Trapezoidal Fuzzy Multi-Number Preference Relations based on Architecture Multi-Criteria Decision-Making Application

Document Type : Research Paper

Authors

1 TBMM Public relations building 2nd Floor, B206 room Ministries, Ankara06543-Turkey

2 Department of Mathematics, Kilis 7 Aralik University, 79000 Kilis, Turkey

3 Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran.

Abstract

The present study aims to described the concept of trapezoidal fuzzy multi number aggregation operators and also provide its application in architecture. Our main focus is on the trapezoidal fuzzy
multi number weighted arithmetic average (TFMNWAA) operator, the trapezoidal fuzzy multi number
weighted geometric average (TFMNWGA) operator and trapezoidal fuzzy multi-number hybrid aggregation (TFMNHA) operator along with their developable properties. Then the notion of the score function of ranking the trapezoidal fuzzy multi numbers is defined. We apply the TFMNHA operator to multi criteria decision making trapezoidal fuzzy multi number. Finally, a comparative analysis is presented with a numerical example is provided to show its applicability and usefulness.

Keywords

Main Subjects


[1] S. Abdullah, A. Fahmi, M. Aslam, Generalized trapezoidal cubic linguistic fuzzy ordered weighted average
operator and group decision-making, Soft Computing, 24(5) (2020), 3155-3171. https://doi.org/10.1007/
s00500-020-04672-7
[2] M. Akram, G. Shahzadi, A hybrid decision-making model under q-rung orthopair fuzzy Yager aggregation operators,
Granular Computing, 6 (2021), 763-777. https://doi.org/10.1007/s41066-020-00229-z
[3] S. Aydin, C. Kahraman, M. Kabak, Development of harmonic aggregation operator with trapezoidal Pythagorean
fuzzy numbers, Soft Computing, 24(15) (2020), 11791-11803. https://doi.org/10.1007/s00500-019-04638-4
[4] J. I. Baek, A. Borumand Saeid, S. H. Han, K. Hur, Some aggregation operators for IVI-octahedron sets and their
application to MCDGM, Iranian Journal of Fuzzy Systems, 20(2) (2023), 135-149. https://doi.org/10.22111/
IJFS.2023.7561
[5] D. Bakbak, Suriyeli siginmacilarin konteyner kamplari´ynailiskin bir arastirma, Gazi Akademik Bakis, 11(23) (2018),
249-287. https://dergipark.org.tr/tr/download/article-file/610381
[6] D. Bakbak, V. Ulu¸cay, M. Sahin, Intuitionistic trapezoidal fuzzy multi-numbers and some arithmetic averaging
operators with their application in architecture, 6th International Multidisciplinary Studies Congress (Multicongress
19) Gaziantep, Turkiye, (2019).
[7] A. Chakraborty, S. Maity, S. Jain, S. P. Mondal, S. Alam, Hexagonal fuzzy number and its distinctive representation,
ranking, defuzzification technique and application in production inventory management problem, Granular
Computing, 6 (2021), 507-521. https://doi.org/10.1007/s41066-020-00212-8
[8] I. A. Da Silva, B. Bedregal, B. Bedregal, R. H. N. Santiago, An interval-valued Atanassov’s intuitionistic fuzzy multiattribute
group decision making method based on the best representation of the WA and OWA operators, Journal of
Fuzzy Extension and Applications, 2(3) (2021), 239-261. https://doi.org/10.22105/jfea.2021.306164.1162
[9] I. Deli, Y. Subas, Single valued neutrosophic numbers and their applications to multicriteria decision
making problem, Neutrosophic Sets and Systems, 2(1) (2014), 1-13. https://fs.unm.edu/SN/
Neutro-SingleValuedNeutroNumbers.pdf
[10] J. Deng, J. Zhan, E. Herrera-Viedma, F. Herrera, Regret theory-based three-way decision method on incomplete
multi-scale decision information systems with interval fuzzy numbers, IEEE Transactions on Fuzzy Systems, 31
(2023), 982-996. https://doi.org/10.1109/TFUZZ.2022.3193453
[11] 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 Computing, 23(14) (2019), 5753-5783. https://doi.org/10.
1007/s00500-018-3242-6
[12] H. Garg, A. Ahmad, K. Ullah, T. Mahmood, Z. Ali, Algorithm for multiple attribute decision-making using Tspherical
fuzzy Maclaurin symmetric mean operator, Iranian Journal of Fuzzy Systems, 19(6) (2022), 111-124.
https://doi.org/10.22111/IJFS.2022.7215
