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.

This book introduces the foundations of multi-criteria decision-making (MCDM) and its relationship with complementary approaches such as multi-objective optimization (MOO) and goal programming (GP). It then presents three major categories of MCDM methods: reference-type methods such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), aggregation-type methods such as Analytic Hierarchy Process (AHP), and outranking-type methods such as Elimination et Choix Traduisant la Realité (ELECTRE). Each method is explained through its underlying principle, step-by-step numerical examples, and a summary of its strengths and limitations. Preprocessing techniques, including normalization and weighting methods, are also covered to ensure a rigorous foundation for analysis. End-of-chapter exercises are also provided to ensure that readers can consolidate their knowledge and develop the skills to apply this to real-world applications. Balancing theoretical foundations with practical application, the book highlights user-friendly software tools, including Microsoft Excel-based programs and open-source Python libraries. These tools render MCDM methods transparent, accessible, and readily applicable in diverse contexts, whether in engineering design, financial investment, business strategy, or public policy.

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 into the techniques and applications and related software.