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AIMS Mathematics

2023, Volume 8, Issue 3: 6520-6542. doi: 10.3934/math.2023329
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Intuitionistic fuzzy credibility Dombi aggregation operators and their application of railway train selection in Pakistan

  • 1.
    Department of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan
  • 2.
    Department of Mathematics Deanship of Applied Sciences Umm Al-Qura University, Makkah, Saudi Arabia
  • Received: 21 August 2022 Revised: 09 October 2022 Accepted: 05 December 2022 Published: 05 January 2023

  • MSC : 03E72, 47S40

  • The degree of credibility of the fuzzy assessment value demonstrates its significance and necessity in the fuzzy decision making problem. The fuzzy assessment values should be closely related to their credibility measures in order to increase the credibility levels and degrees of fuzzy assessment values. This will increase the abundance and the credibility of the assessment information. As a new extension of the intuitionistic fuzzy concept, this study suggests the idea of an intuitionistic fuzzy credibility number (IFCN). So, based on Dombi norms, we proposed some new operational laws for intuitionistic fuzzy credibility numbers. Different intuitionistic fuzzy credibility aggregation operators are defined using Dombi t-norm and t-conorm operations. i.e., intuitionistic fuzzy credibility Dombi weighted averaging (IFCDWA), intuitionistic fuzzy credibility Dombi ordered weighted averaging (IFCDOWA), intuitionistic fuzzy credibility Dombi hybrid weighted averaging (IFCDHWA) operators. Next, we defined multiple criteria group decisions (MCGDM) approach. To ensure that their results are reliable and applicable, we also gave an example of railway train selection and discussed comparative result analysis.

    Citation: Muhammad Qiyas, Neelam Khan, Muhammad Naeem, Saleem Abdullah. Intuitionistic fuzzy credibility Dombi aggregation operators and their application of railway train selection in Pakistan[J]. AIMS Mathematics, 2023, 8(3): 6520-6542. doi: 10.3934/math.2023329

    Related Papers:

  • The degree of credibility of the fuzzy assessment value demonstrates its significance and necessity in the fuzzy decision making problem. The fuzzy assessment values should be closely related to their credibility measures in order to increase the credibility levels and degrees of fuzzy assessment values. This will increase the abundance and the credibility of the assessment information. As a new extension of the intuitionistic fuzzy concept, this study suggests the idea of an intuitionistic fuzzy credibility number (IFCN). So, based on Dombi norms, we proposed some new operational laws for intuitionistic fuzzy credibility numbers. Different intuitionistic fuzzy credibility aggregation operators are defined using Dombi t-norm and t-conorm operations. i.e., intuitionistic fuzzy credibility Dombi weighted averaging (IFCDWA), intuitionistic fuzzy credibility Dombi ordered weighted averaging (IFCDOWA), intuitionistic fuzzy credibility Dombi hybrid weighted averaging (IFCDHWA) operators. Next, we defined multiple criteria group decisions (MCGDM) approach. To ensure that their results are reliable and applicable, we also gave an example of railway train selection and discussed comparative result analysis.