[13] N. Hassan, V. Ulucay, M. Sahin, Q-neutrosophic soft expert set and its application in decision making, International
Journal of Fuzzy System Applications (IJFSA), 7(4) (2018), 37-61.
[14] W. Jianqiang, Z. Zhong, Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to
multi-criteria decision making problems, Journal of Systems Engineering and Electronics, 20(2) (2009), 321-326.
https://ieeexplore.ieee.org/document/6074660
[15] F. Jin, Z. Ni, H. Chen, Interval-valued hesitant fuzzy Einstein prioritized aggregation operators and their applications
to multi-attribute group decision making, Soft Computing, 20(5) (2016), 1863-1878. https://doi.org/10.
1007/s00500-015-1887-y
[16] H. Karimi, M. Sadeghi-Dastaki, M. Javan, A fully fuzzy best-worst multi attribute decision making method with
triangular fuzzy number: A case study of maintenance assessment in the hospitals, Applied Soft Computing, 86
(2020). https://doi.org/10.1016/j.asoc.2019.105882
[17] A. Kaufmann, M. M. Gupta, Fuzzy mathematical models in engineering and management science, Elsevier Science
Publishers, Amsterdam, Netherland, 1988.
[18] Y. Liu, J. Liu, Y. Qin, Pythagorean fuzzy linguistic Muirhead mean operators and their applications to multiattribute
decision-making, International Journal of Intelligent Systems, 35(2) (2020), 300-332. https://doi.org/10.1002/
int.22212
[19] S. Miyamoto, Fuzzy multisets and their generalizations, multiset processing, lecture notes in computer science,
Springer-Verlag, Berlin, 2235 (2001), 225-235. https://doi.org/10.1007/3-540-45523-X_11
[20] S. Miyamoto, Data structure and operations for fuzzy multisets, transactions on rough sets II, Lecture Notes in Computer
Science, Springer-Verlag, Berlin, 3135 (2004), 189-200. https://doi.org/10.1007/978-3-540-27778-1_10
[21] S. Miyamoto, Multisets and fuzzy multisets as a framework of information systems, In International Conference
on Modeling Decisions for Artificial Intelligence, Springer, Berlin, Heidelberg, (2004, August), 27-40. https://doi.
org/10.1007/978-3-540-27774-3_4
[22] S. Mohan, A. P. Kannusamy, V. Samiappan, A new approach for ranking of intuitionistic fuzzy numbers, Journal
of fuzzy extension and applications, 1(1) (2020), 15-26. https://www.journal-fea.com/article_114137_
f4d9bae346ee774968748b4e0c43960c.pdf
[23] S. Narayanamoorthy, T. N. Parthasarathy, S. Pragathi, P. Shanmugam, D. Baleanu, A. Ahmadian, D. Kang, The
novel augmented Fermatean MCDM perspectives for identifying the optimal renewable energy power plant location,
Sustainable Energy Technologies and Assessments, 53 (2022), 102488. https://doi.org/10.1016/j.seta.2022.
102488
[24] S. Orlovsky, Decision-making with a fuzzy preference relation, Fuzzy Sets and Systems, 1(3) (1978), 155-167.
https://doi.org/10.1016/0165-0114(78)90001-5
[25] N. Parveen, P. N. Kamble, Decision-making problem using fuzzy TOPSIS method with hexagonal fuzzy number,
In Computing in Engineering and Technology, Springer, Singapore, (2020), 421-430. https://doi.org/10.1007/
978-981-32-9515-5_40
[26] K. Rahman, Approaches to some induced Einstein geometric aggregation operators based on interval-valued
Pythagorean fuzzy numbers and their application, New Mathematics and Natural Computation, 16(02) (2020),
211-230. https://doi.org/10.1142/S1793005720500131
[27] L. Ramya, S. Narayanamoorthy, S. Kalaiselvan, J. V. Kureethara, V. Annapoorani, D. Kang, A congruent approach
to normal wiggly interval-valued hesitant Pythagorean fuzzy set for thermal energy storage technique selection
applications, International Journal of Fuzzy Systems, 23 (2021), 1581-1599. https://doi.org/10.1007/
s40815-021-01057-2
[28] M. Sahin, I. Deli, V. Ulucay, Extension principle based on neutrosophic multi-fuzzy sets and algebraic operations,
Journal of Mathematical Extension, 12(1) (2018), 69-90.
[29] M. Sahin, V. Ulucay, M. Menek¸se, Some new operations of (α, β, γ) interval cut set of interval valued neutrosophic
sets, Journal of Mathematical and Fundamental Sciences, 50(2) (2018), 103-120. https://doi.org/10.5614/J.
MATH.FUND.SCI.2018.50.2.1
