This thesis investigates the use of both upstream and downstream information about the
current and expected states of a semiconductor manufacturing facility (fab) in making
batch size decisions. First, three control strategies for loading a batch machine, all of
which incorporate knowledge of future arrivals into the decision structure, are critically
evaluated. A new control strategy is then developed which addresses some limitations of
the previously described methods. The local performances of the different batch size
control strategies are then compared with regard to flowtimes through the batch step. The
results show that the new method outperforms, at a local level, any of the existing
methods. Next, the impact of the control strategies on the larger system of a batch
machine followed by a serial processor is examined. This is done by augmenting the new
control strategy to use both upstream and downstream information and making comparisons
based on normalized flowtimes through the two steps. The results indicate that some
improvement can be gained at low to moderate traffic intensities by looking downstream.
However, as traffic intensity increases, use of downstream information can actually lead
to longer delays. This indicates that the scheduling of the batch machine may be more
important than the scheduling of the serial machine in a congested system.
