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Multi-resource constrained scheduling considering process plan flexibility and lot streaming for the CNC machining industry

Author

Listed:
  • James C. Chen

    (National Tsing Hua University)

  • Tzu-Li Chen

    (National Taiwan University of Science and Technology)

  • Yin-Yann Chen

    (National Formosa University)

  • Min-Yu Chung

    (National Tsing Hua University)

Abstract

The CNC machining scheduling problem is a classic flexible job shop scheduling problem (FJSSP) that has been proven to be NP-hard. Although there has been extensive research on FJSSP, its applications in real-world CNC machining scheduling problems have received limited attention, with few related papers. In reality, several critical characteristics within the CNC machining industry can significantly impact the scheduling problem, such as process plan flexibility, multiple resource constraints, and lot streaming. This study aims to establish a scheduling system for the CNC machining industry that integrates order information, work in process information, and the aforementioned characteristics, using a hybrid multi-objective genetic algorithm (HMOGA) combined with local search. The optimal parameter combination for HMOGA is determined through a design of experiments, while the effectiveness of local search is evaluated using computational analysis. Finally, real data from a company is used to validate the proposed method. The results demonstrate that the scheduling system developed in this study effectively addresses real-world CNC machining scheduling problems.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:flsman:v:36:y:2024:i:3:d:10.1007_s10696-023-09514-w
    DOI: 10.1007/s10696-023-09514-w
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    References listed on IDEAS

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    1. Deyun Wang & Olivier Grunder & Abdellah EL Moudni, 2014. "Using genetic algorithm for lot sizing and scheduling problem with arbitrary job volumes and distinct job due date considerations," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(8), pages 1694-1707, August.
    2. Gökçe Candan & Harun Resit Yazgan, 2015. "Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 53(3), pages 897-915, February.
    3. Sarker, Ruhul & Liang, Ko-Hsin & Newton, Charles, 2002. "A new multiobjective evolutionary algorithm," European Journal of Operational Research, Elsevier, vol. 140(1), pages 12-23, July.
    4. Abedinnia, Hamid & Glock, C. H. & Schneider, Michael, 2017. "Machine scheduling in production: a content analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 87242, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Lei, Deming & Guo, Xiuping, 2015. "An effective neighborhood search for scheduling in dual-resource constrained interval job shop with environmental objective," International Journal of Production Economics, Elsevier, vol. 159(C), pages 296-303.
    6. Das, Sidhartha R. & Canel, Cem, 2005. "An algorithm for scheduling batches of parts in a multi-cell flexible manufacturing system," International Journal of Production Economics, Elsevier, vol. 97(3), pages 247-262, September.
    7. Čapek, R. & Šůcha, P. & Hanzálek, Z., 2012. "Production scheduling with alternative process plans," European Journal of Operational Research, Elsevier, vol. 217(2), pages 300-311.
    8. Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
    9. 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.
    10. Xiao-long Zheng & Ling Wang, 2016. "A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(18), pages 5554-5566, September.
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