Abstract
This paper describes a genetic algorithm approach for sequencing problems especially for job shop scheduling. In actual problems, setup time should be optimized. In order to reduce setup time, we developed a new genetic representation and an efficient crossover operator called Common Cluster Crossover (CCX). In our representation, chromosomes represent the shift of the order in a sequence. To preserve sub-sequences in crossover operations, we implemented the process to identify the cluster of the sub-sequences and applied CCX that exchanges common clusters between two parents. The approach was tested on two standard benchmarks and applied to an audio parts manufacturer. CCX achieved remarkable results on the actual job shop scheduling problem.
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References
Lawrence Davis: Job Shop Scheduling with Genetic Algorithms. Proceedings of the First Intl. Conference on Genetic Algorithms and their Applications (1985) 136–140
Sugato Bagchi, Serdar Uckun, Yutaka Miyabe, and Kazuhiko Kawamura: Exploring Problem-Specific Recombination Operators for Job Shop Scheduling. Proceedings of the Forth Intl. Conference on Genetic Algorithms (1991) 10–17
Ryohei Nakano and Takeshi Yamada: Conventional Genetic Algorithm for Job Shop Problems. Proceedings of the Forth Intl. Conference on Genetic Algorithms (1991) 474–479
Christian Bierwirth: A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spektrum Vol. 17 (1995) 87–92
David E. Glodberg and Robert Lingle, Jr.: Alleles, Loci, and Traveling Salesman Problem. Proceedings of the First Intl. Conference on Genetic Algorithms and their Applications (1985) 154–159
Gilbert Syswerda: Schedule Optimization Using Genetic Algorithms. In Handbook of Genetic Algorithms. Lawrence Davis ed. Van Nostrand Reinhold, New York (1991)
Colin R. Reeves: A Genetic Algorithm for Flowshop Sequencing. Computers and Operations Research Vol. 22, No. 1 (1995) 5–13
H. Fisher, G. L. Thompson: Probabilistic learning combinations of local job-shop scheduling rules. In Industrial Scheduling. J.F. Muth, G. L. Thompson ed. Prentice Hall, Englewood Cliffs, New Jersey (1963)
Takeshi Yamada and Ryohei Nakano: A Genetic Algorithm Applicable to Large Scale Job Shop Problems. Parallel Problem Solving from Nature, 2 (1992) 281–290
Shigenobu Kobayashi, Isao Ono, and Masayuki Yamamura: An Efficient Genetic Algorithm for Job Shop Scheduling Problems. Proceedings Of the 6th Intl. Conference On Genetic Algorithms (1995) 506–511
D. C. Mattfeld, H. Kopfer, and C. Bierwirth: Control of Parallel Population Dynamics by Social-like Behavior of GA-Individuals. Parallel Problem Solving from Nature, 3 (1994) 15–24
Shyh-Chang Lin, Erik D. Goodman, and William F. Punch, III: Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems. Evolutionary Programming VI, Proceedings of Sixth International Conference EP97 (1997), 383–394
B. Giffler and G. L. Thompson: Algorithm for Solving Production Schedule Problems. Operations Research, Vol.8 (1960) 487–503
Robert H. Storer, S. David Wu, and Renzo Vaccari: New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling, Management Science, Vol.38 (1992) 1495–1509
James E. Baker: Adaptive Selection Methods for Genetic Algorithms. Proceedings of the First Intl. Conference on Genetic Algorithms and their Applications (1985) 101–111
Gunar E. Liepins and W. D. Potter: A Genetic Algorithm Approach to Multiple-Fault Diagnosis. In Handbook of Genetic Algorithms. Lawrence Davis ed. Van Nostrand Reinhold, New York (1991)
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© 2000 Springer-Verlag Berlin Heidelberg
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Masaru, T., Masahiro, H., Kazunori, M., Keigo, O. (2000). A New Genetic Representation and Common Cluster Crossover for Job Shop Scheduling Problems. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_29
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DOI: https://doi.org/10.1007/3-540-45561-2_29
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