Beschreibung
Inhaltsangabe1. Introduction.- 1.1 What is Multiple Criteria Decision Making.- 1.2 Relevance of MCDM to Engineering Design.- 1.2.1 The Structure of a Design Problem.- 1.2.2 The Principal Issues in Multiple Criteria Decision Making.- 1.2.3 Issues of Complexity, Subjectivity and Uncertainty.- 1.3 Design Selection vs Design Synthesis.- 1.4 Outline of the Book.- 2. MCDM and The Nature of Decision Making in Design.- 2.1 Introduction.- 2.2 Pareto Optimality: What are the Options?.- 2.3 MCDM Methods and Some Key Terminology.- 2.4 Concluding Comments.- 3. Multiple Attribute Decision Making.- 3.1 Problem Formulations and Method Classification.- 3.1.1 MADM Problems.- 3.1.2 Classification of MADM Methods.- 3.2 Techniques for Weight Assignment.- 3.2.1 Direct Assignment.- 3.2.2 Eigenvector Method.- 3.2.3 Entropy Method.- 3.2.4 Minimal Information Method.- 3.2.4.1 General Pairwise Comparisons and Minimal Information.- 3.2.4.2 Linear Programming Models for Weight Assignment.- 3.2.4.3 An Example.- 3.3 Typical MADM Methods and Applications.- 3.3.1 AHP Method and Application.- 3.3.2 UTA Method and Application.- 3.3.3 TOPSIS Method and Application.- 3.3.4 CODASID Method and Applications.- 3.3.4.1 Information Requirement and Normalization.- 3.3.4.2 New Concordance and Discordance Analyses.- 3.3.4.3 Preference Matrix and CODASID Algorithm.- 3.3.4.4 Applications.- 3.3.5 Comments.- 3.4 A Hierarchical Evaluation Process.- 3.4.1 Design Decision Problems with Subjective Factors.- 3.4.2 A Hierarchical Evaluation Process.- 3.4.3 The Ship Choice Problem.- 3.5 Concluding Comments.- 4. Multiple Objective Decision Making.- 4.1 Multiobjective Optimisation and Method Classification.- 4.1.1 Multiobjective Optimisation and Utility Functions.- 4.1.2 Classification of MODM Methods.- 4.2 Techniques for Single-Objective Optimisation.- 4.2.1 Optimality Conditions.- 4.2.2 Sequential Linear Programming.- 4.2.3 Penalty Methods.- 4.3 Typical MODM Methods.- 4.3.1 Goal Programming.- 4.3.2 Geoffrion's Method.- 4.3.3 Minimax Method.- 4.3.4 ISTM Method.- 4.3.5 Local Utility Function Method.- 4.4 Multiobjective Ship Design.- 4.4.1 A Nonlinear Preliminary Ship Design Model.- 4.4.2 Generation of Subsets of Efficient Ship Designs.- 4.4.3 Progressive Design.- 4.4.4 Design by Setting Target Values.- 4.4.5 Adaptive and Compromise Design.- 4.5 Concluding Comments.- 5. Multiple Criteria Decision Making and Genetic Algorithms.- 5.1 Introduction.- 5.2 The Mechanics of the Simple Genetic Algorithm.- 5.2.1 Selection, Crossover and Mutation.- 5.2.2 A Bi-Modal Optimisation Problem.- 5.2.3 The Need for a Multiple Criteria Approach.- 5.3 Multiple Criteria Genetic Algorithms.- 5.3.1 Some Comparative Multiple Criteria G A Approaches.- 5.3.2 Common Issues in Multiple Criteria Genetic Algorithms in Engineering Design.- 5.3.3 Crowding and Niching.- 5.3.4 Estimating Niche Sizes.- 5.4 The Multiple Criteria Genetic Algorithm (MCGA): A Summary.- 5.5 A Numerical Example.- 5.6 An MCGA Schedule for a Generalised Job Shop.- 5.6.1 Problem Data.- 5.6.2 String Configuration.- 5.6.3 The Results from MCGA.- 5.7 Concluding Comments.- 6. An Integrated Multiple Criteria Decision Support System.- 6.1 System Structure and Method Selection.- 6.1.1 General Structure of IMC-DSS.- 6.1.2 The Routine Base for MCDM Techniques.- 6.1.3 Rules for Selection of MADM and MODM Methods.- 6.2 Data Base and Model Base.- 6.2.1 Decision Models and File Systems.- 6.2.2 Semi-Automatic Model Generation.- 6.3 A User Interface and Interactive Decision Making.- 6.3.1 Menu-Driven Interfaces.- 6.3.2 A Unified Approach for Generating and Ranking Design.- 6.4 Application of IMC-DSS.- 6.4.1 A Multiattribute Vessel Choice Problem.- 6.4.2 A Multiobjective Semi-Submersible Design Problem.- 6.4.3 Design Using the Unified Approach.- 6.5 Concluding Comments.- 7. Past, Present and the Future.- 7.1 Introduction.- 7.2 Case Studies.- 7.2.1 Designing product development processes to minimize lead times.- 7.2.2 Multicriteria robust optimisation under uncertainty of cat
