The impetus of this paper is to broaden the structure of linguistic soft set (LSS) to a new domain namely sigmoid valued fuzzy soft set (SVFSS). Some operating laws on SVFSS are also provided. Using the complement concept on SVFSS we define maximum rejection. This maximum rejection paves a way for defining a new similarity measure on SVFSS termed as maximum likely ratio (MLR). A new MCGDM algorithm for SVFSS is proposed using MLR. An illustrative example of haze equipment problem on sigmoid valued fuzzy soft set setting is also given. A comparative analysis of our approach with the existing approaches are also presented to justify our work.
AgbodahK. and DarkoA.P., Probabilistic linguistic aggregation operators based on Einstein t-norm and t-conorm and their application in multi-criteria group decision making, Symmetry11 (2019), 39.
2.
AmaliniL., AbdullahL., AbdullahL., AlkhazalehS. and KaraaslanF., Multi-Valued Interval Neutrosophic Soft Set: Formulation and Theory, Neutrosophic Sets and Systems30 (2019), 149–170.
3.
AygünE. and Kamaci husiyinH., Some generalized operations in soft set theory and their role in similarity and decision making, Journal of Intelligent & Fuzzy Systems36 (2019), 6537–6547.
4.
DongY.C., XuY.F. and YuS., Computing the numerical scale of the LTS for the 2-tuple fuzzy linguistic representation model, IEEE Trans Fuzzy Syst17(6) (2009), 1366–1378.
5.
DongY.C., LiC.C. and HerreraF., Connecting the linguistic hierarchy and the numerical scale for the 2-tuple linguistic model and its use to deal with hesistant unbalanced linguistic information, Information Sciences367–368 (2016), 259–278.
6.
EnginoğluS., MemişS. and KaraaslanF., A new approach to group decision-making method based on TOPSIS under fuzzy soft environment, Journal of New Results in Sciences8(2) (2019), 42–52.
7.
EraslanS. and KaraaslanF., A group decision making method based on TOPSIS under fuzzy soft environment, Journal of New Theory3 (2015), 30–40.
8.
HassanN. and AlhazaymehK., Vague SS expert set theory, In AIP conference proccedings (pp. 953–958), Volume 1522.
9.
HayatK., AliM.I., KaraaslanF., CaoB.Y. and ShahM.H., Design concept evaluation using soft sets based on acceptable and satisfactory levels: an integrated TOPSIS and Shannon entropy, Soft Comput24 (2020), 2229–2263.
10.
HayatK., AliM.I., CaoB.Y., KaraaslanF. and YangX.P., Another view of aggregation operators on group-based generalized intuitionistic fuzzy soft sets: multi-attribute decision making methods, Symmetry10(12) (2018), 253.
11.
KamacìH., Selectivity analysis of parameters in soft set and its effect on decision making, Int J Mach Learn & Cyber11 (2020), 313–324.
12.
KamacÌH., Similarity measure for soft matrices and its applications, Journal of Intelligent & Fuzzy System36 (2019), 3061–3072.
13.
KamacìH., A Novel Approach to Similarity of Soft Sets, Adiyaman University Journal of Science9(1) (2019), 23–35.
14.
KaraaslanF., Correlation coefficients of single-valued neutrosophic refined soft sets and their applications in clustering analysis, Neural Comput & Applic28 (2017), 2781–2793.
15.
KaraaslanF., Possibility neutrosophic soft sets and PNSdecision making method, Applied Soft Computing54 (2017), 403–414.
16.
KaraaslanF. and SerkanK., OR and AND-products of ifpintuitionistic fuzzy soft sets and their applications in decision making, Journal of Intelligent & Fuzzy Systems31 (2016), 1427–1434.
17.
KaraaslanF., Intuitionistic fuzzy parameterized intuitionistic fuzzy soft sets with applications in decision making, Annals of Fuzzy Mathematics and Informatics11(4) (2016), 607–619.
18.
KongZ., GaoL. and WangL., Comment on A fuzzy soft set theoretic approach to decision making problems, Journal of computational and applied mathematics223 (2009), 540–542.
19.
LinM., ChenZ. and LiaoH., ELECTRE II method to deal with probability linguistic term sets and its application to edge computing, Nonlinear Dyn96(3) (2019), 2125–2143.
20.
MaZ.M. and XuZ.S., Hyperbolic scales involving appetitesbased intuitionistic multiplicative preference relations for group decision making, Information Sciences451–452 (2018), 310–325.
21.
MajiP.K., More on Intuitionistic fuzzy soft sets, Rough sets, Fuzzy sets, Data mining and granular computing, Proccedings5908 (2009), 231–240.
22.
MajumdarP. and SamantaS.K., Similarity measure of soft sets, New Mathematics and Natural Computation4(1) (2008), 1–12.
23.
MolodtsovD., soft set Theory First Results, Computers and Mathematics with Applications37 (1999), 19–31.
24.
NamburuA., Kumar SamayS. and EdaraS.R., Soft fuzzy rough set based MR brain image segmentation, Applied Soft Computing54 (2017), 456–466.
25.
PawlakZ., Rough sets, International Journal of Computer and Information Sciences11 (1982), 341–356.
26.
PetchimuthuS. and KamacìH., The row-products of inverse soft matrices in multicriteria decision making, Journal of Intelligent & Fuzzy Systems36 (2019), 6425–6441.
27.
SulaimanN.H. and MohamadD., A Jaccard-based Similarity Measure for soft sets, 2012 IEEE Symposium on Humanities, Science and Engineering Research 659–663.
28.
TaoZ., ChenH., SongX., ZhouL. and LiuJ., Uncertain linguistic fuzzy soft sets and their applications in group decision making, Applied Soft Computing Journal34 (2015), 587–605.
29.
TaoZ., ChenH., ZhouL. and LiuJ., 2-Tuple linguistic soft set and its application to group decision making, Soft Comput19(5) (2015), 1201–1213.
30.
VijayabalajiS. and RameshA., Uncertain multiplicative linguistic soft sets and their application to group decision making, Journal of Intelligent & Fuzzy Systems35 (2018), 3883–3893.
31.
VijayabalajiS. and RameshA., Belief interval-valued soft set, Expert Systems With Applications119 (2019), 262–271.
32.
WangJ.H. and HaoJ.Y., A new version of 2-tuple fuzzy linguistic representation model for computing with words, IEEE Transactions on Fuzzy Systems14 (2006), 435–445.
33.
WangF.Q., LiX.H. and ChenX.H., Hesistant fuzzy soft set and its applications in multicriteria decision making, Journal of Applied Mathematics (2014), 1–10.
34.
XuZ.S., Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment, Information Sciences168 (2014), 171–184.
35.
YangW., New Similarity Measures for soft sets and Their Application, Fuzzy Inf Eng1 (2013), 19–25.
ZadehL.A., Fuzzy logic = computing with words, IEEE Trans Fuzzy Syst4(2) (1996), 103–111.
38.
ZhanJ.M., AliM.I. and MohmoodN., On a novel uncertain soft set model: Z-soft fuzzy rough set model and corresponding decision making methods, Applied Soft Computing56 (2017), 446–457.
39.
ZhangZ. and ZhangS., A novel approach to multi-attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets, Applied Mathematical Modelling37 (2013), 4948–4971.
40.
ZhouW. and XuZ., Asymmetric hesistant fuzzy sigmoid preference relations in the analytic hierarchy process, Information Sciences358–359 (2016), 191–207.
