Decision support algorithm under SV-neutrosophic hesitant fuzzy rough information with confidence level aggregation operators

Muhammad Kamran; Rashad Ismail; Shahzaib Ashraf; Nadeem Salamat; Seyma Ozon Yildirim; Ismail Naci Cangul; Muhammad Kamran; Rashad Ismail; Shahzaib Ashraf; Nadeem Salamat; Seyma Ozon Yildirim; Ismail Naci Cangul

doi:10.3934/math.2023605

AIMS Mathematics

2023, Volume 8, Issue 5: 11973-12008. doi: 10.3934/math.2023605

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Decision support algorithm under SV-neutrosophic hesitant fuzzy rough information with confidence level aggregation operators

1.
Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan
2.
Department of Mathematics, Faculty of Science and Arts, King Khalid University, Muhayl Assir 61913, Saudi Arabia
3.
Department of Mathematics and Computer, Faculty of Science, Ibb University, Ibb 70270, Yemen
4.
Department of Mathematics, Bursa Uludag University, Gorukle 16059, Turkey

Received: 29 January 2023 Revised: 01 March 2023 Accepted: 06 March 2023 Published: 21 March 2023
MSC : 03B52, 03E72

To deal with the uncertainty and ensure the sustainability of the manufacturing industry, we designed a multi criteria decision-making technique based on a list of unique operators for single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) environments with a high confidence level. We show that, in contrast to the neutrosophic rough average and geometric aggregation operators, which are unable to take into account the level of experts' familiarity with examined objects for a preliminary evaluation, the neutrosophic average and geometric aggregation operators have a higher level of confidence in the fundamental idea of a more networked composition. A few of the essential qualities of new operators have also been covered. To illustrate the practical application of these operators, we have given an algorithm and a practical example. We have also created a manufacturing business model that takes sustainability into consideration and is based on the neutrosophic rough model. A symmetric comparative analysis is another tool we use to show the feasibility of our proposed enhancements.
- confidence level,
- neutrosophic information,
- aggregation operators,
- hesitant information,
- rough sets,
- decision-making
Citation: Muhammad Kamran, Rashad Ismail, Shahzaib Ashraf, Nadeem Salamat, Seyma Ozon Yildirim, Ismail Naci Cangul. Decision support algorithm under SV-neutrosophic hesitant fuzzy rough information with confidence level aggregation operators[J]. AIMS Mathematics, 2023, 8(5): 11973-12008. doi: 10.3934/math.2023605

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Abstract

To deal with the uncertainty and ensure the sustainability of the manufacturing industry, we designed a multi criteria decision-making technique based on a list of unique operators for single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) environments with a high confidence level. We show that, in contrast to the neutrosophic rough average and geometric aggregation operators, which are unable to take into account the level of experts' familiarity with examined objects for a preliminary evaluation, the neutrosophic average and geometric aggregation operators have a higher level of confidence in the fundamental idea of a more networked composition. A few of the essential qualities of new operators have also been covered. To illustrate the practical application of these operators, we have given an algorithm and a practical example. We have also created a manufacturing business model that takes sustainability into consideration and is based on the neutrosophic rough model. A symmetric comparative analysis is another tool we use to show the feasibility of our proposed enhancements.

