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

We present an integrated procedure to build and solve big stochastic progra mming models. The individual components of the system - the modeling langua ge, the solver and the hardware - are easily accessible, or a least afforda ble to a large audience. The procedure is applied to a simple financial mod el, which can be expanded to arbitrarily large sizes by enlarging the numbe r of scenarios. We generated a model with one million scenarios, whose dete rministic 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.