top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Multicriteria Analysis in Finance [[electronic resource] /] / by Michael Doumpos, Constantin Zopounidis
Multicriteria Analysis in Finance [[electronic resource] /] / by Michael Doumpos, Constantin Zopounidis
Autore Doumpos Michael
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (111 p.)
Disciplina 332
Collana SpringerBriefs in Operations Research
Soggetto topico Operations research
Decision making
Management science
Finance
Economics, Mathematical 
Operations Research/Decision Theory
Operations Research, Management Science
Finance, general
Quantitative Finance
ISBN 3-319-05864-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction to financial decision making -- An Overview of multiple criteria decision aid.- Banking management -- Credit Scoring -- Portfolio management.- Other applications of multi criteria analysis in finance -- Conclusions and future perspectives.
Record Nr. UNINA-9910298164303321
Doumpos Michael  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multicriteria decision aid and artificial intelligence [[electronic resource] ] : links, theory and applications / / edited by Michael Doumpos and Evangelos Grigoroudis
Multicriteria decision aid and artificial intelligence [[electronic resource] ] : links, theory and applications / / edited by Michael Doumpos and Evangelos Grigoroudis
Autore Doumpos Michael
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Blackwell, 2013
Descrizione fisica 1 online resource (369 p.)
Disciplina 658.4/033
Altri autori (Persone) GrigoroudisEvangelos
Soggetto topico Multiple criteria decision making
Artificial intelligence
ISBN 1-118-52251-6
1-299-15953-2
1-118-52250-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: List of Contributors Preface Part One The Contributions of Intelligent Techniques in Multicriteria Decision Aiding 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview 1.1 Introduction 1.2 The MCDA Paradigm 1.2.1 Modeling Process 1.2.2 Methodological Approaches 1.3 Computational Intelligence in MCDA 1.3.1 Statistical Learning and Data Mining 1.3.2 Fuzzy Modeling 1.3.3 Metaheuristics 1.4 Conclusions References 2 Intelligent Decision Support Systems 2.1 Introduction 2.2 Fundamentals of Human Decision Making 2.3 Decision Support System 2.4 Intelligent Decision Support Systems 2.4.1 Artificial Neural Networks for Intelligent Decision Support 2.4.2 Fuzzy Logic for Intelligent Decision Support 2.4.3 Expert Systems for Intelligent Decision Support 2.4.4 Evolutionary Computing for Intelligent Decision Support 2.4.5 Intelligent Agents for Intelligent Decision Support 2.5 Evaluating Intelligent Decision Support Systems 2.5.1 Determining Evaluation Criteria 2.5.2 Multi-Criteria Model for IDSS Assessment 2.6 Summary and Future Trends References Part Two Intelligent Technologies for Decision Support and Preference Modeling 3 Designing Distributed Multi-Criteria Decision Support Systems for Complex and Uncertain Situations 3.1 Introduction 3.2 Example Applications 3.3 Key Challenges 3.4 Making Trade-offs: Multi-criteria Decision Analysis 3.4.1 Multi-attribute Decision Support 3.4.2 Making Trade-offs Under Uncertainty 3.5 Exploring the Future: Scenario-based Reasoning 3.6 Making Robust Decisions: Combining MCDA and SBR 3.6.1 