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arXiv · 2501.17991

Investigating the Monte-Carlo Tree Search Approach for the Job Shop Scheduling Problem

Laurie Boveroux·Damien Ernst·Quentin Louveaux
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Abstract

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 minimising 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 complexity 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.

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Laurie Boveroux, Damien Ernst, Quentin Louveaux. 2025-01-29. Investigating the Monte-Carlo Tree Search Approach for the Job Shop Scheduling Problem. https://arxiv.org/abs/2501.17991

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