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

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.
This study highlights the potential of using MCTS to tackle large and complex job shop scheduling problems.