[30] S. Sebastian, T. V. Ramakrishnan, Multi-fuzzy sets, International Mathematical Forum, 5(50) (2010), 2471-2476.
[31] S. Sebastian, T. V. Ramakrishnan, Multi-fuzzy subgroups, International Journal of Contemporary Mathematical
Sciences, 6(8) (2011), 365-372.
[32] T. Senapati, R. R. Yager, Fermatean fuzzy weighted averaging/geometric operators and its application in multicriteria
decision-making methods, Engineering Applications of Artificial Intelligence, 85 (2019), 112-121. https:
//doi.org/10.1016/j.engappai.2019.05.012
[33] M. Shakeel, S. Abdullah, M. Aslam, M. Jamil, Ranking methodology of induced Pythagorean trapezoidal fuzzy
aggregation operators based on Einstein operations in group decision making, Soft Computing, 24(10) (2020), 7319-
7334. https://doi.org/10.1007/s00500-019-04356-x
[34] A. Syropoulos, On generalized fuzzy multisets and their use in computation, Iranian Journal of Fuzzy Systems,
9(2) (2012), 115-127.
[35] V. Ulu¸cay, I. Deli, M. Sahin, Trapezoidal fuzzy multi-number and its application to multi-criteria decisionmaking
problems, Neural Computing and Applications, 30(5) (2018), 1469-1478. https://doi.org/10.1007/
s00521-016-2760-3
[36] V. Ulu¸cay, I. Deli, M. Sahin, Similarity measures of bipolar neutrosophic sets and their application to multiple
criteria decision making, Neural Computing and Applications, 29(3) (2018), 739-748. https://doi.org/10.1007/
s00521-016-2479-1
[37] V. Ulu¸cay, A. Kilic, I. Yildiz, M. Sahin, A new approach for multi-attribute decision-making problems in bipolar neutrosophic
sets, Neutrosophic Sets and Systems, 23(1) (2018), 12-22. https://doi.org/10.5281/zenodo.2154873
[38] V. Ulu¸cay, M. Sahin, N. Hassan, Generalized neutrosophic soft expert set for multiple-criteria decision-making,
Symmetry, 10(10) (2018), 437-447. https://doi.org/10.3390/sym10100437
[39] C. Veeramani, R. Venugopal, S. A. Edalatpanah, Neutrosophic DEMATEL approach for financial ratio performance
evaluation of the NASDAQ exchange, Neutrosophic Sets and Systems, 51(1) (2022), 766-782. https:
//digitalrepository.unm.edu/cgi/viewcontent.cgi?article=2160&context=nss_journal
[40] G. W. Wei, Pythagorean fuzzy Hamacher power aggregation operators in multiple attribute decision making, Fundamenta
Informaticae, 166(1) (2019), 57-85. https://doi.org/10.1002/int.21946
[41] Z. Xu, H. Liao, A survey of approaches to decision making with intuitionistic fuzzy preference relations, Knowledge-
Based Systems, 80 (2015), 131-142. https://doi.org/10.1016/j.knosys.2014.12.034
[42] R. R. Yager, On the theory of bags, International Journal of General System, 13 (1986), 23-37. https://doi.org/
10.1080/03081078608934952
[43] R. R. Yager, Generalized OWA aggregation operators, Fuzzy Optimization and Decision Making, 3(1) (2004),
93-107. https://doi.org/10.1023/B:FODM.0000013074.68765.97
[44] J. Ye, Some weighted aggregation operators of trapezoidal neutrosophic numbers and their multiple attribute decision
making method, Informatica, 28(2) (2017), 387-402. https://doi.org/10.15388/Informatica.2017.108
[45] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338-353. https://doi.org/10.1016/
S0019-9958(65)90241-X
[46] J. Zhan, W. Wang, J. C. R. Alcantud, J. Zhan, A three-way decision approach with prospect-regret theory via
fuzzy set pair dominance degrees for incomplete information systems, Information Sciences, 617 (2022), 310-330.
https://doi.org/10.1016/j.ins.2022.10.107
[47] J. Zhan, J. Wang, W. Ding, Y. Yao, Three-way behavioral decision making with hesitant fuzzy information systems:
Survey and challenges, IEEE/CAA Journal of Automatica Sinica, 10(2) (2023), 330-350. https://doi.org/10.
1109/JAS.2022.106061
[48] J. Zhu, X. Ma, L. Mart´ınez, J. Zhan, A probabilistic linguistic three-way decision method with regret theory via fuzzy
C-means clustering algorithm, IEEE Transactions on Fuzzy Systems, (2023). https://doi.org/10.1109/TFUZZ.
2023.3236386
[49] H. J. Zimmermann, Fuzzy set theory and its applications, Kluwer Academic Publishers, 1993. https://doi.org/
10.1007/978-94-010-0646-0