Intelligent Strategies for Meta Multiple Criteria Decision Making

  • Authors
  • Thomas Hanne

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 33)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Thomas Hanne
    Pages 1-14
  3. Thomas Hanne
    Pages 63-78
  4. Thomas Hanne
    Pages 99-133
  5. Thomas Hanne
    Pages 135-139
  6. Back Matter
    Pages 141-197

About this book

Introduction

Multiple criteria decision-making research has developed rapidly and has become a main area of research for dealing with complex decision problems which require the consideration of multiple objectives or criteria. Over the past twenty years, numerous multiple criterion decision methods have been developed which are able to solve such problems. However, the selection of an appropriate method to solve a particular decision problem is today's problem for a decision support researcher and decision-maker.
Intelligent Strategies for Meta Multiple Criteria Decision-Making deals centrally with the problem of the numerous MCDM methods that can be applied to a decision problem. The book refers to this as a `meta decision problem', and it is this problem that the book analyzes. The author provides two strategies to help the decision-makers select and design an appropriate approach to a complex decision problem. Either of these strategies can be designed into a decision support system itself. One strategy is to use machine learning to design an MCDM method. This is accomplished by applying intelligent techniques, namely neural networks as a structure for approximating functions and evolutionary algorithms as universal learning methods. The other strategy is based on solving the meta decision problem interactively by selecting or designing a method suitable to the specific problem, for example, the constructing of a method from building blocks. This strategy leads to a concept of MCDM networks. Examples of this approach for a decision support system explain the possibilities of applying the elaborated techniques and their mutual interplay. The techniques outlined in the book can be used by researchers, students, and industry practitioners to better model and select appropriate methods for solving complex, multi-objective decision problems.

Keywords

algorithms decision problem decision support system evolution evolutionary algorithm learning linear optimization machine learning neural networks

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-1595-1
  • Copyright Information Kluwer Academic Publishers 2001
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5632-5
  • Online ISBN 978-1-4615-1595-1
  • Series Print ISSN 0884-8289
  • About this book