References

[1]	L. A. Zadeh, Similarity relations and fuzzy orderings, Inform. Sci., 3 (1971), 177–200. https://doi.org/10.1016/s0020-0255(71)80005-1 doi: 10.1016/s0020-0255(71)80005-1
[2]	S. T. Chang, K. P. Lu, M. S. Yang, Fuzzy change-point algorithms for regression models, IEEE T. Fuzzy Syst., 23 (2015), 2343–2357. https://doi.org/10.1109/TFUZZ.2015.2421072 doi: 10.1109/TFUZZ.2015.2421072
[3]	R. M. Zulqarnain, X. L. Xin, M. Saeed, Extension of TOPSIS method under intuitionistic fuzzy hypersoft environment based on correlation coefficient and aggregation operators to solve decision making problem, AIMS Math., 6 (2021), 2732–2755. https://doi.org/10.3934/math.2021167 doi: 10.3934/math.2021167
[4]	A. Satirad, R. Chinram, A. Iampan, Pythagorean fuzzy sets in UP-algebras and approximations, AIMS Math., 6 (2021), 6002–6032. https://doi.org/10.3934/math.2021354 doi: 10.3934/math.2021354
[5]	A. U. Rahman, M. Saeed, H. A. E. W. Khalifa, W. A. Afifi, Decision making algorithmic techniques based on aggregation operations and similarity measures of possibility intuitionistic fuzzy hypersoft sets, AIMS Math., 7 (2022), 3866–3895. http://dx.doi.org/10.3934/math.2022214 doi: 10.3934/math.2022214
[6]	Q. Han, W. Li, Y. Song, T. Zhang, R. Wang, A new method for MAGDM based on improved TOPSIS and a novel pythagorean fuzzy soft entropy, Symmetry, 11 (2019), 905. https://doi.org/10.3390/sym11070905 doi: 10.3390/sym11070905
[7]	M. Akram, G. Shahzadi, A hybrid decision-making model under q-rung orthopair fuzzy Yager aggregation operators, Granular Comput., 6 (2021), 763–777. https://doi.org/10.1007/s41066-020-00229-z doi: 10.1007/s41066-020-00229-z
[8]	M. J. Khan, P. Kumam, W. Deebani, W. Kumam, Z. Shah, Bi-parametric distance and similarity measures of picture fuzzy sets and their applications in medical diagnosis, Egypt. Inform. J., 22 (2021), 201–212. https://doi.org/10.2307/j.ctv1cftj6q.24 doi: 10.2307/j.ctv1cftj6q.24
[9]	S. Ashraf, S. Abdullah, T. Mahmood, M. Aslam, Cleaner production evaluation in gold mines using novel distance measure method with cubic picture fuzzy numbers, Int. J. Fuzzy Syst., 21 (2019), 2448–2461. https://doi.org/10.1007/s40815-019-00681-3 doi: 10.1007/s40815-019-00681-3
[10]	K. Ullah, H. Garg, T. Mahmood, N. Jan, Z. Ali, Correlation coefficients for T-spherical fuzzy sets and their applications in clustering and multi-attribute decision making, Soft Comput., 24 (2020), 1647–1659. https://doi.org/10.1007/s00500-019-03993-6 doi: 10.1007/s00500-019-03993-6
[11]	M. Riaz, M. Saba, M. A. Khokhar, M. Aslam, Novel concepts of m-polar spherical fuzzy sets and new correlation measures with application to pattern recognition and medical diagnosis, AIMS Math., 6 (2021), 11346–11379. https://doi.org/10.3934/math.2021659 doi: 10.3934/math.2021659
[12]	L. A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Set. Syst., 1 (1978), 3–28. https://doi.org/10.1016/0165-0114(78)90029-5 doi: 10.1016/0165-0114(78)90029-5
[13]	A. Kechris, Classical descriptive set theory, Springer Science & Business Media, 156 (2012).
[14]	F. Smarandache, Neutrosophic set is a generalization of intuitionistic fuzzy set, inconsistent intuitionistic fuzzy set (picture fuzzy set, ternary fuzzy set), pythagorean fuzzy set, spherical fuzzy set, and q-rung orthopair fuzzy set, while neutrosophication is a generalization of regret theory, grey system theory, and three-ways decision (revisited), J. New Theory, 29 (2019), 1–31.
[15]	Z. Shahbazi, Y. C. Byun, A procedure for tracing supply chains for perishable food based on blockchain, machine learning and fuzzy logic, Electronics, 10 (2020), 41. https://doi.org/10.3390/electronics10010041 doi: 10.3390/electronics10010041
[16]	A. Si, S. Das, S. Kar, Picture fuzzy set-based decision-making approach using Dempster-Shafer theory of evidence and grey relation analysis and its application in COVID-19 medicine selection, Soft Comput., 2021, 1–15. https://doi.org/10.1007/s00500-021-05909-9 doi: 10.1007/s00500-021-05909-9
[17]	R. Costache, M. C. Popa, D. T. Bui, D. C. Diaconu, N. Ciubotaru, G. Minea, Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning, J. Hydrology, 585 (2020), 124808. https://doi.org/10.1016/j.jhydrol.2020.124808 doi: 10.1016/j.jhydrol.2020.124808
