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An effective detailed operation scheduling in MES based on hybrid genetic algorithm

Author

Listed:
  • Li Zhou

    (Huazhong University of Science and Technology)

  • Zhuoning Chen

    (Huazhong University of Science and Technology)

  • Shaoping Chen

    (Huazhong University of Science and Technology)

Abstract

A detailed operation scheduling solution based on hybrid genetic algorithm is proposed and integrated with the manufacturing execution system (MES) for multi-objective scheduling. The constraints and influences from real-time production information collected by MES will all be considered in scheduling procedures. Each order can be scheduled forward or backward and the various constraints such as the ones from working calendar, processing capacity of manufacturing resources and the connection type between the operation and the previous operation will be obeyed in scheduling. A genetic algorithm is designed according to the features of the scheduling problem. Two methods of operation sequence (OS) initialization (named as OSIOP and ROSI) and three methods of manufacturing resource selection (named as RSAPT, RSWTB and RRS) are designed for population initialization. A variable neighborhood search is designed and implanted in the process of GA to improve the scheduling results. The experiments are made and the results have proved the feasibility of the hybrid GA. This scheduling solution is programmed in $$\hbox {C}^{\#}$$ C # and applied to a commercial MES software successfully.

Suggested Citation

  • Li Zhou & Zhuoning Chen & Shaoping Chen, 2018. "An effective detailed operation scheduling in MES based on hybrid genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 135-153, January.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1097-6
    DOI: 10.1007/s10845-015-1097-6
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    References listed on IDEAS

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    1. Ho, Nhu Binh & Tay, Joc Cing & Lai, Edmund M.-K., 2007. "An effective architecture for learning and evolving flexible job-shop schedules," European Journal of Operational Research, Elsevier, vol. 179(2), pages 316-333, June.
    2. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
    3. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    4. Zhang, Rui & Chang, Pei-Chann & Wu, Cheng, 2013. "A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production," International Journal of Production Economics, Elsevier, vol. 145(1), pages 38-52.
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    Cited by:

    1. James C. Chen & Tzu-Li Chen & Yin-Yann Chen & Min-Yu Chung, 2024. "Multi-resource constrained scheduling considering process plan flexibility and lot streaming for the CNC machining industry," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 946-993, September.

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