  The following interdisciplinary article presents a memetic algorithm with
applying deep reinforcement learning (DRL) for solving practically oriented
dual resource constrained flexible job shop scheduling problems (DRC-FJSSP).
From research projects in industry, we recognize the need to consider flexible
machines, flexible human workers, worker capabilities, setup and processing
operations, material arrival times, complex job paths with parallel tasks for
bill of material (BOM) manufacturing, sequence-dependent setup times and
(partially) automated tasks in human-machine-collaboration. In recent years,
there has been extensive research on metaheuristics and DRL techniques but
focused on simple scheduling environments. However, there are few approaches
combining metaheuristics and DRL to generate schedules more reliably and
efficiently. In this paper, we first formulate a DRC-FJSSP to map complex
industry requirements beyond traditional job shop models. Then we propose a
scheduling framework integrating a discrete event simulation (DES) for schedule
evaluation, considering parallel computing and multicriteria optimization.
Here, a memetic algorithm is enriched with DRL to improve sequencing and
assignment decisions. Through numerical experiments with real-world production
data, we confirm that the framework generates feasible schedules efficiently
and reliably for a balanced optimization of makespan (MS) and total tardiness
(TT). Utilizing DRL instead of random metaheuristic operations leads to better
results in fewer algorithm iterations and outperforms traditional approaches in
such complex environments.
