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A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems

Author

Listed:
  • Libin Han

    (Xi’an Jiaotong University)

  • Keyi Xing

    (Xi’an Jiaotong University)

  • Xiao Chen

    (Xi’an Jiaotong University)

  • Fuli Xiong

    (Xi’an Jiaotong University)

Abstract

This paper proposes an effective hybrid particle swarm optimization (HPSO) algorithm to solve the deadlock-free scheduling problem of flexible manufacturing systems (FMSs) that are characterized with lot sizes, resource capacities, and routing flexibility. Based on the timed Petri net model of FMS, a random-key based solution representation is designed to encode the routing and sequencing information of a schedule into one particle. For the existence of deadlocks, most of the particles cannot be directly decoded to a feasible schedule. Therefore, a deadlock controller is applied in the decoding scheme to amend deadlock-prone schedules into feasible ones. Moreover, two improvement strategies, the particle normalization and the simulated annealing based local search, are designed and incorporated into particle swarm optimization algorithm to enhance the searching ability. The proposed HPSO is tested on a set of FMS examples, showing its superiority over existing algorithms in terms of both solution quality and robustness.

Suggested Citation

  • Libin Han & Keyi Xing & Xiao Chen & Fuli Xiong, 2018. "A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1083-1096, June.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:5:d:10.1007_s10845-015-1161-2
    DOI: 10.1007/s10845-015-1161-2
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    References listed on IDEAS

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    1. Tasgetiren, M. Fatih & Liang, Yun-Chia & Sevkli, Mehmet & Gencyilmaz, Gunes, 2007. "A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1930-1947, March.
    2. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
    3. Moslehi, Ghasem & Mahnam, Mehdi, 2011. "A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search," International Journal of Production Economics, Elsevier, vol. 129(1), pages 14-22, January.
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    Cited by:

    1. Yiying Zhang & Aining Chi, 2023. "Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1547-1571, April.
    2. Cosmena Mahapatra & Ashish Payal & Meenu Chopra, 2020. "Swarm intelligence based centralized clustering: a novel solution," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1877-1888, December.
    3. Jiaxing Wang & Sibin Gao & Zhejun Tang & Dapeng Tan & Bin Cao & Jing Fan, 2023. "A context-aware recommendation system for improving manufacturing process modeling," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1347-1368, March.
    4. Xinnian Wang & Keyi Xing & Chao-Bo Yan & Mengchu Zhou, 2019. "A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems," Complexity, Hindawi, vol. 2019, pages 1-14, June.
    5. G. Cherif & E. Leclercq & D. Lefebvre, 2023. "Scheduling of a class of partial routing FMS in uncertain environments with beam search," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 493-514, February.
    6. Wattana Viriyasitavat & Li Xu & Zhuming Bi & Assadaporn Sapsomboon, 2020. "Blockchain-based business process management (BPM) framework for service composition in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1737-1748, October.

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