single-au.php

IJAT Vol.9 No.3 pp. 235-247
doi: 10.20965/ijat.2015.p0235
(2015)

Paper:

A Novel Algorithm for Continuous Steel Casting Scheduling with Focus on Quality Property Constraint and Slab Width Maximization

Taiki Ogata*1,*2, Tsuyoshi Okubo*3, Hidetoshi Nagai*3, Masashi Yamamoto*3, Masao Sugi*4, and Jun Ota*1

*1Reseaerch into Artifacts, Center for Engineering, The University of Tokyo
5-1-5 Kakinoha, Kashiwa, Chiba

*2Tokyo Institute of Technology, Kanagawa, Japan

*3NS Solutions Corporation, Kanagawa, Japan

*4The University of Electro-Communications, Tokyo, Japan

Received:
December 2, 2014
Accepted:
February 25, 2015
Published:
May 5, 2015
Keywords:
scheduling, casting scheduling problem, simulated annealing, shortest path problem
Abstract
This paper proposes a solution for casting scheduling, an important process in steel manufacturing. A constraint due to secular changes in quality properties, including slab property degradation in casting, and problems with evaluation indices for slab casting width for the improvement of productivity, are taken into account in the study. These factors have not been focused on in previous studies. In this study, the casting scheduling problem is divided into two. One problem involves determining a schedule frame for each slab, and the other is the problem of determining the slab width. To be more specific, after an initial solution for slab order is reached based on heuristics with the quality property constraint taken into account, a hierarchical solving method of determining the slab width is employed in accordance with the shortest path problem-solving method, and obtained solutions are improved using a simulated annealing technique. The simulation is performed with the data created from actual operation data and an initial solution that satisfies the constraint is derived. An improved solution is then obtained using the solution search technique.
Cite this article as:
T. Ogata, T. Okubo, H. Nagai, M. Yamamoto, M. Sugi, and J. Ota, “A Novel Algorithm for Continuous Steel Casting Scheduling with Focus on Quality Property Constraint and Slab Width Maximization,” Int. J. Automation Technol., Vol.9 No.3, pp. 235-247, 2015.
Data files:
References
  1. [1] L. Tang, J. Liu, A. Rong, and Z. Yang, “A review of planning and scheduling systems and methods for integrated steel production,” Eur. J. Oper. Res., Vol.133, No.1, pp. 1-20, 2001.
  2. [2] A. Bellabdaoui and J. Teghem, “A mixed-integer linear programming model for the continuous casting planning,” Int. J. Prod. Econ., Vol.104, No.2, pp. 260-270, 2006.
  3. [3] A. Atighehchian, M. Bijari, and H. Tarkesh, “A novel hybrid algorithm for scheduling steel-making continuous casting production,” Comput. Oper. Res., Vol.36, No.8, pp. 2450-2461, 2009.
  4. [4] L. Tang, J. Liu, A. Rong, and Z. Yang, “A mathematical programming model for scheduling steelmaking-continuous casting production,” Eur. J. Oper. Res., Vol.120, No.2, pp. 423-435, 2000.
  5. [5] L. Tang and J. A. Luo, “A new ILS algorithm for cast planning problem in steel industry,” ISIJ international, Vol.47, No.3, pp. 443-452 2007.
  6. [6] R. Zhang, K. Lu, K. Huang, and D. Wang, “Graph-based model of cast planning problem and its optimization,” Proceedings of the 2011 Chinese Control and Decision Conference (CCDC), pp. 1606-1611, May, 2011.
  7. [7] Y. K. Park and J.-M. Yang, “Optimization of mixed casting processes considering discrete ingot sizes,” J. Mech. Sci. Technol., Vol.23, No.7, pp. 1899-1910, 2009.
  8. [8] Y. K. Park and J.-M. Yang, “Maximizing average efficiency of process time for pressure die casting in real foundries,” Int. J. Adv. Manuf. Technol., Vol.53, No.9-12, pp. 889-897, 2011.
  9. [9] H. Dong, M. Huang, W. H. Ip, and X. Wang, “Improved variable neighbourhood search for integrated tundish planning in primary steelmaking processes,” Int. J. Prod. Res., Vol.50, No.20, pp. 5747-5761, 2012.
  10. [10] L. Tang, Y. Zhao, and J. Liu, “An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production,” IEEE T. Evolut. Comput., Vol.18, No.2, pp. 209-225, 2013.
  11. [11] R. Tamura, M. Nagai, Y. Nakagawa, T. Tanizaki, and H. Nakajima, “Synchronized scheduling method in manufacturing steel sheets,” Int. T. Oper. Res., Vol.5, No.3, pp. 189-199, 1998.
  12. [12] P. Cowling and W. Rezig, “Integration of continuous caster and hot strip mill planning for steel production,” J. Sched., Vol.3, No.4, pp. 185-208, 2000.
  13. [13] L. Tang, P. B. Luh, J. Liu, and L. Fang, “Steel-making process scheduling using Lagrangian relaxation,” Int. J. Prod. Res., Vol.40, No.1, pp. 55-70, 2002.
  14. [14] S. Zanoni and L. Zavanella, “Model and analysis of integrated production–inventory system: The case of steel production,” Int. J. Prod. Econ., Vol.93-94, No.8, pp. 197-205, 2005.
  15. [15] M. Gravel, W. L. Price, and C. Gagnée, “Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic,” Eur. J. Oper. Res., Vol.143, No.1, pp. 218-229, 2002.
  16. [16] S. Liu, J. Tang, and J. Song, “Order-planning model and algorithm for manufacturing,” Int. J. Prod. Econ., Vol.100, No.1, pp. 30-43, 2006.
  17. [17] L. Tang and S. Jiang, “The charge batching planning problem in steelmaking process using Lagrangian relaxation algorithm,” Ind. Eng. Chem. Res., Vol.48, No.16, pp. 7780-7787, 2009.
  18. [18] L. Tang and G. Wang, “Decision support system for the batching problems of steelmaking and continuous-casting production,” Omega, Vol.36, No.6, pp. 976-991, 2008.
  19. [19] H. Dong, M. Huang, W. H. Ip, and X. Wang, “On the integrated charge planning with flexible jobs in primary steelmaking processes,” Int. J. Prod. Res., Vol.48, No.21, pp. 6499-6535, 2010.
  20. [20] L. Tang, G. Wang, J. Liu, and J. Liu, “A combination of Lagrangian relaxation and column generation for order batching in steelmaking and continuous-casting production,” Naval Research Logistics, Vol.58, No.4, pp. 370-388, 2011.
  21. [21] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, Vol.200, No.4598, pp. 671-680, 1983.
  22. [22] S. Furao and O. Hasegawa, “Fractal image coding with Simulated Annealing search,” Journal of Advanced Computational Intelligence, Vol.9, No.1, pp. 80-88, 2005.
  23. [23] M. Cheng, H. Itoh Ozaku, N. Kuwahara, K. Kogure, and J. Ota, “Dynamic Scheduling in Inpatient Nursing,” Int. J. of Automation Technology, Vol.3, No.2, pp. 174-184, 2009.
  24. [24] E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, Vol.1, issue 1, pp. 269-271, 1959.
  25. [25] J. Y. Chai, T. Sakaguchi, and K. Shirase, “Dynamic Controls of Genetic Algorithm Scheduling in Supply Chain,” Int. J. of Automation Technology, Vol.4, No.2, pp. 169-177, 2010.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Dec. 06, 2024