In linear programming we assume that all the parameters of the problem, i.e., the coefficients of the objective function, the inequalities and the availabilities are known numbers. This is frequently a not very realistic assumption. In stochastic linear programming the parameters become random variables, i.e., we know only their distribution. In the passive approach to stochastic linear programming the distribution of the objective function is approximated and decisions are based upon this distribution. In the active approach the decision variables are the amounts of resources to be devoted to the various activities.