Stochastic Programming with Incomplete Information

Dupacova, J. (1986). Stochastic Programming with Incomplete Information. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-86-008

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Abstract

The possibility of successful applications of stochastic programming decision models has been limited by the assumed complete knowledge of the distribution F of the random parameters as well as by the limited scope of the existing numerical procedures.

We shall introduce selected methods which can be used to deal with the incomplete knowledge of the distribution F, 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.

The research was mostly carried out at the Department of Statistics, Charles University, Prague and it was stimulated by a close collaboration of the author with the ADO project of SDS. The present version of the paper was written at IIASA Laxenburg.

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