Decisions Under Uncertainty: The Concept of Robustness 3.6.2 Combining Scenarios and MCDA 3.6.3 Collecting, Sharing and Processing Information: A Distributed Approach 3.6.4 Keeping Track of Future Developments: Constructing Comparable Scenarios 3.6.5 Respecting Constraints and Requirements: Scenario Management 3.6.6 Assisting Evaluation: Assessing Large Numbers of Scenarios 3.7 Discussion 3.8 Conclusion References 4 Preference Representation with Ontologies 4.1 Introduction 4.1.1 Structure of the Chapter 4.2 Ontology-based Preference Models 4.3 Maintaining the User's Profile up to Date 4.4 Decision Making Methods Exploiting the Preference Information Stored in Ontologies 4.4.1 Recommendation Based on Aggregation 4.4.2 Recommendation Based on Similarities 4.4.3 Recommendation Based on Rules 4.5 Discussion and Open Questions References Part Three Decision Models 5 Neural Networks in Multicriteria Decision Support 5.1 Introduction 5.2 Basic Concepts of Neural Networks 5.2.1 Neural Networks for Intelligent Decision Support 5.3 Basics in Multicriteria Decision Aid 5.3.1 MCDM Problems 5.3.2 Solutions of MCDM Problems 5.4 Neural Networks and Multicriteria Decision Support 5.4.1 Review of Neural Network Applications to MCDM Problems 5.4.2 Discussion 5.5 Summary and Conclusions References 6 Rule-Based Approach to Multicriteria Ranking 6.1 Introduction 6.2 Problem Setting 6.3 Pairwise Comparison Table (PCT) 6.4 Rough Approximation of Outranking and Non-outranking Relations 6.5 Induction and Application of Decision Rules 6.6 Exploitation of Preference Graphs 6.7 Illustrative Example 6.8 Summary and Conclusions References 7 About the Application of Evidence Theory in MultiCriteria Decision Aid 7.1 Introduction 7.2 Evidence Theory: Some Concepts 7.2.1 Knowledge Model 7.2.2 Combination 7.2.3 Decision Making 7.3 New Concepts in Evidence Theory for MCDA 7.3.1 First Belief Dominance 7.3.2 RBBD Concept 7.4 Multicriteria Methods modeled by Evidence Theory 7.4.1 Evidential Reasoning Approach 7.4.2 DS/AHP 7.4.3 DISSET 7.4.4 A Choice Model Inspired by ELECTRE I 7.4.5 A Ranking Model Inspired by Xu et al.'s Method 7.5 Discussion 7.6 Conclusion References Part Four Multiobjective Optimization 8 Interactive Approaches Applied to Multiobjective Evolutionary Algorithms 8.1 Introduction 8.1.1 Methods Analyzed in this Chapter 8.2 Basic Concepts and Notation 8.2.1 Multiobjective Optimization Problems 8.2.2 Classical Interactive Methods 8.3 MOEAs Based on Reference Point Methods 8.3.1 A Weighted Distance Metric 8.3.2 Light Beam Search Combined with NSGA-II 8.3.3 Controlling the Accuracy of the Pareto Front Approximation 8.3.4 Light Beam Search Combined with PSO 8.3.5 A Preference Relation Based on a Weighted Distance Metric 8.3.6 The Chebyshev Preference Relation 8.4 MOEAs Based on Value Function Methods 8.4.1 Progressive Approximation of a Value Function 8.4.2 Value Function by Ordinal Regression 8.5 Miscellaneous Methods 8.5.1 Desirability Functions 8.6 Conclusions and Future Work References 9 Generalized DEA and Computational Intelligence in Multiple Criteria Decision Making 9.1 Introduction 9.2 Generalized Data Envelopment Analysis 9.2.1 Basic DEA Models: CCR, BCC and FDH Models 9.2.2 GDEA Model 9.3 Generation of Pareto Optimal Solutions using Generalized DEA and Computational