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Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times

Author

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  • Waiman Cheung
  • Hong Zhou

Abstract

The importance of job shop scheduling as a practical problem has attracted the attention of many researchers. However, most research has focused on special cases such as single machine, parallel machine, and flowshop environments due to the “hardness” of general job shop problems. In this paper, a hybrid algorithm based on an integration of a genetic algorithm and heuristic rules is proposed for a general job shop scheduling problem with sequence-dependent setups (Jm|s jk |C max ). An embedded simulator is employed to implement the heuristic rules, which greatly enhances the flexibility of the algorithm. Knowledge relevant to the problem is inherent in the heuristic rules making the genetic algorithm more efficient, while the optimization procedure provided by the genetic algorithm makes the heuristic rules more effective. Extensive numerical experiments have been conducted and the results have shown that the hybrid approach is superior when compared to recently published existing methods for the same problem. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • Waiman Cheung & Hong Zhou, 2001. "Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times," Annals of Operations Research, Springer, vol. 107(1), pages 65-81, October.
    • Handle: RePEc:spr:annopr:v:107:y:2001:i:1:p:65-81:10.1023/a:1014990729837
    DOI: 10.1023/A:1014990729837
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    Citations

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    Cited by:

    1. Ansis Ozolins, 2020. "Bounded dynamic programming algorithm for the job shop problem with sequence dependent setup times," Operational Research, Springer, vol. 20(3), pages 1701-1728, September.
    2. Zhou, Hong & Cheung, Waiman & Leung, Lawrence C., 2009. "Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm," European Journal of Operational Research, Elsevier, vol. 194(3), pages 637-649, May.
    3. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    4. Hamed Piroozfard & Kuan Yew Wong & Adnan Hassan, 2016. "A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems," Journal of Optimization, Hindawi, vol. 2016, pages 1-13, April.
    5. Sels, Veronique & Craeymeersch, Kjeld & Vanhoucke, Mario, 2011. "A hybrid single and dual population search procedure for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 215(3), pages 512-523, December.
    6. Jacomine Grobler & Andries Engelbrecht & Schalk Kok & Sarma Yadavalli, 2010. "Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time," Annals of Operations Research, Springer, vol. 180(1), pages 165-196, November.
    7. Yashar Ahmadov & Petri Helo, 2018. "A cloud based job sequencing with sequence-dependent setup for sheet metal manufacturing," Annals of Operations Research, Springer, vol. 270(1), pages 5-24, November.

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