Application of Stochastic Programming to Tool Portfolio Planning In Semiconductor Manufacturing

Tool portfolio planning is an important task in semiconductor manufacturing in which the types and quantities of processing tools are determined. The challenging facing tool portfolio planning is the uncertainty in product demand and process technology. The paper is made up of two parts. Stochastic programming is a method that is suitable for problems with uncertain factors. However, its utility will depend on the level of uncertainty. We evaluate the economic benefits of using stochastic programming in this thesis. We show that the benefits are very significant and recommend that the method of stochastic programming should be used in tool portfolio planning in the semiconductor manufacturing industry.
Although the stochastic programming method has more tolerant of demand forecast inaccuracy, it causes to increase solution space complexity relatively. The core issues of this study how to select representative scenario outcomes and planning horizons will be reduce solution space efficiently in order to replace the viewpoint of developing algorithm.