Cubic bipolar fuzzy aggregation operator with priority degree with multi-criteria decision-making

Document Type : Article

Authors

Department of Mathematics, University of the Punjab, Lahore, Pakistan

Abstract

A cubic bipolar fuzzy number (CBFN) is extremely useful for conveying ambiguous data in real-world settings. The idea of a priority degree is used in an MCDM in which the parameters have a prioritization relationship. Aggregation operators (AOs) are created by assigning non-negative real numbers called priority degrees to tight priority levels. As a result, "cubic bipolar fuzzy prioritized averaging operator with priority degrees (CBFPDA)" and "cubic bipolar fuzzy prioritized geometric operator with priority degrees (CBFPGD)" are presented as prioritized operators with CBFNs. The proposed approaches results are compared to those of numerous other related studies. The existing method's properties are frequently compared to those of other current methods, emphasizing the superiority of the provided work over currently employed operators. In addition, the impact of priority degrees on information fusion and object ranking is investigated. The discussion of a third party reverse logistic provider (3PRLP) optimization problem's practical implementation is a secondary goal. A numerical example is used to examine the effectiveness, superiority, and logic of the recommended way to refer about 3PRLP. Following the procedure of choosing the best strategy and rating the viable alternatives, a comparison analysis is performed.

Keywords

Main Subjects

References

References:
1. Zadeh, L.A. "Fuzzy sets", Information and Control, 8(3) pp. 338-353 (1965). http://dx.doi.org/10.1016/S0019-9958(65)90241-X.
2. Zadeh, L.A.," Similarity relations and fuzzy ordering", Information Sciences, 3(2), pp. 177-200 (1971). DOI: 10.1016/s0020-0255(71)80005-1.
3. Atanassov, K.T. "Intuitionistic fuzzy sets", Fuzzy Sets and Systems, 20(1), pp. 87-96 (1986). http://dx.doi.org/10.1016/S0165-0114(86)80034-3.
4. Atanossov, K.T. "Intuitionistic fuzzy sets", In Intuitionistic Fuzzy Sets. Studies in Fuzziness and Soft Computing, Physica, Heidelberg, 35, pp. 1-137 (1999). https://doi.org/10.1007/978-3-7908-1870-3-1.
5. Riaz, M. and Jamil, N. "Topological structures on cubic bipolar fuzzy sets with linear assign- ment model and SIR method for healthcare", Journal of Intelligent and Fuzzy Systems, 44(3), pp. 1191-1212 (2022). DOI: 10.3233/JIFS-222224.
6. Xu, Z. "Intuitionistic fuzzy aggregation operators", IEEE Transactions on Fuzzy Systems, 15(6), pp. 1179- 1187 (2007). DOI: 10.1109/TFUZZ.2006.890678.
7. Li, B. and Xu, Z. "Prioritized aggregation operators based on the priority degrees in multicriteria decision making", International Journal of Intelligent Systems, 34(9), pp. 1985-2018 (2019). DOI: 10.1002/int.22123.
8. Riaz, M. and Tehrim, S.T. "Cubic bipolar fuzzy set with application to multi-criteria group decision making using geometric aggregation operators", Soft Computing, 24(21) pp. 16111-16133 (2020).DOI:10.1007/s00500-020-04927-3.
9. Koohathongsumrit, N. and Meethom, W. "An integrated approach of fuzzy risk assessment model and data envelopment analysis for route selection in multimodal transportation networks", Expert Systems with Applications, 171, p. 114342 (2021). DOI: 10.1016/j.eswa.2020.114342.
10. Buyukozkan, G., Karabulut, Y., and Arsenyan, J. "RFID service provider selection: An integrated fuzzy MCDM approach", Measurement, 112, pp. 88-98 (2017). DOI: 10.1016/j.measurement.2017.08.018.
11. Raut, R., Kharat, M., Kamble, S., et al. "Sustainable evaluation and selection of po- tential thirdparty logistics (3PL) providers: An integrated MCDM approach", Benchmarking: An International Journal, 25(1), pp. 76-97 (2018). DOI: 10.1108/BIJ-05-2016- 0065.
