How to cite this paper
Korunović, N., Madić, M., Trajanović, M & Radovanović, M. (2015). A procedure for multi-objective optimization of tire design parameters.International Journal of Industrial Engineering Computations , 6(2), 199-210.
Refrences
Cho, J. R., Jeong, H. S., & Yoo, W. S. (2002). Multi-objective optimization of tire carcass contours using a systematic aspiration-level adjustment procedure. Computational Mechanics, 29(6), 498-509.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.
De Eskinazi, J., Ishihara, K., Volk, H., & Warholic, T. C. (1990). Towards predicting relative belt edge endurance with the finite element method. Tire Science and Technology, 18(4), 216-235.
Gent, A. N., & Walter, J.D. (2006). The pneumatic tire. Washington D.C., National Higway Traffic Safety Administration, U.S. Department of Transportation.
Ghoreishy, M. H. R. (2006). Finite element analysis of steady rolling tyre with slip angle: Effect of belt angle. Plastics, Rubber and Composites, 35(2), 83-90.
Koishi, M., & Shida, Z. (2006). Multi-objective design problem of tire wear and visualization of its Pareto solutions, Tire Science and Technology, 34(3), 170-194.
Korunovi?, N., Trajanovi?, M., & Stojkovi?, M. (2007). Finite element model for steady-state rolling tire analysis. Journal of the Serbian Society for Computational Mechanics, 1(1), 63-79.
Kova?evi?, M., Madi?, M., Radovanovi?, M., & Ran?i?, D. (2014). Software prototype for solving multi-objective machining optimization problems: Application in non-conventional machining processes. Expert Systems with Applications, 41(13), 5657-5668.
Michalewicz, Z. (1996). Genetic algorithms+ data structures= evolution programs. Springer.
Nakajima, Y., Kadowaki, H., Kamegawa, T., & Ueno, K. (1999). Application of a neural network for the optimization of tire design. Tire Science and Technology, 27(2), 62-83.
Ngatchou, P., Zarei, A., & El-Sharkawi, M. A. (2005). Pareto multi objective optimization. In Intelligent Systems Application to Power Systems, 2005. Proceedings of the 13th International Conference on (pp. 84-91). IEEE.
Olatunbosun, O. A., & Bolarinwa, O. (2004). FE simulation of the effect of tire design parameters on lateral forces and moments. Tire science and Technology, 32(3), 146-163.
Rao, S. S. (2009). Engineering optimization: theory and practice. John Wiley & Sons.
Serafinska, A., Kaliske, M., Zopf, C., & Graf, W. (2013). A multi-objective optimization approach with consideration of fuzzy variables applied to structural tire design. Computers and Structures, 116, 7-19.
Yang, X., Behroozi, M., & Olatunbosun, O. A. (2014). A Neural Network Approach to Predicting Car Tyre Micro-Scale and Macro-Scale Behaviour. Journal of Intelligent Learning Systems and Applications, 6, 11-20.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.
De Eskinazi, J., Ishihara, K., Volk, H., & Warholic, T. C. (1990). Towards predicting relative belt edge endurance with the finite element method. Tire Science and Technology, 18(4), 216-235.
Gent, A. N., & Walter, J.D. (2006). The pneumatic tire. Washington D.C., National Higway Traffic Safety Administration, U.S. Department of Transportation.
Ghoreishy, M. H. R. (2006). Finite element analysis of steady rolling tyre with slip angle: Effect of belt angle. Plastics, Rubber and Composites, 35(2), 83-90.
Koishi, M., & Shida, Z. (2006). Multi-objective design problem of tire wear and visualization of its Pareto solutions, Tire Science and Technology, 34(3), 170-194.
Korunovi?, N., Trajanovi?, M., & Stojkovi?, M. (2007). Finite element model for steady-state rolling tire analysis. Journal of the Serbian Society for Computational Mechanics, 1(1), 63-79.
Kova?evi?, M., Madi?, M., Radovanovi?, M., & Ran?i?, D. (2014). Software prototype for solving multi-objective machining optimization problems: Application in non-conventional machining processes. Expert Systems with Applications, 41(13), 5657-5668.
Michalewicz, Z. (1996). Genetic algorithms+ data structures= evolution programs. Springer.
Nakajima, Y., Kadowaki, H., Kamegawa, T., & Ueno, K. (1999). Application of a neural network for the optimization of tire design. Tire Science and Technology, 27(2), 62-83.
Ngatchou, P., Zarei, A., & El-Sharkawi, M. A. (2005). Pareto multi objective optimization. In Intelligent Systems Application to Power Systems, 2005. Proceedings of the 13th International Conference on (pp. 84-91). IEEE.
Olatunbosun, O. A., & Bolarinwa, O. (2004). FE simulation of the effect of tire design parameters on lateral forces and moments. Tire science and Technology, 32(3), 146-163.
Rao, S. S. (2009). Engineering optimization: theory and practice. John Wiley & Sons.
Serafinska, A., Kaliske, M., Zopf, C., & Graf, W. (2013). A multi-objective optimization approach with consideration of fuzzy variables applied to structural tire design. Computers and Structures, 116, 7-19.
Yang, X., Behroozi, M., & Olatunbosun, O. A. (2014). A Neural Network Approach to Predicting Car Tyre Micro-Scale and Macro-Scale Behaviour. Journal of Intelligent Learning Systems and Applications, 6, 11-20.