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A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems

Author

Listed:
  • M. Saqlain

    (Chungbuk National University)

  • S. Ali

    (Chungbuk National University)

  • J. Y. Lee

    (Chungbuk National University)

Abstract

Flexible job-shop scheduling problem (FJSP) is an extension of the simple JSP with additional features of routing flexibility. It is an essential class of sequencing and planning problems that can apply in many real-life applications, especially in the field of manufacturing systems and production management. Finding a scheduling solution of sequential operations of various jobs by processing them on a defined number of machines and following various constraints with the goal to minimize the completion time of all operations, known as Makespan, is a big challenging issue. To address this issue, we proposed a Monte Carlo Tree Search-based flexible job-shop scheduling algorithm called MCTS-FJS for scheduling highly complex jobs in a real-time job-shop environment. An MCTS is a tree search technique aimed at making sequential decisions with uncertainty, calculate reward values from sub-tees, and regularly explore the most promising sub-tree. Experimental results showed that MCTS-scheduler outperformed various baseline scheduling algorithms and got the best evaluation performance for our sample dataset. More importantly, results showed that the performance of the proposed algorithm improved with increasing the number of jobs. Hence, this novel approach can be used to solve the complex FJSP in manufacturing systems.

Suggested Citation

  • M. Saqlain & S. Ali & J. Y. Lee, 2023. "A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 548-571, June.
    • Handle: RePEc:spr:flsman:v:35:y:2023:i:2:d:10.1007_s10696-021-09437-4
    DOI: 10.1007/s10696-021-09437-4
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    References listed on IDEAS

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    1. Abhijit Gosavi, 2009. "Reinforcement Learning: A Tutorial Survey and Recent Advances," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 178-192, May.
    2. Marwin H. S. Segler & Mike Preuss & Mark P. Waller, 2018. "Planning chemical syntheses with deep neural networks and symbolic AI," Nature, Nature, vol. 555(7698), pages 604-610, March.
    3. Christodoulos Floudas & Xiaoxia Lin, 2005. "Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications," Annals of Operations Research, Springer, vol. 139(1), pages 131-162, October.
    4. 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.
    5. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    6. Yuxia Cheng & Zhiwei Wu & Kui Liu & Qing Wu & Yu Wang, 2019. "Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method," Sustainability, MDPI, vol. 11(7), pages 1-16, March.
    7. Christophe Sauvey & Wajdi Trabelsi & Nathalie Sauer, 2020. "Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems," Mathematics, MDPI, vol. 8(1), pages 1-17, January.
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