. and Engino~glu, S. "Fuzzy soft matrix theory and its application in decision making", Iranian Journal of Fuzzy Systems, 9(1), pp. 109-119 (2012).
29. Borah, M.J., Neog, T.J., and Sut, D.K. "Fuzzy soft matrix theory and its decision making", International Journal of Modern Engineering Research, 2(2), pp. 121-127 (2012).
30. Razak, S.A. and Mohamad, D. "A decision making method using fuzzy soft sets", Malaysian Journal of Fundamental and Applied Sciences, 9(2), pp. 99-104 (2013).
31. Inthumathi, V., Chitra, V., and Jayasree, S. "Fuzzy soft min-max decision making and its applications", Journal of Informatics and Mathematical Sciences, 9(3), pp. 827-834 (2017).
32. Razak, S.A., Mohamad, D., and Abdullah, I.I. "A group decision making problem using hierarchical based fuzzy soft matrix approach", International Journal of Advanced and Applied Sciences, 4(10), pp. 26-32 (2017).
33. Cetkin, V., Aygunoglu, A., and Aygun, H. "A new approach in handling soft decision making problems", Journal of Nonlinear Science and Applications, 9, pp. 231-239 (2016).
34. Khalil, A.M. and Hassan, N. "Inverse fuzzy soft set and its application in decision making", International Journal of Information and Decision Sciences, 11(1), pp. 73-92 (2019).
35. Maji, P.K., Roy, A.R., and Biswas, R. "An application of soft sets in a decision making problem", Comput. Math. Appl., 44, pp. 1077-1083 (2002).
36. Petchimuthu, S. and Kamaci, H. "The row-products of inverse soft matrices in multicriteria decision making", Journal of Intelligent and Fuzzy Systems, 36(6), pp. 6425-6441 (2019).
Volume 29, Issue 4
Transactions on Industrial Engineering (E)
July and August 2022
Pages 2166-2190
  • Receive Date: 23 August 2019
  • Revise Date: 14 June 2020
  • Accept Date: 21 September 2020