  Highly automated assembly lines enable significant productivity gains in the
manufacturing industry, particularly in mass production condition. Nonetheless,
challenges persist in job scheduling for make-to-job and mass customization,
necessitating further investigation to improve efficiency, reduce tardiness,
promote safety and reliability. In this contribution, an advantage actor-critic
based reinforcement learning method is proposed to address scheduling problems
of distributed flexible assembly lines in a real-time manner. To enhance the
performance, a more condensed environment representation approach is proposed,
which is designed to work with the masks made by priority dispatching rules to
generate fixed and advantageous action space. Moreover, a Monte-Carlo tree
search based soft shielding component is developed to help address
long-sequence dependent unsafe behaviors and monitor the risk of overdue
scheduling. Finally, the proposed algorithm and its soft shielding component
are validated in performance evaluation.
