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Reinforcement learning applications to machine scheduling problems: a comprehensive literature review

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  • Behice Meltem Kayhan

    (Dokuz Eylul University)

  • Gokalp Yildiz

    (Dokuz Eylul University)

Abstract

Reinforcement learning (RL) is one of the most remarkable branches of machine learning and attracts the attention of researchers from numerous fields. Especially in recent years, the RL methods have been applied to machine scheduling problems and are among the top five most encouraging methods for scheduling literature. Therefore, in this study, a comprehensive literature review about RL methods applications to machine scheduling problems was conducted. In this regard, Scopus and Web of Science databases were searched very inclusively using the proper keywords. As a result of the comprehensive research, 80 papers were found, published between 1995 and 2020. These papers were analyzed considering different aspects of the problem such as applied algorithms, machine environments, job and machine characteristics, objectives, benchmark methods, and a detailed classification scheme was constructed. Job shop scheduling, unrelated parallel machine scheduling, and single machine scheduling problems were found as the most studied problem type. The main contributions of the study are to examine essential aspects of reinforcement learning in machine scheduling problems, identify the most frequently investigated problem types, objectives, and constraints, and reveal the deficiencies and promising areas in the related literature. This study can help researchers who wish to study in this field through the comprehensive analysis of the related literature.

Suggested Citation

  • Behice Meltem Kayhan & Gokalp Yildiz, 2023. "Reinforcement learning applications to machine scheduling problems: a comprehensive literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 905-929, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01847-3
    DOI: 10.1007/s10845-021-01847-3
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    References listed on IDEAS

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    1. Jack P. C. Kleijnen, 2015. "Simulation Optimization," International Series in Operations Research & Management Science, in: Design and Analysis of Simulation Experiments, edition 2, chapter 6, pages 241-300, Springer.
    2. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    3. Arezoo Atighehchian & Mohammad Mehdi Sepehri, 2013. "An environment-driven, function-based approach to dynamic single-machine scheduling," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 7(1), pages 100-118.
    4. Tapas K. Das & Abhijit Gosavi & Sridhar Mahadevan & Nicholas Marchalleck, 1999. "Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning," Management Science, INFORMS, vol. 45(4), pages 560-574, April.
    5. Seunghoon Lee & Yongju Cho & Young Hoon Lee, 2020. "Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    6. Zhang, Zhicong & Zheng, Li & Hou, Forest & Li, Na, 2011. "Semiconductor final test scheduling with Sarsa([lambda], k) algorithm," European Journal of Operational Research, Elsevier, vol. 215(2), pages 446-458, December.
    7. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    8. Kfir Arviv & Helman Stern & Yael Edan, 2016. "Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1196-1209, February.
    9. Manuel Parente & Gonçalo Figueira & Pedro Amorim & Alexandra Marques, 2020. "Production scheduling in the context of Industry 4.0: review and trends," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5401-5431, September.
    10. Hao-Xiang Wang & Hong-Sen Yan, 2016. "An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1085-1095, October.
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