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Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

Author

Listed:
  • Choo Jun Tan

    (Wawasan Open University)

  • Siew Chin Neoh

    (UCSI University)

  • Chee Peng Lim

    (Deakin University)

  • Samer Hanoun

    (Deakin University)

  • Wai Peng Wong

    (University Science of Malaysia)

  • Chu Kong Loo

    (University of Malaya)

  • Li Zhang

    (Northumbria University)

  • Saeid Nahavandi

    (Deakin University)

Abstract

In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.

Suggested Citation

  • Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1291-1
    DOI: 10.1007/s10845-016-1291-1
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    Cited by:

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    2. Ying Sun & Jeng-Shyang Pan & Pei Hu & Shu-Chuan Chu, 2023. "Enhanced Equilibrium Optimizer algorithm applied in job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1639-1665, April.
    3. 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.
    4. Yanwei Sang & Jianping Tan, 2022. "Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration," Energies, MDPI, vol. 15(5), pages 1-17, March.

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