A Multi-population Parallel Genetic Algorithm for Highly Constrained Continuous Galvanizing Line Scheduling
The steelmaking process consists of two phases: primary steelmaking and finishing lines. The scheduling of the continuous galvanizing lines (CGL) is regarded as the most difficult process among the finishing lines due to its multi-objective and highly-constrained nature. In this paper, we present a multi-population parallel genetic algorithm (MPGA) with a new genetic representation called k th nearest neighbor representation, and with a new communication operator for performing better communication between subpopulations in the scheduling of CGL. The developed MPGA consists of two phases. Phase one generates schedules from a primary work in process (WIP) inventory filtered according to the production campaign, campaign tonnage, priorities of planning department, and the due date information of each steel coil. If the final schedule includes the violations of some constraints, phase two repairs these violations by using a secondary WIP inventory of steel coils. The developed scheduling system is currently being used in a steel making company with encouraging preliminary results.
Keywordsmulti population genetic algorithm real world application continuous galvanizing line scheduling
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- 1.Cantú-Paz, E.: A survey of parallel genetic algorithms. IlliGAL Report 97003, Illinois Genetic Algorithms Lab., University of Illinois (1997)Google Scholar
- 4.Cohoon, J.P., Martin, W.N., Richards, D.S.: A multi-population genetic algorithm for solving the K-partition problem on hyper-cubes. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 244–248. Morgan Kaufmann, San Mateo (1991)Google Scholar
- 5.Fang, H.-L., Tsai, C.-H.: A Genetic Algorithm Approach to Hot Strip Mill Rolling Scheduling Problems. In: Proceedings of the International Conference on Tools with Artificial Intelligence, pp. 264–271. IEEE, Piscataway (1998)Google Scholar
- 6.Grosso, P.B.: Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model, Ph.D. Thesis, The University of Michigan (1985)Google Scholar
- 7.Kapanoglu, M., Koc, I.O., Kara, İ., Aktürk, M.S.: Multi-population genetic algorithm using a new genetic representation for the Euclidean traveling salesman problem. In: Durmusoglu, M.B., Kahraman, C. (eds.) Proceedings of the 35th International Conference on Computers & Industrial Engineering, Turkey, vol. 1, pp. 1047–1052 (2005)Google Scholar
- 11.Petty, C.B., Leuze, M.R., Grefenstette, J.J.: A parallel genetic algorithm. In: Grefenstette, J.J. (ed.) Proceedings of the Second International Conference on Genetic Algorithms, pp. 155–161. Lawrence Erlbaum Associates, Hillsdale (1987)Google Scholar
- 12.Reinelt, G.: The Traveling Salesman. LNCS, vol. 840. Springer, Berlin (1994)Google Scholar
- 13.Yasuda, H., Tokuyama, H., Tarui, K., Tanimoto, Y., Nagano, M.: Two-Stage Algorithm for Production Scheduling of Hot Strip Mill. Operations Research 32, 695–707 (1984)Google Scholar