Research article

The q-rung orthopair fuzzy-valued neutrosophic sets: Axiomatic properties, aggregation operators and applications

  • Received: 21 October 2023 Revised: 11 December 2023 Accepted: 12 December 2023 Published: 24 January 2024
  • MSC : 03E72, 90B50, 68T35

  • During the transitional phase spanning from the realm of fuzzy logic to the realm of neutrosophy, a multitude of hybrid models have emerged, each surpassing its predecessor in terms of superiority. Given the pervasive presence of indeterminacy in the world, a higher degree of precision is essential for effectively handling imprecision. Consequently, more sophisticated variants of neutrosophic sets (NSs) have been conceived. The key objective of this paper is to introduce yet another variant of NS, known as the q-rung orthopair fuzzy-valued neutrosophic set (q-ROFVNS). By leveraging the extended spatial range offered by q-ROFS, q-ROFVNS enables a more nuanced representation of indeterminacy and inconsistency. Our endeavor commences with the definitions of q-ROFVNS and q-ROFVN numbers (q-ROFVNNs). Then, we propose several types of score and accuracy functions to facilitate the comparison of q-ROFVNNs. Fundamental operations of q-ROFVNSs and some algebraic operational rules of q-ROFVNNs are also provided with their properties, substantiated by proofs and elucidated through illustrative examples. Drawing upon the operational rules of q-ROFVNNs, the q-ROFVN weighted average operator (q-ROFVNWAO) and q-ROFVN weighted geometric operator (q-ROFVNWGO) are proposed. Notably, we present the properties of these operators, including idempotency, boundedness and monotonicity. Furthermore, we emphasize the applicability and significance of the q-ROFVN operators, substantiating their utility through an algorithm and a numerical application. To further validate and evaluate the proposed model, we conduct a comparative analysis, examining its accuracy and performance in relation to existing models.

    Citation: Ashraf Al-Quran, Faisal Al-Sharqi, Atiqe Ur Rahman, Zahari Md. Rodzi. The q-rung orthopair fuzzy-valued neutrosophic sets: Axiomatic properties, aggregation operators and applications[J]. AIMS Mathematics, 2024, 9(2): 5038-5070. doi: 10.3934/math.2024245

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  • During the transitional phase spanning from the realm of fuzzy logic to the realm of neutrosophy, a multitude of hybrid models have emerged, each surpassing its predecessor in terms of superiority. Given the pervasive presence of indeterminacy in the world, a higher degree of precision is essential for effectively handling imprecision. Consequently, more sophisticated variants of neutrosophic sets (NSs) have been conceived. The key objective of this paper is to introduce yet another variant of NS, known as the q-rung orthopair fuzzy-valued neutrosophic set (q-ROFVNS). By leveraging the extended spatial range offered by q-ROFS, q-ROFVNS enables a more nuanced representation of indeterminacy and inconsistency. Our endeavor commences with the definitions of q-ROFVNS and q-ROFVN numbers (q-ROFVNNs). Then, we propose several types of score and accuracy functions to facilitate the comparison of q-ROFVNNs. Fundamental operations of q-ROFVNSs and some algebraic operational rules of q-ROFVNNs are also provided with their properties, substantiated by proofs and elucidated through illustrative examples. Drawing upon the operational rules of q-ROFVNNs, the q-ROFVN weighted average operator (q-ROFVNWAO) and q-ROFVN weighted geometric operator (q-ROFVNWGO) are proposed. Notably, we present the properties of these operators, including idempotency, boundedness and monotonicity. Furthermore, we emphasize the applicability and significance of the q-ROFVN operators, substantiating their utility through an algorithm and a numerical application. To further validate and evaluate the proposed model, we conduct a comparative analysis, examining its accuracy and performance in relation to existing models.



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    [1] L. A. Zadeh, Fuzzy sets, Inform. Contr., 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
    [2] S. Sniazhko, Uncertainty in decision-making: A review of the international business literature, Cogent Bus. Manag., 6 (2019), 1650692. https://doi.org/10.1080/23311975.2019.1650692 doi: 10.1080/23311975.2019.1650692
    [3] L. S. Jin, Uncertain probability, regular probability interval and relative proximity, Fuzzy Set. Syst., 467 (2023), 108579. https://doi.org/10.1016/j.fss.2023.108579 doi: 10.1016/j.fss.2023.108579
    [4] B. Bishesh, Fuzzy decision making, In: Fuzzy computing in data science, John Wiley & Sons, Ltd, 2022, 33–75. https://doi.org/10.1002/9781394156887
    [5] M. Pouyakian, A. Khatabakhsh, M. Yazdi, E. Zarei, Optimizing the allocation of risk control measures using fuzzy MCDM approach: Review and application, In: Linguistic methods under fuzzy information in system safety and reliability analysis, Springer, Cham, 414 (2022), 53–89. https://doi.org/10.1007/978-3-030-93352-4_4
    [6] H. Li, M. Yazdi, Developing failure modes and effect analysis on offshore wind turbines using two-stage optimization probabilistic linguistic preference relations, In: Advanced decision-making methods and applications in system safety and reliability problems, Studies in Systems, Decision and Control, Springer, Cham, 211 (2022), 47–68. https://doi.org/10.1007/978-3-031-07430-1_4
    [7] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Set. Syst., 20 (1986), 87–96.
