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A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

Author

Listed:
  • Zigao Wu

    (Department of Industrial Engineering, Northwestern Polytechnical University, Xi’an 710072, China)

  • Shaohua Yu

    (Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, 91190 Saint-Aubin, France)

  • Tiancheng Li

    (Key Laboratory of Information Fusion Technology (Ministry of Education), School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract

In the real-world manufacturing system, various uncertain events can occur and disrupt the normal production activities. This paper addresses the multi-objective job shop scheduling problem with random machine breakdowns. As the key of our approach, the robustness of a schedule is considered jointly with the makespan and is defined as expected makespan delay, for which a meta-model is designed by using a data-driven response surface method. Correspondingly, a multi-objective evolutionary algorithm (MOEA) is proposed based on the meta-model to solve the multi-objective optimization problem. Extensive experiments based on the job shop benchmark problems are conducted. The results demonstrate that the Pareto solution sets of the MOEA are much better in both convergence and diversity than those of the algorithms based on the existing slack-based surrogate measures. The MOEA is also compared with the algorithm based on Monte Carlo approximation, showing that their Pareto solution sets are close to each other while the MOEA is much more computationally efficient.

Suggested Citation

  • Zigao Wu & Shaohua Yu & Tiancheng Li, 2019. "A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling," Mathematics, MDPI, vol. 7(6), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:6:p:529-:d:238656
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    References listed on IDEAS

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    1. von Hoyningen-Huene, W. & Kiesmüller, G.P., 2015. "Evaluation of the expected makespan of a set of non-resumable jobs on parallel machines with stochastic failures," European Journal of Operational Research, Elsevier, vol. 240(2), pages 439-446.
    2. Al-Fawzan, M. A. & Haouari, Mohamed, 2005. "A bi-objective model for robust resource-constrained project scheduling," International Journal of Production Economics, Elsevier, vol. 96(2), pages 175-187, May.
    3. Selcuk Goren & Ihsan Sabuncuoglu, 2010. "Optimization of schedule robustness and stability under random machine breakdowns and processing time variability," IISE Transactions, Taylor & Francis Journals, vol. 42(3), pages 203-220.
    4. 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.
    5. Shichang Xiao & Shudong Sun & Jionghua (Judy) Jin, 2017. "Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems," Energies, MDPI, vol. 10(4), pages 1-26, April.
    6. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
    7. Evgeny Gafarov & Frank Werner, 2019. "Two-Machine Job-Shop Scheduling with Equal Processing Times on Each Machine," Mathematics, MDPI, vol. 7(3), pages 1-11, March.
    8. Ali Hosseinabadi & Hajar Siar & Shahaboddin Shamshirband & Mohammad Shojafar & Mohd Nasir, 2015. "Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises," Annals of Operations Research, Springer, vol. 229(1), pages 451-474, June.
    9. Fei Luan & Zongyan Cai & Shuqiang Wu & Tianhua Jiang & Fukang Li & Jia Yang, 2019. "Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem," Mathematics, MDPI, vol. 7(5), pages 1-14, April.
    10. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    11. Jian Xiong & Xu Tan & Ke-wei Yang & Li-ning Xing & Ying-wu Chen, 2012. "A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-27, August.
    12. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
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    Cited by:

    1. Yuri N. Sotskov & Natalja M. Matsveichuk & Vadzim D. Hatsura, 2020. "Schedule Execution for Two-Machine Job-Shop to Minimize Makespan with Uncertain Processing Times," Mathematics, MDPI, vol. 8(8), pages 1-51, August.

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