The first work that pointed out the obvious that most applications of linear programming have stochastic nature was published by George Dantzig in 1955. Still the interest for optimization by linear programming was sparse, although many possible applications could already be identified. Not until the first computers became reliable in the late 1950s, did Stochastic Optimization become a field of study.
Until the increase of computer power the scope of solvable applications for Stochastic Optimization had been very limited, because the high dimensionality of stochastic programs requires considerable calculation efforts.
The gradual availability of sufficient calculation capacity inspired researchers to investigate methods, which provided decision support by taking into account uncertainties. Thereby, a series of techniques to choose most representative scenarios and to constrain calculation efforts were proposed.
In recent years, the field of applications for Stochastic Optimization has become broader. Whereas for decades the main practical interest came from finance institutions, nowadays much research is going on in applications for the power industry, where utilities have to cope with massive uncertainties that emerged in particular after the liberalization of the markets.
Today, Stochastic Optimization is available in commercialized products and practitioners more and more begin to apply it. Thereby, most recent hard- and software technology is used to overcome the enormous computational efforts that it involves. Distributed computing, massive random access memory and high clock rates allow for the introduction of new Stochastic Optimization approaches into the gas and power industry.