Job shop scheduling problems are one of the most important and challenging
combinatorial optimization problems that have been tackled mainly by exact or
approximate solution approaches. However, finding an exact solution can be
infeasible for real-world problems, and even with an approximate solution
approach, it can require a prohibitive amount of time to find a near-optimal
solution, and the found solutions are not applicable to new problems in
general. To address these challenges, we propose an attention-based
reinforcement learning method for the class of job shop scheduling problems by
integrating policy gradient reinforcement learning with a modified transformer
architecture. An important result is that our trained learners in the proposed
method can be reused to solve large-scale problems not used in training and
demonstrate that our approach outperforms the results of recent studies and
widely adopted heuristic rules.

我们提出了一种基于注意力机制的强化学习方法来解决作业车间调度问题，通过将策略梯度强化学习与改进的 Transformer 架构相结合，我们的方法在解决大规模问题上表现优于最近的研究和广泛采用的启发式规则。