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Using a family of critical ratio-based approaches to minimize the number of tardy jobs in the job shop with sequence dependent setup times

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  • Chiang, Tsung-Che
  • Fu, Li-Chen

Abstract

This paper addresses the job shop scheduling problem to minimize the number of tardy jobs, considering the sequence dependent setup time. This problem is taken as a sequencing problem, and a family of approaches with different levels of intricacy is proposed. The simplest form is a critical ratio-based dispatching rule, which leads to satisfactory solutions by taking into account the group information rather than only the individual information of jobs. Then, an enhanced approach consisting of an iterative schedule refining mechanism will be given. Its feature is to iteratively adjust the estimation of the remaining processing times of jobs in a dynamic and operation-specific manner. Finally, a genetic algorithm which takes the dispatching rule and the refining mechanism as the core is proposed. The performance of these approaches is carefully examined by a comprehensive experimental study.

Suggested Citation

  • Chiang, Tsung-Che & Fu, Li-Chen, 2009. "Using a family of critical ratio-based approaches to minimize the number of tardy jobs in the job shop with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 196(1), pages 78-92, July.
  • Handle: RePEc:eee:ejores:v:196:y:2009:i:1:p:78-92
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    References listed on IDEAS

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    1. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    2. Mattfeld, Dirk C. & Bierwirth, Christian, 2004. "An efficient genetic algorithm for job shop scheduling with tardiness objectives," European Journal of Operational Research, Elsevier, vol. 155(3), pages 616-630, June.
    3. Lodree, Emmett & Jang, Wooseung & Klein, Cerry M., 2004. "A new rule for minimizing the number of tardy jobs in dynamic flow shops," European Journal of Operational Research, Elsevier, vol. 159(1), pages 258-263, November.
    4. Yang, Taho & Kuo, Yiyo & Cho, Chiwoon, 2007. "A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1859-1873, February.
    5. Sevaux, Marc & Dauzere-Peres, Stephane, 2003. "Genetic algorithms to minimize the weighted number of late jobs on a single machine," European Journal of Operational Research, Elsevier, vol. 151(2), pages 296-306, December.
    6. Bertel, S. & Billaut, J. -C., 2004. "A genetic algorithm for an industrial multiprocessor flow shop scheduling problem with recirculation," European Journal of Operational Research, Elsevier, vol. 159(3), pages 651-662, December.
    7. Franca, Paulo M. & Mendes, Alexandre & Moscato, Pablo, 2001. "A memetic algorithm for the total tardiness single machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 132(1), pages 224-242, July.
    8. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
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    Cited by:

    1. Branke, Juergen & Pickardt, Christoph W., 2011. "Evolutionary search for difficult problem instances to support the design of job shop dispatching rules," European Journal of Operational Research, Elsevier, vol. 212(1), pages 22-32, July.
    2. Georgiadis, Patroklos & Michaloudis, Charalampos, 2012. "Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis," European Journal of Operational Research, Elsevier, vol. 216(1), pages 94-104.

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