Research article

Prioritized Hamy mean operators based on Dombi t-norm and t-conorm for the complex interval-valued Atanassov-Intuitionistic fuzzy sets and their applications in strategic decision-making problems

  • Received: 20 September 2024 Revised: 23 December 2024 Accepted: 10 January 2025 Published: 25 March 2025
  • MSC : 03B52, 68T27, 68T37, 94D05, 03E72

  • The complex interval-valued Atanassov intuitionistic fuzzy set theory is an advanced modification of traditional fuzzy information that combines elements of both interval-valued fuzzy information and Atanassov intuitionistic fuzzy set theory, and incorporates complex numbers. Additionally, aggregating a finite number of alternatives into a singleton set is very important, where the Hamy mean operator and prioritized aggregation operator are much more suitable and flexible for depicting such kinds of problems. Our main goal of this manuscript was to analyze the Dombi operational laws based on complex interval-valued Atanassov intuitionistic fuzzy numbers. Further, the prioritized Hamy mean operators based on Dombi operational laws for complex interval-valued Atanassov intuitionistic fuzzy values, called the complex interval-valued Atanassov intuitionistic fuzzy Dombi Hamy mean operator, complex interval-valued Atanassov intuitionistic fuzzy weighted Dombi prioritized Hamy mean operator, complex interval-valued Atanassov intuitionistic fuzzy Dombi Dual Hamy mean operator, and complex interval-valued Atanassov intuitionistic fuzzy weighted Dombi Dual prioritized Hamy mean operator, were proposed. Some dominant and flexible properties of the evaluated operators were also examined. Further, in some multi-attribute decision-making problems, biased conclusions may be produced due to the deficiency of consideration for many relationships between the criteria of decision-making. Therefore, to evaluate the proficiency and reliability of the proposed operators, the multi-attribute decision-making technique based on derived operators for complex interval-valued Atanassov intuitionistic fuzzy values was developed. Finally, the proposed method with some prevailing techniques was compared to show its advantages and benefits.

    Citation: Shichao Li, Zeeshan Ali, Peide Liu. Prioritized Hamy mean operators based on Dombi t-norm and t-conorm for the complex interval-valued Atanassov-Intuitionistic fuzzy sets and their applications in strategic decision-making problems[J]. AIMS Mathematics, 2025, 10(3): 6589-6635. doi: 10.3934/math.2025302

    Related Papers:

  • The complex interval-valued Atanassov intuitionistic fuzzy set theory is an advanced modification of traditional fuzzy information that combines elements of both interval-valued fuzzy information and Atanassov intuitionistic fuzzy set theory, and incorporates complex numbers. Additionally, aggregating a finite number of alternatives into a singleton set is very important, where the Hamy mean operator and prioritized aggregation operator are much more suitable and flexible for depicting such kinds of problems. Our main goal of this manuscript was to analyze the Dombi operational laws based on complex interval-valued Atanassov intuitionistic fuzzy numbers. Further, the prioritized Hamy mean operators based on Dombi operational laws for complex interval-valued Atanassov intuitionistic fuzzy values, called the complex interval-valued Atanassov intuitionistic fuzzy Dombi Hamy mean operator, complex interval-valued Atanassov intuitionistic fuzzy weighted Dombi prioritized Hamy mean operator, complex interval-valued Atanassov intuitionistic fuzzy Dombi Dual Hamy mean operator, and complex interval-valued Atanassov intuitionistic fuzzy weighted Dombi Dual prioritized Hamy mean operator, were proposed. Some dominant and flexible properties of the evaluated operators were also examined. Further, in some multi-attribute decision-making problems, biased conclusions may be produced due to the deficiency of consideration for many relationships between the criteria of decision-making. Therefore, to evaluate the proficiency and reliability of the proposed operators, the multi-attribute decision-making technique based on derived operators for complex interval-valued Atanassov intuitionistic fuzzy values was developed. Finally, the proposed method with some prevailing techniques was compared to show its advantages and benefits.