12. Koohathongsumrit, N. and Meethom, W. "Route selection in multimodal transportation networks: a hybrid multiple criteria decision-making approach", Journal of Industrial and Production Engineering, 38(3), pp. 171-185 (2021).DOI: 10.1080/21681015.2020.1871084.
13. Yager, R.R. "Pythagorean fuzzy subsets", In Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), Edmonton, AB, Canada, pp. 57-61 (2013). DOI: 10.1109/IFSA-NAFIPS.2013.6608375.
14. Yager, R.R. "Pythagorean membership grades in multicriteria decision making", IEEE Transactions on Fuzzy Systems, 22(4), pp. 958-965 (2014). DOI: 10.1109/TFUZZ.2013.2278989.
15. Senapati, T. and Yager, R.R. "Fermatean fuzzy sets", Journal of Ambient Intelligence and Humanized Computing, 11(2), pp. 663-674 (2020). DOI: 10.1007/s12652-019-01377-0.
16. Senapati, T. and Yager, R.R. "Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods", Engineering Applications of Artificial Intelligence, 85, pp. 112-121 (2019). DOI: 10.1016/J.ENGAPPAI.2019.05.012.
17. Yager, R.R. "Generalized orthopair fuzzy sets", IEEE Transactions on Fuzzy Systems, 25(5), pp. 1220-1230 (2017). DOI: 10.1109/TFUZZ.2016.2604005.
18. Zhang, W.R. "Bipolar fuzzy sets and relations: a computational framework for cognitive modeling and multiagent decision analysis", NAFIPS/IFIS/NASA'94, Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference, The Industrial Fuzzy Control and Intelligence, San Antonio, TX, USA, pp. 305-309 (1994). DOI: 10.1109/IJCF.1994.375115.
19. Zhang, W.R. "(Yin) (Yang) bipolar fuzzy sets", IEEE International Conference on Fuzzy Systems Proceedings, IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), pp. 835-840 (1998). DOI: 10.1109/FUZZY.1998.687599.
20. Jun, Y.B., Kim, C.S., and Yung, K.O. "Cubic Sets", Annals of Fuzzy Mathematics and Informatics, 4(1), pp. 83-98 (2012). https://doi.org/10.1155/2015/523129.
21. Riaz, M. and Tehrim, S.T. "Multi-attribute group decision making based on cubic bipolar fuzzy information using averaging aggregation operators", Journal of Intelligent and Fuzzy Systems, 37(2), pp. 2473-2494 (2019). DOI: 10.3233/JIFS-182751.
22. Yager, R.R. "On ordered weighted averaging aggregation operators in multicriteria decision-making", IEEE Transactions on Systems, Man, and Cybernetics, 18(1), pp. 183-190 (1988). https://doi.org/10.1109/21.87068.
23. Xu, Z.S. and Yager, R.R. "Some geometric aggregation operators based on intuitionistic fuzzy sets", International Journal of General Systems, 35(4), pp. 417-433 (2006). DOI: 10.1080/03081070600574353.
24. Xu, Z.S. "Intuitionistic fuzzy aggreagtion operators", IEEE Transactions on Fuzzy Systems, 15(6), pp. 1179- 1187 (2007). DOI: 10.1109/TFUZZ.2006.890678.
25. Wang, W. and Liu, X. "Intuitionistic fuzzy information aggregation using Einstein operations", IEEE Transactions on Fuzzy Systems, 20(5), pp. 923-938 (2012). DOI: 10.1109/tfuzz.2012.2189405.
26. Wei, G., Alsaadi, F.E., Hayat, T., et al. "Bipolar fuzzy Hamacher aggregation operators in multiple attribute decision making", International Journal of Fuzzy Systems, 33(6), pp. 1-12 (2017). DOI: 10.1007/s40815- 017-0338-6.
27. Kaur, G. and Garg, H. "Multi-attribute decisionmaking based on Bonferroni mean operators under cubic intuitionistic fuzzy set environment", Entropy, 20(1), p. 65 (2018). DOI: 10.3390/e20010065.
28. Shi, L. and Ye, J. "Dombi aggregation operators of neutrosophic cubic sets for multiple attribute decisionmaking", Algorithms, 11(3), p. 29 (2018). DOI: 10.3390/a11030029.
29. Akram, M., Dudek, W.A., and Dar, J.M. "Pythagorean Dombi fuzzy aggregation operators with application in multicriteria decision-making", International Journal of Intelligent Systems, 34(11), pp. 3000-3019 (2019).https://doi.org/10.1002/int.22183.
