Abstract
Equipment costs constitute approximately 3 quarters of overall manufacturing costs in semiconductor manufacturing. Preventive maintenance(PM) is an important cause pertaining to maintain high equipment availability. However PM trigger timing are not clear. Yet, timing is needed for planning of PM resources. Therefore, forecasting equipment preventive maintenance(PM) timing is very important in semiconductor fabrication plant. After having the timing of preventive maintenance, we not only can plan the manpower at the timing to optimize the utility of manpower but also minimize negative impacts on manufacturing efficiency. This study used genetic algorithm to locate optimal parameter, α. and used residual error to compensate original grey model. Tests on three kinds of semiconductor equipment showed significant improvement over the original grey forecast model.