reinforcement learning for scheduling

**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.