Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Scheduling of semiconductor wafer testing processes can be seen as a resource constraint project scheduling problem (RCPSP). However, it includes uncertainties caused by human factors, wafer errors and so on. Because some uncertainties are not simply quantitative, the range estimation of the parameters would not be very useful. Considering such uncertainties, finding the meta rule to select a dispatching rule depending on the situation would be more suitable than solving the RCPSP under uncertainties. Moreover, this meta rule would be advantageous in the adaptation to unexpected changes. In this paper we apply some machine learning approaches to acquire the meta rule selecting a dispatching rule depending on the situation. We compare the obtained rules with the simple dispatching rules and examine the effectiveness and usefulness of the obtained rules in the problems with unpredictable wafer errors.