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    [1] K. T. Atanassov, More on intuitionistic fuzzy sets, Fuzzy Set. Syst., 33 (1989), 37–45. https://doi.org/10.1016/0165-0114(89)90215-7 doi: 10.1016/0165-0114(89)90215-7
    [2] S. Ayouni, L. J. Menzli, F. Hajjej, M. Maddeh, S. Al-Otaibi, Fuzzy Vikor application for learning management systems evaluation in higher education, IJICTE, 17 (2021), 17–35. https://doi.org/10.4018/IJICTE.2021040102 doi: 10.4018/IJICTE.2021040102
    [3] F. E. Boran, S. Genç, M. Kurt, D. Akay, A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, Expert Syst. Appl., 36 (2009), 11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039 doi: 10.1016/j.eswa.2009.03.039
    [4] I. Beg, T. Rashid, Multi-criteria trapezoidal valued intuitionistic fuzzy decision making with Choquet integral based TOPSIS, Opsearch, 51 (2014), 98–129. https://doi.org/10.1007/s12597-013-0134-5 doi: 10.1007/s12597-013-0134-5
    [5] 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
    [6] S. K. De, R. Biswas, A. R. Roy, An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy Set. Syst., 117 (2001), 209–213. https://doi.org/10.1016/S0165-0114(98)00235-8 doi: 10.1016/S0165-0114(98)00235-8
    [7] K. Guo, Q. Song, On the entropy for Atanassov's intuitionistic fuzzy sets: An interpretation from the perspective of amount of knowledge, Appl. Soft Comput., 24 (2014), 328–340. https://doi.org/10.1016/j.asoc.2014.07.006 doi: 10.1016/j.asoc.2014.07.006
    [8] H. Garg, A new generalized Pythagorean fuzzy information aggregation using Einstein operations and its application to decision making, Int. J. Intell. Syst., 31 (2016), 886–920. https://doi.org/10.1002/int.21809 doi: 10.1002/int.21809
    [9] C. C. Hung, L. H. Chen, A fuzzy TOPSIS decision making model with entropy weight under intuitionistic fuzzy environment, Proceedings of the international multiconference of engineers and computer scientists, 1 (2009).
    [10] G. Q. Huang, L. M. Xiao, G. B. Zhang, Assessment and prioritization method of key engineering characteristics for complex products based on cloud rough numbers, Adv. Eng. Inform., 49 (2021), 101309. https://doi.org/10.1016/j.aei.2021.101309 doi: 10.1016/j.aei.2021.101309
    [11] A. Hussain, A. Alsanad, Novel Dombi aggregation operators in spherical cubic fuzzy information with applications in multiple attribute decision-making, Math. Probl. Eng., 2021 (2021), 9921553. https://doi.org/10.1155/2021/9921553 doi: 10.1155/2021/9921553
    [12] G. Q. Huang, L. M. Xiao, W. Pedrycz, D. Pamucar, G. B. Zhang, L. Martínez, Design alternative assessment and selection: A novel Z-cloud rough number-based BWM-MABAC model, Inform. Sci., 603 (2022), 149–189. https://doi.org/10.1016/j.ins.2022.04.040 doi: 10.1016/j.ins.2022.04.040
    [13] G. Q. Huang, L. M. Xiao, W. Pedrycz, G. B. Zhang, L. Martinez, Failure mode and effect analysis using T-spherical fuzzy maximizing deviation and combined comparison solution methods, IEEE Trans. Reliab., 2022, 1–22. https://doi.org/10.1109/TR.2022.3194057
    [14] D. Kumar, Analysis of issues of generic medicine supply chain using fuzzy AHP: A Pilot study of Indian public drug distribution scheme, Int. J. Pharm. Healthcare Mark., 15 (2021), 18–42. https://doi.org/10.1108/IJPHM-12-2019-0078 doi: 10.1108/IJPHM-12-2019-0078
    [15] D. F. Li, Multiattribute decision making models and methods using intuitionistic fuzzy sets, J. Comput. Syst. Sci., 70 (2005), 73–85. https://doi.org/10.1016/j.jcss.2004.06.002 doi: 10.1016/j.jcss.2004.06.002
    [16] P. D. Liu, J. L. Liu, S.M. Chen, Some intuitionistic fuzzy Dombi Bonferroni mean operators and their application to multi-attribute group decision making, J. Oper. Res. Soc., 69 (2018), 1–24. https://doi.org/10.1057/s41274-017-0190-y doi: 10.1057/s41274-017-0190-y
