Abstract
This paper presents an application of a genetic algorithm (GA) to the scheduling of hot rolling mills. The objective function used is based on earlier developments on flow stress modeling of steels. A hybrid two-phase procedure was applied in order to calculate the optimal pass reductions, in terms of minimum total rolling time. In the first phase, a non-linear optimization function was applied to evaluate the computational cost to the problem solution. For the second phase, a GA was applied. A comparison with two-point and simulated binary (SBX) crossover operators was established. The results were validated with data of industrial schedules. A GA with SBX crossover operator is shown to be an efficient method to calculate the multi-pass schedules at reduced processing time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boubel, D., Fazan, B., Ratte, P.: Optimum Computer Control of the Plate Mill. Results of Industrial Operation. IRSID Report. No. RE501 (1978)
Akgerman, N., Lahoti, G., Altan, T.: Computer Aided Roll Pass Design. J. Appl. Metalwork. 30–40 (1980)
Tamura, R., Nagai, M., Nakagawa, Y., Tanizaki, T., Nakajima, H.: Synchronized Scheduling Method in Manufacturing Steel Sheets Int. Trans. Opl. Res. 5, 189–199 (1998)
Medina, S. F., Hernández, C. A.: General Expression of the Zener-Hollomon Parameter as a Function of the Chemical Composition of Low Alloy and the Microalloyed Steels. Acta Met. Mat. 44, 137–148 (1996)
Medina, S. F., Hernández, C. A.: Influence of Chemical Compo on Peak Strain of Deformed Austenite in Low Alloy and Microalloyed Steels. Acta Met. Mat. 44, 149–154 (1996)
Hernández, C. A., Medina, S. F., Ruiz, J.: Modelling Austenite Flow Curves in Low Alloy and Microalloyed Steels. Acta Met. Mat. 44, 155–163 (1996)
Ford, H., Alexander, J.: Simplified Hot-Rolling Calculations. J. Inst. Met. 92, 397–404 (1963)
Haupt, R., Haupt, S.: Practical Genetic Algorithms. John Wiley & Sons, New York (1998)
Fraire, H., Castilla, G., Hernández, A., Aguilar, S., Castillo, G., Camacho, C.: Reducción de la Varianza en la Evaluación Experimental de Algoritmos Metaheurísticos. In: Proc. 13th Int. Conf. on Computer Sci. Res. CIICC’06, pp. 14–22 (2006)
Deb, K., Beyer, H.-G.: Self-Adaptive Genetic Algorithms with Simulated Binary Crossover. Technical Report No. CI-61/99, Dept. Computer Science, University of Dortmund, (1999)
Wright, H., Hope, T.: Rolling of stainless steel in wide hot strip mills. Metals Tech. 9, 565–576 (1975)
Chakraborti, N., Kumar, A.: The Optimal Scheduling of a Reversing Strip Mill: Studies Using Multipopulation Genetic Algorithms and Differential Evolution. Mat. and Manuf. Proc. 18, 433–445 (2003)
Chakraborti, N., Kumar, B., Babu, S., Moitra, S., Mukhopadhyay, A.: Optimizing Surface Profiles during Hot Rolling: A Genetic Algorithms Based Multi-Objetive Optimization. Comp. Mat. Sci. 37, 159–165 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hernández Carreón, C.A., Fraire Huacuja, H.J., Fernandez, K.E., Valdez, G.C., Mancilla Tolama, J.E. (2007). Application of Genetic Algorithms to Strip Hot Rolling Scheduling. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-74972-1_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74971-4
Online ISBN: 978-3-540-74972-1
eBook Packages: EngineeringEngineering (R0)