    加载中


    [1] L. A. Zadeh, Fuzzy sets, Infor. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
    [2] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Set. Syst, 20 (1986), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3 doi: 10.1016/S0165-0114(86)80034-3
    [3] K. T. Atanassov, Interval-valued intuitionistic fuzzy sets, In: Intuitionistic fuzzy sets, 35 (1999), 139–177. https://doi.org/10.1007/978-3-7908-1870-3_2
    [4] D. Ramot, R. Milo, M. Friedman, A. Kandel, Complex fuzzy sets, IEEE T. Fuzzy Syst., 10 (2002), 171–186. https://doi.org/10.1109/91.995119 doi: 10.1109/91.995119
    [5] A. M. J. S. Alkouri, A. R. Salleh, Complex intuitionistic fuzzy sets, AIP Conf. Proc., 1482 (2012), 464–470. https://doi.org/10.1063/1.4757515 doi: 10.1063/1.4757515
    [6] H. Garg, D. Rani, Complex interval-valued intuitionistic fuzzy sets and their aggregation operators, Fund. Inform., 164 (2019), 61–101. https://doi.org/10.3233/FI-2019-1755 doi: 10.3233/FI-2019-1755
    [7] T. Mahmood, Z. Ali, Fuzzy superior mandelbrot sets, Soft Comput., 26 (2022), 9011–9020. https://doi.org/10.1007/s00500-022-07254-x doi: 10.1007/s00500-022-07254-x
    [8] I. İnce, S. Ersoy, Generalized fuzzy Mandelbrot and Mandelbrot sets, Commun. Nonlinear Sci. Numer. Simul., 118 (2023), 107045. https://doi.org/10.1016/j.cnsns.2022.107045 doi: 10.1016/j.cnsns.2022.107045
    [9] O. Castillo, P. Melin, A review on interval type-2 fuzzy logic applications in intelligent control. Inform. Sciences, 279 (2014), 615–631. https://doi.org/10.1016/j.ins.2014.04.015 doi: 10.1016/j.ins.2014.04.015
    [10] R. John, S. Coupland, Type-2 fuzzy logic: A historical view, IEEE Comput. Intell. Magaz., 2 (2007), 57–62. https://doi.org/10.1109/MCI.2007.357194 doi: 10.1109/MCI.2007.357194
    [11] O. Kaleva, Fuzzy differential equations, Fuzzy Set. Syst., 24 (1987), 301–317. https://doi.org/10.1016/0165-0114(87)90029-7 doi: 10.1016/0165-0114(87)90029-7
    [12] J. J. Buckley, T. Feuring, Fuzzy differential equations, Fuzzy Set. Syst., 110 (2000), 43–54. https://doi.org/10.1007/978-3-642-35221-8_9 doi: 10.1007/978-3-642-35221-8_9
    [13] O. Kaleva, A note on fuzzy differential equations, Nonlinear Anal. Theor., 64 (2006), 895–900. https://doi.org/10.1016/j.na.2005.01.003 doi: 10.1016/j.na.2005.01.003
    [14] J. Y. Park, H. K. Han, Fuzzy differential equations, Fuzzy Set. Syst., 110 (2000), 69–77. https://doi.org/10.1016/S0165-0114(98)00150-X doi: 10.1016/S0165-0114(98)00150-X
    [15] D. Pan, H. J. Zhou, X. X. Yan, Characterizations for the negativity of continuous t-conorms over fuzzy implications, Fuzzy Set. Syst., 456 (2023), 173–196. https://doi.org/10.1016/j.fss.2022.04.006 doi: 10.1016/j.fss.2022.04.006
    [16] B. Gohain, R. Chutia, P. Dutta, A distance measure for optimistic viewpoint of the information in interval-valued intuitionistic fuzzy sets and its applications, Eng. Appl. Artif. Intel., 119 (2023), 105747. https://doi.org/10.1016/j.engappai.2022.105747 doi: 10.1016/j.engappai.2022.105747
    [17] K. Sharma, V. P. Singh, A. Ebrahimnejad, D. Chakraborty, Solving a multi-objective chance constrained hierarchical optimization problem under intuitionistic fuzzy environment with its application, Expert Syst. Appl., 217 (2023), 119595. https://doi.org/10.1016/j.eswa.2023.119595 doi: 10.1016/j.eswa.2023.119595
    [18] A. R. Mishra, P. Rani, F. Cavallaro, I. M. Hezam, Intuitionistic fuzzy fairly operators and additive ratio assessment-based integrated model for selecting the optimal sustainable industrial building options, Sci. Rep., 13 (2023), 5055. https://doi.org/10.1038/s41598-023-31843-x doi: 10.1038/s41598-023-31843-x
