A Handbook on Multi-Attribute Decision-Making Methods

Clear and effective instruction on MADM methods for students, researchers, and practitioners.

A Handbook on Multi-Attribute Decision-Making Methods describes multi-attribute decision-making (MADM) methods and provides step-by-step guidelines for applying them. The authors describe the most important MADM methods and provide an assessment of their performance in solving problems across disciplines. After offering an overview of decision-making and its fundamental concepts, this book covers 20 leading MADM methods and contains an appendix on weight assignment methods. Chapters are arranged with optimal learning in mind, so you can easily engage with the content found in each chapter. Dedicated readers may go through the entire book to gain a deep understanding of MADM methods and their theoretical foundation, and others may choose to review only specific chapters. Each standalone chapter contains a brief description of prerequisite materials, methods, and mathematical concepts needed to cover its content, so you will not face any difficulty understanding single chapters. Each chapter:

• Describes, step-by-step, a specific MADM method, or in some cases a family of methods
• Contains a thorough literature review for each MADM method, supported with numerous examples of the method's implementation in various fields
• Provides a detailed yet concise description of each method's theoretical foundation
• Maps each method's philosophical basis to its corresponding mathematical framework
• Demonstrates how to implement each MADM method to real-world problems in a variety of disciplines

In MADM methods, stakeholders' objectives are expressible through a set of often conflicting criteria, making this family of decision-making approaches relevant to a wide range of situations. A Handbook on Multi-Attribute Decision-Making Methods compiles and explains the most important methodologies in a clear and systematic manner, perfect for students and professionals whose work involves operations research and decision making.

Omid Bozorg-Haddad, PhD, is Professor in the Department of Irrigation & Reclamation Engineering at University of Tehran, Iran. Dr. Bozorg-Haddad is co-author of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization (Wiley, 2017).

Babak Zolghadr-Asli, M.Sc., received M.Sc. in Irrigation Engineering, Water Resources Management, from Tehran University in Tehran, Iran. Dr. Aolghadr-Asli is a member of American Society of Civil Engineers (ASCE) and International Association of Hydrological Science (IAHS).

Hugo A. Loaiciga, PhD, is Professor of Geography in the Department of Geography at the University of California, Santa Barbara, CA, USA. Dr. Loaiciga is co-author of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization (Wiley, 2017).

Chapter 1: An overview of the art of decision-making

Summary

1.1. Introduction

1.2. Classification of MADM methods

1.2.1. Preference evaluation mechanism

1.2.2. Attributes’ Interactions

1.2.3. The mathematical nature of attributes’ values

1.2.3.1. Deterministic vs. non-deterministic

1.2.3.2. Fuzzy vs. crisp

1.2.4. Number of involved decision-makers

1.3. Brief chronicle of MADM methods

1.4. Conclusion

References

Chapter 2: Simple Weighting Methods: Weighted Sum and Weighted Product Methods

Summary

2.1. Introduction

2.2. The weighted sum method

2.3. The Weighted product method

2.4. Conclusion

References

Chapter 3: Analytic Hierarchy Process (AHP)

Summary

3.1. Introduction

3.2. The hierarchical structure

3.3. The pairwise comparison

3.4. Inconsistency

3.5. Quadruple axioms of the AHP

3.6. Stepwise description of the AHP method

3.7. Conclusion

Reference

Chapter 4: Analytic Network Process (ANP)

Summary

4.1. Introduction

4.2. Network vs. hierarchy structure

4.3. Stepwise instruction to the ANP method

4.4. Conclusion

References

Chapter 5: The Best-Worst Method (BWM)

Summary

5.1. Introduction

5.2. Basic principles of the BWM

5.3. Stepwise description of the BWM

5.4. Conclusion

References

Chapter 6: TOPSIS

Summary

6.1. Introduction

6.2. Stepwise instruction to the TOPSIS method

6.3. A common misinterpretation of TOPSIS results

6.4. Conclusion

Reference

Chapter 7: VIKOR

Summary

7.1. Introduction

7.2. Stepwise description of the VIKOR method

7.3. Conclusion

References

Chapter 8: ELECTRE

Summary

8.1. Introduction

8.2. A brief history of the ELECTRE family of methods

8.3. ELECTRE I

8.4. ELECTRE II

8.5. ELECTRE III

8.6. ELECTRE IV

8.7. Conclusion

References

Chapter 9: PROMETHEE

Summary

9.1. Introduction

9.2. Common ground of the PROMETHEE family

9.3. PROMETHEE I

9.4. PROMETHEE II

9.5. PROMETHEE III

9.6. PROMETHEE IV

9.7. Conclusion

Reference

Chapter 10: Superiority and Inferiority Ranking (SIR)

Summary

10.1. Introduction

10.2. Foundational bases of the SIR method

10.3. Stepwise instruction to SIR method

10.4. Conclusion

References

Chapter 11: PAPRIKA

Summary

11.1. Introduction

11.2. Stepwise description of PAPRIKA

11.3. Conclusion

References

Chapter 12: PAPRIKA

Summary

12.1. Introduction

12.2. Grey system theory: The foundation and basic principles

12.3. Gray relational modeling

12.4. Grey theory in relation to MADM

12.5. Conclusion

References

Appendix I: Weight assignment approaches

Appendix II: A benchmark example and a comparison between objective- and subjective-based MADM methods

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