Deep RL Tackles Dynamic Job Shop Scheduling with Random Arrivals

arXiv·low signal

Applies deep reinforcement learning to the flexible job shop scheduling problem with stochastic job arrivals — a more realistic formulation than the static FJSP typically studied. Addresses both optimal machine allocation and dynamic demand handling. Practical for manufacturing and logistics optimization where schedules must adapt in real time.