[18]	M. Riaz, M. Saba, M. A. Khokhar, M. Aslam, Medical diagnosis of nephrotic syndrome using m-polar spherical fuzzy sets, Int. J. Biomath., 15 (2022), 2150094. https://doi.org/10.1142/S1793524521500947 doi: 10.1142/S1793524521500947
[19]	Y. Jin, M. Kamran, N. Salamat, S. Zeng, R. H. Khan, Novel distance measures for single-valued neutrosophic fuzzy sets and their applications to multicriteria group decision-making problem, J. Funct. Space., 2022 (2022). https://doi.org/10.1155/2022/7233420 doi: 10.1155/2022/7233420
[20]	K. Ullah, H. Garg, T. Mahmood, N. Jan, Z. Ali, Correlation coefficients for T-spherical fuzzy sets and their applications in clustering and multi-attribute decision making, Soft Comput., 24 (2020), 1647–1659. https://doi.org/10.1007/s00500-019-03993-6 doi: 10.1007/s00500-019-03993-6
[21]	J. B. Liu, N. Salamat, M. Kamran, S. Ashraf, R. H. Khan, Single valued neutrosophic sets: A promising approach to assess image quality, Fractals, 2023. https://doi.org/10.1142/S0218348X23400741 doi: 10.1142/S0218348X23400741
[22]	A. Fahmi, S. Abdullah, F. Amin, M. S. A. Khan, Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making, Soft Comput., 23 (2019), 5753–5783. https://doi.org/10.1007/s00500-018-3242-6 doi: 10.1007/s00500-018-3242-6
[23]	T. Alsboui, R. Hill, H. Al-Aqrabi, H. M. A. Farid, M. Riaz, S. Iram, A dynamic multi-mobile agent itinerary planning approach in wireless sensor networks via intuitionistic fuzzy set, Sensors, 22 (2022), 8037. https://doi.org/10.3390/s22208037 doi: 10.3390/s22208037
[24]	R. M. Zulqarnain, X. L. Xin, I. Siddique, W. A. Khan, M. A. Yousif, TOPSIS method based on correlation coefficient under pythagorean fuzzy soft environment and its application towards green supply chain management, Sustainability, 13 (2021), 1642. https://doi.org/10.3390/su13041642 doi: 10.3390/su13041642
[25]	M. J. Khan, P. Kumam, P. Liu, W. Kumam, S. Ashraf, A novel approach to generalized intuitionistic fuzzy soft sets and its application in decision support system, Mathematics, 7 (2019), 742. https://doi.org/10.3390/math7080742 doi: 10.3390/math7080742
[26]	S. Zeng, A. Hussain, T. Mahmood, M. I. Ali, S. Ashraf, M. Munir, Covering-based spherical fuzzy rough set model hybrid with TOPSIS for multi-attribute decision-making, Symmetry, 11 (2019), 547. https://doi.org/10.3390/sym11040547 doi: 10.3390/sym11040547
[27]	H. Wang, F. Smarandache, Y. Zhang, Single valued neutrosophic sets, Infinite study, 12 (2010).
[28]	M. Saqlain, N. Jafar, S. Moin, M. Saeed, S. Broumi, Single and multi-valued neutrosophic hypersoft set and tangent similarity measure of single valued neutrosophic hypersoft sets, Neutrosophic Sets Sy., 32 (2020), 317–329.
[29]	M. A. Khan, S. Ashraf, S. Abdullah, F. Ghani, Applications of probabilistic hesitant fuzzy rough set in decision support system, Soft Comput., 24 (2020), 16759–16774. https://doi.org/10.1007/s00500-020-04971-z doi: 10.1007/s00500-020-04971-z
[30]	R. Sahin, P. Liu, Correlation coefficient of single-valued neutrosophic hesitant fuzzy sets and its applications in decision making, Neural Comput. Appl., 28 (2017), 1387–1395. https://doi.org/10.1007/s00521-015-2163-x doi: 10.1007/s00521-015-2163-x
[31]	C. Zhang, D. Li, S. Broumi, A. K. Sangaiah, Medical diagnosis based on single-valued neutrosophic probabilistic rough multisets over two universes, Symmetry, 10 (2018), 213. https://doi.org/10.3390/sym10060213 doi: 10.3390/sym10060213
[32]	R. Sahin, M. Karabacak, A novel similarity measure for single-valued neutrosophic sets and their applications in medical diagnosis, taxonomy, and clustering analysis, Optimization theory based on neutrosophic and plithogenic sets, Academic Press, 2020,315–341. https://doi.org/10.1016/B978-0-12-819670-0.00014-7
[33]	Y. Guo, A. S. Ashour, Neutrosophic sets in dermoscopic medical image segmentation, Neutrosophic Set in Medical Image Analysis, Academic Press, 2019,229–243. https://doi.org/10.1016/B978-0-12-818148-5.00011-4
[34]	H. M. A. Farid, M. Riaz, Single-valued neutrosophic Einstein interactive aggregation operators with applications for material selection in engineering design: Case study of cryogenic storage tank, Complex Intell. Syst., 8 (2022), 2131–2149. https://doi.org/10.1007/s40747-021-00626-0 doi: 10.1007/s40747-021-00626-0
