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Adaptive job shop scheduling strategy based on weighted Q-learning algorithm

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  • Yu-Fang Wang

    (Nanjing University of Information Science & Technology)

Abstract

Given the dynamic and uncertain production environment of job shops, a scheduling strategy with adaptive features must be developed to fit variational production factors. Therefore, a dynamic scheduling system model based on multi-agent technology, including machine, buffer, state, and job agents, was built. A weighted Q-learning algorithm based on clustering and dynamic search was used to determine the most suitable operation and to optimize production. To address the large state space problem caused by changes in the system state, four state features were extracted. The dimension of the system state was decreased through the clustering method. To reduce the error between the actual system states and clustering ones, the state difference degree was defined and integrated with the iteration formula of the Q function. To select the optimal state-action pair, improved search and iteration update strategies were proposed. Convergence analysis of the proposed algorithm and simulation experiments indicated that the proposed adaptive strategy is well adaptable and effective in different scheduling environments, and shows better performance in complex environments. The two contributions of this research are as follows: (1) a dynamic greedy search strategy was developed to avoid blind searching in traditional strategy. (2) Weighted iteration update of the Q function, including the weighted mean of the maximum fuzzy earning, was designed to improve the speed and accuracy of the improved learning algorithm.

Suggested Citation

  • Yu-Fang Wang, 2020. "Adaptive job shop scheduling strategy based on weighted Q-learning algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 417-432, February.
  • Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1454-3
    DOI: 10.1007/s10845-018-1454-3
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    References listed on IDEAS

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    1. Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
    2. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    3. Liu, Zhixin & Lu, Liang & Qi, Xiangtong, 2018. "Cost allocation in rescheduling with machine unavailable period," European Journal of Operational Research, Elsevier, vol. 266(1), pages 16-28.
    4. Yao, Shiqing & Jiang, Zhibin & Li, Na & Zhang, Huai & Geng, Na, 2011. "A multi-objective dynamic scheduling approach using multiple attribute decision making in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 130(1), pages 125-133, March.
    5. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    6. Kusiak, Andrew & Li, Wenyan, 2010. "Short-term prediction of wind power with a clustering approach," Renewable Energy, Elsevier, vol. 35(10), pages 2362-2369.
    7. Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
    8. 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.
    9. Kenneth R. Baker, 1984. "Sequencing Rules and Due-Date Assignments in a Job Shop," Management Science, INFORMS, vol. 30(9), pages 1093-1104, September.
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    Cited by:

    1. Sebastian Mayer & Tobias Classen & Christian Endisch, 2021. "Modular production control using deep reinforcement learning: proximal policy optimization," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2335-2351, December.
    2. Rami Naimi & Maroua Nouiri & Olivier Cardin, 2021. "A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives," Sustainability, MDPI, vol. 13(23), pages 1-36, November.
    3. Kapil Deshpande & Philipp Möhl & Alexander Hämmerle & Georg Weichhart & Helmut Zörrer & Andreas Pichler, 2022. "Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience," Energies, MDPI, vol. 15(19), pages 1-35, October.

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