Elsevier < Decision Sciences Publications < Handbooks in Operations Research and Management Science < Volume 10: Stochastic Programming  < Preface

STOCHASTIC PROGRAMMING
Edited by A. Ruszczynski and A. Shapiro

PREFACE

The area of stochastic programming was created in the middle of the last century, following fundamental achievements in linear and nonlinear programming. While it has been quickly realized that the presence of uncertainty in optimization models creates a need for new problem formulations, many years have passed until the basic stochastic programming models have been formulated and analyzed. Today, stochastic programming theory offers a variety of models to address the presence of random data in optimization problems: chance-constrained models, two- and multi-stage models, models involving risk measures. New problem formulations appear almost every year and this variety is one of the strengths of the field.

Stochastic programming can be quite involved, starting with sophisticated modeling and is based on advanced mathematical tools such as nonsmooth calculus, abstract optimization, probability theory and statistical techniques. One of the objectives of this Handbook is to bring these techniques together and to show how they can be used to analyze and solve stochastic programming models.

Because of the inherent difficulty of stochastic optimization problems, it took a long time until efficient solution methods have been developed. In the last two decades a dramatic change in our abilities to solve stochastic programming problems took place. It is partially due to the progress in large scale linear and nonlinear programming, in nonsmooth optimization and integer programming, but mainly it follows the development of techniques exploiting specific properties of stochastic programming problems. Computational advances are also due to modern parallel processing technology. Nowadays we can solve stochastic optimization problems involving tens of millions of variables and constraints.

Our intention was to bring together leading experts in the most important sub-fields of stochastic programming to present a rigorous overview of basic models, methods, and applications of stochastic programming. We hope that this Handbook will prove useful to researchers, students, engineers and economists, who encounter in their work optimization problems involving uncertainty. We also hope that our work will encourage many to undertake research in this exciting and practically important field.

We want to thank all the Authors involved in this project for their contributions. We also want to thank Darinka Dentcheva, Shabbir Ahmed, Tito Homem-de-Mello and Anton Kleywegt, who have helped us to review and improve several chapters of this Handbook.

Andrzej Ruszczynski
Alexander Shapiro
December 2002
 
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