Stuart Bermon∗

We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing. Given multiple demand scenarios together with associated prob-abilities, our aim is to identify a set of tools that is a good compromise for all these scenarios. More precisely, we formulate a mixed-integer program in which expected value of the unmet demand is minimized subject to capacity and budget constraints. This is a difficult two-stage stochastic mixed-integer program which can not be solved to optimality in a reasonable amount of time. We instead propose a heuristic that can produce near-optimal solutions. Our heuristic strengthens the linear programming relaxation of the formulation with cutting planes and performs limited enumeration. Analyses of the results in some real-life situations are also presented