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Introducing hesitant fuzzy equations and determining market equilibrium price

Open Access
|Jun 2022

References

  1. Abbasbandy, S. and Asady, B. (2004) Newton’s method for solving fuzzy nonlinear equations. Appl Math Comput, 159, 349–356.10.1016/j.amc.2003.10.048
  2. Allahviranloo, T. and Babakordi, F. (2017) Algebraic solution of fuzzy linear system as: $$\widetilde {A}\widetilde {X}+\widetilde {B}\widetilde {X}= \widetilde {Y}$$ A˜X˜+ B˜X˜= Y˜. Soft Computing, 21(24), 7463-7472.10.1007/s00500-016-2294-8
  3. Amirfakhrian, M. (2012) Analyzing the solution of a system of fuzzy linear equations by a fuzzy distance. Soft Comput, 16(6), 1035–1041.10.1007/s00500-012-0801-0
  4. Babakordi, F. and Firozja, A. (2020) Solving Fully Fuzzy Dual Matrix System With Optimization Problem. International Journal of Industrial Mathematics, 12(2), 109-119.
  5. Babakordi, F., Allahviranloo, T. and Adabitabarrozja, T. (2016) An efficient method for solving LR fuzzy dual matrix system. Journal of Intelligent & Fuzzy Systems, 30, 575–581.10.3233/IFS-151781
  6. Baumol, W. J. (1972). Economic Theory and Operations Analysis. Prentice-Hall, Inc., Englewood Cliffs, New Jersey.
  7. Begnini, M., Bertol, W. and Martins, N. A. (2018). Design of an adaptive fuzzy variable structure compensator for the nonholonomic mobile robot in trajectory tracking task. Control and Cybernetics, 47(3), 239-275.
  8. Boyacı, A. Ç. (2020) Selection of eco-friendly cities in Turkey via a hybrid hesitant fuzzy decision making approach. Applied Soft Computing, 89.10.1016/j.asoc.2020.106090
  9. Buckley, J. (1991) Solving fuzzy equations: a new solution concept. Fuzzy Sets Syst, 39(3), 291–301.10.1016/0165-0114(91)90099-C
  10. Buckley, J., Feuring, T. and Hayashi, Y. (2002) Solving fuzzy equations using evolutionary algorithms and neural nets. Soft Computing, 6(2), 116–123.10.1007/s005000100147
  11. Chen, N., Xu, Z. and Xia, M. (2013) Interval-valued hesitant preference relations and their applications to group decision making. Knowledge Based Systems, 37, 528–540.10.1016/j.knosys.2012.09.009
  12. Farahani, H., Nehi, H. and Paripour, M. (2016) Solving fuzzy complex system of linear equations using eigenvalue method. J. Intell. Fuzzy Syst., 31(3), 1689–1699.10.3233/JIFS-152046
  13. Farhadinia, B. (2014a) Correlation for dual hesitant fuzzy sets and dual interval-valued hesitant fuzzy sets. Int. J. Intell. Syst., 29, 184–205.10.1002/int.21633
  14. Farhadinia, B. (2014b) Distance and similarity measures for higher order hesitant fuzzy sets. Knowledge Based Systems, 55, 43–48.10.1016/j.knosys.2013.10.008
  15. Farhadinia, B. and Herrera-Viedma, E. (2018) Sorting of decision-making methods based on their outcomes using dominance-vector hesitant fuzzy-based distance. Soft Computing. doi:https://doi.org/10.1007/s00500-018-3143-8
  16. Fiedler, M., Nedoma, J., Ramik, J., Rohn, J. and Zimmermann, K. (2006) Optimization Problems with Inexact Data. Springer.
  17. Hesamian, Gh. (2017) Fuzzy similarity measure based on fuzzy sets. Control and Cybernetics, 46 (1), 71-86.
  18. Kalshetti, S. C. and Dixit, S. K. (2018) Self-adaptive grey wolf optimization based adaptive fuzzy aided sliding mode control for robotic manipulator. Control & Cybernetics, 47(4), 383-409.
  19. Khalili Goodarzi, F., Taghinezhad, N. A. and Nasseri, S. H. (2014) A new fuzzy approach to solve a novel model of open shop scheduling problem. University Politehnica of Bucharest Scientific Bulletin-Series A-Applied Mathematics and Physics, 76(3), 199-210.
  20. Liu, P. and Zhang, X. (2020) A new hesitant fuzzy linguistic approach for multiple attribute decision making based on Dempster–Shafer evidence theory. Applied Soft Computing, 86.10.1016/j.asoc.2019.105897
  21. Lodwick, W. (1990) Analysis of structure in fuzzy linear programs. Fuzzy Sets and Systems, 38, 15-26.10.1016/0165-0114(90)90097-P
  22. Nasseri, S. H., Khalili, F., Taghi-Nezhad, N. and Mortezania, S. (2014) A novel approach for solving fully fuzzy linear programming problems using membership function concepts. Ann. Fuzzy Math. Inform., 7(3), 355-368.
  23. Noor’ani, A., Kavikumar, J., Mustaf, M. and Nor, S. (2011) Solving dual fuzzy polynomial equation by ranking method. Far East J Math Sci, 51(2), 151–163.