    [8] M. Gulzar, M. H. Mateen, D. Alghazzawi, N. Kausar, A novel applications of complex intuitionistic fuzzy sets in group theory, IEEE Access, 8 (2020), 196075–196085. https://doi.org/10.1109/ACCESS.2020.3034626 doi: 10.1109/ACCESS.2020.3034626
    [9] J. C. R. Alcantud, A. Z. Khameneh, A. Kilicman, Aggregation of infinite chains of intuitionistic fuzzy sets and their application to choices with temporal intuitionistic fuzzy information, Inform. Sciences, 514 (2020), 106–117. https://doi.org/10.1016/j.ins.2019.12.008 doi: 10.1016/j.ins.2019.12.008
    [10] A. U. Rahman, M. R. Ahmad, M. Saeed, M. Ahsan, M. Arshad, M. Ihsan, A study on fundamentals of refined intuitionistic fuzzy set with some properties, J. Fuzzy Ext. Appl., 1 (2020), 279–292. https://doi.org/10.22105/jfea.2020.261946.1067 doi: 10.22105/jfea.2020.261946.1067
    [11] R. R. Yager, Pythagorean fuzzy subsets, IEEE, 2013, 57–61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375 doi: 10.1109/IFSA-NAFIPS.2013.6608375
    [12] D. Q. Li, W. Y. Zeng, Distance measure of Pythagorean fuzzy sets, Int. J. Intell. Syst., 33 (2018), 348–361. https://doi.org/10.1002/int.21934 doi: 10.1002/int.21934
    [13] G. W. Wei, Y. Wei, Similarity measures of Pythagorean fuzzy sets based on the cosine function and their applications, Int. J. Intell. Syst., 33 (2018), 634–652. https://doi.org/10.1002/int.21965 doi: 10.1002/int.21965
    [14] F. Y. Xiao, W. P. Ding, Divergence measure of Pythagorean fuzzy sets and its application in medical diagnosis, Appl. Soft Comput., 79 (2019), 254–267. https://doi.org/10.1016/j.asoc.2019.03.043 doi: 10.1016/j.asoc.2019.03.043
    [15] N. X. Thao, F. Smarandache, A new fuzzy entropy on Pythagorean fuzzy sets, J. Intell. Fuzzy Syst., 37 (2019), 1065–1074. https://doi.org/10.3233/JIFS-182540 doi: 10.3233/JIFS-182540
    [16] X. Z. Gao, Y. Deng, Generating method of Pythagorean fuzzy sets from the negation of probability, Eng. Appl. Artif. Intel., 105 (2021), 104403. https://doi.org/10.1016/j.engappai.2021.104403 doi: 10.1016/j.engappai.2021.104403
    [17] A. Hussain, K. Ullah, M. N. Alshahrani, M. S. Yang, D. Pamucar, Novel Aczel-Alsina operators for Pythagorean fuzzy sets with application in multi-attribute decision making, Symmetry, 14 (2022), 940. https://doi.org/10.3390/sym14050940 doi: 10.3390/sym14050940
    [18] K. Ullah, T. Mahmood, Z. Ali, N. Jan, On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition, Complex Intell. Syst., 6 (2020), 15–27. https://doi.org/10.1007/s40747-019-0103-6 doi: 10.1007/s40747-019-0103-6
    [19] Z. Wang, F. Y. Xiao, Z. H. Cao, Uncertainty measurements for Pythagorean fuzzy set and their applications in multiple-criteria decision making, Soft Comput., 26 (2022), 9937–9952. https://doi.org/10.1007/s00500-022-07361-9 doi: 10.1007/s00500-022-07361-9
    [20] T. M. Athira, S. J. John, H. Garg, A novel entropy measure of Pythagorean fuzzy soft sets, AIMS Math., 5 (2020), 1050–1061. https://doi.org/10.3934/math.20200073 doi: 10.3934/math.20200073
    [21] M. Rasheed, E. Tag-Eldin, N. A. Ghamry, M. A. Hashmi, M. Kamran, U. Rana, Decision-making algorithm based on Pythagorean fuzzy environment with probabilistic hesitant fuzzy set and Choquet integral, AIMS Math., 8 (2023), 12422–12455. https://doi.org/10.3934/math.2023624 doi: 10.3934/math.2023624
    [22] R. R. Yager, Generalized orthopair fuzzy sets, IEEE T. Fuzzy Syst., 25 (2016), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005 doi: 10.1109/TFUZZ.2016.2604005
    [23] P. D. Liu, P. Wang, Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making, Int. J. Intell. Syst., 33 (2017), 259–280. https://doi.org/10.1002/int.21927 doi: 10.1002/int.21927