Intelligence 9.3.1 GDEA in Fitness Evaluation 9.3.2 GDEA in Deciding the Parameters of Multi-objective PSO 9.3.3 Expected Improvement for Multi-objective Optimization Using GDEA 9.4 Summary References 10 Fuzzy Multiobjective Optimization 10.1 Introduction 10.2 Solution Concepts for Multiobjective Programming 10.3 Interactive Multiobjective Linear Programming 10.4 Fuzzy Multiobjective Linear Programming 10.5 Interactive Fuzzy Multiobjective Linear Programming 10.6 Interactive Fuzzy Multiobjective Linear Programming with Fuzzy Parameters 10.7 Interactive Fuzzy Stochastic Multiobjective Linear Programming 10.8 Related Works and Applications References Part Five Applications in Management and Engineering 11 MCDA & Agents: Supporting Effective Resource Federation in Virtual Organizations 11.1 Introduction 11.2 The Intuition of Multiple Criteria Decision Aid in Multi-agent Systems 11.3 Resource Federation Applied 11.3.1 Describing the Problem in a Cloud Computing Context 11.3.2 Problem Modeling 11.3.3 Assessing Agents' Value Function for Resource Federation 11.4 An Illustrative Example 11.5 Conclusions References 12 Fuzzy AHP Using Type II Fuzzy Sets: An Application to Warehouse Location Selection 12.1 Introduction 12.2 Multicriteria Selection 12.2.1 The ELECTRE (Elimination Et Choix Traduisant la Realite) Method 12.2.2 PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) 12.2.3 TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) 12.2.4 The WSM (Weighted Sum Model) Method 12.2.5 MAUT (Multi-attribute Utility Theory) 12.2.6 AHP (Analytic Hierarchy Process) 12.3 Literature Review on Fuzzy AHP 12.4 Buckley's Type-1 Fuzzy AHP 12.5 Type-2 Fuzzy Sets 12.6 Type-2 Fuzzy AHP 12.7 An Application: Warehouse Location Selection 12.8 Conclusion References 13 Applying Genetic Algorithms to Optimize Energy Efficiency in Buildings 13.1 Introduction 13.2 State-of-the-Art Review 13.3 An Example Case Study 13.3.1 Basic Principles and Problem Definition 13.3.2 Decision Variables 13.3.3 Decision Criteria 13.3.4 Decision Model 13.4 Development and Application of a Genetic Algorithm for the Example Case Study 13.4.1 Development of the Genetic Algorithm 13.4.2 Application of the Genetic Algorithm, Analysis of Results and Discussion 13.5 Conclusions References 14 Nature-Inspired Intelligence for Pareto Optimality Analysis in Portfolio Optimization 14.1 Introduction 14.2 Literature Review 14.3 Methodological Issues 14.4 Pareto Optimal Sets in Portfolio Optimization 14.4.1 Pareto Efficiency 14.4.2 Mathematical Formulation of the Portfolio Optimization Problem 14.5 Computational Results 14.5.1 Experimental Setup 14.5.2 Efficient Frontier 14.6 Conclusion References Index.
Record Nr. UNINA-9910141473803321
Doumpos Michael  
Hoboken, N.J., : Wiley-Blackwell, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multicriteria decision aid and artificial intelligence [[electronic resource] ] : links, theory and applications / / edited by Michael Doumpos and Evangelos Grigoroudis
Multicriteria decision aid and artificial intelligence [[electronic resource] ] : links, theory and applications / / edited by Michael Doumpos and Evangelos Grigoroudis
Autore Doumpos Michael
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Blackwell, 2013
Descrizione fisica 1 online resource (369 p.)
