Models for evaluating the regional carbon emissions performance under the background of urban industrial agglomerations with hesitant fuzzy linguistic information
Restricted accessResearch articleFirst published online November, 2017
Models for evaluating the regional carbon emissions performance under the background of urban industrial agglomerations with hesitant fuzzy linguistic information
In this paper, we investigate the multiple attribute decision making (MADM) problem based on the induced OWA (IOWA) operator with hesitant fuzzy linguistic information. Then, motivated by the ideal of induced OWA (IOWA) operator and Einstein operations, we have developed the induced hesitant fuzzy linguistic Einstein ordered weighted average (IHFLEOWA) operator. Then, we have utilized induced hesitant fuzzy linguistic Einstein ordered weighted average (IHFLEOWA) operator to develop an approach to solve the multiple attribute decision making problems for evaluating the regional carbon emissions performance under the background of urban industrial agglomerations. Finally, a practical example for evaluating the regional carbon emissions performance under the background of urban industrial agglomerations is given to verify the developed approach and to demonstrate its practicality and effectiveness.
XuZ.S. and YagerR.R., Some geometric aggregation operators based on intuitionistic fuzzy sets, International Journal of General Systems35 (2006), 417–433.
4.
XuZ.S., Choquet integrals of weighted intuitionistic fuzzy information, Information Sciences180 (2010), 726–736.
5.
ChenT.-Y., An interval-valued intuitionistic fuzzy permutation method with likelihood-based preference functions and its application to multiple criteria decision analysis, Applied Soft Computing42 (2016), 390–409.
6.
ChenT.-Y., The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making, Applied Soft Computing26 (2015), 57–73.
7.
TangY.WenL.L. and WeiG.W., Approaches to multiple attribute group decision making based on the generalized Dice similarity measures with intuitionistic fuzzy information, International Journal of Knowledge-Based and Intelligent Engineering Systems21(2) (2017), 85–95.
8.
YeJ., Cosine similarity measures for intuitionistic fuzzy sets and their applications, Mathematical and Computer Modelling53(1–2) (2011), 91–97.
9.
LiD.-F. and RenH.-P., Multi-attribute decision making method considering the amount and reliability of intuitionistic fuzzy information, Journal of Intelligent and Fuzzy Systems28(4) (2015), 1877–1883.
10.
WanS.-P. and LiD.-F., Fuzzy mathematical programming approach to heterogeneous multiattribute decision-making with interval-valued intuitionistic fuzzy truth degrees, Information Science325 (2015), 484–503.
11.
WeiG.W., Picture fuzzy aggregation operators and their application to multiple attribute decision making, Journal of Intelligent and Fuzzy Systems33(2) (2017), 713–724.
12.
WeiG.W., Some geometric aggregation functions and their application to dynamic multiple attribute decision making in intuitionistic fuzzy setting, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems17(2) (2009), 179–196.
13.
WeiG.W., Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making, Applied Soft Computing10(2) (2010), 423–431.
14.
WeiG.W. and ZhaoX.F., Some induced correlated aggregating operators with intuitionistic fuzzy information and their application to multiple attribute group decision making, Expert Systems with Applications39(2) (2012), 2026–2034.
15.
WeiG., Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making, Expert Systems with Applications38(9) (2011), 11671–11677.
16.
WeiG.W.AlsaadiF.E.HayatT. and AlsaediA., Hesitant bipolar fuzzy aggregation operators in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems33(2) (2017), 1119–1128.
17.
ZhaoX. and WeiG., Some intuitionistic fuzzy Einstein hybrid aggregation operators and their application to multiple attribute decision making, Knowledge-Based Systems37 (2013), 472–479.
18.
TorraV., Hesitant fuzzy sets, International Journal of Intelligent Systems25 (2010), 529–539.
19.
XiaM. and XuZ.S., Hesitant fuzzy information aggregation in decision making, International Journal of Approximate Reasoning52(3) (2011), 395–407.
20.
WeiG.ZhaoX.WangH. and LinR., Hesitant fuzzy choquet integral aggregation operators and their applications to multiple attribute decision making, Information: An International Interdisciplinary Journal15(2) (2012), 441–448.
21.
WeiG.W., Hesitant fuzzy prioritized operators and their application to multiple attribute group decision making, Knowledge-Based Systems31 (2012), 176–182.
22.
FarhadiniaB., A novel method of ranking hesitant fuzzy values for multiple attribute decision-making problems, Int J Intell Syst28(8) (2013), 752–767.
23.
LiaoH. and XuZ., A VIKOR-based method for hesitant fuzzy multi-criteria decision making, FO & DM12(4) (2013), 373–392.
24.
