Simulation-based optimization for groups of cluster tools in semiconductor manufacturing using simulated annealing

Abstract

Simulation-based optimization is an established approach to handle complex scheduling problems. The problem examined in this study is scheduling jobs for groups of cluster tools in semiconductor manufacturing including a combination of sequencing, partitioning, and grouping of jobs with additional constraints. We use a specialized fast simulator to evaluate the generated schedules which allows us to run a large number of optimization iterations. For optimization we propose a simulated annealing algorithm to generate the schedules. It is implemented as a special instance of our adaptable evolutionary algorithm framework. As a consequence it is easy to adapt and extend the algorithm. For example, we can make use of various already existing problem representations that are geared to excel at certain aspects of our problem. Furthermore, we are able to parallelize the algorithm by using a population of optimization runs.