Intelligent Strategies for Meta Multiple Criteria Decision Making

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 de...

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Main Author: Hanne, Thomas
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY Springer US 2001, 2001
Edition:1st ed. 2001
Series:International Series in Operations Research & Management Science
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 2. Method integration, openness, and object oriented implementation
  • 3. A class concept for LOOPS
  • 4. Problem solving and learning from an object oriented point of view
  • 5. MADM methods in LOOPS
  • 6. Neural networks in LOOPS
  • 7. Neural MCDM networks in LOOPS
  • 8. Evolutionary algorithms in LOOPS
  • 9. An extended interactive framework
  • 10. Summary and conclusions
  • 6. Examples of the Application of Loops
  • 1. Some remarks on the application of LOOPS
  • 2. The learning of utility functions
  • 3. Stock selection
  • 4. Stock price prediction and the learning of time series
  • 5. Stock analysis and long-term prediction
  • 6. Method learning
  • 7. Meta learning
  • 8. An integrated proposal for the application of LOOPS
  • 9. Summary and conclusions
  • 7. Critical Resume and Outlook
  • References
  • Appendices
  • A- Some basic concepts of MCDM theory
  • 1. Relations
  • 2. Efficiency concepts and scalarizing theorems
  • 3. Utility concepts and other axiomatics 166 B- Some selected MCDM methods
  • 1. Simple additive weighting
  • 2. Achievement levels
  • 3. Reference point approaches
  • 4. The outranking method PROMETHEE 171 C- Neural networks
  • 1. Introduction to neural networks
  • 2. Neural networks for intelligent decision support 178 D- Evolutionary algorithms
  • 1. Introduction to evolutionary algorithms
  • 2. The generalization of evolutionary algorithms 186 E- List of symbols 189 F- List of abbreviations
  • 1. Introduction
  • 1. MCDM problems
  • 2. Solutions of MCDM problems
  • 3. Decision processes and the application of MCDM methods
  • 4. Concepts of ‘correct’ decision making in MCDM methods
  • 5. Summary and conclusions
  • 2. The Meta Decision Problem in MCDM
  • 1. Methodological criticism in MCDM
  • 2. The met a decision problem in MCDM
  • 3. Summary and conclusions
  • 3. Neural Networks and Evolutionary Learning For MCDM
  • 1. Neural networks and MCDM
  • 2. Evolutionary learning
  • 3. Summary and conclusions
  • 4. On the Combination of MCDM Methods
  • 1. Introduction
  • 2. Properties of MCDM methods
  • 3. Properties of specific MCDM methods
  • 4. Properties of neurons and neural networks
  • 5. The combination of algorithms
  • 6. Neural MCDM networks
  • 7. Termination and runtime of the algorithm
  • 8. Summary and conclusions
  • 5. Loops - An Object Oriented DSS for Solving Meta Decision Problems
  • 1. Preliminary remarks