Abstract
This paper presents a heuristic algorithm to schedule a hot rolling mill in the aluminum industry. One problematic issue is the tight coupling between the homogenizing furnaces and the mill, which needs to be integrated into the heuristic design. The latter also takes into account standard technological constraints like alloy hardness transitions, roll wear, homogenization code compatibilities and width transitions. The objective is to minimize the idle time on the mill and penalties for soft constraint violations related to production quality. The heuristic is divided into two phases. First, batches of ingots are constructed for the furnaces. These batches, called blocks, are then sequenced on the mill. Numerical results are reported on test instances derived from real-world data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Cowling P and Rezig W (2000). Integration of continuous caster and hot strip mill planning for steel production. J Scheduling 3: 185–208.
Dupont L, Rézard H and Allaili A (1997). Ordonnancement sous contraintes d'un laminoir à chaud, Deuxième Congrès International Franco-Québécois de Génie Industriel. École des Mines d'Albi-Carmaux, Albi, 1997.
Fang H-L and Tsai C-H (1998). A genetic algorithm approach to hot strip mill rolling scheduling problems. In: Proceedings of the Tenth IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer Society Press, Piscataway, NJ, pp 264–271.
Frontini GF and Bonhomme J (1999). Optimizing asset utilization in aluminum hot rolling mills. In: Langevin A, Ropel D and Ladet P (eds). 3iéme Congrès International de Génie Industriel, Presses Internationales Polytechnique, Montreal, Canada.
Lopez L, Carter MW and Gendreau M (1998). The hot strip mill production scheduling problem: a tabu search approach. Eur J Opl Res 106: 317–335.
Mayrand É, Lefrançois P, Kettani O and Jobin M-H (1995). A genetic search algorithm to optimize job sequencing under a technological constraint in a rolling-mill facility. OR Spektrum 17: 183–191.
Stauffer L and Liebling TM (1997). Rolling horizon scheduling in a rolling-mill. Ann Opns Res 69: 323–349.
Tamaki H, Mori M and Araki M (1994). Multi-criteria optimization by genetic algorithms: a case of scheduling in hot rolling process. In: Fushimi M and Tone K (eds). Proceedings of APORS'94, Association of Asia-Pacific Operational Research Societies, World Scientific, Singapore, pp 374–381.
Tang L, Liu J, Rong A and Yang Z (2000). A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex. Eur J Opl Res 124: 267–282.
Acknowledgements
This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and by Keops Technologies Inc. Their support is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
de Ladurantaye, D., Gendreau, M. & Potvin, JY. Scheduling a hot rolling mill. J Oper Res Soc 58, 288–300 (2007). https://doi.org/10.1057/palgrave.jors.2602137
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1057/palgrave.jors.2602137