Preface
Reference
Contents
Chapter 1: MCDM Methods: Modelling, Feasibility, and Sensitivity Analysis
1.1 Introduction
1.1.1 Present-Day Condition of MCDM
1.1.2 Are MCDM Methods Reliable or They Are Only a Useful Guide?
1.1.3 Modelling
1.1.4 Project Feasibility
1.1.4.1 A Fundamental Question: Is a Problem Feasible?
1.1.5 As Done at Present, Sensitivity Analysis Is Not Reliable
1.2 Conclusion
References
Chapter 2: Linear Programming and the SIMUS Method
2.1 Linear Programming
2.2 The SIMUS Method
2.2.1 SIMUS and Sensitivity Analysis
2.2.2 How SIMUS Considers the Significance of Criteria?
2.2.3 The Role of the DM
2.3 Conclusion
References
Chapter 3: Analysis of Facts: Issues and Questions Answered in MCDM Practice
3.1 Which Are Complex Scenarios?
3.2 Why There Could Be Precedence Between Projects, What Do They Mean?
3.3 Why Most Methods Do Not Consider Resources?
3.4 Why There Is a Necessity to Consider All Aspects of a Project and Not Only the Most Important?
3.5 How Are Resources Considered?
3.6 Which Are Determined First, Alternatives or Criteria, and Why?
3.7 How to Work with Multiple Scenarios?
3.8 Can a Criterion Impact on Another?
3.9 Are Alternatives Related or They Are Always Independent?
3.10 What Is a Binary Criterion, and What Is Its Purpose?
3.11 What Happens When Projects in a Portfolio Need to Be Started at Different Times?
3.12 How Do We Work with Group Decision-Making?
3.13 What Relationships May Exist Among Resources?
3.14 Is it Possible to Work with Negative Performance Values?
3.15 What Is Rank Reversal (RR)?
3.16 How Do we Know How Well Each Criterion Is Satisfied?
3.17 What Is the Strength of a Solution?
3.18 How Criteria Are Chosen for Sensitivity Analysis?
3.19 How to Proceed When the Result Shows Ties Between Alternatives Scores?
3.20 What Happens When There Is a Shortage of Resources?
3.21 How to Choose Alternatives When Subject to Conditional Criteria?
3.22 How to Compute Objective Weights?
3.23 How to Proceed When Alternatives Are Already Specified, but Must Be Performed at Different Time Periods?
3.24 How Do We Work with Geographic Information System (GIS)?
3.25 Considering Technical Aspects
3.26 What Are Multiples Scenarios?
3.27 Forecasting Risks
3.28 When to Use Fuzzy Logic?
3.29 What Are the Aichi Biodiversity Targets and How Can They Be Used as Criteria?
3.30 What Is the Meaning of Structures in MCDM Methods?
3.31 Why in Some Problems Not All Alternatives Get Ranked?
3.32 Is it Possible That a Certain Criterion Calls for Maximization and Also for Minimization?
3.33 What Is a Composite Index and How Can We Build It?
3.34 What Is Planning at Macro Level?
3.35 Is It Possible to Validate a Result or a MCDM Method?
3.36 How to Work with Negative Objectives?
3.37 Why Do We Need to Work with Objective Weights Instead of Subjective Weights?
3.38 How to Proceed When in Two Portfolios with Different Projects, the Second Portfolio Depends on the First?
3.39 How to Proceed with Portfolios of Projects in Series?
3.40 Can Resources Be Interrelated?
3.41 Is It Convenient to Partition a Project, Solve Each Part and Then Solving?
3.42 How to Work with Projects That Must Be Forcefully Shared Between Several Sites?
3.43 How to Work When in the Initial Decision Matrix, the Performance Values Are Probabilities?
3.44 How to Work with Weights?
3.45 Conclusion
References
Chapter 4: Analysis of Questions Normally Formulated on MCDM
4.1 Is the Top-Down Approach, Correct?
4.2 Is It Reasonable to Use Pair-Wise Comparisons in MCDM?
4.3 The Entropy Concept
4.4 Why Different Methods Give a Different Ranking for the Same Problem?
4.5 Which Is the Best MCDM Method?
4.6 Why Decision-Making Using Preferences Should Be Avoided?
4.7 Which Is the Best Normalization Procedure?
4.8 Is It Convenient to Use a Linear Hierarchy in MCDM?
4.9 Is It Possible to Affirm That a MCDM Solution Can Be Validated?
4.10 What Information Can Be Extracted from MCDM Methods?
4.11 Ties in Alternatives
4.12 Conditions to Be Met to Perform an Efficient Sensitivity Analysis
4.13 Strategic Analysis
4.14 How to Select the MCDM Method That Best Fits a Problem?
4.15 Hidden Factors
4.16 How to Reduce the Number of Alternatives?
4.17 Is It Always the Best Alternative Selected the One to Be Adopted?
4.18 Is Fuzzy Logic Useful in the MCDM Context?
4.19 What Are the Criteria?
4.20 How to Proceed When Alternatives Are Also Subject to Compliance with a Certain Number of Criteria?
4.21 What If We Need the Results Expressed in Quantities?
4.22 Planning Resources: How to Compute the Quantity of Resources Required for a Scenario?
4.23 What Is a Compromise Solution?
4.24 What Is Compensation?
4.25 Can We Disaggregate or Partition a Project?
4.26 How to Perform Sensitivity Analysis Considering Performance Values?
4.27 A Case: Not Sufficient Research Rendered an Unacceptable Solution
4.28 Conclusion
References
Further Reading
Chapter 5: Putting SIMUS to Work
5.1 What Platform Does SIMUS Use?
5.2 What Can the User Do with SIMUS?
5.3 Examples of Applications in Different Areas
5.4 Background Information
5.5 How SIMUS Works?
5.5.1 Sensitivity Analysis
5.6 Step-by-Step Procedure Using SIMUS
5.7 Operation
5.7.1 Additional Characteristics
5.8 Particular Scenarios
5.8.1 When There Is Dependency Between Projects
5.8.2 When There Are Project Alliances (Joint Ventures)
5.8.3 When the Result Must Be Expressed in Binary
5.8.4 When There Is a Need to Have Results in Integers
5.8.5 When Alternatives Are Inclusive or Exclusive According to Criteria
5.8.6 When There Is Need to Rank Projects in a Portfolio
5.8.7 When There Is Shortage of Funds to Execute All Projects in the Portfolio
5.8.8 When Selection for Projects Is Linked with Planning and Scheduling Along Several Years
5.8.9 When Projects Are Related to Funds Availability
5.8.10 When Selection Must Consider That Projects May Have Different Percentages of Completion
5.9 Sensitivity Analysis
5.9.1 Checking Robustness
5.10 Conclusion
References
Appendix
Chapter 12 (No Author Mentioned)
Alternatives
Compensatory Methods
Correlation
Criteria
Different Results
Entropy
Forecasting
Inconsistency
Independence of Criteria
Modelling
Normalization
Objectives
Partitioning
Preferences
Pair-Wise Comparisons
Publishing
Rankings
Rank Reversal
Results
Scales
Stakeholders
Weights
References