A continuous-time strategic capacity planning model

Capacity planning decisions affect a significant portion of future revenue. In the semiconductor industry, they need to be made in the presence of both highly volatile demand and long capacity installation lead-times. In contrast to traditional discrete-time models, we present a continuous-time stochastic programming model for multiple resource types and product families. We show how this approach can solve capacity planning problems of reasonable size and complexity with provable efficiency. This is achieved by an application of the divide-and-conquer algorithm, convexity, submodularity, and the open-pit mining problem.