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An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time

Author

Listed:
  • W. Qin

    (Shanghai Jiao Tong University)

  • J. Zhang

    (Shanghai Jiao Tong University)

  • D. Song

    (Shanghai Jiao Tong University)

Abstract

In this article the scheduling problem of dynamic hybrid flow shop with uncertain processing time is investigated and an ant colony algorithm based rescheduling approach is proposed. In order to reduce the rescheduling frequency the concept of due date deviation is introduced, according to which a rolling horizon driven strategy is specially designed. Considering the importance of computational efficiency in the dynamic environment, the traditional ant colony optimization is improved. On the one hand, a strategy of available routes compression to restrict ants’ movement is proposed so that the ants’ searching cycle for new solutions could be shorten. On the other hand, illuminating function in state transfer possibility is improved to facilitate the exploration of low pheromone trail. Performance of rolling horizon procedure and rescheduling algorithm are evaluated respectively through simulations, the results show the best parameters of rolling horizon procedure and demonstrate the feasibility and efficiency of rescheduling algorithm. An example from the practical production is addressed to verify the effectiveness of the proposed approach.

Suggested Citation

  • W. Qin & J. Zhang & D. Song, 2018. "An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 891-904, April.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:4:d:10.1007_s10845-015-1144-3
    DOI: 10.1007/s10845-015-1144-3
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    References listed on IDEAS

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    1. Kis, Tamas & Pesch, Erwin, 2005. "A review of exact solution methods for the non-preemptive multiprocessor flowshop problem," European Journal of Operational Research, Elsevier, vol. 164(3), pages 592-608, August.
    2. Huang, Rong-Hwa, 2010. "Multi-objective job-shop scheduling with lot-splitting production," International Journal of Production Economics, Elsevier, vol. 124(1), pages 206-213, March.
    3. Drótos, Márton & Erdos, Gábor & Kis, Tamás, 2009. "Computing lower and upper bounds for a large-scale industrial job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 197(1), pages 296-306, August.
    4. Ji, P. & Sze, M. T. & Lee, W. B., 2001. "A genetic algorithm of determining cycle time for printed circuit board assembly lines," European Journal of Operational Research, Elsevier, vol. 128(1), pages 175-184, January.
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    Cited by:

    1. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    2. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    3. Muren, & Wu, Jianjun & Zhou, Li & Du, Zhiping & Lv, Ying, 2019. "Mixed steepest descent algorithm for the traveling salesman problem and application in air logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 87-102.
    4. Hongtao Tang & Jiahao Zhou & Yiping Shao & Zhixiong Yang, 2023. "Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    5. Zheng, Meimei & Ye, Hongqing & Wang, Dong & Pan, Ershun, 2021. "Joint Optimization of Condition-Based Maintenance and Spare Parts Orders for Multi-Unit Systems with Dual Sourcing," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    6. Hongliang Zhang & Yujuan Wu & Ruilin Pan & Gongjie Xu, 2021. "Two-stage parallel speed-scaling machine scheduling under time-of-use tariffs," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 91-112, January.
    7. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    8. Máté Hegyháti & Krisztián Attila Bakon & Tibor Holczinger, 2023. "Optimization with uncertainties: a scheduling example," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1239-1263, December.
    9. Tomoko Sakiyama & Ikuo Arizono, 2018. "Coordination of Pheromone Deposition Might Solve Time-Constrained Travelling Salesman Problem," Complexity, Hindawi, vol. 2018, pages 1-5, December.

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