Robust Capacity Planning in Semiconductor Manufacturing

We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing. Given multiple demand scenarios together with associated probabilities, our aim is to arrive at a set of tools that does well across all of these scenarios. We formulate the problem as 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 be solved to near-optimality with the help of cutting planes and limited enumeration. Analysis of the results in some real-life situations are also presented.