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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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    1. Prot, D. & Bellenguez-Morineau, O. & Lahlou, C., 2013. "New complexity results for parallel identical machine scheduling problems with preemption, release dates and regular criteria," European Journal of Operational Research, Elsevier, vol. 231(2), pages 282-287.
    2. Homa Amirian & Rashed Sahraeian, 2015. "Augmented ε-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect," International Journal of Production Research, Taylor & Francis Journals, vol. 53(19), pages 5962-5976, October.
    3. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    4. Tapan Sen & Farhad M. E. Raiszadeh & Parthasarati Dileepan, 1988. "Note---A Branch-and-Bound Approach to the Bicriterion Scheduling Problem Involving Total Flowtime and Range of Lateness," Management Science, INFORMS, vol. 34(2), pages 254-260, February.
    5. Chiang, Tsung-Che & Lin, Hsiao-Jou, 2013. "A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 87-98.
    6. Anurag Tiwari & Pei-Chann Chang & M.K. Tiwari & Nevin John Kollanoor, 2015. "A Pareto block-based estimation and distribution algorithm for multi-objective permutation flow shop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 53(3), pages 793-834, February.
    7. Eugen Lounkine & Michael J. Keiser & Steven Whitebread & Dmitri Mikhailov & Jacques Hamon & Jeremy L. Jenkins & Paul Lavan & Eckhard Weber & Allison K. Doak & Serge Côté & Brian K. Shoichet & Laszlo U, 2012. "Large-scale prediction and testing of drug activity on side-effect targets," Nature, Nature, vol. 486(7403), pages 361-367, June.
    8. Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    9. Van Wassenhove, L. & Gelders, L., 1978. "Four solution techniques for a general one machine scheduling problem : A comparative study," European Journal of Operational Research, Elsevier, vol. 2(4), pages 281-290, July.
    10. Nagar, Amit & Haddock, Jorge & Heragu, Sunderesh, 1995. "Multiple and bicriteria scheduling: A literature survey," European Journal of Operational Research, Elsevier, vol. 81(1), pages 88-104, February.
    11. Chen, Shih-Hsin & Chen, Min-Chih, 2013. "Addressing the advantages of using ensemble probabilistic models in Estimation of Distribution Algorithms for scheduling problems," International Journal of Production Economics, Elsevier, vol. 141(1), pages 24-33.
    12. Jérémy Besnard & Gian Filippo Ruda & Vincent Setola & Keren Abecassis & Ramona M. Rodriguiz & Xi-Ping Huang & Suzanne Norval & Maria F. Sassano & Antony I. Shin & Lauren A. Webster & Frederick R. C. S, 2012. "Automated design of ligands to polypharmacological profiles," Nature, Nature, vol. 492(7428), pages 215-220, December.
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    Cited by:

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    4. 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.

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