Disciplina 658.4/033
Altri autori (Persone) GrigoroudisEvangelos
Soggetto topico Multiple criteria decision making
Artificial intelligence
ISBN 1-118-52251-6
1-299-15953-2
1-118-52250-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: List of Contributors Preface Part One The Contributions of Intelligent Techniques in Multicriteria Decision Aiding 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview 1.1 Introduction 1.2 The MCDA Paradigm 1.2.1 Modeling Process 1.2.2 Methodological Approaches 1.3 Computational Intelligence in MCDA 1.3.1 Statistical Learning and Data Mining 1.3.2 Fuzzy Modeling 1.3.3 Metaheuristics 1.4 Conclusions References 2 Intelligent Decision Support Systems 2.1 Introduction 2.2 Fundamentals of Human Decision Making 2.3 Decision Support System 2.4 Intelligent Decision Support Systems 2.4.1 Artificial Neural Networks for Intelligent Decision Support 2.4.2 Fuzzy Logic for Intelligent Decision Support 2.4.3 Expert Systems for Intelligent Decision Support 2.4.4 Evolutionary Computing for Intelligent Decision Support 2.4.5 Intelligent Agents for Intelligent Decision Support 2.5 Evaluating Intelligent Decision Support Systems 2.5.1 Determining Evaluation Criteria 2.5.2 Multi-Criteria Model for IDSS Assessment 2.6 Summary and Future Trends References Part Two Intelligent Technologies for Decision Support and Preference Modeling 3 Designing Distributed Multi-Criteria Decision Support Systems for Complex and Uncertain Situations 3.1 Introduction 3.2 Example Applications 3.3 Key Challenges 3.4 Making Trade-offs: Multi-criteria Decision Analysis 3.4.1 Multi-attribute Decision Support 3.4.2 Making Trade-offs Under Uncertainty 3.5 Exploring the Future: Scenario-based Reasoning 3.6 Making Robust Decisions: Combining MCDA and SBR 3.6.1 Decisions Under Uncertainty: The Concept of Robustness 3.6.2 Combining Scenarios and MCDA 3.6.3 Collecting, Sharing and Processing Information: A Distributed Approach 3.6.4 Keeping Track of Future Developments: Constructing Comparable Scenarios 3.6.5 Respecting Constraints and Requirements: Scenario Management 3.6.6 Assisting Evaluation: Assessing Large Numbers of Scenarios 3.7 Discussion 3.8 Conclusion References 4 Preference Representation with Ontologies 4.1 Introduction 4.1.1 Structure of the Chapter 4.2 Ontology-based Preference Models 4.3 Maintaining the User's Profile up to Date 4.4 Decision Making Methods Exploiting the Preference Information Stored in Ontologies 4.4.1 Recommendation Based on Aggregation 4.4.2 Recommendation Based on Similarities 4.4.3 Recommendation Based on Rules 4.5 Discussion and Open Questions References Part Three Decision Models 5 Neural Networks in Multicriteria Decision Support 5.1 Introduction 5.2 Basic Concepts of Neural Networks 5.2.1 Neural Networks for Intelligent Decision Support 5.3 Basics in Multicriteria Decision Aid 5.3.1 MCDM Problems 5.3.2 Solutions of MCDM Problems 5.4 Neural Networks and Multicriteria Decision Support 5.4.1 Review of Neural Network Applications to MCDM Problems 5.4.2 Discussion 5.5 Summary and Conclusions References 6 Rule-Based Approach to Multicriteria Ranking 6.1 Introduction 6.2 Problem Setting 6.3 Pairwise Comparison Table (PCT) 6.4 Rough Approximation of Outranking and Non-outranking Relations 6.5 Induction and Application of Decision Rules 6.6 Exploitation of Preference Graphs 6.7 Illustrative Example 6.8 Summary and Conclusions References 7 About the Application of Evidence Theory in MultiCriteria Decision Aid 7.1 Introduction 7.2 Evidence Theory: Some Concepts 7.2.1 Knowledge Model 7.2.2 Combination 7.2.3 Decision Making 7.3 New Concepts in Evidence Theory for MCDA 7.3.1 First Belief Dominance 7.3.2 RBBD Concept 7.4 Multicriteria Methods modeled by Evidence Theory 7.4.1 Evidential Reasoning Approach 7.4.2 DS/AHP 7.4.3 DISSET 7.4.4 A Choice Model Inspired by ELECTRE I 7.4.5 A Ranking Model Inspired by Xu et al.'s Method 7.5 Discussion 7.6 Conclusion References Part Four