30. Khan, F., Khan, M.S.A., Shahzad, M., et al. "Pythagorean cubic fuzzy aggregation operators and their application to multi-criteria decision making problems", Journal of Intelligent and Fuzzy Systems, 36(1), pp. 595-607 (2019). DOI: 10.3233/JIFS-18943.
31. Riaz, M., Fareed, H.M.A., Shakeel, H.M., et al. "Innovative q-rung orthopair fuzzy prioritized aggregation operators based on priority degrees with application to sustainable energy planning: A case study of Gwadar", AIMS Mathematics, 6(11) pp. 12795-12831 (2021). DOI: 10.3934/math.2021739.
32. Riaz, M., Pamucar, D., Farid H.M.A., et al. "qrung orthopair fuzzy prioritized aggregation operators and their application towards green supplier chain management", Symmetry, 12(6), p. 976 (2020). https://doi.org/10.3390/sym12060976.
33. Akram, M., Khan, A., Alcantud, J.C.R., et al. "A hybrid decision-making frame- work under complex spherical fuzzy prioritized weighted aggregation operators", Expert Systems, 38(6), e12712 (2021). DOI: 10.1111/exsy.12712.
34. Garg, H. and Rani, D. "New prioritized aggregation operators with priority degrees among priority orders for complex intuitionistic fuzzy information", Journal of Ambient Intelligence and Humanized Computing, 14(3), pp. 1373-1399 (2021). DOI: 10.1007/s12652- 021-03164-2.
35. Riaz, M., Farid, H.M.A., Shakeel, H.M.M., et al. "Innovative q-rung orthopair fuzzy prioritized aggregation operators based on priority degrees with application to sustainable energy planning: A case study of Gwadar", AIMS Mathematics, 6(11), pp. 12795- 12831 (2021). DOI: 10.3934/math.2021739.
36. Farid, H.M.A., Riaz, M., Khan, M.J., et al. "Sustainable thermal power equipment supplier selection by Einstein prioritized linear Diophantine fuzzy aggregation operators", AIMS Mathematics, 7, pp. 11201- 11242 (2022). DOI: 10.3934/math.2022627.
37. Farid, H.M.A., Garg, H., Riaz, M., et al. "Multicriteria group decision-making algorithm based on single-valued neutrosophic Einstein prioritized aggregation operators and its applications", Management Decision, 61(2), 382-420 (2022).
https://doi.org/10.1108/MD-04-2022-0484.
38. Farid, H.M.A. and Riaz, M. "Innovative q-rung orthopair fuzzy prioritized interactive aggregation operators to evaluate efficient autonomous vehicles for freight transportation", Scientia Iranica, pp. 1-24 (2022). DOI: 10.24200/SCI.2022.59601.6326.
39. Akman, G. and Baynal, K. "Logistics service provider selection through an integrated fuzzy multicriteria decision making approach", Journal of Industrial Engineering, 2014, pp. 1-26 (2014). https://doi.org/10.1155/2014/794918.
40. Riaz, M. and Tehrim, S.T. "Cubic bipolar fuzzy ordered weighted geometric aggregation operators and their application using internal and external cubic bipolar fuzzy data", Computational and Applied Mathematics, 38(2), pp. 1-25 (2019).
41. Riaz, M., Habib, A., and Aslam, M. "Cubic bipolar fuzzy Dombi averaging aggregation opera- tors with application to multi-criteria decision-making", Journal of Intelligent and Fuzzy Systems, 41(2), pp. 3373-3393(2021). DOI: 10.3233/JIFS-210667.
42. Jamil N. and Riaz, M. "Bipolar disorder diagnosis with cubic bipolar fuzzy informa- tion using TOPSIS and ELECTRE-I", International Journal of Biomathematics, (2022). https://doi.org/10.1142/S1793524522500309.
Scientia Iranica
Volume 31, Issue 5 - Serial Number 5
Transactions on Computer Science & Engineering and Electrical Engineering (D)
March and April 2024
Pages 388-416

Files

History

  • Receive Date: 01 August 2022
  • Revise Date: 16 November 2022
  • Accept Date: 03 December 2023

Share

How to cite

Statistics