A simulation optimization-based framework for capacity planning under uncertainty

Capacity planning in semiconductor manufacturing industry is a challenging task due to its high capital investments, volatile demands, and the long lead time. Current approaches to handle this problem are to model it as an optimization problem where the uncertain demand is either based on a single forecast, or is decomposed via a finite-scenario structure with an assigned probability for each scenario to reflect the likelihood of occurrence. However, when the uncertainty cannot be decomposed into finite scenarios or when the number of possible scenarios is extremely large, traditional approaches such as the mathematical programming either cannot deal with the problem or may require unreasonable computing time. In this paper, we consider a multiple-period capacity planning problem where the uncertain demand is modeled as a continuous stochastic process over the planning horizon. Moreover, the long lead time and capacity migrations between different products are taken into account to accurately determine the optimal capacity plan. A new framework based on sample path method in simulation optimization is proposed to solve the problem. Comparing to traditional methods, the new framework is much more efficient in terms of the required computing time, as is demonstrated in the computational study.