    [19] Z. A. Xue, M. M. Jing, Y. X. Li, Y. Zheng, Variable precision multi-granulation covering rough intuitionistic fuzzy sets, Granul. Comput., 8 (2023), 577–596. https://doi.org/10.1007/s41066-022-00342-1 doi: 10.1007/s41066-022-00342-1
    [20] J. Więckowski, B. Kizielewicz, W. Sałabun, Handling decision-making in intuitionistic fuzzy environment: PyIFDM package, SoftwareX, 22 (2023), 101344. https://doi.org/10.1016/j.softx.2023.101344 doi: 10.1016/j.softx.2023.101344
    [21] A. İlbaş, A. Gürdere, F. E. Boran, An integrated intuitionistic fuzzy set and stochastic multi-criteria acceptability analysis approach for supplier selection, Neural Comput. Applic., 35 (2023), 3937–3953. https://doi.org/10.1007/s00521-022-07919-6 doi: 10.1007/s00521-022-07919-6
    [22] I. M. Hezam, N. R. D. Vedala, B. R. Kumar, A. R. Mishra, F. Cavallaro, Assessment of Biofuel industry sustainability factors based on the intuitionistic fuzzy symmetry point of criterion and Rank-Sum-Based MAIRCA method, Sustainability, 15 (2023), 6749. https://doi.org/10.3390/su15086749 doi: 10.3390/su15086749
    [23] K. Atanassov, On intuitionistic fuzzy temporal topological structures, Axioms, 2 (2023), 182. https://doi.org/10.3390/axioms12020182 doi: 10.3390/axioms12020182
    [24] P. Liu, Z. Ali, T. Mahmood, The distance measures and cross-entropy based on complex fuzzy sets and their application in decision making, J. Intell. Fuzzy Syst., 39 (2020), 3351–3374. https://doi.org/10.3233/JIFS-191718 doi: 10.3233/JIFS-191718
    [25] T. Mahmood, Z. Ali, A. Gumaei, Interdependency of complex fuzzy neighborhood operators and derived complex fuzzy coverings, IEEE Access, 9 (2021), 73506–73521. https://doi.org/10.1109/ACCESS.2021.3074590 doi: 10.1109/ACCESS.2021.3074590
    [26] C. Li, T. W. Chiang, Complex neurofuzzy ARIMA forecasting-a new approach using complex fuzzy sets, IEEE T. Fuzzy Syst., 21 (2012), 567–584. https://doi.org/10.1109/TFUZZ.2012.2226890 doi: 10.1109/TFUZZ.2012.2226890
    [27] C. Li, T. Wu, F. T. Chan, Self-learning complex neuro-fuzzy system with complex fuzzy sets and its application to adaptive image noise canceling, Neurocomputing, 94 (2012), 121–139. https://doi.org/10.1016/j.neucom.2012.04.011 doi: 10.1016/j.neucom.2012.04.011
    [28] T. Mahmood, Z. Ali, M. Albaity, Aggregation operators based on algebraic t-Norm and t-Conorm for complex linguistic fuzzy sets and their applications in strategic decision making, Symmetry, 14 (2022), 1990. https://doi.org/10.3390/sym14101990 doi: 10.3390/sym14101990
    [29] W. Azeem, W. Mahmood, T. Mahmood, Z. Ali, M. Naeem, Analysis of Einstein aggregation operators based on complex intuitionistic fuzzy sets and their applications in multi-attribute decision-making, AIMS Math., 8 (2023), 6036–6063. https://doi.org/10.3934/math.2023305 doi: 10.3934/math.2023305
    [30] Z. Ali, T. Mahmood, M. Aslam, R. Chinram, Another view of complex intuitionistic fuzzy soft sets based on prioritized aggregation operators and their applications to multiattribute decision making, Mathematics, 9 (2021), 1922. https://doi.org/10.3390/math9161922 doi: 10.3390/math9161922
    [31] H. Garg, J. Vimala, S. Rajareega, D. Preethi, L. Perez-Dominguez, Complex intuitionistic fuzzy soft SWARA-COPRAS approach: An application of ERP software selection, AIMS Math., 7 (2022), 5895–5909. https://doi.org/10.3934/math.2022327 doi: 10.3934/math.2022327
    [32] J. Dombi, A general class of fuzzy operators, the DeMorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators, Fuzzy Set. Syst., 8 (1982), 149–163. https://doi.org/10.1016/0165-0114(82)90005-7 doi: 10.1016/0165-0114(82)90005-7
