This book brings together, in a single volume, the fields of multicriteria decision making and multiobjective optimization that are traditionally covered separately. Both fields have in common the presence of multiple perspectives of looking at and evaluating decisions to be taken but they differ in the number of available alternatives. Multicriteria approaches deal with decision processes where a finite number of alternatives have to be evaluated while, in multiobjective optimization, this number is infinite and the space of alternatives continuous. This book is written for students of applied mathematics, engineering, and economics and management, with no assumed previous knowledge on the subject, as well as for practitioners in industry looking for techniques to support decision making. The mathematical formalism is very low, so that all materials are accessible to most readers. Nonetheless, a rich bibliography allows interested readers to access more technical literature.
The textbook is organized in eleven chapters, each corresponding to a class of about two hours. A comprehensive set of examples is presented, allowing for a didactic approach when presenting the methodologies. Each chapter ends with exercises that are designed to develop problem-solving skills and to promote concepts retention.
Author(s): Maria Isabel Gomes, Nelson Chibeles Martins
Publisher: CRC Press
Year: 2022
Language: English
Pages: 300
City: Boca Raton
Cover
Title Page
Copyright Page
Preface
Table of Contents
1. Single Criterion Decision Making
1.1 Introduction
1.2 Single Criterion Decision Making Methodologies
1.2.1 Decision Making in an Uncertainty Context
1.2.2 Decision Making in an Risk Context
1.3 Final Remarks
1.4 Proposed Exercises
2. Sequential Decisions and Introduction to Utility Theory
2.1 Introduction
2.2 Sequential Decision Making and Decision Trees
2.2.1 Drawing the Decision Tree
2.2.2 Assessing the Leaves
2.2.3 Assessing the Nodes
2.2.4 Some Comments about Sequential Decisions
2.3 Utility Theory
2.3.1 Probability Equivalence Method
2.3.2 Certainty Equivalence Method
2.3.3 Types of Utility Functions
2.3.4 Final Remarks Concerning Utility Theory
2.4 Proposed Exercises
3. Simple Multi-Attribute Rating Technique – SMART
3.1 Introduction
3.2 The Method
3.2.1 Value Trees
3.2.2 Alternatives’ Assessment
3.2.3 Value Functions
3.2.4 Dominance Analysis
3.2.5 Assigning Weights to the Criteria – Swing Weight
3.2.6 Calculate the Weighted Sum for All Alternatives
3.2.7 Sensitivity Analysis
3.3 Final Remarks
3.4 Proposed Exercises
4. The ELECTRE Methods
4.1 Introduction
4.2 ELECTRE I
4.2.1 Outranking Relation
4.2.2 Concordance Matrix
4.2.3 Discordance and Veto
4.2.4 The Nucleus
4.3 Electre III
4.3.1 Preference and Indifference Relations
4.3.2 Outranking Relation
4.3.3 Concordance Matrix
4.3.4 Discordance Matrices
4.3.5 Credibility Matrix
4.3.6 Alternatives Ranking
4.4 Final Remarks
4.5 Proposed Exercises
5. Analytic Hierarchy Process
5.1 Introduction
5.2 The Method
5.2.1 Pairwise Comparisons, Judgement Matrix and Priority Vector
5.2.2 Consistency of Judgement Matrices. Consistency Index. Random Consistency Index. Consistency Ratio
5.2.3 Concerning the Alternatives
5.2.4 Non-Consistency
5.2.5 Integrating the Problem’s Hierarchic Levels
5.2.6 More Complex Hierarchies
5.3 Final Remarks
5.4 Exercises
6. TOPSIS and Fuzzy TOPSIS
6.1 Introduction
6.2 The Method
6.3 Some of the Method’s Limitations
6.3.1 Normalization Step
6.3.2 Weights
6.3.3 Rank Reversal
6.4 Fuzzy TOPSIS
6.4.1 Fuzzy Sets and Fuzzy Numbers
6.4.2 The Method
6.4.3 Fuzzy TOPSIS for Group Decision Making
6.5 Final Remarks
6.6 Proposed Exercises
7. Multi-Objective Linear Programming
7.1 Introduction
7.2 Efficient Solutions and Objective Functions Space
7.3 Weakly Efficient Solution
7.4 Payoff Table, and Ideal and Nadir Solutions
7.5 Final Remarks
7.6 Exercises
7.6.1 Solved Exercises
7.6.2 Proposed Exercises
8. Lexicographic and ε-Constraint Methods
8.1 Introduction
8.2 The Lexicographic Method
8.2.1 The Algorithm
8.2.2 The Lexicographic Method as a Tool to Identify Multiple Optimal Solutions
8.2.3 Final Remarks
8.3 ε-Constraint Method
8.3.1 Infeasibility
8.3.2 Weak Efficiency
8.3.3 How to Approximate the Pareto Front using ε-Constraint Method?
8.3.4 Final Remarks
8.4 Exercises
8.4.1 Solved Exercises
8.4.2 Proposed Exercises
9. Weighted Sum and Distance Minimization Methods
9.1 Introduction
9.2 Weighted Sum Model
9.2.1 Definition of a Weighted Sum Objective Function
9.2.2 Dealing with Functions in Different Scales
9.2.3 Final Remarks
9.3 Distance Minimization Model
9.3.1 Distance Minimization to the Ideal Solution
9.3.2 L∞-Distance Minimization to the Ideal Solution
9.3.3 L2-Distance Minimization to the Iideal Solution
9.3.4 Final Remarks
9.4 Final Remarks
9.5 Exercises
9.5.1 Solved Exercises
9.5.2 Proposed Eexercises
10. Interactive Methods
10.1 Introduction
10.2 The Step Method (STEM)
10.2.1 Augmented Weighted Tchebychev Distance
10.2.2 Iterative Process
10.2.3 Final Rremarks
10.3 Other Methods
10.3.1 Zionts-Wallenius Method
10.3.2 Pareto Race
10.3.3 NAUTILUS
10.4 Final Remarks
10.5 Exercises
10.5.1 Solved Exercises
10.5.2 Proposed Exercises
Appendix A
Models in GAMS
11. Goal Programming
11.1 Introduction
11.2 Goals and the Utopian Set
11.2.1 What if the Utopian Set Does Not Intersect the Feasible Region?
11.2.2 What if the Utopian Set is an Empty Set?
11.3 Goal Programming Models
11.3.1 Preemptive GP Model
11.3.2 The Archimedean GP Model
11.3.3 A GP Model Combining Preemptive Priorities and Weighting
11.3.4 The Tchebychev GP Model
11.4 Final Remarks
11.5 Proposed Exercises
Appendix B: Definitions
A.1 Multi-Criteria Methods
A.1.1 An Example
A.2 Multi-Objective Methods
Index
