Robust investment model for long-range capacity expansion of chemical processing networks under uncertain demand forecast scenarios

The problem of long-range capacity expansion planning for chemical processing networks under uncertain demand forecast scenarios is addressed. This optimization problem involves capacity expansion timing and sizing of each chemical processing unit to maximize the expected net present value while considering the deviation of net present values and the excess capacity over a given time horizon. A multiperiod mixed integer nonlinear programming optimization model that is both solution and model robust for any realization of demand scenarios is developed using the two-stage stochastic programming modeling framework. Two example problems are considered to illustrate the effectiveness of the model. Especially, the use of the model is illustrated on a real problem arising from investment planning in Korean petrochemical industry.