Introduction to Stochastic Programming

✍ Scribed by John R. Birge, François Louveaux (auth.)

• Publisher: Springer-Verlag New York
• Year: 2011
• Tongue: English
• Leaves: 512
• Series: Springer Series in Operations Research and Financial Engineering
• Edition: 2
• Category: Library

✦ Synopsis

The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.

In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.

The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest.

Review of First Edition:

"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)

✦ Table of Contents

Front Matter....Pages i-xxv
Front Matter....Pages 1-1
Introduction and Examples....Pages 3-54
Uncertainty and Modeling Issues....Pages 55-100
Front Matter....Pages 101-101
Basic Properties and Theory....Pages 103-161
The Value of Information and the Stochastic Solution....Pages 163-177
Front Matter....Pages 179-179
Two-Stage Recourse Problems....Pages 181-263
Multistage Stochastic Programs....Pages 265-287
Stochastic Integer Programs....Pages 289-338
Front Matter....Pages 339-339
Evaluating and Approximating Expectations....Pages 341-387
Monte Carlo Methods....Pages 389-415
Multistage Approximations....Pages 417-448
Back Matter....Pages 449-485

✦ Subjects

Operations Research, Management Science; Statistics and Computing/Statistics Programs; Optimization