    [24] P. D. Liu, P. Wang, Multiple-attribute decision-making based on archimedean bonferroni operators of q-rung orthopair fuzzy numbers, IEEE T. Fuzzy Syst., 27 (2018), 834–848. https://doi.org/10.1109/TFUZZ.2018.2826452 doi: 10.1109/TFUZZ.2018.2826452
    [25] P. Wang, J. Wang, G. W. Wei, C. Wei, Similarity measures of q-rung orthopair fuzzy sets based on cosine function and their applications, Mathematics, 7 (2019), 340. https://doi.org/10.3390/math7040340 doi: 10.3390/math7040340
    [26] D. H. Liu, X. H. Chen, D. Peng, Some cosine similarity measures and distance measures between q‐rung orthopair fuzzy sets, Int. J. Intell. Syst., 34 (2019), 1572–1587. https://doi.org/10.1002/int.22108 doi: 10.1002/int.22108
    [27] C. Dhankhar, A. K. Yadav, K. Kumar, A ranking method for q-rung orthopair fuzzy set based on possibility degree measure, Soft Comput. Theor. Appl., 425 (2022), 15–24. https://doi.org/10.1007/978-981-19-0707-4_2 doi: 10.1007/978-981-19-0707-4_2
    [28] M. Deveci, D. Pamucar, I. Gokasar, M. Köppen, B. B. Gupta, Personal mobility in metaverse with autonomous vehicles using Q-rung orthopair fuzzy sets based OPA-RAFSI model, IEEE T. Intell. Transp., 24 (2022), 15642–15651. https://doi.org/10.1109/TITS.2022.3186294 doi: 10.1109/TITS.2022.3186294
    [29] M. W. Lin, X. M. Li, L. Y. Chen, Linguistic q-rung orthopair fuzzy sets and their interactional partitioned Heronian mean aggregation operators, Int. J. Intell. Syst., 35 (2020), 217–249. https://doi.org/10.1002/int.22136 doi: 10.1002/int.22136
    [30] H. X. Li, S. Y. Yin, Y. Yang, Some preference relations based on q‐rung orthopair fuzzy sets, Int. J. Intell. Syst., 34 (2019), 2920–2936. https://doi.org/10.1002/int.22178 doi: 10.1002/int.22178
    [31] X. D. Peng, J. G. Dai, H. Garg, Exponential operation and aggregation operator for q‐rung orthopair fuzzy set and their decision‐making method with a new score function, Int. J. Intell. Syst., 33 (2018), 2255–2282. https://doi.org/10.1002/int.22028 doi: 10.1002/int.22028
    [32] M. Deveci, D. Pamucar, U. Cali, E. Kantar, K. Kölle, J. O. Tande, Hybrid q-rung orthopair fuzzy sets based cocoso model for floating offshore wind farm site selection in Norway, CSEE J. Power Energy Syst., 8 (2022), 1261–1280. https://doi.org/10.17775/CSEEJPES.2021.07700 doi: 10.17775/CSEEJPES.2021.07700
    [33] M. Deveci, I. Gokasar, P. R. Brito-Parada, A comprehensive model for socially responsible rehabilitation of mining sites using Q-rung orthopair fuzzy sets and combinative distance-based assessment, Expert Syst. Appl., 200 (2022), 117155. https://doi.org/10.1016/j.eswa.2022.117155 doi: 10.1016/j.eswa.2022.117155
    [34] K. Alnefaie, Q. Xin, A. Almutlg, E. S. A. Abo-Tabl, M. H. Mateen, A novel framework of q-Rung orthopair fuzzy sets in field, Symmetry, 15 (2022), 114. https://doi.org/10.3390/sym15010114 doi: 10.3390/sym15010114
    [35] A. Habib, M. Akram, A. Farooq, q-Rung orthopair fuzzy competition graphs with application in the soil ecosystem, Mathematics, 7 (2019), 91. https://doi.org/10.3390/math7010091 doi: 10.3390/math7010091
    [36] H. Garg, J. Gwak, T. Mahmood, Z. Ali, Power aggregation operators and VIKOR methods for complex q-Rung orthopair fuzzy sets and their applications, Mathematics, 8 (2020), 538. https://doi.org/10.3390/math8040538 doi: 10.3390/math8040538
    [37] F. Smarandache, Neutrosophy: Neutrosophic probability, set, and logic: Analytic synthesis & synthetic analysis, Rehoboth, NM: American Research Press, 1998.
