Abstract
This paper presents a genetic algorithm specially designed for job shop problems. The algorithm has a simple coding scheme and new crossover and mutation operators. A simple local search scheme is incorporated in the algorithm leading to a combined genetic algorithm(CGA). It is evaluated in three famous Muth and Thompson problems (i.e. MT6×6, MT10×10, MT20×5). The simulation study shows that this algorithm possesses high efficiency and is able to find out the optimal solutions for the job shop problems.
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References
Coffman, E. G., et al., Computer and Job-Shop Scheduling Theory, U.S.A., John Wiley & Sons, 1976.
French, S., Sequencing and Scheduling: An Introduction to The Mathematics of The Job-Shop, England, Ellis Horwood Ltd., 1982.
Barker, J. R. and McMahon, G. B., ‘Scheduling the general job shop’, Management Science, 1985, 31(5), 594–598.
Carlier, J. and Pinson, E., ‘An algorithm for solving the job-shop problem’, Management Science, 1989, 35(2), 164–176.
Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, 1989.
Starkweather, T., et al., ‘A comparison of genetic sequencing operators’, Proceedings of The Fourth International Conference on Genetic Algorithms, SAN DIEGO, 1991, pp.69–76 41.
Bagchi, S., et al., ‘Exploring problem-specific recombination operators for job shop scheduling’, Proceedings of The Fourth International Conference on Genetic Algorithms, SAN DIEGO, 1991, pp.10–17.
Cao, Y.J., Wu, Q.H., ‘Mechanical design optimization by mixed-variable evolutionary programming’, Proc. IEEE International Conference on Evolutionary Computation, 1997, Indianapolis, USA, pp.443–446.
Wu, Q.H., Cao, Y.J., ‘Stochastic optimization of control parameters in genetic algorithms’, Proc. IEEE International Conference on Evolutionary Computation, 1997, Indianapolis, USA., pp77–80.
Nakano, R. and Yamada, T., ‘Conventional genetic algorithm for job shop problems’, Proceedings of The Fourth International Conference on Genetic Algorithms, SAN DIEGO, 1991, pp.474–479.
Federico Delia Croce, et al., ‘A genetic algorithm for the job shop problem’, Computers & Operations Research, 1995, 22(1), 15–24.
Shi, G., ‘A genetic algorithm applied to a classic job-shop scheduling problem’, International Journal of Systems Science, 1997, 28(1), 25–32.
Yamada, T. and Nakano, R., ‘A genetic algorithm with multi-step crossover for job shop scheduling problems’, First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications: GALESIA, 1st, Sheffield, 1995, pp.146–151.
Muth, J. F. and Thompson, G. L., Industrial scheduling, Prentice-Hall, Englewood Cliffs, New Jersey, 1963.
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© 2000 Springer-Verlag Berlin Heidelberg
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Cai, L.W., Wu, Q.H., Yong, Z.Z. (2000). A Genetic Algorithm with Local Search for Solving Job 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_11
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DOI: https://doi.org/10.1007/3-540-45561-2_11
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