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Practical Bilevel Optimization

Algorithms and Applications

  • Jonathan F. Bard

Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 30)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Mathematical Programming

    1. Front Matter
      Pages 1-1
    2. Jonathan F. Bard
      Pages 3-16
    3. Jonathan F. Bard
      Pages 17-75
    4. Jonathan F. Bard
      Pages 76-136
    5. Jonathan F. Bard
      Pages 137-192
  3. Bilevel Programming

    1. Front Matter
      Pages 193-193
    2. Jonathan F. Bard
      Pages 195-231
    3. Jonathan F. Bard
      Pages 232-268
    4. Jonathan F. Bard
      Pages 269-300
    5. Jonathan F. Bard
      Pages 301-360
    6. Jonathan F. Bard
      Pages 361-388
  4. Applications

    1. Front Matter
      Pages 389-389
    2. Jonathan F. Bard
      Pages 391-413
    3. Jonathan F. Bard
      Pages 414-427
  5. Back Matter
    Pages 455-476

About this book

Introduction

The use of optimization techniques has become integral to the design and analysis of most industrial and socio-economic systems. Great strides have been made recently in the solution of large-scale problems arising in such areas as production planning, airline scheduling, government regulation, and engineering design, to name a few. Analysts have found, however, that standard mathematical programming models are often inadequate in these situations because more than a single objective function and a single decision maker are involved. Multiple objective programming deals with the extension of optimization techniques to account for several objective functions, while game theory deals with the inter-personal dynamics surrounding conflict. Bilevel programming, the focus of this book, is in a narrow sense the combination of the two. It addresses the problern in which two decision makers, each with their individual objectives, act and react in a noncooperative, sequential manner. The actions of one affect the choices and payoffs available to the other but neither player can completely dominate the other in the traditional sense.

Keywords

Variable algorithm algorithms complexity game theory linear optimization management mathematical programming mathematics model network nonlinear optimization operations research optimization programming

Authors and affiliations

  • Jonathan F. Bard
    • 1
  1. 1.Graduate Program in Operations Research, Department of Mechanical EngineeringThe University of TexasAustinUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-2836-1
  • Copyright Information Springer-Verlag US 1998
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-4807-6
  • Online ISBN 978-1-4757-2836-1
  • Series Print ISSN 1571-568X
  • Buy this book on publisher's site