The core competence, which is the new development of strategy government theory, is able to bring about differential competitive advantages. The innovation is the progressive soul of a nation, the inexhaustible motive force of a country’s boom. The discussion plays an important part in the sustainable development of corporations in theory and in practice in the way of integrating technological innovation into the core competence of enterprises. The textile industry, which is China’s traditional industry, plays a decisive role in the development of economy and society. Then, we investigate the multiple attribute decision making (MADM) problems for evaluating the core competitiveness of China’s cotton textile enterprises with intuitionistic fuzzy information. We utilize the intuitionistic fuzzy Bonferroni mean (IFBM) operator to aggregate the intuitionistic fuzzy information, then rank the alternatives and select the most desirable one(s) according to the score function and accuracy function. Finally an illustrative example has been given to show the developed approach.
XuA. and LiY., The evaluation for competitiveness of high-techology zone on creative industrial cluster with uncertain linguistic information, IJACT: International Journal of Advancements in Computing Technology3(5) (2011), 243–248.
2.
WeiG., Approaches to interval intuitionistic trapezoidal fuzzy multiple attribute decision making with incomplete weight information, International Journal of Fuzzy Systems17(3) (2015), 484–489.
3.
LvH., Application of fuzzy evaluating model to the assessment of marine service industry in triangular fuzzy setting, AISS: Advances in Information Sciences and Service Sciences3(9) (2011), 162–167.
4.
HuangH., Evaluation model construction of enterprises knowledge management with 2-tuple linguistic information, AISS: Advances in Information Sciences and Service Sciences3(9) (2011), 121–125.
5.
ZhangD.ZengX.ChenH. and HeW., Research on the evaluation models of customer value of brokers in the circumstances of electronic commerce with intuitionistic fuzzy information, AISS: Advances in Information Sciences and Service Sciences3(9) (2011), 76–81.
6.
JiangY. and JiangD., An approach to wireless sensor network security assessment with fuzzy number intuitionistic fuzzy information, AISS: Advances in Information Sciences and Service Sciences3(9) (2011), 140–146.
7.
LiuD., E-commerce system security assessment based on grey relational analysis comprehensive evaluation, JDCTA: International Journal of Digital Content Technology and its Applications5(10) (2011), 279–284.
8.
LiY.ShanX. and WuG., Comprehensive evaluation model for computer network security with linguistic information, AISS: Advances in Information Sciences and Service Sciences3(9) (2011), 126–131.
9.
WeiG., Hesitant fuzzy prioritized operators and their application to multiple attribute group decision making, Knowledge-Based Systems31, (2012), 176–182.
10.
LiD.F., A ratio ranking method of triangular intuitionistic fuzzy numbers and its application to MADM problems, Computers & Mathematics with Applications60(6) (Sep 2010), 1557–1570.
11.
LiD.F., Linear programming method for MADM with interval-valued intuitionistic fuzzy sets, Expert Systems with Applications37(8) (Aug 2010), 5939–5945.
12.
LiD.F., Mathematical-programming approach to matrix games with payoffs represented by atanassov’s interval-valued intuitionistic fuzzy sets, Ieee Transactions on Fuzzy Systems18(6) (2010), 1112–1128.
13.
WangJ.WeiG.W. and WeiY., Models for green supplier selection with some 2-tuple linguistic neutrosophic number bonferroni mean operators, Symmetry10(5) (2018), 131. doi: 10.3390/sym10050131.
WeiG., Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making, Applied Soft Computing10(2) (2010), 423–431.
16.
WeiG., GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting, Knowledge-Based Systems23(3) (2010), 243–247.
17.
AtanassovK., More on intuitionistic fuzzy sets, Fuzzy Sets and Systems33(5) (1989), 37–46.
18.
ChenS.M. and TanJ.M., Handling multicriteria fuzzy decision-making problems based on vague set theory, Fuzzy Sets and Systems67(4) (1994), 163–172.
19.
