Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing
This addresses a scalability problem for smart manufacturing systems, offering an incremental improvement over prior DRL methods.
The paper tackles the scale generalization challenge in deep reinforcement learning for flexible job-shop scheduling with transportation constraints in smart manufacturing, introducing a graph-based method that outperforms existing approaches on large-scale instances.
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.