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Flexible job shop scheduling with due window—a two-pheromone ant colony approach

Author

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  • Huang, Rong-Hwa
  • Yang, Chang-Lin
  • Cheng, Wei-Che

Abstract

Recently, the companies reduce the manufacturing costs and increase capacity efficiency in the competitive environment. Therefore, to balance workstation loading, the hybrid production system is necessary, so that, the flexible job shop system is the most common production system, and there are parallel machines in each workstation. In this study, the due window and the sequential dependent setup time of jobs are considered. To satisfy the customers’ requirement, and reduce the cost of the storage costs at the same time, the sum of the earliness and tardiness costs is the objective. In this study, to improve the traditional ant colony system, we developed the two pheromone ant colony optimization (2PH-ACO) to approach the flexible job shop scheduling problem. Computational results indicate that 2PH-ACO performs better than ACO in terms of sum of earliness and tardiness time.

Suggested Citation

  • Huang, Rong-Hwa & Yang, Chang-Lin & Cheng, Wei-Che, 2013. "Flexible job shop scheduling with due window—a two-pheromone ant colony approach," International Journal of Production Economics, Elsevier, vol. 141(2), pages 685-697.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:2:p:685-697
    DOI: 10.1016/j.ijpe.2012.10.011
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    References listed on IDEAS

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    1. Hani, Y. & Amodeo, L. & Yalaoui, F. & Chen, H., 2007. "Ant colony optimization for solving an industrial layout problem," European Journal of Operational Research, Elsevier, vol. 183(2), pages 633-642, December.
    2. R-H Huang & C-L Yang & H-T Huang, 2010. "Parallel machine scheduling with common due windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 640-646, April.
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    Citations

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    Cited by:

    1. Liang Tang & Zhihong Jin & Xuwei Qin & Ke Jing, 2019. "Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design," Annals of Operations Research, Springer, vol. 275(2), pages 685-714, April.
    2. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    3. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    4. Cheng, Shuenn-Ren, 2014. "Some new problems on two-agent scheduling to minimize the earliness costs," International Journal of Production Economics, Elsevier, vol. 156(C), pages 24-30.
    5. Jiae Zhang & Jianjun Yang, 2016. "Flexible job-shop scheduling with flexible workdays, preemption, overlapping in operations and satisfaction criteria: an industrial application," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4894-4918, August.
    6. Yang, Dar-Li & Lai, Chien-Jung & Yang, Suh-Jenq, 2014. "Scheduling problems with multiple due windows assignment and controllable processing times on a single machine," International Journal of Production Economics, Elsevier, vol. 150(C), pages 96-103.
    7. Julien Autuori & Faicel Hnaien & Farouk Yalaoui, 2016. "A mapping technique for better solution exploration: NSGA-II adaptation," Journal of Heuristics, Springer, vol. 22(1), pages 89-123, February.
    8. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.

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