Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing

arXiv:2402.08979v1 Announce Type: cross
Abstract: 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 …

abstract arxiv automated challenge constraints cs.ai cs.lg cs.sy eess.sy flexibility job manufacturing production productivity reinforcement reinforcement learning scalable scale scheduling smart solutions systems transportation type vehicles