FarhadiniaB., Distance and similarity measures for higher order hesitant fuzzy sets, Knowl-Based Syst55 (2014), 43–48.
25.
LiX.Y. and WeiG.W., GRA method for multiple criteria group decision making with incomplete weight information under hesitant fuzzy setting, Journal of Intelligent and Fuzzy Systems27(3) (2014), 1095–1105.
26.
ZhangZ.WangC.TianD. and LiK., Induced generalized hesitant fuzzy operators and their application to multiple attribute group decision making, Computers & Industrial Engineering67 (2014), 116–138.
27.
HeY.HeZ.WangG. and ChenH., Hesitant fuzzy power Bonferroni means and their application to multiple attribute decision making, IEEE Trans Fuzzy Systems23(5) (2015), 1655–1668.
28.
LiaoH.XuZ. and ZengX.-J., Novel correlation coefficients between hesitant fuzzy sets and their application in decision making, Knowl-Based Syst82 (2015), 115–127.
29.
LinR.ZhaoX.F.WangH.J. and WeiG.W., Hesitant fuzzy linguistic aggregation operators and their application to multiple attribute decision making, Journal of Intelligent and Fuzzy Systems27 (2014), 49–63.
30.
LinR.ZhaoX.F. and WeiG.W., Models for selecting an ERP system with hesitant fuzzy linguistic information, Journal of Intelligent and Fuzzy Systems26(5) (2014), 2155–2165.
31.
WeiG.W.WangH.J.ZhaoX.F. and LinR., Hesitant triangular fuzzy information aggregation in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems26(3) (2014), 1201–1209.
32.
ZhaoX.F.LinR. and WeiG.W., Hesitant triangular fuzzy information aggregation based on Einstein operations and their application to multiple attribute decision making, Expert Systems with Applications41(4) (2014), 1086–1094.
33.
LiQ.X.ZhaoX.F. and WeiG.W., Model for software quality evaluation with hesitant fuzzy uncertain linguistic information, Journal of Intelligent and Fuzzy Systems26(6) (2014), 2639–2647.
34.
WeiG.W.ZhaoX.F. and LinR., Some hesitant interval-valued fuzzy aggregation operators and their applications to multiple attribute decision making, Knowledge-Based Systems46 (2013), 43–53.
35.
WeiG.W.XuX.R. and DengD.X., Interval-valued dual hesitant fuzzy linguistic geometric aggregation operators in multiple attribute decision making, International Journal of Knowledge-Based and Intelligent Engineering Systems20(4) (2016), 189–196.
36.
WeiG.W., Interval valued hesitant fuzzy uncertain linguistic aggregation operators in multiple attribute decision making, International Journal of Machine Learning and Cybernetics7(6) (2016), 1093–1114.
37.
WeiG.W.AlsaadiF.E.HayatT. and AlsaediA., Hesitant fuzzy linguistic arithmetic aggregation operators in multiple attribute decision making, Iranian Journal of Fuzzy Systems13(4) (2016), 1–16.
38.
LuM.WeiG.W.AlsaadiF.E.HayatT. and AlsaediA., Hesitant pythagorean fuzzy hamacher aggregation operators and their application to multiple attribute decision making, Journal of Intelligent and Fuzzy Systems33(2) (2017), 1105–1117.
39.
WeiG.W.AlsaadiF.E.HayatT. and AlsaediA., A linear assignment method for multiple criteria decision analysis with hesitant fuzzy sets based on fuzzy measure, International Journal of Fuzzy Systems, 2016. doi: 10.1007/s40815-016-0177-x.
40.
ZhouL.Y.ZhaoX.F. and WeiG.W., Hesitant fuzzy Hamacher aggregation operators and their application to multiple attribute decision making, Journal of Intelligent and Fuzzy Systems26(6) (2014), 2689–2699.
41.
LiX.Y. and WeiG.W., GRA method for multiple criteria group decision making with incomplete weight information under hesitant fuzzy setting, Journal of Intelligent and Fuzzy Systems27 (2014), 1095–1105.
42.
WangH. and XuZ.S., Admissible orders of typical hesitant fuzzy elements and their application in ordered information fusion in multi-criteria decision making, Information Fusion29 (2016), 98–104.
43.
XuZ.S.XiaM. and ChenN., Some hesitant fuzzy aggregation operators with their application in group decision making, Group Decision and Negotiation22(2) (2013), 259–279.
44.
ZhangN. and WeiG.W., Extension of VIKOR method for decision making problem based on hesitant fuzzy set, Applied Mathematical Modelling37 (2013), 4938–4947.
45.
HerreraF. and Herrera-ViedmaE., Linguistic decision analysis: Steps for solving decision problems under linguistic information, Fuzzy Sets and Systems115 (2000), 67–82.
