Multicriteria Decision Aid and Artificial Intelligence

Informationen zum Autor Michael Doumpos, Technical University of Crete, Department of Production Engineering and Management, Greece. Evangelos Grigoroudis, Technical University of Crete, Department of Production Engineering and Management, Greece. Klappentext Presents recent advances in both models and systems for intelligent decision making.Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems.The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering.Multicriteria Decision Aid and Artificial Intelligence:* Covers all of the recent advances in intelligent decision making.* Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems.* Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments.* Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications.* Is written by experts in the field.This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial. Zusammenfassung Presents recent advances in both models and systems for intelligent decision making.Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems.The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering.Multicriteria Decision Aid and Artificial Intelligence:* Covers all of the recent advances in intelligent decision making.* Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems.* Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments.* Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications.* Is written by experts in the field.This book provides an excellent reference tool for the in...

List of contents

Preface xi
 
Notes on Contributors xv
 
Part I THE CONTRIBUTIONS OF INTELLIGENT TECHNIQUES IN MULTICRITERIA DECISION AIDING 1
 
1 Computational intelligence techniques for multicriteria decision aiding: An overview 3
Michael Doumpos and Constantin Zopounidis
 
1.1 Introduction 3
 
1.2 The MCDA paradigm 4
 
1.2.1 Modeling process 4
 
1.2.2 Methodological approaches 6
 
1.3 Computational intelligence in MCDA 9
 
1.3.1 Statistical learning and data mining 9
 
1.3.2 Fuzzy modeling 12
 
1.3.3 Metaheuristics 15
 
1.4 Conclusions 17
 
References 18
 
2 Intelligent decision support systems 25
Gloria Phillips-Wren
 
2.1 Introduction 25
 
2.2 Fundamentals of human decision making 26
 
2.3 Decision support systems 29
 
2.4 Intelligent decision support systems 30
 
2.4.1 Artificial neural networks for intelligent decision support 31
 
2.4.2 Fuzzy logic for intelligent decision support 34
 
2.4.3 Expert systems for intelligent decision support 35
 
2.4.4 Evolutionary computing for intelligent decision support 35
 
2.4.5 Intelligent agents for intelligent decision support 36
 
2.5 Evaluating intelligent decision support systems 38
 
2.5.1 Determining evaluation criteria 38
 
2.5.2 Multi-criteria model for IDSS assessment 39
 
2.6 Summary and future trends 40
 
Acknowledgment 41
 
References 41
 
Part II INTELLIGENT TECHNOLOGIES FOR DECISION SUPPORT AND PREFERENCE MODELING 45
 
3 Designing distributed multi-criteria decision support systems for complex and uncertain situations 47
Tina Comes, Niek Wijngaards and Frank Schultmann
 
3.1 Introduction 47
 
3.2 Example applications 49
 
3.3 Key challenges 51
 
3.4 Making trade-offs: Multi-criteria decision analysis 53
 
3.4.1 Multi-attribute decision support 53
 
3.4.2 Making trade-offs under uncertainty 55
 
3.5 Exploring the future: Scenario-based reasoning 56
 
3.6 Making robust decisions: Combining MCDA and SBR 57
 
3.6.1 Decisions under uncertainty: The concept of robustness 57
 
3.6.2 Combining scenarios and MCDA 58
 
3.6.3 Collecting, sharing and processing information: A distributed approach 59
 
3.6.4 Keeping track of future developments: Constructing comparable scenarios 61
 

3.6.5 Respecting constraints and requirements: Scenario management 64
 
3.6.6 Assisting evaluation: Assessing large numbers of scenarios 66
 
3.7 Discussion 69
 
3.8 Conclusion 71
 
Acknowledgment 71
 
References 72
 
4 Preference representation with ontologies 77
Aida Valls, Antonio Moreno and Joan Borr`as
 
4.1 Introduction 77
 
4.2 Ontology-based preference models 80
 
4.3 Maintaining the user profile up to date 85
 
4.4 Decision making methods exploiting the preference information stored in ontologies 88
 
4.4.1 Recommendation based on aggregation 91
 
4.4.2 Recommendation based on similarities 92
 
4.4.3 Recommendation based on rules 93
 
4.5 Discussion and open questions 94
 
Acknowledgments 95
 
References 96
 
Part III DECISION MODELS 101
 
5 Neural networks in multicriteria decision support 103
Thomas Hanne
 
5.1 Introduction 103
 
5.2 Basic concepts of neural networks 104
 
5.2.1 Neural networks for intelligent decision support 109
 
5.3 Basics in multicriteria decision aid 111
 
5.3.1 MCDM problems 111
 
5.3.2 Solutions of MCDM problems 112
 

Product details

• Authors: M Doumpos, Michael Doumpos, Michael (Technical University of Crete) Doumpos, Doumpos Michael, Evangelos Grigoroudis, Evangelos (Technical University of Crete Grigoroudis, Grigoroudis Evangelos
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 05.04.2013
• EAN: 9781119976394
• ISBN: 978-1-119-97639-4
• No. of pages: 368
• Dimensions: 178 mm x 255 mm x 24 mm
• Subjects: Social sciences, law, business > Business > Management; Statistik, Operations Research, Data Mining, Statistics, Business & management, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Wirtschaft u. Management, Data Mining Statistics, Betriebswirtschaft u. Operationsforschung, Management Science/Operational Research, Intelligente Systeme u. Agenten, Intelligent Systems & Agents