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Robust capacity planning in semiconductor manufacturing. (English) Zbl 1072.90517

Summary: 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 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 cannot 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.

MSC:

90B30 Production models
90B90 Case-oriented studies in operations research
90C15 Stochastic programming

Software:

OSL
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References:

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