Event-driven dynamic Job-shop scheduling method with strong process constraints

Authors

  • Chenlu Zhang
  • Ruijuan Zheng

DOI:

https://doi.org/10.54097/jceim.v10i3.8705

Keywords:

Flexible Job-shop, NSGA-II, Dynamic event-driven

Abstract

In the actual production environment, there are many uncertainties that need to be handled. Since the occurrence time and frequency of dynamic events cannot be predicted, frequent global scheduling calculation will cause waste of time and resources. Therefore, facing the challenge of job shop scheduling problem in dynamic scenarios, this paper studied the dynamic event-driven job shop scheduling problem with complex process constraints, and proposed an improved NSGAII algorithm. At the end of this paper, the designed dynamic scheduling method is applied to a variety of benchmarks through experiments to verify the effectiveness of the proposed method in solving the job shop scheduling problem in a dynamic environment.

References

Oluyisola O E, Bhalla S, Sgarbossa F, et al. Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study[J]. Journal of Intelligent Manufacturing, 2022, 33(1): 311-332.

Wang G G, Gao D, Pedrycz W. Solving multiobjective fuzzy job-shop scheduling problem by a hybrid adaptive differential evolution algorithm[J]. IEEE Transactions on Industrial Informatics, 2022, 18(12): 8519-8528.

Pan Y, Gao K, Li Z, et al. Solving biobjective distributed flow-shop scheduling problems with lot-streaming using an improved Jaya algorithm[J]. IEEE Transactions on Cybernetics, 2022.

Zhang F, Mei Y, Nguyen S, et al. Evolving scheduling heuristics via genetic programming with feature selection in dynamic flexible job-shop scheduling[J]. IEEE Transactions on Cybernetics, 2021, 51(4): 1797-1811.

An Y, Chen X, Gao K, et al. Multiobjective flexible job-shop rescheduling with new job insertion and machine preventive maintenance[J]. IEEE Transactions on Cybernetics, 2022.

Zhang Y, Zhu H, Tang D, et al. Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems[J]. Robotics and Computer-Integrated Manufacturing, 2022, 78: 102412.

Wen X, Lian X, Qian Y, et al. Dynamic scheduling method for integrated process planning and scheduling problem with machine fault[J]. Robotics and Computer-Integrated Manufacturing, 2022, 77: 102334.

Zhang F, Mei Y, Nguyen S, et al. Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling[J]. IEEE Transactions on Evolutionary Computation, 2021, 25(4): 651-665.

Wen X, Lian X, Qian Y, et al. Dynamic scheduling method for integrated process planning and scheduling problem with machine fault[J]. Robotics and Computer-Integrated Manufacturing, 2022, 77: 102334.

Li X, Gao L, Pan Q, et al. An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(10):1933-1945.

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Published

24-05-2023

Issue

Section

Articles

How to Cite

Zhang, C., & Zheng, R. (2023). Event-driven dynamic Job-shop scheduling method with strong process constraints. Journal of Computing and Electronic Information Management, 10(3), 72-79. https://doi.org/10.54097/jceim.v10i3.8705