This text presents a broad overview of the main themes and methods in stochastic programming. It first introduces worked examples of stochastic programming and demonstrates how a stochastic model is formally built. Then it develops the properties of stochastic programs and the basic solution techniques used to solve them. Finally, it covers approximation and sampling techniques and also offers a case study in-depth.Programming. Consider a linear program (L.P.) of the form max{cTx\Ax = b, xagt; 0}, (9.1) where A is an m x n matrix, x and c are nxl ... For a better understanding, some examples and exercises also use manual solutions of linear programs.

Title | : | Introduction to Stochastic Programming |

Author | : | John R. Birge, François Louveaux |

Publisher | : | Springer Science & Business Media - 1997-01-01 |

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