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Advances in Sensitivity Analysis and Parametric Programming

  • Book
  • © 1997

Overview

Editors:
  1. Tomas Gal

    1. FernUniversität, Hagen, Germany

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    You can also search for this editor in PubMed   Google Scholar

  2. Harvey J. Greenberg

    1. University of Colorado at Denver, USA

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    You can also search for this editor in PubMed   Google Scholar

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

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. A Historical Sketch on Sensitivity Analysis and Parametric Programming

    • Tomas Gal
    Pages 1-10

  3. A Systems Perspective: Entity Set Graphs

    • Heiner Müller-Merbach
    Pages 11-55

  4. Linear Programming 1: Basic Principles

    • Harvey J. Greenberg
    Pages 57-100

  5. Linear Programming 2: Degeneracy Graphs

    • Tomas Gal
    Pages 101-136

  6. Linear Programming 3: The Tolerance Approach

    • Richard E. Wendell
    Pages 137-157

  7. The Optimal Set and Optimal Partition Approach to Linear and Quadratic Programming

    • Arjan B. Berkelaar, Kees Roos, Tamás Terlaky
    Pages 159-202

  8. Network Models

    • Gerald L. Thompson
    Pages 203-236

  9. Qualitative Sensitivity Analysis

    • Antoine Gautier, Daniel Granot, Frieda Granot
    Pages 237-289

  10. Integer and Mixed-Integer Programming

    • Charles Blair
    Pages 291-315

  11. Nonlinear Programming

    • Arne Stolbjerg Drud, Leon Lasdon
    Pages 317-361

  12. Multi-Criteria and Goal Programming

    • Jerald Dauer, Yi-Hsin Liu
    Pages 363-393

  13. Stochastic Programming and Robust Optimization

    • Hercules Vladimirou, Stavros A. Zenios
    Pages 395-447

  14. Redundancy

    • Richard J. Caron, Arnon Boneh, Shahar Boneh
    Pages 449-489

  15. Feasibility and Viability

    • John W. Chinneck
    Pages 491-531

  16. Fuzzy Mathematical Programming

    • Hans-Jürgen Zimmermann
    Pages 533-572

  17. Back Matter

    Pages 573-581
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Keywords

About this book

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.

Reviews

`... well-organized book can be highly recommended to all who are engaged in operations research and decision making with incomplete information and/or multiple criteria. I do not hesitate to congratulate the two editors on their excellent work.'
OR Spektrum, 22 (2000)

Editors and Affiliations

  • FernUniversität, Hagen, Germany

    Tomas Gal

  • University of Colorado at Denver, USA

    Harvey J. Greenberg

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