Long-term capacity planning and production scheduling present significant challenges for the aviation industry. Our research has integrated three different modeling methodologies to effectively forecast future demand for aircraft painting and then assess and manage the capacity that is needed to meet these requirements. First, an innovative forecasting approach was developed in which stochastic processes were used to model aircraft demand over a selected time interval. These demand forecasts were used as inputs to an integer programming model, which was used to find optimal monthly aircraft painting schedules. This approach supports for resource allocation that is based on optimal scheduling, rather than the existing heuristic-based methods. The optimal monthly schedules can then serve as inputs to a discrete event simulation model of the painting operation, which can be used to test the robustness of the optimal schedules under conditions of uncertain demand and processing times.