Multiobjective Optimization 8 Interactive Approaches Applied to Multiobjective Evolutionary Algorithms 8.1 Introduction 8.1.1 Methods Analyzed in this Chapter 8.2 Basic Concepts and Notation 8.2.1 Multiobjective Optimization Problems 8.2.2 Classical Interactive Methods 8.3 MOEAs Based on Reference Point Methods 8.3.1 A Weighted Distance Metric 8.3.2 Light Beam Search Combined with NSGA-II 8.3.3 Controlling the Accuracy of the Pareto Front Approximation 8.3.4 Light Beam Search Combined with PSO 8.3.5 A Preference Relation Based on a Weighted Distance Metric 8.3.6 The Chebyshev Preference Relation 8.4 MOEAs Based on Value Function Methods 8.4.1 Progressive Approximation of a Value Function 8.4.2 Value Function by Ordinal Regression 8.5 Miscellaneous Methods 8.5.1 Desirability Functions 8.6 Conclusions and Future Work References 9 Generalized DEA and Computational Intelligence in Multiple Criteria Decision Making 9.1 Introduction 9.2 Generalized Data Envelopment Analysis 9.2.1 Basic DEA Models: CCR, BCC and FDH Models 9.2.2 GDEA Model 9.3 Generation of Pareto Optimal Solutions using Generalized DEA and Computational Intelligence 9.3.1 GDEA in Fitness Evaluation 9.3.2 GDEA in Deciding the Parameters of Multi-objective PSO 9.3.3 Expected Improvement for Multi-objective Optimization Using GDEA 9.4 Summary References 10 Fuzzy Multiobjective Optimization 10.1 Introduction 10.2 Solution Concepts for Multiobjective Programming 10.3 Interactive Multiobjective Linear Programming 10.4 Fuzzy Multiobjective Linear Programming 10.5 Interactive Fuzzy Multiobjective Linear Programming 10.6 Interactive Fuzzy Multiobjective Linear Programming with Fuzzy Parameters 10.7 Interactive Fuzzy Stochastic Multiobjective Linear Programming 10.8 Related Works and Applications References Part Five Applications in Management and Engineering 11 MCDA & Agents: Supporting Effective Resource Federation in Virtual Organizations 11.1 Introduction 11.2 The Intuition of Multiple Criteria Decision Aid in Multi-agent Systems 11.3 Resource Federation Applied 11.3.1 Describing the Problem in a Cloud Computing Context 11.3.2 Problem Modeling 11.3.3 Assessing Agents' Value Function for Resource Federation 11.4 An Illustrative Example 11.5 Conclusions References 12 Fuzzy AHP Using Type II Fuzzy Sets: An Application to Warehouse Location Selection 12.1 Introduction 12.2 Multicriteria Selection 12.2.1 The ELECTRE (Elimination Et Choix Traduisant la Realite) Method 12.2.2 PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) 12.2.3 TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) 12.2.4 The WSM (Weighted Sum Model) Method 12.2.5 MAUT (Multi-attribute Utility Theory) 12.2.6 AHP (Analytic Hierarchy Process) 12.3 Literature Review on Fuzzy AHP 12.4 Buckley's Type-1 Fuzzy AHP 12.5 Type-2 Fuzzy Sets 12.6 Type-2 Fuzzy AHP 12.7 An Application: Warehouse Location Selection 12.8 Conclusion References 13 Applying Genetic Algorithms to Optimize Energy Efficiency in Buildings 13.1 Introduction 13.2 State-of-the-Art Review 13.3 An Example Case Study 13.3.1 Basic Principles and Problem Definition 13.3.2 Decision Variables 13.3.3 Decision Criteria 13.3.4 Decision Model 13.4 Development and Application of a Genetic Algorithm for the Example Case Study 13.4.1 Development of the Genetic Algorithm 13.4.2 Application of the Genetic Algorithm, Analysis of Results and Discussion 13.5 Conclusions References 14 Nature-Inspired Intelligence for Pareto Optimality Analysis in Portfolio Optimization 14.1 Introduction 14.2 Literature Review 14.3 Methodological Issues 14.4 Pareto Optimal Sets in Portfolio Optimization 14.4.1 Pareto Efficiency 14.4.2 Mathematical Formulation of the Portfolio Optimization Problem 14.5 Computational Results 14.5.1 Experimental Setup 14.5.2 Efficient Frontier 14.6 Conclusion References Index.
Record Nr. UNINA-9910812188803321
Doumpos Michael  
Hoboken, N.J., : Wiley-Blackwell, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui