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A Genetic Algorithm with a Quasi-local Search for the Job Shop Problem with Recirculation

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Applied Soft Computing Technologies: The Challenge of Complexity

Part of the book series: Advances in Soft Computing ((AINSC,volume 34))

Abstract

In this work we present a genetic algorithm for the job shop problem with recirculation. The genetic algorithm includes a local search procedure that is implemented as a genetic operator. This strategy differs from the memetic algorithm because it is not guaranteed that the local minimum is achieved in each iteration.

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References

  • Aiex, R.M., Binato, S., and Resende, M.G.C. (2003),“Parallel GRASP with path-relinking for job shop scheduling,” Parallel Computing, vol. 29, pp. 393–430.

    Article  MathSciNet  Google Scholar 

  • Aydin, E., and Fogarty, T.C. (2002),“Modular simulated annealing for job shop scheduling running on distributed resource machine (DRM),” Technical Report, South Bank University, London, (Downloadable from website http://www.dcs.napier.ac.uk/~benp/dream/dreampaperl3.pdf).

    Google Scholar 

  • Beasley, J.E. (1990),“OR-Library: distributing test problems by electronic mail,” Journal of the Operational Research Society, vol. 41, pp. 1069–1072. (website http://mscmga.ms.ic.ac.uk/info.html).

    Article  Google Scholar 

  • Binato, S., Hery, W.J., Loewenstern, D.M., and Resende, M.G.C. (2001),“A GRASP for job shop scheduling, in: C.C. Ribeiro, P. Hansen, (Eds.), Essays and Surveys in Metaheuristics, Kluwer Academic Publishers, Dordrecht, pp. 58–79.

    Google Scholar 

  • Brucker, P. and Knust, S. (2003), Complexity results for scheduling problems, Department of Mathematics/Computer Science, University of Osnabrück, (Downloadable from website http://www.mathematik.uniosnabrueck.de/research/OR/class/).

    Google Scholar 

  • French, S. (1982), Sequencing and Scheduling – An Introduction to the Mathematics of the Job-Shop, Ellis Horwood Limited, Chicester.

    MATH  Google Scholar 

  • Garey, M., Johnson, D.S., and Sethi, R. (1976),“The complexity of flowshop and jobshop scheduling,” Mathematics of Operation Research, vol. 1, pp. 117–129.

    MATH  MathSciNet  Google Scholar 

  • Giffler, B. and Thompson, G.L. (1960),“Algorithms for solving production scheduling problems,” Operations Research, vol. 8, pp. 487–503.

    MATH  MathSciNet  Google Scholar 

  • Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.

    MATH  Google Scholar 

  • Gonçalves, J.F. and Beirão, N.C. (1999),“Urn algoritmo genético baseado em chaves aleatórias para sequenciamento de operações,” Revista Associação Portuguesa de Desenvolvimento e Investigaçõo Operacional, vol. 19, pp. 123–137.

    Google Scholar 

  • Gonçalves, J.F., Mendes, J.J.M., and Resende, M.G.C. (2002),“A hybrid genetic algorithm for the job shop scheduling problem,” AT&T Labs Research Technical Report TD-5EAL6J, Florham Park, NJ, (Downloadable from website http://www.research.att.com/~mgcr/doc/hgajss.pdf).

    Google Scholar 

  • Jain, A.S. and Meeran, S. (1999),“A state-of-the-art review of job-shop scheduling techniques,” European Journal of Operations Research, vol. 113, pp. 390–434.

    Article  MATH  Google Scholar 

  • Kolonko, M. (1999),“Some new results on simulated annealing applied to the job shop scheduling problem,” European Journal of Operational Research, vol. 113, pp. 123–136.

    Article  MATH  Google Scholar 

  • Lenstra, J.K. and Rinnooy Kan, A.H.G. (1979),“Computational complexity of discrete optimization problems,” Annals of Discrete Mathematics, vol. 4, pp. 121–140.

    Article  MATH  MathSciNet  Google Scholar 

  • Nowicki, E. and Smutnicki, C. (1996),“A fast taboo search algorithm for the job-shop problem,” Management Science, vol. 42, pp. 797–813.

    Article  MATH  Google Scholar 

  • Oliveira, J.A. (2001), Aplicação de Modelos e Algoritmos de Investigação Operacional ao Planeamento de Operações em Armazéns, Ph.D. Thesis, Universidade do Minho, Braga.

    Google Scholar 

  • Ono, I., Yamamura, M., and Kobayashi, S. (1996),“A genetic algorithm for job-shop scheduling problems using job-based order crossover,” Proceedings of ICE′96, pp. 547–552.

    Google Scholar 

  • Roy, B. and Sussmann, B. (1964),“Les Problemes D’Ordonnancement Avec

    Google Scholar 

  • Vaessens, R.J.M., Aarts, E.H.L., and Lenstra, J.K. (1996),“Job Shop Scheduling by local search,” INFORMS Journal on Computing, vol. 8, pp. 302–317. (Downloadable from web site http://joc.pubs.informs.org/BackIssues/Vol008/ Vol008No03Paper09.pdf).

    MATH  Google Scholar 

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Oliveira, J.A. (2006). A Genetic Algorithm with a Quasi-local Search for the Job Shop Problem with Recirculation. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_18

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  • DOI: https://doi.org/10.1007/3-540-31662-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31649-7

  • Online ISBN: 978-3-540-31662-6

  • eBook Packages: EngineeringEngineering (R0)

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