Abstract
In this paper, a method of applying genetic algorithms (GAs) to multi-objective scheduling problems is proposed. The key points are (1) an alphabetical representation (i.e., genotype) of feasible schedules (i.e., phenotype), and (2) a reproduction operator of GAs which combines the parallel selection with the Pareto reservation strategy. In the paper, through computational experiments, it is shown that not only one of the Pareto-optimal schedules of a problem but a set of such solutions can be obtained by a single run of the proposed method.
Chapter PDF
Similar content being viewed by others
Keywords
References
Fonseca, C. M. and Fleming, P. J. (1995), An overview of evolutionary algorithms in multiobjective optimization, Evolutionary Computation, 3 (1), pp. 1–16, MIT press.
Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning,Addison-Wesley.
Reeves, C. R. (1993), Modern Heuristic Techniques for Combinatorial Problems,Blackwell Scientific.
Tamaki, H., Hasegawa, Y., Kozasa, J. and Araki, M. (1993), Scheduling in Plastics Forming Plant: A Binary Representation Approach, Proc. of the 32nd IEEE Conf. on Decision and Control, pp. 3845–3850.
Tamaki, H., Taguchi, K. and Araki, M. (1995a), Application of Meta-Heuristics to Scheduling Problems in Plastics Forming Plant, Proc. of the 7th IFAC/IFORS/IMACS Symp. on Large Scale Systems, pp. 409–414.
Tamaki, H., Mori, M., and Araki, M. (1995b), Generation of a Set of Pareto-Optimal Solutions by Genetic Algorithms, Trans. of the Society of Instrument and Control, 31 (8), pp. 1185–1192 (in Japanese).
Tamaki, H., Kita, H. and Kobayashi, S. (1996), Multi-Objective Optimization by Genetic Algorithms: A Review, Proc. of the 1996 IEEE Int. Conf. on Evolutionary Computation, pp. 517–522.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Tamaki, H., Mukai, T., Kawakami, K., Araki, M. (1998). Genetic algorithm approach to multi-objective scheduling problem in plastics forming plant. In: Okino, N., Tamura, H., Fujii, S. (eds) Advances in Production Management Systems. IFIP — The International Federation for Information Processing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35304-3_38
Download citation
DOI: https://doi.org/10.1007/978-0-387-35304-3_38
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-4455-2
Online ISBN: 978-0-387-35304-3
eBook Packages: Springer Book Archive