46.
HerreraF. and MartínezL., A 2-tuple fuzzy linguistic representation model for computing with words, IEEE Transactions on Fuzzy Systems8 (2000), 746–752.
47.
XuZ.S., A note on linguistic hybrid arithmetic averaging operator in multiple attribute group decision making with linguistic information, Group Decision and Negotiation15(6) (2006), 593–604.
48.
WeiG.W., Interval-valued dual hesitant fuzzy uncertain linguistic aggregation operators in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems33(3) (2017), 1881–1893.
49.
WeiG.W.LuM.AlsaadiF.E.HayatT. and AlsaediA., Pythagorean 2-tuple linguistic aggregation operators in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems33(2) (2017), 1129–1142.
50.
WeiG.W., Some harmonic averaging operators with 2-tuple linguistic assessment information and their application to multiple attribute group decision making, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems19(6) (2011), 977–998.
51.
WeiG.W. and ZhaoX.F., Some dependent aggregation operators with 2-tuple linguistic information and their application to multiple attribute group decision making, Expert Systems with Applications39 (2012), 5881–5886.
52.
MerigóJ.M. and Gil-LafuenteA.M., Induced 2-tuple linguistic generalized aggregation operators and their application in decision-making, Information Sciences236(1) (2013), 1–16.
53.
WeiG.W., Uncertain linguistic hybrid geometric mean operator and its application to group decision making under uncertain linguistic environment, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems17(2) (2009), 251–267.
54.
WeiG.W.ZhaoX.F.LinR. and WangH.J., Uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making, Applied Mathematical Modelling37(7) (2013), 5277–5285.
55.
JiangX.P. and WeiG.W., Some Bonferroni mean operators with 2-tuple linguistic information and their application to multiple attribute decision making, Journal of Intelligent and Fuzzy Systems27 (2014), 2153–2162.
56.
WeiG.W., Grey relational analysis model for dynamic hybrid multiple attribute decision making, Knowledge-Based Systems24(5) (2011), 672–679.
57.
YagerR.R. and FilevD.P., Induced ordered weighted averaging operators, IEEE Transactions on Systems, Man, and Cybernetics – Part B29 (1999), 141–150.
58.
WeiG.W.WangJ.M. and ChenJ., Potential optimality and robust optimality in multiattribute decision analysis with incomplete information: A comparative study, Decision Support Systems55(3) (2013), 679–684.
59.
ZhaoX.F.LinR. and WeiG.W., Fuzzy prioritized operators and their application to multiple attribute group decision making, Applied Mathematical Modelling37 (2013), 4759–4770.
60.
WeiG.W., Picture 2-tuple linguistic Bonferroni mean operators and their application to multiple attribute decision making, International Journal of Fuzzy Systems19(4) (2017), 997–1010.
61.
WeiG.W.AlsaadiF.E.HayatT. and AlsaediA., Picture 2-tuple linguistic aggregation operators in multiple attribute decision making, Soft Computing, 2016. doi: 10.1007/s00500-016-2403-8.
62.
WeiG.W., Picture fuzzy cross-entropy for multiple attribute decision making problems, Journal of Business Economics and Management17(4) (2016), 491–502.
63.
WeiG.AlsaadiF.E.HayatT. and AlsaediA., Projection models for multiple attribute decision making with picture fuzzy information, International Journal of Machine Learning and Cybernetics, 2016. doi: 10.1007/s13042-016-0604-1.
64.
WangH.J.ZhaoX.F. and WeiG.W., Dual hesitant fuzzy aggregation operators in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems26(5) (2014), 2281–2290.
65.
ZhouS.H. and ChangW.B., Approach to multiple attribute decision making based on the Hamacher operation with fuzzy number intuitionistic fuzzy information and their application, Journal of Intelligent and Fuzzy Systems27(3) (2014), 1087–1094.
66.
WangY.PengX.G. and BianJ., Study on the security of information system authentication scheme based on the fuzzy number intuitionistic fuzzy information, Journal of Intelligent and Fuzzy Systems28(5) (2015), 2225–2232.
67.
LuM.WeiG.W.AlsaadiF.E.HayatT. and AlsaediA., Bipolar 2-tuple linguistic aggregation operators in multiple attribute decision making, Journal of Intelligent and Fuzzy Systems33(2) (2017), 1197–1207.
68.
TianM.Y., A new fuzzy similarity based on cotangent function for medical diagnosis, Advanced Modeling and Optimization15(2) (2013), 151–156.
69.
RajarajeswariP. and UmaN., Intuitionistic fuzzy multi similarity measure based on cotangent function, International Journal of Engineering Research & Technology2(11) (2013), 1323–1329.
