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Full Description
This textbook for advanced undergraduate and graduate students provides a clear and practical guide to the principles and methods for structuring and solving complex problems that include multiple, often conflicting criteria.
The book introduces the foundations of multi-criteria decision-making (MCDM) and its relationship with complementary approaches such as multi-objective optimization and goal programming. It then presents three major MCDM methods: reference-type methods such as TOPSIS, aggregation-type methods such as AHP, and outranking-type methods such as ELECTRE. Each method is explained with numerical examples, and strengths and limitations discussed. Preprocessing techniques are also covered. The book highlights user-friendly software tools, including Microsoft Excel-based programs and open-source Python libraries. End-of-chapter exercises are also provided to ensure that readers can consolidate their knowledge and apply this to real-world applications.
This textbook is vital for advanced undergraduate and graduate students taking decision science and optimization courses in disciplines such as engineering, business, finance, and management. Its focus on recent methods and developments will ensure that students are well prepared for employment in their field or for further research alike.
Contents
1. Introduction 2. Multi-Objective Optimization 3. Goal Programming 4. Normalization Methods 5. Objective Weighting Methods 6. Subjective Weighting Methods 7. Multi-Criteria Decision-Making — Reference-Type Methods 8. Multi-Criteria Decision-Making — Aggregation-Type Methods 9. Multi-Criteria Decision-Making — Outranking-Type Methods 10. Programs and Software for Multi-Criteria Decision-Making. Appendix: Alternatives-Criteria Matrix (ACM) for Selected Applications
