Learning-enabled Flexible Job-shop Scheduling for Scalable Smart
  Manufacturing
Sihoon Moon Sanghoon Lee Kyung-Joon Park
In smart manufacturing systems (SMSs), flexible job-shop scheduling with
transportation constraints (FJSPT) is essential to optimize solutions for
maximizing productivity, considering production flexibility based on automated
guided vehicles (AGVs). Recent developments in deep reinforcement learning
(DRL)-based methods for FJSPT have encountered a scale generalization
challenge. These methods underperform when applied to environment at scales
different from their training set, resulting in low-quality solutions. To
address this, we introduce a novel graph-based DRL method, named the
Heterogeneous Graph Scheduler (HGS). Our method leverages locally extracted
relational knowledge among operations, machines, and vehicle nodes for
scheduling, with a graph-structured decision-making framework that reduces
encoding complexity and enhances scale generalization. Our performance
evaluation, conducted with benchmark datasets, reveals that the proposed method
outperforms traditional dispatching rules, meta-heuristics, and existing
DRL-based approaches in terms of makespan performance, even on large-scale
instances that have not been experienced during training.