Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning

Existing studies on the permutation flow shop problem (PFSP) commonly assume that jobs are produced on a single line. However, manufacturers may speed up their production by employing multiple lines, where each line produces sub-parts of the final product; which must be assembled by a synchronization machine. This study presents a novel reinforcement learning (RL) approach for the PFSP with multiple lines and demand plans. Our approach differs from existing RL-based scheduling methods as we train the policy to directly generate the sequence in an iterative way, where actions denote the job type to be sequenced next. During cutoff time, we follow a multistart approach that generates sequences with the trained policy, which are subsequently optimized by local search. Our numerical evaluation based on 1050 problem instances with up to three production lines shows that our approach outperforms existing methods on the multi-line problems for short cutoff times, while there is a tie with existing methods for medium and long cutoff times. A further analysis suggests that our approach can also be applied to problems with imbalanced demand plans.