In this paper, we propose the theory of possibility Pythagorean fuzzy soft set and define some related the operations concerning this notion including the complement, union, intersection, AND and OR. Then a similarity measure is introduced to compare two possibility Pythagorean fuzzy soft sets for dealing with decision problems. Finally, a practical example is provided to illustrate the validity of this similarity measure in decision making problem.
ZadehL.A., Fuzzy sets, Information and control8(3) (1965), 338–353.
2.
AtanassovK., Intuitionistic fuzzy sets, Fuzzy sets and Systems20(1) (1986), 87–96.
3.
YagerR.R., Pythagorean membership grades in multi-criteria decision making, IEEE Transaction on Fuzzy Systems22(4) (2014), 958–965.
4.
MolodtsovD., Soft set theory-First results, Computers and Mathematics with Applications37 (1999), 19–31.
5.
JunY.B., Soft BCK/BCI-algebras, Computers and Mathematics with Applications56 (2008), 1408–1413.
6.
JunY.B. and ParkC.H., Applications of soft sets in ideal theory of BCK/BCI-algebras, Information Sciences178 (2008), 2466–2475.
7.
FengF., JunY.B. and ZhaoX.Z., Soft semirings, Computers and Mathematics with Applications56 (2008), 2621–2628.
8.
AliM.I., FengF., LiuX., MinW.K. and ShabirM., On some new operations in soft set theory, Computers and Mathematics with Applications57 (2009), 1547–1553.
9.
QinK.Y. and HongZ.Y., On soft equality, Journal of Computational and Applied Mathematics234 (2010), 1347–1355.
10.
ParkJ.H., KimaO.H. and KwunY.C., Some properties of equivalence soft set relations, Computers and Mathematics with Applications63 (2012), 1079–1088.
11.
ZhanJ.M., LiuQ. and HerawanT., A novel soft rough set: soft rough hemirings and its multicriteria group decision making, Applied Soft Computing54 (2017), 393–402.
12.
ZhanJ.M., AliM.I. and MehmoodN., On a novel uncertain soft set model: Z-soft fuzzy rough set model and corresponding decision making methods, Applied Soft Computing56 (2017), 446–457.
13.
ZhanJ.M. and ZhuK.Y., A novel soft rough fuzzy set: Z-soft rough fuzzy ideals of hemirings and corresponding decision making, Soft Computing21 (2017), 1923–1936.
14.
MaX.L., et al., A survey of decision making methods based on two classes of hybrid soft set models, Artificial Intelligence Review49(4) (2018), 511–529.
15.
Mathew, et al., A multimodal adaptive approach on soft set based diagnostic risk prediction system, Journal of Intelligent & Fuzzy Systems34(3) (2018), 1609–1618.
16.
MajiP. K.,
BiswasR.,
RoyA. R., Soft set theory, Computer sand Mathematics with Applications45 (2003), 555–562.
17.
MajiP. K.,
BiswasR.,
RoyA. R., Fuzzy Soft Set, Journal of Fuzzy Mathematics9 (3) (2001), 589–602.
18.
MajiP. K.,
BiswasR.,
RoyA. R., On intuitionistic Fuzzy Soft Set, Journal of Fuzzy Mathematics9 (3) (2001), 677–692.
19.
XiaoZ., ChenW. J. and LiL. L., A method based on interval-valued fuzzy soft set for multiattribute group decision-making problems under uncertain environment, Knowledge and Information Systems34 (2013), 653–669.
20.
DeschrijverG. and KerreE. E., Implicators based on binary aggregation operators in interval-valued fuzzy set theory, Fuzzy Sets and Systems153 (2005), 229–248.
21.
YangY.,
TanX.,
MengC. C., The multi-fuzzy soft set and its application in decision making, Applied Mathematical Modelling37 (2013), 4915–4923.
22.
AlkhazalehS.,
SallehA. R.,
HassanN., Possibility fuzzy soft set, Advances in Decision Sciences, Volume 2011, Article ID 479756, 18 pages.
23.
FatimahF., et al., N-soft sets and their decision making algorithms, Soft Computing22 (12) (2018), 3829–3842.
24.
AkramM., et al., Group decision-making methods based on hesitant N-soft sets, Expert Systems with Applications115 (2019), 95–105.
25.
AkramM., et al., Fuzzy N-Soft Sets: A Novel Model with Applications, Journal of Intelligent & Fuzzy Systems (2018), doi: 10.3233/JIFS-18244.
26.
PengX.D., YangY., SongJ.P., et al., Pythagorean fuzzy soft set and its application, Computer Engineering41(7) (2015), 224–229.
27.
ZhangX.L. and XuZ.S., Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets, International Journal of Intelligent Systems29(12) (2014), 1061–1078.
28.
MuthukumarP. and SaiG., Sundara Krishnan, A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis, Applied Soft Computing41 (2016), 148–156.
29.
GargH., Generalized Pythagorean Fuzzy Geometric Aggregation Operators Using Einstein t-Norm and t-Conorm for Multicriteria Decision-Making Process, International journal of intelligent systems32(6) (2017), 597–630.
30.
SanayeiA., Farid MousaviS. and YazdankhahA., Group decision making process for supplier selection with VIKOR under fuzzy environment, Expert Systems with Applications37(1) (2010), 24–30.
31.
Hassanzadeh AminS., RazmiJ. and ZhangG., Supplier selection and order allocation based on fuzzy SWOT anal-ysis and fuzzy linear programming, Expert Systems with Applications38(1) (2011), 334–342.