Multiple Criteria Decision Support in Engineering Design

ISBN-13: 9781447130222 / Angielski / Miękka / 2011 / 264 str.

Pratyush Sen; Jian-Bo Yang

Opis

Multiple Criteria Decision Support in Engineering Design examines some of the underlying issues and related modelling strategies, with a view to exploring the rich potential of a generalised multiple-criteria approach to design decision-making. The arguments are supported by numerical examples. It can be argued that, within the classic monocriterion paradigm, the optimal solution is inarguably identified once the feasible alternatives are established and an objective function agreed on. It is only when conflict resolution is involved that decision-making truly becomes important, and many design situations exist where stated functional requirements may be in actual or potential conflict. The most preferred solution under such circumstances depends on the designer's or decision-maker's priorities, so that the chosen solution is based on a combination of technical possibilities and designer preferences. This book addresses the key concepts in multiple criteria decision-making and provides valuable insight into how such problems arise and can be solved, in the area of decision-making in general and in the domain of engineering design in particular.

Spis treści

1. 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 catamarans from a seakeeping point of view.- 7.3 Concluding Comments.- References.- Topic Index.