Introduction to Stochastic Programming

  • John R. Birge
  • François Louveaux

Table of contents

  1. Front Matter
    Pages i-xix
  2. Models

    1. Front Matter
      Pages 1-1
  3. Basic Properties

  4. Solution Methods

  5. Approximation and Sampling Methods

    1. Front Matter
      Pages 283-283
    2. Pages 331-352
  6. A Case Study

    1. Front Matter
      Pages 373-373
    2. Pages 375-384
  7. Back Matter
    Pages 385-421

About this book


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.


Stochastic Programming Stochastic model linear optimization model modeling nonlinear optimization operations research programming

Authors and affiliations

  • John R. Birge
    • 1
  • François Louveaux
    • 2
  1. 1.McCormick School of Engineering and Applied ScienceNorthwestern UniversityEvanstonUSA
  2. 2.Département des Méthodes QuantitativesFacultés Universitaires Notre Dame de la PaixNamurBelgium

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York, Inc. 1997
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-98217-5
  • Online ISBN 978-0-387-22618-7
  • Series Print ISSN 1431-8598
  • Buy this book on publisher's site