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Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

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  • Hao-Chin Chang

    (National Kaohsiung First University of Science and Technology)

  • Tung-Kuan Liu

    (National Kaohsiung First University of Science and Technology)

Abstract

In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.

Suggested Citation

  • Hao-Chin Chang & Tung-Kuan Liu, 2017. "Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1973-1986, December.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:8:d:10.1007_s10845-015-1084-y
    DOI: 10.1007/s10845-015-1084-y
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    References listed on IDEAS

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    1. Ho, Nhu Binh & Tay, Joc Cing & Lai, Edmund M.-K., 2007. "An effective architecture for learning and evolving flexible job-shop schedules," European Journal of Operational Research, Elsevier, vol. 179(2), pages 316-333, June.
    2. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
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    Cited by:

    1. Lilan Liu & Kai Guo & Zenggui Gao & Jiaying Li & Jiachen Sun, 2022. "Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    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. Du, Yu & Li, Jun-qing, 2024. "A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling," International Journal of Production Economics, Elsevier, vol. 268(C).
    4. Wenxiang Xu & Shunsheng Guo, 2019. "A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    5. Guiliang Gong & Raymond Chiong & Qianwang Deng & Qiang Luo, 2020. "A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1443-1466, August.

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