Intelligent Strategies for Meta Multiple Criteria Decision Making

Name: Intelligent Strategies for Meta Multiple Criteria Decision Making
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ISBN: 9780792372516

International Series in Operations Research & Management Science

By: Thomas Hanne

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Hardcover Published: 31st December 2000
ISBN: 9780792372516
Number Of Pages: 197

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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.

List of Figures	p. ix
List of Tables	p. xi
Preface	p. xiii
Foreword	p. xvii
Introduction	p. 1
MCDM problems	p. 1
Solutions of MCDM problems	p. 4
Decision processes and the application of MCDM methods	p. 5
Concepts of 'correct' decision making in MCDM methods	p. 9
Summary and conclusions	p. 14
The Meta Decision Problem in MCDM	p. 15
Methodological criticism in MCDM	p. 15
Criticism on single concepts and methods	p. 15
The discussion on the descriptive orientation of MCDM	p. 19
Foundations by axioms of rational behavior	p. 22
The meta decision problem in MCDM	p. 24
Formulation and foundation of the problem	p. 24
Criteria for method selection	p. 25
The suitability for a type of problem	p. 25
Criteria based on solution concepts	p. 26
Criteria oriented towards implementation	p. 28
Criteria based on the specific decision situation	p. 30
Scalar and multicriteria meta decision problems	p. 31
Scalar evaluations of MCDM methods	p. 31
Method choice as an MADM problem	p. 32
The meta decision problem as a problem of method design	p. 34
Determining the parameters of an MCDM method	p. 34
Formalization of MCDM methods	p. 36
A parameter optimization model	p. 37
The problem of information acquisition	p. 39
Implicit information	p. 40
Explicit information	p. 41
Summary and conclusions	p. 44
Neural Networks and Evolutionary Learning for MCDM	p. 47
Neural networks and MCDM	p. 47
Introduction	p. 47
The construction of neural networks working as traditional MCDM methods	p. 49
Neural networks as an adaptive MCDM method	p. 54
Evolutionary learning	p. 55
Evolutionary algorithms and neural networks	p. 56
Evolutionary algorithms and MCDM	p. 59
Summary and conclusions	p. 61
On the Combination of MCDM Methods	p. 63
Introduction	p. 63
Properties of MCDM methods	p. 69
Properties of specific MCDM methods	p. 71
Properties of neurons and neural networks	p. 73
The combination of algorithms	p. 74
Neural MCDM networks	p. 75
Termination and runtime of the algorithm	p. 76
Summary and conclusions	p. 77
Loops--An Object Oriented DSS for Solving Meta Decision Problems	p. 79
Preliminary remarks	p. 79
Method integration, openness, and object oriented implementation	p. 80
A class concept for LOOPS	p. 84
Problem solving and learning from an object oriented point of view	p. 84
MADM methods in LOOPS	p. 87
Neural networks in LOOPS	p. 89
Neural MCDM networks in LOOPS	p. 90
Evolutionary algorithms in LOOPS	p. 91
An extended interactive framework	p. 95
Summary and conclusions	p. 98
Examples of the Application of LOOPS	p. 99
Some remarks on the application of LOOPS	p. 99
The learning of utility functions	p. 100
Stock selection	p. 106
Stock price prediction and the learning of time series	p. 113
Stock analysis and long-term prediction	p. 121
Method learning	p. 124
Meta learning	p. 127
An integrated proposal for the application of LOOPS	p. 131
Summary and conclusions	p. 132
Critical Resume and Outlook	p. 135
References	p. 141
Appendices	p. 162
Some basic concepts of MCDM theory	p. 163
Relations	p. 163
Efficiency concepts and scalarizing theorems	p. 165
Utility concepts and other axiomatics	p. 166
Some selected MCDM methods	p. 169
Simple additive weighting	p. 169
Achievement levels	p. 169
Reference point approaches	p. 170
The outranking method Promethee	p. 171
Neural networks	p. 173
Introduction to neural networks	p. 173
Neural networks for intelligent decision support	p. 178
Evolutionary algorithms	p. 181
Introduction to evolutionary algorithms	p. 181
The generalization of evolutionary algorithms	p. 186
List of symbols	p. 189
List of abbreviations	p. 193
Index	p. 195
Table of Contents provided by Syndetics. All Rights Reserved.

ISBN: 9780792372516
ISBN-10: 0792372514
Series: International Series in Operations Research & Management Science
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 197
Published: 31st December 2000
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 23.5 x 15.5 x 1.91
Weight (kg): 1.08