Stochastic programming with incomplete information:a surrey of results on postoptimization and sensitivity analysis

The possibility of successful applications of stochastic programming decision models has been limited by the assumed complete knowledge of the distribution Fof the random parameters as well as by the limited scope of the existing numerical procedures. We shall give a survey of selected methods which can be used to deal with the incomplete knowledge of the distribution F, namely to study robustness of the optimal solution and the optimal value of the objective function relative to small changes of the underlying distribution and to get error bounds in approximation schemes.