    [17] J. H. Park, Intuitionistic fuzzy metric spaces, Chaos, Soliton. Fract., 22 (2004), 1039–1046. https://doi.org/10.1016/j.chaos.2004.02.051 doi: 10.1016/j.chaos.2004.02.051
    [18] M. Qiyas, T. Madrar, S. Khan, S. Abdullah, T. Botmart, A. Jirawattanapaint, Decision support system based on fuzzy credibility Dombi aggregation operators and modified TOPSIS method, AIMS Mathematics, 7 (2022), 19057–19082. https://doi.org/10.3934/math.20221047 doi: 10.3934/math.20221047
    [19] M. Qiyas, M. Yahya, S. Abdullah, N. Khan, M. Naeem, Extended GRA method for multi-criteria group decision making problem based on fuzzy credibility geometric aggregation operator, 2022. Available from: https://doi.org/10.21203/rs.3.rs-1419758/v1.
    [20] E. Szmidt, J. Kacprzyk, Entropy for intuitionistic fuzzy sets, Fuzzy Set. Syst., 118 (2001), 467–477. https://doi.org/10.1016/S0165-0114(98)00402-3 doi: 10.1016/S0165-0114(98)00402-3
    [21] Y. Sun, J. S. Mi, J. K. Chen, W. Liu, A new fuzzy multi-attribute group decision-making method with generalized maximal consistent block and its application in emergency management, Knowl.-Based Syst., 215 (2021), 106594. https://doi.org/10.1016/j.knosys.2020.106594 doi: 10.1016/j.knosys.2020.106594
    [22] Y. M. Wang, H. Y. Yang, K. Y. Qin, The consistency between cross-entropy and distance measures in fuzzy sets, Symmetry, 11 (2019), 386. https://doi.org/10.3390/sym11030386 doi: 10.3390/sym11030386
    [23] Z. S. Xu, An integrated model-based interactive approach to FMAGDM with incomplete preference information, Fuzzy Optim. Decis. Making, 9 (2010), 333–357. https://doi.org/10.1007/s10700-010-9083-0 doi: 10.1007/s10700-010-9083-0
    [24] L. M. Xiao, G. Q. Huang, W. Pedrycz, D. Pamucar, L. Martínez, G. B. Zhang, A q-rung orthopair fuzzy decision-making model with new score function and best-worst method for manufacturer selection, Inform. Sci., 608 (2022), 153–177. https://doi.org/10.1016/j.ins.2022.06.061 doi: 10.1016/j.ins.2022.06.061
    [25] Z. L. Yue, An extended TOPSIS for determining weights of decision makers with interval numbers, Knowl.-Based Syst., 24 (2011), 146–153. https://doi.org/10.1016/j.knosys.2010.07.014 doi: 10.1016/j.knosys.2010.07.014
    [26] L. M. Xiao, G. Q. Huang, G. B. Zhang, An integrated risk assessment method using Z-fuzzy clouds and generalized TODIM, Qual. Reliab. Eng. Int., 38 (2022), 1909–1943. https://doi.org/10.1002/qre.3062 doi: 10.1002/qre.3062
    [27] E. Yadegaridehkordi, M. Hourmand, M. Nilashi, E. Alsolami, S. Samad, M. Mahmoud, et al., Assessment of sustainability indicators for green building manufacturing using fuzzy multi-criteria decision making approach, J. Cleaner Prod., 277 (2020), 122905. https://doi.org/10.1016/j.jclepro.2020.122905 doi: 10.1016/j.jclepro.2020.122905
    [28] M. Yahya, S. Abdullah, M. Qiyas, Analysis of medical diagnosis based on fuzzy credibility Dombi Bonferroni mean operator, J. Ambient Intell. Human. Comput., 2022. https://doi.org/10.1007/s12652-022-04203-2
    [29] M. Yahya, S. Abdullah, A. O. Almagrabi, T. Botmart, Analysis of S-box based on image encryption application using complex fuzzy credibility Frank aggregation operators, IEEE Access, 10 (2022), 88858–88871. https://doi.org/10.1109/ACCESS.2022.3197882 doi: 10.1109/ACCESS.2022.3197882
    [30] J. Ye, J. M. Song, S. G. Du, R. Yong, Weighted aggregation operators of fuzzy credibility numbers and their decision-making approach for slope design schemes, Comp. Appl. Math., 40 (2021), 155. https://doi.org/10.1007/s40314-021-01539-x doi: 10.1007/s40314-021-01539-x
    [31] L. A. Zadeh, Fuzzy sets, Information and control, J. Symbolic Logic, 8 (1965), 338–353. https://doi.org/10.2307/2272014
    [32] H. J. Zimmermann, Fuzzy set theory, WIREs Comp. Stats., 2 (2010), 317–332. https://doi.org/10.1002/wics.82
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  • © 2023 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)
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