    [38] J. Ye, A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets, J. Intell. Fuzzy Syst., 26 (2014), 2459–2466. https://doi.org/10.3233/IFS-130916 doi: 10.3233/IFS-130916
    [39] A. R. Mishra, P. Rani, R. S. Prajapati, Multi-criteria weighted aggregated sum product assessment method for sustainable biomass crop selection problem using single-valued neutrosophic sets, Appl. Soft Comput., 113 (2021), 108038. https://doi.org/10.1016/j.asoc.2021.108038 doi: 10.1016/j.asoc.2021.108038
    [40] M. Ali, F. Smarandache, Complex neutrosophic set, Neural Comput. Appl., 28 (2017), 1817–1834. https://doi.org/10.1007/s00521-015-2154-y doi: 10.1007/s00521-015-2154-y
    [41] A. Al-Quran, A. Ahmad, F. Al-Sharqi, A. Lutfi, Q-complex neutrosophic set, Int. J. Neutrosophic Sci., 20 (2023), 8–19. https://doi.org/10.54216/IJNS.200201 doi: 10.54216/IJNS.200201
    [42] A. Al-Quran, N. Hassan, S. Alkhazaleh, Fuzzy parameterized complex neutrosophic soft expert set for decision under uncertainty, Symmetry, 11 (2019), 382. https://doi.org/10.3390/sym11030382 doi: 10.3390/sym11030382
    [43] F. Al-Sharqi, A. G. Ahmad, A. Al-Quran, Fuzzy parameterized-interval complex neutrosophic soft sets and their applications under uncertainty, J. Intell. Fuzzy Syst., 44 (2023), 1453–1477. https://doi.org/10.3233/JIFS-221579 doi: 10.3233/JIFS-221579
    [44] D. Karabašević, D. Stanujkić, E. K. Zavadskas, P. Stanimirović, G. Popović, A. Ulutaş, et al., A novel extension of the TOPSIS method adapted for the use of single-valued neutrosophic sets and hamming distance for E-commerce development strategies selection, Symmetry, 12 (2020), 1263. https://doi.org/10.3390/sym12081263 doi: 10.3390/sym12081263
    [45] M. Abdel-Basset, A. Gamal, G. Manogaran, L. H. Son, H. V. Long, A novel group decision making model based on neutrosophic sets for heart disease diagnosis, Multimed. Tools Appl., 79 (2020), 9977–10002. https://doi.org/10.1007/s11042-019-07742-7 doi: 10.1007/s11042-019-07742-7
    [46] C. Jana, M. Pal, A robust single-valued neutrosophic soft aggregation operators in multi-criteria decision making, Symmetry, 11 (2019), 110. https://doi.org/10.3390/sym11010110 doi: 10.3390/sym11010110
    [47] P. Ji, J. Q. Wang, H. Y. Zhang, Frank prioritized Bonferroni mean operator with single-valued neutrosophic sets and its application in selecting third-party logistics providers, Neural Comput. Appl., 30 (2018), 799–823. https://doi.org/10.1007/s00521-016-2660-6 doi: 10.1007/s00521-016-2660-6
    [48] D. S. Xu, C. Wei, G. W. Wei, TODIM method for single-valued neutrosophic multiple attribute decision making, Information, 8 (2017), 125. https://doi.org/10.3390/info8040125 doi: 10.3390/info8040125
    [49] K. L. Hu, L. P. Zhao, S. Feng, S. D. Zhang, Q. W. Zhou, X. Z. Gao, et al., Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement, Comput. Biol. Med., 147 (2022), 105760. https://doi.org/10.1016/j.compbiomed.2022.105760 doi: 10.1016/j.compbiomed.2022.105760
    [50] J. Ye, Trapezoidal neutrosophic set and its application to multiple attribute decision-making, Neural Comput. Appl., 26 (2015), 1157–1166. https://doi.org/10.1007/s00521-014-1787-6 doi: 10.1007/s00521-014-1787-6
    [51] G. Kaur, H. Garg, A new method for image processing using generalized linguistic neutrosophic cubic aggregation operator, Complex Intell. Syst., 8 (2022), 4911–4937. https://doi.org/10.1007/s40747-022-00718-5 doi: 10.1007/s40747-022-00718-5
    [52] C. Jana, M. Pal, F. Karaaslan, J. Q. Wang, Trapezoidal neutrosophic aggregation operators and their application to the multi-attribute decision-making process, Sci. Iran., 27 (2020), 1655–1673. https://doi.org/10.24200/sci.2018.51136.2024 doi: 10.24200/sci.2018.51136.2024
    [53] M. Bhowmik, M. Pal, Intuitionistic neutrosophic set, J. Inform. Comput. Sci., 4 (2009), 142–152.