[35]	M. Kamran, S. Nadeem, S. Ashraf, A. Alam, Novel decision modeling for manufacturing sustainability under single-valued neutrosophic hesitant fuzzy rough aggregation information, Comput. Intell. Neurosci., 2022 (2022). https://doi.org/10.1155/2022/7924094 doi: 10.1155/2022/7924094
[36]	M. Kamran, S. Ashraf, N. Salamat, M. Naeem, T. Botmart, Cyber security control selection based decision support algorithm under single valued neutrosophic hesitant fuzzy Einstein aggregation information, AIMS Math., 8 (2023), 5551–5573. https://doi.org/10.3934/math.2023280 doi: 10.3934/math.2023280
[37]	A. Yolcu, A. Benek, T. Y. Öztürk, A new approach to neutrosophic soft rough sets, Knowl. Inform. Syst., 2023, 1–18. https://doi.org/10.1007/s10115-022-01824-z doi: 10.1007/s10115-022-01824-z
[38]	L. Jiao, H. L. Yang, S. G. Li, Three-way decision based on decision-theoretic rough sets with single-valued neutrosophic information, Int. J. Mach. Learn. Cyb., 11 (2020), 657–665. https://doi.org/10.1007/s13042-019-01023-3 doi: 10.1007/s13042-019-01023-3
[39]	R. Chinram, A. Hussain, T. Mahmood, M. I. Ali, EDAS method for multi-criteria group decision making based on intuitionistic fuzzy rough aggregation operators, IEEE Access, 9 (2021), 10199–10216. https://doi.org/10.1109/ACCESS.2021.3049605 doi: 10.1109/ACCESS.2021.3049605
[40]	M. Z. Hanif, N. Yaqoob, M. Riaz, M. Aslam, Linear Diophantine fuzzy graphs with new decision-making approach, AIMS Math., 7 (2022), 14532–14556. https://doi.org/10.3934/math.2022801 doi: 10.3934/math.2022801
[41]	S. Shao, X. Zhang, Y. Li, C. Bo, Probabilistic single-valued (interval) neutrosophic hesitant fuzzy set and its application in multi-attribute decision making, Symmetry, 10 (2018), 419. https://doi.org/10.3390/sym10090419 doi: 10.3390/sym10090419
[42]	J. R. Trillo, F. J. Cabrerizo, F. Chiclana, M. Á. Martínez, F. Mata, E. Herrera-Viedma, Theorem verification of the quantifier-guided dominance degree with the mean operator for additive preference relations, Mathematics, 10 (2022), 2035. https://doi.org/10.3390/math10122035 doi: 10.3390/math10122035
[43]	M. Kamran, R. Ismail, E. H. A. Al-Sabri, N. Salamat, M. Farman, S. Ashraf, An optimization strategy for MADM framework with confidence level aggregation operators under probabilistic neutrosophic hesitant fuzzy rough environment, Symmetry, 15 (2023), 578. https://doi.org/10.3390/sym15030578 doi: 10.3390/sym15030578
[44]	B. Sun, X. Zhou, N. Lin, Diversified binary relation-based fuzzy multigranulation rough set over two universes and application to multiple attribute group decision making, Inform. Fusion, 55 (2020), 91–104. https://doi.org/10.1016/j.inffus.2019.07.013 doi: 10.1016/j.inffus.2019.07.013
[45]	C. Wang, Y. Huang, W. Ding, Z. Cao, Attribute reduction with fuzzy rough self-information measures, Inform. Sci., 549 (2021), 68–86. https://doi.org/10.1016/j.ins.2020.11.021 doi: 10.1016/j.ins.2020.11.021
[46]	Economic report of the president, transmitted to the congress February 2010 together with the annual report of the Council of Economic Advisors, Council of Economic Advisers, 2010.
[47]	X. Wang, E. Triantaphyllou, Ranking irregularities when evaluating alternatives by using some ELECTRE methods, Omega, 36 (2008), 45–63. https://doi.org/10.1016/j.omega.2005.12.003 doi: 10.1016/j.omega.2005.12.003
[48]	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
[49]	C. Jana, M. Pal, Multi-criteria decision making process based on some single-valued neutrosophic Dombi power aggregation operators, Soft Comput., 25 (2021), 5055–5072. https://doi.org/10.1007/s00500-020-05509-z doi: 10.1007/s00500-020-05509-z
[50]	S. B. Said, M. Lathamaheswari, P. K. Singh, A. A. Ouallane, A. Bakhouyi, A. Bakali, et al., An intelligent traffic control system using neutrosophic sets, rough sets, graph theory, fuzzy sets and its extended approach: A literature review, Neutrosophic Set. Syst., 50 (2022).
[51]	A. Al-Quran, A new multi attribute decision making method based on the T-spherical hesitant fuzzy sets, IEEE Access, 9 (2021), 156200–156210. https://doi.org/10.1109/ACCESS.2021.3128953 doi: 10.1109/ACCESS.2021.3128953

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