Reinforcement Learning (RL) for Scheduling is the application of RL agents to optimize wafer lot dispatching and tool scheduling in semiconductor fabs — learning scheduling policies that minimize cycle time, maximize throughput, or optimize other objectives through trial-and-error in simulated fab environments.

How RL Scheduling Works

• State: Current fab state (WIP levels, tool availability, lot priorities, queue lengths).
• Action: Dispatching decisions (which lot to process next on which tool).
• Reward: Negative cycle time, throughput, or weighted priority completion.
• Training: Train in a discrete-event simulation of the fab, then deploy the learned policy.

Why It Matters

• Dynamic: RL adapts to real-time fab conditions (tool downs, hot lots, priority changes) unlike static dispatching rules.
• Complexity: Modern fabs have 1000+ tools and 10,000+ lots — too complex for exact optimization.
• Performance: RL policies outperform traditional dispatching rules (FIFO, CR, EDD) by 5-15% on cycle time.

RL for Scheduling is the AI dispatcher — using reinforcement learning to make real-time lot dispatching decisions that outperform human-designed rules.