Abstract
The main part of the paper (Section 3) provides an overview of case studies based on stochastic programming models in which reality of incomplete information about distribution of random parameters is clearly accepted and treated. Also, several techniques of scenario generation are surveyed in this context. Basic ideas of various approaches to stochastic programming models under incomplete information are briefly explained on illustrative examples in Section 2.
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