Hybrid Simulation and Learning Framework for WIP Prediction in Semiconductor Fabs

This paper presents a hybrid framework that combines discrete-event simulation (DES) with neural networks to forecast Work-In-Progress (WIP) in semiconductor fabs. The model integrates three learned components: a dispatching model, an inter-start time predictor, and a processing time estimator. These models drive a lightweight simulation engine that accurately predicts WIP across various aggregation levels.