Large-scale simulation-based optimization of semiconductor dispatching rules

Developing dispatching rules for complex production systems such as semiconductor manufacturing is an involved task usually performed manually. In a tedious trial-and-error process, a human expert attempts to improve existing rules, which are evaluated using discrete-event simulation. A significant improvement in this task can be achieved by coupling a discrete-event simulator with heuristic optimization algorithms. In this paper we show that this approach is feasible for large manufacturing scenarios as well, and it is also useful to quantify the value of information for the scheduling process. Using the objective of minimizing the mean cycle time of lots, we show that rules created automatically using Genetic Programming (GP) can clearly outperform standard rules. We compare their performance to manually developed rules from the literature.