Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Spherical fuzzy sets (SFSs) provide more free space for decision makers (DMs) to express preference information from four aspects: approval, objection, abstention and refusal. The partitioned Maclaurin symmetric mean (PMSM) operator is an effective information fusion tool, which can fully capture the interrelationships among any multiple attributes in the same block whereas attributes in different block are unrelated. Therefore, in this paper, we first extend PMSM operator to spherical fuzzy environment and develop spherical fuzzy PMSM (SFPMSM) operator as well as spherical fuzzy weighted PMSM (SFWPMSM) operator. Meanwhile, we discuss some properties and special cases of these two operators. To diminish the impact of extreme evaluation values on decision-making results, then we integrate power average (PA) operator and PMSM operator to further develop spherical fuzzy power PMSM (SFPPMSM) operator and spherical fuzzy weighted power PMSM (SFWPPMSM) operator and also investigate their desirable properties. Subsequently, a new multiple attribute group decision making (MAGDM) method is established based on SFWPPMSM operator under spherical fuzzy environment. Finally, two numerical examples are used to illustrate the proposed method, and comparative analysis with the existing methods to further testy the validity and superiority of the proposed method.
Czasopismo
Rocznik
Tom
Strony
179--238
Opis fizyczny
Bibliogr. 50 poz., rys., tab., wzory
Twórcy
autor
- School of Mathematics and Statistics, Liupanshui Normal University, Liupanshui 553004, Guizhou, P.R. China
- School of Mathematical Sciences, Sichuan Normal University, Chengdu, 610101, P.R. China
autor
- School of Business, Sichuan Normal University, Chengdu, 610101, P.R. China
autor
- School of Mathematical Sciences, Sichuan Normal University, Chengdu, 610101, P.R. China
- School of Business, Sichuan Normal University, Chengdu, 610101, P.R. China
Bibliografia
- [1] L.A. Zadeh: Fuzzy sets. Information and Control, 8 (1965), 338-353.
- [2] K.T. Atanassov: Intuitionistic fuzzy sets. Fuzzy Sets and System, 20 (1986), 87-96.
- [3] M. Rahimi, P. Kumar, B. Moomivand, and G. Yari: An intuitionistic fuzzy entropy approach for supplier selection. Complex & Intelligent Systems, 7 (2021) 1869-1876. DOI: 10.1007/s40747-020-00224-6.
- [4] M.W. Zhao, G.W. Wei, X.D. Chen, and Y. Wei: Intuitionistic fuzzy MABAC method based on cumulative prospect theory for multiple attribute group decision making. International Journal of Intelligent Systems, 36(11), (2021), 6337-6359. DOI: 10.1002/int.22552.
- [5] J. Bao: Algorithms for MAGDM with intuitionistic fuzzy sets and their application for evaluating the green technological innovation ability of the enterprises. Journal of Intelligent & Fuzzy Systems, 40(5), (2021), 9687-9707. DOI: 10.3233/JIFS-202194.
- [6] F.Y. Xiao: A Distance measure for intuitionistic fuzzy sets and its application to pattern classification problems. IEEE Transactions on Systems Man Cybernetics: Systems, 51(6), (2021), 3980-3992. DOI: 10.1109/TSMC.2019.2958635.
- [7] S.L. Liu: Research on the teaching quality evaluation of physical education with intuitionistic fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems, 40(5), (2021), 9227-9236. DOI: 10.3233/jifs-201672.
- [8] Y. Liang: An EDAS method for Multiple Attribute Group Decision-Making under intuitionistic fuzzy environment and its application for evaluating green building energy-saving design projects. Symmetry, 12(3), (2020), 484. DOI: 10.3390/sym12030484.
- [9] K.H. Chang and C.H. Cheng: A risk assessment methodology using intuitionistic fuzzy set in FMEA. International Journal of Systems Science, 41(12), (2010), 1457-1471. DOI: 10.1080/00207720903353633.
- [10] M. Dursun: BWM integrated intuitionistic fuzzy approach for sustainable transportation service provider selection. Journal of Multiple-Valued Logic and Soft Computing, 37(3-4), (2021), 277-294.
- [11] R.R. Yager: Pythagorean membership grades in Multicriteria Decision Making. IEEE Transactions on Fuzzy Systems, 22(4), (2014), 958-965. DOI: 10.1109/TFUZZ.2013.2278989.
- [12] B.C. Cuong: Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), (2014), 409-420. DOI: 10.15625/1813-9663/30/4/5032.
- [13] V. Arya and S. Kumar: A new picture fuzzy information measure based on shannon entropy with applications in opinion polls using extended VIKOR-TODIM approach. Computational & Applied Mathematics, 39 (2020), 197. DOI: 10.1007/s40314-020-01228-1.
- [14] G.W. Wei and H. Gao: The generalized Dice similarity measures for picture fuzzy sets and their applications. Informatica, 29(1), (2018) 107-124. DOI: 10.15388/Informatica.2018.160.
