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Investigating the Monte-Carlo Tree Search approach for the job shop scheduling problem

Laurie Boveroux et al.

EURO Journal on Computational Optimization2025https://doi.org/10.1016/j.ejco.2025.100118article
AJG 2
Weight
0.50

What the paper says

The Job Shop Scheduling Problem (JSSP) is a well-known optimization problem in manufacturing, where the goal is to determine the optimal sequence of jobs across different machines to minimize a given objective. In this work, we focus on minimizing the weighted sum of job completion times. We explore the potential of Monte Carlo Tree Search (MCTS), a heuristic-based reinforcement learning technique, to solve large-scale JSSPs, especially those with recirculation. We propose several Markov Decision Process (MDP) formulations to model the JSSP for the MCTS algorithm. In addition, we introduce a new synthetic benchmark derived from real manufacturing data, which captures the computational burden of large, non-rectangular instances often encountered in practice. Our experimental results show that MCTS effectively produces good-quality solutions for large-scale JSSP instances, outperforming our constraint programming approach. • New benchmark of large-scale complex industrial JSSP instances with uneven workloads. • Different MDP formulations to represent the JSSP. • Monte-Carlo Tree Search demonstrates its effectiveness in solving largescale JSSP.

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https://doi.org/https://doi.org/10.1016/j.ejco.2025.100118

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@article{laurie2025,
  title        = {{Investigating the Monte-Carlo Tree Search approach for the job shop scheduling problem}},
  author       = {Laurie Boveroux et al.},
  journal      = {EURO Journal on Computational Optimization},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ejco.2025.100118},
}

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Investigating the Monte-Carlo Tree Search approach for the job shop scheduling problem

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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