    [33] D. J. Yu, Intuitionistic fuzzy geometric Heronian mean aggregation operators, Appl. Soft Comput., 13 (2013), 1235–1246. https://doi.org/10.1016/j.asoc.2012.09.021 doi: 10.1016/j.asoc.2012.09.021
    [34] L. P. Wu, G. W. Wei, J. Wu, C. Wei, Some interval-valued intuitionistic fuzzy dombi heronian mean operators and their application for evaluating the ecological value of forest ecological tourism demonstration areas, Int. J. Environ. Res. Public Health, 17 (2020), 829. https://doi.org/10.3390/ijerph17030829 doi: 10.3390/ijerph17030829
    [35] D. J. Yu, Y. Y. Wu, T. Lu, Interval-valued intuitionistic fuzzy prioritized operators and their application in group decision making, Knowl.-Based Syst, 30 (2012), 57–66. https://doi.org/10.1016/j.knosys.2011.11.004 doi: 10.1016/j.knosys.2011.11.004
    [36] H. Garg, D. Rani, New prioritized aggregation operators with priority degrees among priority orders for complex intuitionistic fuzzy information, J. Ambient Intell. Human. Comput., 14 (2023), 1373–1399. https://doi.org10.1007/s12652-021-03164-2 doi: 10.1007/s12652-021-03164-2
    [37] H. Garg, D. Rani, Novel aggregation operators and ranking method for complex intuitionistic fuzzy sets and their applications to decision-making process, Artif. Intell. Rev., 53 (2020), 3595–3620. https://doi.org/10.1007/s10462-019-09772-x doi: 10.1007/s10462-019-09772-x
    [38] X. M. Shi, Z. Ali, T. Mahmood, P. D. Liu, Power aggregation operators of interval-valued Atanassov-Intuitionistic fuzzy sets based on Aczel-Alsina t-Norm and t-Conorm and their applications in decision making, Int. J. Comput. Intell. Syst., 16 (2023), 43. https://doi.org/10.1007/s44196-023-00208-7 doi: 10.1007/s44196-023-00208-7
    [39] T. Y. Chen, A prioritized aggregation operator-based approach to multiple criteria decision making using interval-valued intuitionistic fuzzy sets: A comparative perspective, Inform. Sciences, 28 (2014), 97–112. https://doi.org/10.1016/j.ins.2014.05.018 doi: 10.1016/j.ins.2014.05.018
    [40] P. Wang, B. Y. Zhu, Y. Yu, Z. Ali, B. Almohsen, Complex intuitionistic fuzzy DOMBI prioritized aggregation operators and their application for resilient green supplier selection, Facta Univ. Ser. Mech. Eng., 21 (2023), 339–357. https://doi.org/10.22190/FUME230805029W doi: 10.22190/FUME230805029W
    [41] H. Fang, T. Mahmood, Z. Ali, S. Zeng, Y. Jin, WASPAS method and Aczel-Alsina aggregation operators for managing complex interval-valued intuitionistic fuzzy information and their applications in decision-making, Peer J Computer Sci., 9 (2023), e1362. https://doi.org/10.7717/peerj-cs.1362 doi: 10.7717/peerj-cs.1362
    [42] S. P. Wan, J. Y. Dong, S. M. Chen, A novel intuitionistic fuzzy best-worst method for group decision making with intuitionistic fuzzy preference relations, Inform. Sciences, 666 (2024), 120404. https://doi.org/10.1016/j.ins.2024.120404 doi: 10.1016/j.ins.2024.120404
    [43] J. Y. Dong, S. P. Wan, Interval-valued intuitionistic fuzzy best-worst method with additive consistency, Expert Syst. Appl., 236 (2024), 121213. https://doi.org/10.1016/j.eswa.2023.121213 doi: 10.1016/j.eswa.2023.121213
    [44] S. P. Wan, T. Rao, J. Y. Dong, Time-series based multi-criteria large-scale group decision making with intuitionistic fuzzy information and application to multi-period battery supplier selection, Expert Syst Appl., 232 (2023), 120749. https://doi.org/10.1016/j.eswa.2023.12074 doi: 10.1016/j.eswa.2023.12074
