An Application of Stochastic Programming in Solving Capacity Allocation and Migration Planning Problem under Uncertainty

The semiconductor packaging and testing industry, which utilizes high-technology manufacturing processes and a variety of machines, belongs to an uncertain make-to-order (MTO) production environment. Order release particularly originates from customer demand; hence, demand fluctuation directly affects capacity planning. Thus, managing capacity allocation is a difficult endeavor. This study aims to determine the best capacity allocation with limited resources to maximize the net profit. Three bottleneck stations in the semiconductor packaging and testing process are mainly investigated, namely, die bond (DB), wire bond (WB), and molding (MD) stations. Deviating from previous studies that consider the deterministic programming model, customer demand in the current study is regarded as an uncertain parameter in formulating a two-stage scenario-based stochastic programming (SP) model. The SP model seeks to respond to sharp demand fluctuations. Even if future demand is uncertain, migration decision for machines and tools will still obtain better robust results for various demand scenarios. A hybrid approach is proposed to solve the SP model. Moreover, two assessment indicators, namely, the expected value of perfect information (EVPI) and the value of the stochastic solution (VSS), are adopted to compare the solving results of the deterministic planning model and stochastic programming model. Sensitivity analysis is performed to evaluate the effects of different parameters on net profit.