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Intelligent Strategies for Meta Multiple Criteria Decision Making : International Series in Operations Research & Management Science - Thomas Hanne

Intelligent Strategies for Meta Multiple Criteria Decision Making

International Series in Operations Research & Management Science

Hardcover

Published: 31st December 2000
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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 Figuresp. ix
List of Tablesp. xi
Prefacep. xiii
Forewordp. xvii
Introductionp. 1
MCDM problemsp. 1
Solutions of MCDM problemsp. 4
Decision processes and the application of MCDM methodsp. 5
Concepts of 'correct' decision making in MCDM methodsp. 9
Summary and conclusionsp. 14
The Meta Decision Problem in MCDMp. 15
Methodological criticism in MCDMp. 15
Criticism on single concepts and methodsp. 15
The discussion on the descriptive orientation of MCDMp. 19
Foundations by axioms of rational behaviorp. 22
The meta decision problem in MCDMp. 24
Formulation and foundation of the problemp. 24
Criteria for method selectionp. 25
The suitability for a type of problemp. 25
Criteria based on solution conceptsp. 26
Criteria oriented towards implementationp. 28
Criteria based on the specific decision situationp. 30
Scalar and multicriteria meta decision problemsp. 31
Scalar evaluations of MCDM methodsp. 31
Method choice as an MADM problemp. 32
The meta decision problem as a problem of method designp. 34
Determining the parameters of an MCDM methodp. 34
Formalization of MCDM methodsp. 36
A parameter optimization modelp. 37
The problem of information acquisitionp. 39
Implicit informationp. 40
Explicit informationp. 41
Summary and conclusionsp. 44
Neural Networks and Evolutionary Learning for MCDMp. 47
Neural networks and MCDMp. 47
Introductionp. 47
The construction of neural networks working as traditional MCDM methodsp. 49
Neural networks as an adaptive MCDM methodp. 54
Evolutionary learningp. 55
Evolutionary algorithms and neural networksp. 56
Evolutionary algorithms and MCDMp. 59
Summary and conclusionsp. 61
On the Combination of MCDM Methodsp. 63
Introductionp. 63
Properties of MCDM methodsp. 69
Properties of specific MCDM methodsp. 71
Properties of neurons and neural networksp. 73
The combination of algorithmsp. 74
Neural MCDM networksp. 75
Termination and runtime of the algorithmp. 76
Summary and conclusionsp. 77
Loops--An Object Oriented DSS for Solving Meta Decision Problemsp. 79
Preliminary remarksp. 79
Method integration, openness, and object oriented implementationp. 80
A class concept for LOOPSp. 84
Problem solving and learning from an object oriented point of viewp. 84
MADM methods in LOOPSp. 87
Neural networks in LOOPSp. 89
Neural MCDM networks in LOOPSp. 90
Evolutionary algorithms in LOOPSp. 91
An extended interactive frameworkp. 95
Summary and conclusionsp. 98
Examples of the Application of LOOPSp. 99
Some remarks on the application of LOOPSp. 99
The learning of utility functionsp. 100
Stock selectionp. 106
Stock price prediction and the learning of time seriesp. 113
Stock analysis and long-term predictionp. 121
Method learningp. 124
Meta learningp. 127
An integrated proposal for the application of LOOPSp. 131
Summary and conclusionsp. 132
Critical Resume and Outlookp. 135
Referencesp. 141
Appendicesp. 162
Some basic concepts of MCDM theoryp. 163
Relationsp. 163
Efficiency concepts and scalarizing theoremsp. 165
Utility concepts and other axiomaticsp. 166
Some selected MCDM methodsp. 169
Simple additive weightingp. 169
Achievement levelsp. 169
Reference point approachesp. 170
The outranking method Prometheep. 171
Neural networksp. 173
Introduction to neural networksp. 173
Neural networks for intelligent decision supportp. 178
Evolutionary algorithmsp. 181
Introduction to evolutionary algorithmsp. 181
The generalization of evolutionary algorithmsp. 186
List of symbolsp. 189
List of abbreviationsp. 193
Indexp. 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