Edited 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
Description
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.
Included in series
Handbooks in Operations Research and Management Science
