Building and solving large-scale stochastic programs on an affordable distributed computing system

We present an integrated procedure to build and solve big stochastic programming models. The individual components of the system - the modeling language, the solver and the hardware - are easily accessible, or a least affordable to a large audience. The procedure is applied to a simple financial model, which can be expanded to arbitrarily large sizes by enlarging the number of scenarios. We generated a model with one million scenarios, whose deterministic equivalent linear program has 1,111,112 constraints and 2,555,556 variables. We have been able to solve it on the cluster of ten PCs in less than 3 hours.