Semiconductor capacity planning: stochastic modelingand computational studies

This paper presents a multistage stochastic programming model for strategic capacity planning at a major US semiconductor manufacturer. Main sources o f uncertainty in this multi-year planning problem include demand of differe nt technologies and capacity estimations for each fabrication (fab) facilit y. We test the model using real-world scenarios requiring the determination of capacity planning for 29 technology categories among five fab facilitie s. The objective of the model is to minimize the gaps between product deman ds and the capacity allocated to the technology specified by each product. We consider two different scenario-analysis constructs: first, an independe nt scenario structure where we assume no prior information and the model sy stematically enumerates possible states in each period. The states from one period to another are independent from each other. Second, we consider an arbitrary scenario construct, which allows the planner to sample/evaluate a rbitrary multi-period scenarios that captures the dependency between period s. In both cases, a scenario is defined as a multi-period path from the roo t to a leaf in the scenario tree. We conduct intensive computational experi ments on these models using real data supplied by the semiconductor manufac turer. The purpose of our experiments is two-fold: first to examine differe nt degree of scenario aggregation and its effects on the independent model to achieve high-quality solution. Using this as a benchmark, we then compar e the results from the arbitrary model and illustrate the different uses of the two scenario constructs. We show that the independent model allows a v arying degree of scenario aggregation without significant prior information , while the arbitrary model allows planners to play out specific scenarios given prior information.