Handbooks in Operations Research and Management Science, 10: Stochastic ProgrammingEdited by
- A. Ruszczynski, Department of Management Science and Information Systems, Rutgers University, Piscataway, NJ, USA
- A. Shapiro, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
This Handbook Volume brings 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. The work is intended for researchers, students, engineers and economists, who encounter in their work optimization problems involving uncertainty.
The area of stochastic programming was created in the middle of the last century, following fundamental achievements in linear and nonlinear programming. However, because of the inherent difficulty of stochastic optimisation problems, it took a long time until efficient solution methods were 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.
Handbooks in Operations Research and Management Science
Published: September 2003
...Ten papers provide a rigorous overview of basic models, methods, and applications of stochastic programming.
Journal of Economic Literature, 2004
...The handbook will be a valuable reference book for experts in Stochastic Programming.
Mathematical Reviews, 2005
- Preface. Chapters. Stochastic Programming Models (A. Ruszczynski, A. Shapiro).Optimality and Duality in Stochastic Programming (A. Ruszczynski, A. Shapiro).Decomposition Methods (A. Ruszczynski).Stochastic Integer Programming (F.V. Louveaux, R. Schultz).Probabilistic Programming (A. Prékopa).Monte Carlo Sampling Methods (A. Shapiro).Stochastic Optimization and Statistical Inference (G. Ch. Pflug).Stability of Stochastic Programming Problems (W. Römisch).Stochastic Programming in Transportation and Logistics (W.B. Powell, H. Topaloglu).Stochastic Programming Models in Energy (S.W. Wallace, S.-E. Fleten).