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Introduction to Stochastic Programming

  • Textbook
  • © 1997

Overview

Authors:
  1. John R. Birge

    1. McCormick School of Engineering and Applied Science, Northwestern University, Evanston, USA

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  2. François Louveaux

    1. Département des Méthodes Quantitatives, Facultés Universitaires Notre Dame de la Paix, Namur, Belgium

    View author publications

    You can also search for this author in PubMed   Google Scholar

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xix
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  2. Models

    1. Front Matter

      Pages 1-1
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    2. Introduction and Examples

      Pages 3-48

    3. Uncertainty and Modeling Issues

      Pages 49-79

  3. Basic Properties

    1. Front Matter

      Pages 81-81
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    2. Basic Properties and Theory

      Pages 83-135

    3. The Value of Information and the Stochastic Solution

      Pages 137-152

  5. Approximation and Sampling Methods

    1. Front Matter

      Pages 283-283
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    2. Evaluating and Approximating Expectations

      Pages 285-329

    3. Monte Carlo Methods

      Pages 331-352

    4. Multistage Approximations

      Pages 353-371

  6. A Case Study

    1. Front Matter

      Pages 373-373
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    2. Capacity Expansion

      Pages 375-384

  7. Back Matter

    Pages 385-421
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Keywords

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.

Authors and Affiliations

  • McCormick School of Engineering and Applied Science, Northwestern University, Evanston, USA

    John R. Birge

  • Département des Méthodes Quantitatives, Facultés Universitaires Notre Dame de la Paix, Namur, Belgium

    François Louveaux

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