Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys

  • Matthias Ehrgott
  • Xavier Gandibleux

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 52)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Christiane Tammer, Alfred Göpfert
    Pages 1-70
  3. Tetsuzo Tanino, Hun Kuk
    Pages 71-128
  4. Dylan F. Jones, Mehrdad Tamiz
    Pages 129-170
  5. Masatoshi Sakawa
    Pages 171-226
  6. Kaisa Miettinen
    Pages 227-276
  7. Carlos A. Coello Coello, Carlos E. Mariano Romero
    Pages 277-331
  8. Hirotaka Nakayama, Masao Arakawa, Ye Boon Yun
    Pages 333-368
  9. Vincent T’Kindt, Jean-Charles Billaut
    Pages 445-491
  10. Back Matter
    Pages 493-496

About this book

Introduction

The generalized area of multiple criteria decision making (MCDM) can be defined as the body of methods and procedures by which the concern for multiple conflicting criteria can be formally incorporated into the analytical process. MCDM consists mostly of two branches, multiple criteria optimization and multi-criteria decision analysis (MCDA). While MCDA is typically concerned with multiple criteria problems that have a small number of alternatives often in an environment of uncertainty (location of an airport, type of drug rehabilitation program), multiple criteria optimization is typically directed at problems formulated within a mathematical programming framework, but with a stack of objectives instead of just one (river basin management, engineering component design, product distribution). It is about the most modern treatment of multiple criteria optimization that this book is concerned. I look at this book as a nicely organized and well-rounded presentation of what I view as ”new wave” topics in multiple criteria optimization. Looking back to the origins of MCDM, most people agree that it was not until about the early 1970s that multiple criteria optimization c- gealed as a field. At this time, and for about the following fifteen years, the focus was on theories of multiple objective linear programming that subsume conventional (single criterion) linear programming, algorithms for characterizing the efficient set, theoretical vector-maximum dev- opments, and interactive procedures.

Keywords

Data Envelopment Analysis Vector optimization algorithms calculus combinatorial optimization data envelopment evolutionary algorithm multi-objective optimization multiple-criteria decision-making optimization scheduling

Editors and affiliations

  • Matthias Ehrgott
    • 1
  • Xavier Gandibleux
    • 2
  1. 1.University of AucklandNew Zealand
  2. 2.Université de ValenciennesFrance

Bibliographic information

  • DOI https://doi.org/10.1007/b101915
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4020-7128-7
  • Online ISBN 978-0-306-48107-9
  • Series Print ISSN 0884-8289
  • About this book