Autorenporträt
Inhaltsangabe1. Introduction.- 1.1 What is Multiple Criteria Decision Making.- 1.2 Relevance of MCDM to Engineering Design.- 1.2.1 The Structure of a Design Problem.- 1.2.2 The Principal Issues in Multiple Criteria Decision Making.- 1.2.3 Issues of Complexity, Subjectivity and Uncertainty.- 1.3 Design Selection vs Design Synthesis.- 1.4 Outline of the Book.- 2. MCDM and The Nature of Decision Making in Design.- 2.1 Introduction.- 2.2 Pareto Optimality: What are the Options?.- 2.3 MCDM Methods and Some Key Terminology.- 2.4 Concluding Comments.- 3. Multiple Attribute Decision Making.- 3.1 Problem Formulations and Method Classification.- 3.1.1 MADM Problems.- 3.1.2 Classification of MADM Methods.- 3.2 Techniques for Weight Assignment.- 3.2.1 Direct Assignment.- 3.2.2 Eigenvector Method.- 3.2.3 Entropy Method.- 3.2.4 Minimal Information Method.- 3.2.4.1 General Pairwise Comparisons and Minimal Information.- 3.2.4.2 Linear Programming Models for Weight Assignment.- 3.2.4.3 An Example.- 3.3 Typical MADM Methods and Applications.- 3.3.1 AHP Method and Application.- 3.3.2 UTA Method and Application.- 3.3.3 TOPSIS Method and Application.- 3.3.4 CODASID Method and Applications.- 3.3.4.1 Information Requirement and Normalization.- 3.3.4.2 New Concordance and Discordance Analyses.- 3.3.4.3 Preference Matrix and CODASID Algorithm.- 3.3.4.4 Applications.- 3.3.5 Comments.- 3.4 A Hierarchical Evaluation Process.- 3.4.1 Design Decision Problems with Subjective Factors.- 3.4.2 A Hierarchical Evaluation Process.- 3.4.3 The Ship Choice Problem.- 3.5 Concluding Comments.- 4. Multiple Objective Decision Making.- 4.1 Multiobjective Optimisation and Method Classification.- 4.1.1 Multiobjective Optimisation and Utility Functions.- 4.1.2 Classification of MODM Methods.- 4.2 Techniques for Single-Objective Optimisation.- 4.2.1 Optimality Conditions.- 4.2.2 Sequential Linear Programming.- 4.2.3 Penalty Methods.- 4.3 Typical MODM Methods.- 4.3.1 Goal Programming.- 4.3.2 Geoffrion's Method.- 4.3.3 Minimax Method.- 4.3.4 ISTM Method.- 4.3.5 Local Utility Function Method.- 4.4 Multiobjective Ship Design.- 4.4.1 A Nonlinear Preliminary Ship Design Model.- 4.4.2 Generation of Subsets of Efficient Ship Designs.- 4.4.3 Progressive Design.- 4.4.4 Design by Setting Target Values.- 4.4.5 Adaptive and Compromise Design.- 4.5 Concluding Comments.- 5. Multiple Criteria Decision Making and Genetic Algorithms.- 5.1 Introduction.- 5.2 The Mechanics of the Simple Genetic Algorithm.- 5.2.1 Selection, Crossover and Mutation.- 5.2.2 A Bi-Modal Optimisation Problem.- 5.2.3 The Need for a Multiple Criteria Approach.- 5.3 Multiple Criteria Genetic Algorithms.- 5.3.1 Some Comparative Multiple Criteria G A Approaches.- 5.3.2 Common Issues in Multiple Criteria Genetic Algorithms in Engineering Design.- 5.3.3 Crowding and Niching.- 5.3.4 Estimating Niche Sizes.- 5.4 The Multiple Criteria Genetic Algorithm (MCGA): A Summary.- 5.5 A Numerical Example.- 5.6 An MCGA Schedule for a Generalised Job Shop.- 5.6.1 Problem Data.- 5.6.2 String Configuration.- 5.6.3 The Results from MCGA.- 5.7 Concluding Comments.- 6. An Integrated Multiple Criteria Decision Support System.- 6.1 System Structure and Method Selection.- 6.1.1 General Structure of IMC-DSS.- 6.1.2 The Routine Base for MCDM Techniques.- 6.1.3 Rules for Selection of MADM and MODM Methods.- 6.2 Data Base and Model Base.- 6.2.1 Decision Models and File Systems.- 6.2.2 Semi-Automatic Model Generation.- 6.3 A User Interface and Interactive Decision Making.- 6.3.1 Menu-Driven Interfaces.- 6.3.2 A Unified Approach for Generating and Ranking Design.- 6.4 Application of IMC-DSS.- 6.4.1 A Multiattribute Vessel Choice Problem.- 6.4.2 A Multiobjective Semi-Submersible Design Problem.- 6.4.3 Design Using the Unified Approach.- 6.5 Concluding Comments.- 7. Past, Present and the Future.- 7.1 Introduction.- 7.2 Case Studies.- 7.2.1 Designing product development processes to minimize lead times.- 7.2.2 Multicriteria robust optimisation under uncertainty of cat