HongD.H. and ChoiC.H., Multicriteria fuzzy problems based on vague set theory, Fuzzy Sets and Systems114(3) (2000), 103–113.
20.
BonferroniC., Sulle medie multiple di potenze, Bolletino Matematica Italiana5 (1950), 267–270.
WuS.WangJ.WeiG. and WeiY., Research on construction engineering project risk assessment with some 2-tuple linguistic neutrosophic hamy mean operators, Sustainability10(5) (2018), 1536. https://doi.org/10.3390/su10051536.
23.
LiuP.-D.ChenS.-M. and LiuJ., Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators, Inf Sci411 (2017), 98–121.
24.
Al-QuranA. and HassanN., The complex neutrosophic soft expert relation and its multiple attribute decision-making method, Entropy20(2) (2018), 101.
25.
FarhadiniaB. and XuZ., Ordered weighted hesitant fuzzy information fusion – based approach to multiple attribute decision making with probabilistic linguistic term Sets, Fundam Inform159(4) (2018), 361–383.
WeiG.W., Picture uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making, Kybernetes46(10) (2017), 1777–1800.
28.
AkramM. and ShahzadiS., Novel intuitionistic fuzzy soft multiple-attribute decision-making methods, Neural Computing and Applications29(7) (2018), 435–447.
29.
WeiG.W.GaoH. and WeiY., Some q-rung orthopair fuzzy heronian mean operators in multiple attribute decision making, International Journal of Intelligent Systems33(7) (2018), 1426–1458.
30.
WeiG.W.LuM.TangX.Y. and WeiY., Pythagorean hesitant fuzzy hamacher aggregation operators and their application to multiple attribute decision making, International Journal of Intelligent Systems33(6) (2018), 1197–1233.
31.
ParkJ.H.KwarkH.E. and KwunY.C., Entropy and cross-entropy for generalized hesitant fuzzy information and their use in multiple attribute decision making, Int J Intell Syst32(3) (2017), 266–290.
32.
WeiG.W.GaoH.WangJ. and HuangY.H., Research on risk evaluation of enterprise human capital investment with interval-valued bipolar 2-tuple linguistic information, IEEE Access6 (2018), 35697–35712.
33.
GaoH.LuM.WeiG.W. and WeiY., Some novel Pythagorean fuzzy interaction aggregation operators in multiple attribute decision making, Fundamenta Informaticae159(4) (2018), 385–428.
34.
RahmanK.AbdullahS.AhmedR. and UllahM., Pythagorean fuzzy Einstein weighted geometric aggregation operator and their application to multiple attribute group decision making, Journal of Intelligent and Fuzzy Systems33(1) (2017), 635–647.
35.
WeiG.W., Some similarity measures for picture fuzzy sets and their applications, Iranian Journal of Fuzzy Systems15(1) (2018), 77–89.
36.
WeiG.W.AlsaadiF.E.HayatT. and AlsaediA., Bipolar fuzzy Hamacher aggregation operators in multiple attribute decision making, International Journal of Fuzzy System20(1) (2018), 1–12.
37.
HuangY.H. and WeiG.W., TODIM method for pythagorean 2-tuple linguistic multiple attribute decision making, Journal of Intelligent and Fuzzy Systems35(1) (2018), 901–915.
38.
VermaR., Multiple attribute group decision making based on generalized trapezoid fuzzy linguistic prioritized weighted average operator, Int J Machine Learning & Cybernetics8(6) (2017), 1993–2007.
39.
WeiG.W., Picture fuzzy Hamacher aggregation operators and their application to multiple attribute decision making, Fundamenta Informaticae157(3) (2018), 271–320.
40.
WangJ.-C. and ChenT.-Y., Likelihood-based assignment methods for multiple criteria decision analysis based on interval-valued intuitionistic fuzzy sets, FO & DM14(4) (2015), 425–457.
41.
WeiG.W. and LuM., Pythagorean fuzzy maclaurin symmetric mean operators in multiple attribute decision making, International Journal of Intelligent Systems33(5) (2018), 1043–1070.