  24. Peng, J., Wang, J., Wu, X. and Tian, C. (2017). Hesitant intuitionistic fuzzy aggregation operators based on the archimedean t-norms and tconorms. Int. J. Fuzzy Syst., 19(3), 702–714.10.1007/s40815-017-0303-4
  25. Ranjbar, M. and Effati, S. (2019) Symmetric and right-hand-side hesitant fuzzy linear programming. IEEE Transactions on Fuzzy Systems, 28(2), 215-227.10.1109/TFUZZ.2019.2902109
  26. Stolfi, J. and de Figueriredo, L. (1997) Self-Validated Numerical Methods and Applications. IMPA, Brazilian Mathematics Colloquium monograph.
  27. Taghi-Nezhad, N. (2019) The p-median problem in fuzzy environment: proving fuzzy vertex optimality theorem and its application. Soft Computing. doi:https://doi.org/10.1007/s00500-019-04074-4
  28. Taleshian, F., Fathali, J., and Taghi-Nezhad, N. A. (2018) Fuzzy majority algorithms for the 1-median and 2-median problems on a fuzzy tree. Fuzzy Information and Engineering, 1-24.10.1080/16168658.2018.1517976
  29. Tang, X., Peng Z, Ding, H., Cheng, M. and Yang, S. (2018) Novel distance and similarity measures for hesitant fuzzy sets and their applications to multiple attribute decision making. J. Intell. Fuzzy Syst., 34, 3903–3916.10.3233/JIFS-169561
  30. Torra, V. (2010) Hesitant Fuzzy Sets. International Journal of Intelligent Systems, 25(6), 529–539.10.1002/int.20418
  31. Torra, V. and Narukawa, Y. (2009) On hesitant fuzzy sets and decision. The 18-th IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 1378-1382.10.1109/FUZZY.2009.5276884
  32. Viattchenin, D. A., Owsiński, J. W. and Kacprzyk, J. (2018) New developments in fuzzy clustering with emphasis on special types of tasks. Control and Cybernetics, 47(2), 115-130.
  33. Wang, J., Wang, D., Zhang, H. and Chen, X. (2014) Multi-criteria outranking approach with hesitant fuzzy sets. OR Spectr, 36, 1001–1019.10.1007/s00291-013-0354-3
  34. Wei, G. and Zhao, X. (2013) Induced hesitant interval-valued fuzzy Einstein aggregation operators and their application to multiple attribute decision making. J. Intell. Fuzzy Syst., 24(4), 789–803.10.3233/IFS-2012-0598
  35. Wu, P., Zhou, L., Chen, H. and Tao, Z. (2020) Multi-stage optimization model for hesitant qualitative decision making with hesitant fuzzy linguistic preference relations. Applied Intelligence, 50(1), 222-240.10.1007/s10489-019-01502-8
  36. Xia, M., Xu, Z. and Chen, N. (2013) Some Hesitant fuzzy aggregation operators with their application in group decision making. Group Decis. Negot., 22(2), 259–279.10.1007/s10726-011-9261-7
  37. Xian, S. and Guo, H. (2020) Novel supplier grading approach based on interval probability hesitant fuzzy linguistic TOPSIS. Engineering Applications of Artificial Intelligence, 87.10.1016/j.engappai.2019.103299
  38. Yu, D., Wu, Y. and Zhou, W. (2011) Multi-criteria decision making based on Choquet integral under hesitant fuzzy environment. J. Comput. Inf. Syst., 12(7), 4506–4513.
  39. Zhang, Z. and Chen, S. M. (2020) Group decision making with hesitant fuzzy linguistic preference relations. Information Sciences, 514, 354-368.10.1016/j.ins.2019.11.030
  40. Zhang, Z., Wang, C., Tian, D. and Li, K. (2014) Induced generalized hesitant fuzzy operators and their application to multiple attribute group decision making. Comput. Ind. Eng., 67, 116–138.10.1016/j.cie.2013.10.011
  41. Zhou, W. (2014) An accurate method for determining hesitant fuzzy aggregation operator weights and its application to project investment. Int. J. Intell. Syst., 29(7), 668–686.10.1002/int.21651
  42. Zhu, B., Xu, Z. and Xia, M. (2011) Hesitant fuzzy information aggregation in decision making. International Journal of Approximate Reasoning, 52(3), 395–407.10.1016/j.ijar.2010.09.002
  43. Zhu, J., Fu, F., Yin, K., Luo, J. and Wei, D. (2014) Approaches to multiple attribute decision making with hesitant interval-valued fuzzy information under correlative environment. J. Intell. Fuzzy Syst., 27, 1057–106510.3233/IFS-131066
DOI: https://doi.org/10.2478/candc-2021-0022 | Journal eISSN: 2720-4278 | Journal ISSN: 0324-8569
Language: English
Page range: 363 - 382
Submitted on: May 1, 2020
Accepted on: Apr 1, 2021
Published on: Jun 27, 2022
Published by: Systems Research Institute Polish Academy of Sciences
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2022 Fatemeh Babakordi, N. A. Taghi-Nezhad, published by Systems Research Institute Polish Academy of Sciences
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.