Multi-Agent Scheduler for the Continuous Dynamic Flexible Job Shop Scheduling Problem

This work addresses the unique production challenges in dynamic smart manufacturing environments caused by the shift towards mass personalisation. Traditional scheduling methods do not perform reliably in a continuously operating environment with unpredictable job order arrivals, a variety of products and urgent orders, leading to increased tardiness and inefficiencies. To reflect the challenges dynamic manufacturing environments face, we introduce a new problem – the Continuous Dynamic Flexible Job Shop Scheduling Problem (C-DFJSP). In response, we propose MAC-Sched – a MultiAgent Continuous Scheduler. MAC-Sched is a Graph-Based Multi-Agent Reinforcement Learning model that takes a graph representation of the manufacturing environment as input, thus enabling the continuous and reliable production of personalised products. Demonstrating its reliability in multiple unseen continuous operating environments, MAC-Sched outperforms popular heuristic rules, effectively minimising the average tardiness over time amid evolving factory dynamics.