  Job shop scheduling problems represent a significant and complex facet of
combinatorial optimization problems, which have traditionally been addressed
through either exact or approximate solution methodologies. However, the
practical application of these solutions is often challenged due to the
complexity of real-world problems. Even when utilizing an approximate solution
approach, the time required to identify a near-optimal solution can be
prohibitively extensive, and the solutions derived are generally not applicable
to new problems. This study proposes an innovative attention-based
reinforcement learning method specifically designed for the category of job
shop scheduling problems. This method integrates a policy gradient
reinforcement learning approach with a modified transformer architecture. A key
finding of this research is the ability of our trained learners within the
proposed method to be repurposed for larger-scale problems that were not part
of the initial training set. Furthermore, empirical evidence demonstrates that
our approach surpasses the results of recent studies and outperforms commonly
implemented heuristic rules. This suggests that our method offers a promising
avenue for future research and practical application in the field of job shop
scheduling problems.
