A simulation-based Approximate Dynamic Programming approach for the control of the Intel Mini-Fab benchmark model

This paper presents initial results on the application of a simulation-based Approximate Dynamic Programming (ADP) for the control of the benchmark model of a semiconductor fab denominated the Intel Mini-Fab. The ADP approach utilized is based on an Average Cost Temporal-Difference TD(¿) learning algorithm and under an Actor-Critic architecture. Results from simulation experiments, on which both policies generated via ADP and commonly utilized dispatching rules were utilized in the Mini-Fab, demonstrated that ADP yielded policies that provided a good performance in average Work-In-Process and average Cycle Time with respect to the dispatching rules considered.