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Introduction to Stochastic Programming

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ISBN-10: 0387982175

ISBN-13: 9780387982175

Edition: 2000

Authors: John R. Birge, Fran�ois Louveaux

List price: $134.00
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Description:

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. Conversely, it is 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.The first chapters introduce some worked examples of stochastic programming and demonstrate how a stochastic model is formally built. Subsequent chapters develop the properties of stochastic programs and the basic solution techniques used to solve them. Three chapters cover approximation and sampling techniques and the final chapter presents a case study in depth.A wide range of students from operations research, industrial engineering, and related disciplines will find this a well-paced and wide-ranging introduction to this subject.
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Book details

List price: $134.00
Copyright year: 2000
Publisher: Springer
Publication date: 2/2/2000
Binding: Hardcover
Pages: 421
Size: 6.50" wide x 9.75" long x 1.00" tall
Weight: 1.694
Language: English

John R. Birge, is a Jerry W. and Carol Lee Levin Professor of Operations Management at the University of Chicago Booth School of Business. François Louveaux is a Professor at the University of Namur(FUNDP) in the Department of Business Administration

Preface
Notation
Models
Introduction and Examples
Uncertainty and Modeling Issues
Basic Properties
Basic Properties and Theory
The Value of Information and the Stochastic Solution
Solution Methods
Two-Stage Linear Recourse Problems
Nonlinear Programming Approaches to Two-Stage Recourse Problems
Multistage Stochastic Programs
Stochastic Integer Programs
Approximation and Sampling Methods
Evaluating and Approximating Expectations
Monte Carlo Methods
Multistage Approximations
A Case Study
Capacity Expansion
Sample Distribution Functions
References
Author Index
Subject Index