In this paper, we define aggregation operators for triangular cubic linguistic hesitant fuzzy sets which include triangular cubic linguistic hesitant fuzzy (geometric) operator, triangular cubic linguistic hesitant fuzzy weighted geometric (TCLHFWG) operator, triangular cubic linguistic hesitant fuzzy ordered weighted geometric (TCHFOWG) operator and triangular cubic linguistic hesitant fuzzy hybrid geometric (TCLHFHG) operator. Furthermore, we relate these aggregation operators to develop an approach to multi-attribute decision-making with triangular cubic linguistic hesitant fuzzy information. Finally, a numerical example is providing to demonstrate the submission of the established approach.
AtanassovK.T., Intuitionistic fuzzy sets, Fuzzy Sets and Systems20(1) (1986), 87–96.
2.
AtanassovK.T., New operations defined over intuitionistic fuzzy sets, Fuzzy Sets and Systems61(2) (1994), 137–142.
3.
ChenX., ZhangH. and DongY., The fusion process with heterogeneous preference structures in group decision making: A survey, Information Fusion24 (2015), 72–83.
4.
DongY., ZhangG., HongW.C. and YuS., Linguistic computational model based on 2-tuples and intervals, IEEE Transactions on Fuzzy Systems21(6) (2013), 1006–1018.
5.
DongY., ChenX. and HerreraF., Minimizing adjusted simple terms in the consensus reaching process with hesitant linguistic assessments in group decision making, Information Sciences297 (2015), 95–117.
6.
DongY., ZhangH. and Herrera-ViedmaE., Consensus reaching model in the complex and dynamic MAGDM problem, Knowledge-Based Systems106 (2016), 206–219.
7.
DongY., LiC.C. and HerreraF., Connecting the linguistic hierarchy and the numerical scale for the 2-tuple linguistic model and its use to deal with hesitant unbalanced linguistic information, Information Sciences367 (2016), 259–278.
8.
DongY., DingZ., MartinezL. and HerreraF., Managing consensus based on leadership in opinion dynamics, Information Sciences397 (2017), 187–205.
9.
DongY., LiuY., LiangH., ChiclanaF. and Herrera-ViedmaE., Strategic weight manipulation in multiple attribute decision making, Omega (2018), 154–164.
10.
JunY.B., KimC.S.Ki and YangO., Cubic sets, Annals of Fuzzy Mathematics and Informatics4(1) (2011), 83–98.
11.
LiC.C., DongY., HerreraF., Herrera-ViedmaE. and MartinezL., Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching, Information Fusion33 (2017), 29–40.
12.
LiuW., DongY., ChiclanaF., CabrerizoF.J. and Herrera-ViedmaE., Group decision-making based on heterogeneous preference relations with self-confidence, Fuzzy Optimization and Decision Making16 (2017), 429–447.
13.
LiuX., JuY. and YangS., Hesitant intuitionistic fuzzy linguistic aggregation operators and their applications to multiple attribute decision making, Journal of Intelligent & Fuzzy Systems27(3) (2014), 1187–1201.
14.
WeiG., WangH., ZhaoX. and LinR., Hesitant triangular fuzzy information aggregation in multiple attribute decision making, Journal of Intelligent & Fuzzy Systems26(3) (2014), 1201–1209.
15.
YuD., Triangular hesitant fuzzy set and its application to teaching quality evaluation, Journal of Information & Computational Science10(7) (2013), 1925–1934.
16.
ZadehL.A., The concept of a linguistic variable and its application to approximate reasoning—I, Information Sciences8(3) (1975), 199–249.
ZhangH., DongY. and ChenX., The 2-rank consensus reaching model in the multigranular linguistic multiple-attribute group decision-making, IEEE Transactions on Systems, Man and Cybernetics: Systems (2017) (in press DOI: 10.1109/TSMC.2017.2694429).
19.
ZhangH., DongY. and Herrera-ViedmaE., Consensus building for the heterogeneous large-scale GDM with the individual concerns and satisfactions, IEEE Transactions on Fuzzy Systems (2017) (In press, DOI: 10.1109/TFUZZ.2017.2697403).
20.
HashemiS.H., KarimiA. and TavanaM., An integrated green supplier selection approach with analytic network process and improved Grey relational analysis, International Journal of Production Economics159 (2015), 178–191.
21.
WangW.J., ChengC.H. and HuangK.C., Fuzzy hierarchical TOPSIS for supplier selection, Applied Soft Computing9(1) (2009), 377–386.
22.
SalmeronJ.L., VidalR. and MenaA., Ranking fuzzy cognitive map based scenarios with TOPSIS, Expert Systems with Applications39(3) (2012), 2443–2450.
23.
AtanassovK.T., VII ITKR’s Session, Sofia, Intuitionistic fuzzy sets1983.
24.
AtanassovK.T., Intuitionistic fuzzy sets: Theory and application, Springer1999.
25.
SzmidtE. and KacprzykJ., Intuitionistic fuzzy sets in some medical applications, Springer, Heidelberg. In International Conference on Computational Intelligence, Springer, Berlin, Heidelberg. 2001. pp. 148–151.
26.
TorraV.Hesitant fuzzy sets, International Journal of Intelligent Systems25(6) (2010), 529–539.
27.
TorraV. and NarukawaY., On hesitant fuzzy sets and decision, in: The 18th IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 2009. pp. 1378–1382.
28.
XuZ.S.
, A method based on linguistic aggregation operators for group decision making with linguistic preference relations. Information Sciences, 166(1) (2004), 19–30.