- [15] S. Singh and A.H. Ganie: Applications of picture fuzzy similarity measures in pattern recognition, clustering, and MADM. Expert Systems with Applications, 168 (2021), 114264. DOI: 10.1016/j.eswa.2020.114264.
- [16] G.W. Wei, S.Q. Zhang, J.P. Lu, J. Wu, and C. Wei: An extended bidirectional projection method for picture fuzzy MAGDM and its application to safety assessment of construction project. IEEE Access, 7 (2019), 166138-166147. DOI: 10.1109/ACCESS.2019.2953316.
- [17] J.L. Jin, P. Zhao, and T.J. You: Picture fuzzy TOPSIS method based on CPFRS model: An application to risk management problems. Scientific Programming, 2021 (2021), 1-15. DOI: 10.1155/2021/6628745.
- [18] M.J. Khan, P. Kumam, W. Deebani, W. Kumam, and Z. Shah: Biparametric distance and similarity measures of picture fuzzy sets and their applications in medical diagnosis. Egyptian Informatics Journal, 22(2), (2021), 201-212. DOI: 10.1016/j.eij.2020.08.002.
- [19] P. Meksavang, H. Shi, S.M. Lin. and H.C. Liu: An extended picture fuzzy VIKOR approach for sustainable supplier management and its application in the beef industry. Symmetry, 11(4), (2019), 468. DOI: 10.3390/sym11040468.
- [20] T. Mahmood, K. Ullah, Q. Khan, and N. Jan: An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing & Applications, 31 (2019), 7041-7053. DOI: 10.1007/s00521-018-3521-2.
- [21] J. Ali: A novel score function based CRITIC-MARCOS method with spherical fuzzy information. Computational & Applied Mathematics, 40(8), (2021), 280. DOI: 10.1007/s40314-021-01670-9.
- [22] O. Dogan: Process mining technology selection with spherical fuzzy AHP and sensitivity analysis. Expert Systems with Applications, 178 (2021), 114999. DOI: 10.1016/j.eswa.2021.114999.
- [23] M. Fernandez-Martinez and J.M. Sanchez-Lozano: Assessment of near-Earth asteroid deflection techniques via spherical fuzzy sets. Advances in Astronomy, 2021 (2021), 6678056. DOI: 10.1155/2021/6678056.
- [24] X. Peng and W. Li: Spherical fuzzy decision making method based on combined compromise solution for IIoT industry evaluation. Artificial Intelligence Review, 55(3), (2022), 1857-1886. DOI: 10.1007/s10462-021-10055-7.
- [25] H.Y. Zhang, G.W. Wei, and X.D. Chen: CPT-MABAC method for spherical fuzzy multiple attribute group decision making and its application to green supplier selection. Journal of Intelligent & Fuzzy Systems, 41(1), (2021), 1009-1019. DOI: 10.3233/JIFS-202954.
- [26] S.A. Seyfi-Shishavan, F.K. Gundogdu, Y. Donyatalab, E. Farrokhizadeh, and C. Kahraman: A novel spherical fuzzy bi-objective linear assignment method and its application to insurance options selection. International Journal of Information Technology & Decision Making, 20(2), (2021), 521-551. DOI: 10.1142/S0219622021500073.
- [27] H. Zhang, G. Wei, and C. Wei: TOPSIS method for spherical fuzzy MAGDM based on cumulative prospect theory and combined weights and its application to residential location. Journal of Intelligent & Fuzzy Systems, 42(3), (2022), 1367-1380. DOI: 10.3233/JIFS-210267.
- [28] G.W. Wei, J. Wang, M. Lu, J. Wu, and C. Wei: Similarity measures of spherical fuzzy sets based on cosine function and their applications. IEEE Access, 7 (2019), 159069-159080. DOI: 10.1109/ACCESS.2019.2949296.
- [29] B. Oztaysi, S.C. Onar, F.K. Gundogdu, and C. Kahraman: Location-based advertisement selection using spherical fuzzy AHP-VIKOR. Journal of Multiple-Valued Logic and Soft Computing, 35(1-2), (2020), 5-23.
- [30] H. Zhang, G. Wei, andd X. Chen: SF-GRA method based on cumulative prospect theory for multiple attribute group decision making and its application to emergency supplies supplier selection. Engineering Applications of Artificial Intelligence, 110 (2022), 104679. DOI: 10.1016/j.engappai.2022.104679.
- [31] A. Aydogdu and S. Gul: A novel entropy proposition for spherical fuzzy sets and its application in multiple attribute decision-making. International Journal of Intelligent Systems, 35 (2020), 1354-1374. DOI: 10.1002/int.22256.
- [32] S. Ashraf, S. Abdullah, T. Mahmood, F. Ghani, and T. Mahmood: Spherical fuzzy sets and their applications in multi-attribute decision making problems. Journal of Intelligent & Fuzzy Systems, 36(3), (2019), 2829-2844. DOI: 10.3233/JIFS-172009.
- [33] F.K. Gundogdu and C. Kahraman: Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems, 36(1), (2019) 337-352. DOI: 10.3233/JIFS-181401.
