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© 2011

Introduction to Stochastic Programming

Textbook

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Models

    1. Front Matter
      Pages 1-1
    2. John R. Birge, François Louveaux
      Pages 3-54
    3. John R. Birge, François Louveaux
      Pages 55-100
  3. Basic Properties

    1. Front Matter
      Pages 101-101
    2. John R. Birge, François Louveaux
      Pages 103-161
    3. John R. Birge, François Louveaux
      Pages 163-177
  4. Solution Methods

    1. Front Matter
      Pages 179-179
    2. John R. Birge, François Louveaux
      Pages 181-263
    3. John R. Birge, François Louveaux
      Pages 265-287
    4. John R. Birge, François Louveaux
      Pages 289-338
  5. Approximation and Sampling Methods

    1. Front Matter
      Pages 339-339
    2. John R. Birge, François Louveaux
      Pages 341-387
    3. John R. Birge, François Louveaux
      Pages 389-415
    4. John R. Birge, François Louveaux
      Pages 417-448
  6. Back Matter
    Pages 449-485

About this book

Introduction

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) 

 

 

Keywords

Stochastic optimization Two-Stage Linear Recourse Problems decision making under uncertainty dynamic programming

Authors and affiliations

  1. 1., Booth School of BusinessUniversity of ChicagoChicagoUSA
  2. 2., Department of Business AdministrationUniversity of NamurNamurBelgium

About the authors

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

Bibliographic information

Reviews

From the reviews of the second edition:

“Help the students to understand how to model uncertainty into mathematical optimization problems, what uncertainty brings to the decision process and which techniques help to manage uncertainty in solving the problems. … certainly attract also the wide spectrum of readers whose main interest lies in possible exploitation of stochastic programming methodology and will help them to find their own way to treat actual problems using stochastic programming methods. As a whole, the three main building blocks of stochastic programming … are well represented and balanced.” (Jitka Dupačová, Zentralblatt MATH, Vol. 1223, 2011)