    [45] X. Y. Lu, J. Y. Dong, S. P. Wan, Y. F. Yuan, Consensus reaching with minimum adjustment and consistency management in group decision making with intuitionistic multiplicative preference relations, Expert Syst. Appl., 232 (2023), 120674. https://doi.org/10.1016/j.eswa.2023.120674 doi: 10.1016/j.eswa.2023.120674
    [46] Z. H. Chen, S. P. Wan, J. Y. Dong, An integrated interval-valued intuitionistic fuzzy technique for resumption risk assessment amid COVID-19 prevention, Inform. Sciences, 619 (2023), 695–721. https://doi.org/10.1016/j.ins.2022.11.028 doi: 10.1016/j.ins.2022.11.028
    [47] X. Gou, Z. Xu, P. Ren, The properties of continuous Pythagorean fuzzy information, Inter J.Intel Syst., 3 (2016), 401–424. https://doi.org/10.1002/int.21788 doi: 10.1002/int.21788
    [48] X. J. Gou, Z. S. Xu, F. Herrera, Consensus reaching process for large-scale group decision making with double hierarchy hesitant fuzzy linguistic preference relations, Knowl.-Based Syst., 157 (2018), 20–33. https://doi.org/10.1016/j.knosys.2018.05.008 doi: 10.1016/j.knosys.2018.05.008
    [49] X. J. Gou, X. R. Xu, Z. S. Xu, M. Skare, Circular economy and fuzzy set theory: A bibliometric and systematic review based on Industry 4.0 technologies perspective, Technol. Econ. Dev. Eco., 30 (2024), 489–526. https://doi.org/10.3846/tede.2024.20286 doi: 10.3846/tede.2024.20286
    [50] X. J. Gou, X. R. Xu, F. M. Deng, W. Zhou, E. Herrera-Viedma, Medical health resources allocation evaluation in public health emergencies by an improved ORESTE method with linguistic preference orderings, Fuzzy Optim. Decis. Making, 23 (2024), 1–27. https://doi.org/10.1007/s10700-023-09409-3 doi: 10.1007/s10700-023-09409-3
    [51] X. T. Cheng, Z. S. Xu, X. J. Gou, A large-scale group decision-making model considering risk attitudes and dynamically changing roles, Expert Syst. Appl., 245 (2024), 123017. https://doi.org/10.1016/j.eswa.2023.123017 doi: 10.1016/j.eswa.2023.123017
    [52] Z. Lian, P. Shi, C. C. Lim, X. Yuan, Fuzzy-model-based lateral control for networked autonomous vehicle systems under hybrid cyber-attacks, IEEE T. Cybernetics, 53 (2023), 2600-2609. https://doi.org/10.1109/TCYB.2022.3151880 doi: 10.1109/TCYB.2022.3151880
    [53] M. Zivkovic, N. Bacanin, K. Venkatachalam, A. Nayyar, A. Djordjevic, I. Strumberger, F. Al-Turjman. COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc., 66 (2021), 102669. https://doi.org/10.1016/j.scs.2020.102669 doi: 10.1016/j.scs.2020.102669
    [54] N. A. Korenevskiy, A. V. Bykov, R. T. Al-Kasasbeh, A. A. Aikeyeva, S. N. Rodionova, I. Maksim, et al., Developing hybrid fuzzy model for predicting severity of end organ damage of the anatomical zones of the lower extremities, Inter Jour Medical Engine Infor., 14 (2023), 323–335. https://doi.org/10.1504/IJMEI.2022.123925 doi: 10.1504/IJMEI.2022.123925
    [55] A. Azizpour, M. A. Izadbakhsh, S. Shabanlou, F. Yosefvand, A. Rajabi, Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model, Environ. Sci. Pollut. Res., 29 (2022), 28414–28430. https://doi.org/10.1007/s11356-021-17879-4 doi: 10.1007/s11356-021-17879-4
    [56] M. Parsajoo, D. J. Armaghani, P. G. Asteris, A precise neuro-fuzzy model enhanced by artificial bee colony techniques for assessment of rock brittleness index, Neural Comput. Applic., 34 (2022), 3263–3281. https://doi.org/10.1007/s00521-021-06600-8 doi: 10.1007/s00521-021-06600-8
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1050) PDF downloads(46) Cited by(0)

Article outline

Figures and Tables

Figures(3)  /  Tables(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog