Application of Fuzzy Soft Set in Selection Decision Making Problem

 International Journal of Engineering Trends and Technology (IJETT) © 2016 by IJETT Journal Volume-42 Number-7 Year of Publication : 2016 Authors : Rajesh Kumar Pal DOI :  10.14445/22315381/IJETT-V42P265

Citation

Rajesh Kumar Pal "Application of Fuzzy Soft Set in Selection Decision Making Problem", International Journal of Engineering Trends and Technology (IJETT), V42(7),370-374 December 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
In our daily life we often face some problems in which the right decision making is highly essential. But in most of the cases we become confused about the right solution. To obtain the best feasible solution of these problems we have to consider various parameters relating to the solution. For this we can use the best mathematical tool called Fuzzy soft set theory. In this paper we select a burning problem for the parents and successfully applied the fs-aggregation algorithm in decision making for selecting a suitable bride by the family.

References

[1] M. I. Ali, F. Feng, X. Liu, W. K. Min and M. Shabir, “On some new operations in soft set theory,” Comput. Math. Appl., vol. 57, pp. 1547-1553, 2009.
[2] N. C.agman and S. Enginoglu, “Soft set theory and uni-int decision making,” Eur. J. Oper. Res., vol. 207, pp. 848- 855, 2010.
[3] N. C.agman and S. Enginoglu, “Soft matrix theory and its decision making, Comput. Math. Appl.,” vol. 59(10), pp. 3308-3314, 2010.
[4] N. C.agman, F. C tak and S. Enginoglu, “Fuzzy parameterized fuzzy soft set theory and its applications,” Turk. J. Fuzzy Syst., vol. 1(1), pp. 21-35, 2010.
[5] D. Chen, E. C. C. Tsang, D. S. Yeung and X. Wang, “The parameterization reduction of soft sets and its applications,” Comput. Math. Appl., vol. 49, pp. 757-763, 2005.
[6] Z. Kong, L. Gao, L. Wang and S. Li, “The normal parameter reduction of soft sets and its algorithm,” Comput. Math. Appl., vol. 56, pp. 3029-3037, 2008.
[7] Z. Kong, L. Gao and L. Wang, “Comment on A fuzzy soft set theoretic approach to decision making problems," J. Comput. Appl. Math.,vol. 223, pp. 540-542, 2009.
[8] D. V. Kovkov, V. M. Kolbanov and D. A. Molodtsov, “Soft sets theory-based optimization,” J. Comput. Sys. Sc. Int., vol. 46(6), pp. 872-880, 2007.
[9] P. K. Maji, R. Biswas and A. R. Roy, “Fuzzy soft sets,” J. Fuzzy Math., vol. 9(3), pp. 589-602, 2001.
[10] P. K. Maji, A. R. Roy and R. Biswas, “An application of soft sets in a decision making problem,” Comput. Math. Appl., vol. 44, pp. 1077-1083, 2002.
[11] P. K. Maji, R. Biswas and A. R. Roy, “Soft set theory,” Comput. Math. Appl., vol. 45, pp. 555-562, 2003.
[12] P. Majumdar and S. K. Samanta, “Similarity measure of soft sets,” New. Math. Nat. Comput., vol. 4(1), pp. 1-12, 2008.
[13] D. A. Molodtsov, “Soft set theory- rst results,” Comput. Math. Appl., vol. 37, pp. 19-31, 1999.
[14] D. A. Molodtsov, “The description of dependence with the help of soft sets,” J. Comput. Sys. Sc. Int., vol. 40(6), pp.977-984, 2001.
[15] D. A. Molodtsov, The theory of soft sets (in Russian), URSS Publishers, Moscow, 2004.
[16] D. A. Molodtsov, V. Yu. Leonov and D. V. Kovkov, “Soft sets technique and its application,” Nechetkie Sistemi I Myakie Vychisleniya, vol. 1(1), pp. 8-39, 2006.
[17] M. M. Mushrif, S. Sengupta and A. K. Ray, “Texture classification using a novel, soft-set theory based classification, Algorithm.” Lecture Notes In Computer Science, 3851, pp. 246-254, 2006.
[18] Z. Pawlak, “Rough sets,” Int. J. Comput. Inform. Sci., vol. 11, pp. 341-356, 1982.
[19] D. Pei and D. Miao, From soft sets to information systems, In: X. Hu, Q. Liu, A. Skowron, T. Y. Lin, R. R. Yager, B. Zhang ,eds., “Proceedings of Granular Computing,” IEEE, vol. 2, pp. 617-621, 2005.
[20] A. R. Roy and P. K. Maji, “A fuzzy soft set theoretic approach to decision making problems,” J. Comput. Appl. Math., vol. 203, pp. 412-418, 2007.
[21] T. Som, “On the theory of soft sets, soft relation and fuzzy soft relation,” Proc. of the National Conference on Uncertainty: A Mathematical Approach, UAMA-06, Burdwan, 2006, pp. 1-9.
[22] Z. Xiao, Y. Li, B. Zhong and X. Yang, Research on synthetically evaluating method for business competitive capacity based on soft set, Stat. Methods. Med. Res., pp. 52-54, 2003.
[23] Z. Xiao, L. Chen, B. Zhong and S. Ye, “Recognition for soft information based on the theory of soft sets,” In: J. Chen ,eds., Proceedings of ICSSSM-05, 2, 2005, pp. 1104-1106.
[24] L. A. Zadeh, Fuzzy Sets, Information and Control, 8 , pp.338-353, 1965.
[25] Y. Zou and Z. Xiao, Data analysis approaches of soft sets under incomplete information, Knowl. Base. Syst., 21, pp. 941-945, 2008.
[26] N. C.agman and S. Enginoglu, and F. Citak, “Fuzzy soft set theory and its applications,” Iranian Journal of Fuzzy Systems, Vol. 8, No. 3, pp. 137-147, 2011.
[27] Krishna Gogoi, Alok Kr. Dutta and Chandra Chutia, “Application of Fuzzy Soft Set in day to day Problems,” International J. of Computer Applications, Vol. 85, No. 7, pp. 27-31, 2014.
[28] R.K. Bhardwaj, S.K. Tiwari and Kailash Chandra Nayak, “A Study of Solving Decision Making Problem using soft set,” IJLTEMAS, Vol. IV, Issue IX, pp. 26-32, 2015 .

Keywords
—Fuzzy set, Fuzzy Soft set, fs-aggregation.