Advertisement

Advances in Sensitivity Analysis and Parametic Programming

  • Tomas Gal
  • Harvey J. Greenberg

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 6)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Heiner Müller-Merbach
    Pages 11-55
  3. Harvey J. Greenberg
    Pages 57-100
  4. Tomas Gal
    Pages 101-136
  5. Richard E. Wendell
    Pages 137-157
  6. Arjan B. Berkelaar, Kees Roos, Tamás Terlaky
    Pages 159-202
  7. Gerald L. Thompson
    Pages 203-236
  8. Antoine Gautier, Daniel Granot, Frieda Granot
    Pages 237-289
  9. Charles Blair
    Pages 291-315
  10. Arne Stolbjerg Drud, Leon Lasdon
    Pages 317-361
  11. Jerald Dauer, Yi-Hsin Liu
    Pages 363-393
  12. Hercules Vladimirou, Stavros A. Zenios
    Pages 395-447
  13. Richard J. Caron, Arnon Boneh, Shahar Boneh
    Pages 449-489
  14. John W. Chinneck
    Pages 491-531
  15. Hans-Jürgen Zimmermann
    Pages 533-572
  16. Back Matter
    Pages 573-581

About this book

Introduction

The standard view of Operations Research/Management Science (OR/MS) dichotomizes the field into deterministic and probabilistic (nondeterministic, stochastic) subfields. This division can be seen by reading the contents page of just about any OR/MS textbook. The mathematical models that help to define OR/MS are usually presented in terms of one subfield or the other. This separation comes about somewhat artificially: academic courses are conveniently subdivided with respect to prerequisites; an initial overview of OR/MS can be presented without requiring knowledge of probability and statistics; text books are conveniently divided into two related semester courses, with deterministic models coming first; academics tend to specialize in one subfield or the other; and practitioners also tend to be expert in a single subfield. But, no matter who is involved in an OR/MS modeling situation (deterministic or probabilistic - academic or practitioner), it is clear that a proper and correct treatment of any problem situation is accomplished only when the analysis cuts across this dichotomy.

Keywords

Stochastic Programming calculus linear optimization nonlinear optimization optimization programming quadratic programming

Editors and affiliations

  • Tomas Gal
    • 1
  • Harvey J. Greenberg
    • 2
  1. 1.FernUniversitätHagenGermany
  2. 2.University of Colorado at DenverUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-6103-3
  • Copyright Information Kluwer Academic Publishers 1997
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7796-2
  • Online ISBN 978-1-4615-6103-3
  • Series Print ISSN 0884-8289
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Automotive
Biotechnology
Finance, Business & Banking
Consumer Packaged Goods
Energy, Utilities & Environment
Oil, Gas & Geosciences
Engineering