- [34] Y. Donyatalab, E. Farokhizadeh, S.D.S. Garmroodi, and S.A.S. Shishavan: Harmonic mean aggregation operators in spherical fuzzy environment and their group decision making applications. Journal of Multiple-Valued Logic and Soft Computing, 33 (2019), 565-592.
- [35] S. Ashraf, S. Abdullah, and T. Mahmood: Spherical fuzzy Dombi aggregation operators and their application in group decision making problems. Journal of Ambient Intelligence and Humanized Computing, 11 (2020), 2731-2749. DOI: 10.1007/s12652-019-01333-y.
- [36] M.S. Sindhu, T. Rashid, and A. Kashif: Multiple criteria decision making based on Hamy mean operators under the environment of spherical fuzzy sets. Journal of Intelligent & Fuzzy Systems, 41(1), (2021), 273-298. DOI: 10.3233/JIFS-201708.
- [37] H. Zhang, G. Wei and X. Chen: Spherical fuzzy Dombi power Heronian mean aggregation operators for multiple attribute group decision-making. Computational & Applied Mathematics, 41(3), (2022), 98. DOI: 10.1007/s40314-022-01785-7.
- [38] S. Ashraf, S. Abdullah, and M. Aslam: Symmetric sum based aggregation operators for spherical fuzzy information: Application in multi-attribute group decision making problem. Journal of Intelligent & Fuzzy Systems, 38(4), (2020), 5241-5255. DOI: 10.3233/JIFS-191819.
- [39] E. Farrokhizadeh, S.A. Seyfi Shishavan, Y. Donyatalab, F. Kutlu Gündogdu, and C. Kahraman: Spherical fuzzy Bonferroni mean aggregation operators and their applications to multiple-attribute decision making. In: C. Kahraman, F. Kutlu Gündogdu (Eds.) Decision Making with Spherical Fuzzy Sets: Theory and Applications, Springer International Publishing, Cham, 2021, pp. 111-134.
- [40] C. Maclaurin: A second letter to Martin Folkes, Esq.; concerning the roots of equations, with demonstration of other rules of algebra. Philos Trans R Soc London Ser A, 36 (1729), 59-96. DOI: 10.1098/rstl.1729.0011.
- [41] K.Y. Bai, X.M. Zhu, J. Wang, and R.T. Zhang: Some partitioned Maclaurin symmetric mean based on q-Rung orthopair fuzzy information for dealing with multi-attribute group decision making. Symmetry, 10 (2018), 383. DOI: 10.3390/sym10090383.
- [42] P.D. Liu, S.M. Chen, and Y.M. Wang: Multiattribute group decision making based on intuitionistic fuzzy partitioned Maclaurin symmetric mean operators. Information Sciences, 512(C), (2020), 830-854. DOI: 10.1016/j.ins.2019.10.013.
- [43] P.D. Liu and X.L. You: Linguistic neutrosophic partitioned Maclaurin symmetric mean operators based on clustering algorithm and their application to multi-criteria group decision-making. Artificial Intelligence Review, 53(3), (2020), 2131-2170. DOI: 10.1007/s10462-019-09729-0.
- [44] J. Ling, M.W. Lin, and L.L. Zhang: Medical waste treatment scheme selection based on single-valued neutrosophic numbers. Aims Mathematics, 6(10), (2021), 10540-10564. DOI: 10.3934/math.2021612.
- [45] J. Ling, X.M. Li, and M.W. Lin: Medical waste treatment station selection based on linguistic q-Rung orthopair fuzzy numbers. Cmes-Computer Modeling in Engineering & Sciences, 129 (2021), 117-148.
- [46] P.D. Liu, Q. Khan, T. Mahmood, and N. Hassan: T-spherical fuzzy power Muirhead mean operator based on novel operational laws and their application in multi-attribute group decision making. IEEE Access, 7(1), (2019), 22613-22632. DOI: 10.1109/ACCESS.2019.2896107.
- [47] R.R. Yager: The power average operator. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 31(6), (2001), 724-731. DOI: 10.1109/3468.983429.
- [48] J. Pecaric, J.J. Wen, W.L. Wang, and T. Lu: A generalization of Maclaurin’s inequalities and its applications. Mathematical Inequalities & Applications, 8(4), (2005), 583-598. DOI: 10.7153/mia-08-55.
- [49] H. Garg, K. Ullah, T. Mahmood, N. Hassan, and N. Jan: T-spherical fuzzy power aggregation operators and their applications in multi-attribute decision making. Journal of Ambient Intelligence and Humanized Computing, 12 (2021), 9067-9080. DOI: 10.1007/s12652-020-02600-z.
- [50] P.D. Liu, B.Y. Zhu, and P. Wang: A multi-attribute decision-making approach based on spherical fuzzy sets for Yunnan Baiyao’s R&D Project Selection Problem. International Journal of Fuzzy Systems, 21 (2019), 2168-2191. DOI: 10.1007/s40815-019-00687-x.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b2e3c9ce-ec07-41c7-8213-d61d11f0fcd8
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.