Open Access
Issue
RAIRO-Oper. Res.
Volume 60, Number 2, March-April 2026
Page(s) 529 - 552
DOI https://doi.org/10.1051/ro/2026006
Published online 18 March 2026
  • J. Chen and L. Zhao, Deep fuzzy inference system fused with probability density function control. J. Clean. Prod. (2025), in press. [Google Scholar]
  • Y. Chen, Y. Gao, Y. Chen, L. Liu, A. Mo and Q. Peng, Nanomaterials-based photothermal therapy and its potentials in antibacterial treatment. J. Control. Release 328 (2020) 251–262. [Google Scholar]
  • K. Chinchpure, Predict Price of Used Cars Regression Problem. Kaggle (2020). [Online]. Available: https://www.kaggle.com/code/karanchinchpure/predict-price-of-used-cars-regression-problem/notebook. [Google Scholar]
  • S.K. Das, Modified method for solving fully fuzzy linear programming problem with triangular fuzzy numbers. Int. J. Res. Ind. Eng. 6 (2017) 293–311. [Google Scholar]
  • G. Dhiman, N. Kumar, R.K. Chandrawat, V. Joshi and A. Kaur, A novel approach to optimize the production cost of railway coaches of India using situational-based composite triangular and trapezoidal fuzzy LPP models. Complex Intell. Syst. 7 (2021) 2053–2068. [Google Scholar]
  • A. Ebrahimnejad, Fuzzy linear programming approach for solving transportation problems with interval-valued trapezoidal fuzzy numbers. Sādhanā 41 (2016) 299–316. [Google Scholar]
  • A. Ebrahimnejad and M. Tavana, A novel method for solving linear programming problems with symmetric trapezoidal fuzzy numbers. Appl. Math. Model. 38 (2014) 4388–4395. [Google Scholar]
  • P. Guo and H. Tanaka, Dual models for possibilistic regression analysis. Comput. Stat. Data Anal. 51 (2006) 253–266. [Google Scholar]
  • I. Hayashi and H. Tanaka, The fuzzy GMDH algorithm by possibility models and its application. Fuzzy Sets Syst. 36 (1990) 245–258. [Google Scholar]
  • D.H. Hong and K.T. Kim, A note on linear regression model using non-symmetric triangular fuzzy number coefficients. J. Korean Data Inf. Sci. Soc. 16 (2005) 445–449. [Google Scholar]
  • H. Ishibuchi and M. Nii, Fuzzy regression analysis with non-symmetric fuzzy number coefficients and its neural network implementation, in Proceedings of IEEE 5th International Fuzzy Systems. Vol. 1. IEEE (1996) 318–324. [Google Scholar]
  • I.I. Ismagilov and G. Alsaied, Fuzzy regression analysis using trapezoidal fuzzy numbers. Ind. Eng. Manage. Syst. 19 (2020) 896–900. [Google Scholar]
  • M. Khan, R. Kumar and G. Dhiman, A comparative study with linear regression and linear regression with fuzzy data for the same data set: LRFD, in AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management. Hershey, IGI Global, PA, USA (2022) 97–116. [Google Scholar]
  • M. Khan, R. Kumar, A.N. Aledaily, E. Kariri, W. Viriyasitavat, K. Yadav, G. Dhiman, A. Kaur, A. Sharma and S. Vimal, A systematic survey on implementation of fuzzy regression models for real life applications. Arch. Comput. Methods Eng. 31 (2024) 291–311. [Google Scholar]
  • R. Kumar and G. Dhiman, A comparative study of fuzzy optimization through fuzzy number. Int. J. Mod. Res. 1 (2021) 1–14. [Google Scholar]
  • R. Kumar, R.K. Chandrawat, B. Sarkar, V. Joshi and A. Majumder, An advanced optimization technique for smart production using a-cut based quadrilateral fuzzy number. Int. J. Fuzzy Syst. 23 (2021) 107–127. [CrossRef] [Google Scholar]
  • H. Lee and H. Tanaka, Fuzzy regression analysis with non-symmetric fuzzy coefficients based on quadratic programming approach, in Proceedings of the Korean Institute of Intelligent Systems Conference. Korean Institute of Intelligent Systems (1998) 63–68. [Google Scholar]
  • I. Listiani, H. Susilo and S. Sueb, Relationship between scientific literacy and critical thinking of prospective teachers. Al-Ishlah: J. Pendidik. 14 (2022) 721–730. [Google Scholar]
  • M. Liu and P. Smith, A large-sample study of fuzzy least-squares estimation: consistency and asymptotic normality. Fuzzy Sets Syst. 14 (2024) 181. [Google Scholar]
  • S. Moreno-Carbonell and E.F. Sánchez-Úbeda, A piecewise linear regression model ensemble for large-scale curve fitting. Algorithms 17 (2024) 147. [Google Scholar]
  • R. Naderkhani, M.H. Behzad, T. Razzaghnia and R. Farnoosh, Fuzzy regression analysis based on fuzzy neural networks using trapezoidal data. Int. J. Fuzzy Syst. 23 (2021) 1267–1280. [Google Scholar]
  • M. Noack and D. Ushizima, Methods and Applications of Autonomous Experimentation. CRC Press, Boca Raton, FL, USA (2024). [Google Scholar]
  • Pinki R., Kumar, S. Vimal, N.S. Alghamdi, G. Dhiman, S. Pasupathi, A. Sood, W. Viriyasitavat, A. Sapsomboon and A. Kaur, Artificial intelligence-enabled smart city management using multi-objective optimization strategies. Expert Syst. 42 (2025) e13574. [Google Scholar]
  • J. Rosset and L. Donzé, Fuzzy least squares and fuzzy orthogonal least squares linear regressions, in Proc. 15th Int. Joint Conf. Comput. Intell. (IJCCI) (2023) 359–368. [Google Scholar]
  • K. Samruddhi and R.A. Kumar, Used car price prediction using K-nearest neighbour-based model. Int. J. Innov. Res. Appl. Sci. Eng. 4 (2020) 686. [Google Scholar]
  • M. Sarwar, N. Jamal, K. Abodayeh, M. Hleili, T. Sitthiwirattham, C. Promsakon and S. Arabia, Existence of solution for fractional differential equations involving symmetric fuzzy numbers. AIMS Math. 9 (2024) 14747–14764. [Google Scholar]
  • P. Škrabánek and J. Marek, Models used in fuzzy linear regression, in Proceedings of the 17th Conference on Applied Mathematics APLIMAT (2018) 955–964. [Google Scholar]
  • P. Škrabánek and N. Martínková, Algorithm 1017: Fuzzyreg: an R package for fitting fuzzy regression models. ACM Trans. Math. Softw. 47 (2021) 1–18. [Google Scholar]
  • P. Škrabánek, J. Marek and A. Pozdílková, Boscovich fuzzy regression line. Mathematics 9 (2021) 685. [Google Scholar]
  • H. Tanaka, Fuzzy data analysis by possibilistic linear models. Fuzzy Sets Syst. 24 (1987) 363–375. [Google Scholar]
  • H. Tanaka and J. Watada, Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets Syst. 27 (1988) 275–289. [Google Scholar]
  • X. Wang and Y. Li, Estimation of fuzzy regression parameters with ANFIS and Bayesian methods. Engineering Reports. Submitted for publication (2025). [Google Scholar]
  • Q. Zheng, Performance of linear programming asymmetric parameter fuzzy regression. J. Adv. Res. Des. 130 (2025) 126–133. [Google Scholar]

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