Summary: Such LP models are good for handling manufacturing facilities with constant throughput time (TPT). However, in a semiconductor supply network, the TPT of a lot starting on a given day depends on the total amount of that day's factory starts. A typical approximation of the nonlinear relationship between TPT and starts is given by a step function. The resulting mixed-integer programming problem becomes far too big to be solved by standard methods. This dissertation develops a hybrid method, combining the heuristics of a genetic algorithm (GA) with a linear programming approach. The GA determines a set of bounds for the allowable starts over the its time horizon. In doing so, the LP acts as a measure of fitness for the GA. This hybrid GA-LP algorithm was tested on several sample problems and its performance was compared with a best-fit LP algorithm. The hybrid algorithm captured the nonlinearity of the TPT much better than the LP algorithm and generated quantitatively correct schedule.