Introduction to Stochastic Programming / by John R. Birge, François Louveaux.

Format:

Book

Author/Creator:

Birge, John R., Author.

Louveaux, François, Author.

Series:

Springer Series in Operations Research and Financial Engineering, 1431-8598

Language:

English

Subjects (All):

Operations research.

Management science.

Statistics.

Mathematical optimization.

Operations Research, Management Science.

Statistics and Computing/Statistics Programs.

Optimization.

Local Subjects:

Operations Research, Management Science.

Statistics and Computing/Statistics Programs.

Optimization.

Physical Description:

1 online resource (499 p.)

Edition:

2nd ed. 2011.

Place of Publication:

New York, NY : Springer New York : Imprint: Springer, 2011.

Language Note:

English

Summary:

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) .

Contents:

Introduction and Examples

Uncertainty and Modeling Issues

Basic Properties and Theory

The Value of Information and the Stochastic Solution

Two-Stage Recourse Problems

Multistage Stochastic Programs

Stochastic Integer Programs

Evaluating and Approximating Expectations

Monte Carlo Methods

Multistage Approximations

Sample Distribution Functions

References.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

ISBN:

1-4614-0237-9

OCLC:

745001039