    [54] M. Unver, E. Turkarslan, N. Celik, M. Olgun, J. Ye, Intuitionistic fuzzy-valued neutrosophic multi-sets and numerical applications to classification, Complex Intell. Syst., 8 (2022), 1703–1721. https://doi.org/10.1007/s40747-021-00621-5 doi: 10.1007/s40747-021-00621-5
    [55] M. Palanikumar, K. Arulmozhi, C. Jana, Multiple attribute decision-making approach for Pythagorean neutrosophic normal interval-valued fuzzy aggregation operators, Comput. Appl. Math., 41 (2022), 90. https://doi.org/10.1007/s40314-022-01791-9 doi: 10.1007/s40314-022-01791-9
    [56] P. Chellamani, D. Ajay, Pythagorean neutrosophic Dombi fuzzy graphs with an application to MCDM, Neutrosophic Sets Sy., 47 (2021), 411–431. https://doi.org/10.5281/zenodo.5775162 doi: 10.5281/zenodo.5775162
    [57] D. Ajay, P. Chellamani, Pythagorean neutrosophic soft sets and their application to decision-making scenario, In: Intelligent and fuzzy techniques for emerging conditions and digital transformation: Proceedings of the INFUS 2021 Conference, Springer International Publishing, 2 (2021), 552–560.
    [58] M. Palanikumar, K. Arulmozhi, MCGDM based on TOPSIS and VIKOR using Pythagorean neutrosophic soft with aggregation operators, Neutrosophic Sets Sy., 51 (2022), 538–555. https://doi.org/10.5281/zenodo.7135376 doi: 10.5281/zenodo.7135376
    [59] J. Rajan, M. Krishnaswamy, Similarity measures of Pythagorean neutrosophic sets with dependent neutrosophic components between T and F, J. New Theory, 33 (2020), 85–94.
    [60] A. Siraj, T. Fatima, D. Afzal, K. Naeem, F. Karaaslan, Pythagorean m-polar fuzzy neutrosophic topology with applications, Neutrosophic Sets Sy., 48 (2022), 251–290. https://doi.org/10.5281/zenodo.6041514 doi: 10.5281/zenodo.6041514
    [61] M. C. Bozyigit, M. Olgun, F. Smarandache, M. Unver, A new type of neutrosophic set in Pythagorean fuzzy environment and applications to multi-criteria decision making, Int. J. Neutrosophic Sci., 20 (2023), 107–134. https://doi.org/10.54216/IJNS.200208 doi: 10.54216/IJNS.200208
    [62] A. Al-Quran, F. Al-Sharqi, K. Ullah, M. U. Romdhini, M. Balti, M. Alomair, Bipolar fuzzy hypersoft set and its application in decision making, Int. J. Neutrosophic Sci., 20 (2023), 65–77. https://doi.org/10.54216/IJNS.200405 doi: 10.54216/IJNS.200405
    [63] A. Sarkar, T. Senapati, L. S. Jin, R. Mesiar, A. Biswas, R. R. Yager, Sugeno-Weber triangular norm-based aggregation operators under T-spherical fuzzy hypersoft context, Inform. Sci., 645 (2023), 119305. https://doi.org/10.1016/j.ins.2023.119305 doi: 10.1016/j.ins.2023.119305
    [64] A. Sarkar, S. Moslem, D. Esztergár-Kiss, M. Akram, L. S. Jin, T. Senapati, A hybrid approach based on dual hesitant q-rung orthopair fuzzy Frank power partitioned Heronian mean aggregation operators for estimating sustainable urban transport solutions, Eng. Appl. Artif. Intel., 124 (2023), 106505. https://doi.org/10.1016/j.engappai.2023.106505 doi: 10.1016/j.engappai.2023.106505
    [65] F. Al-Sharqi, A. Al-Quran, A. G. Ahmad, S. Broumi, Interval-valued complex neutrosophic soft set and its applications in decision-making, Neutrosophic Sets Sy., 40 (2021), 149–168.
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