A Handbook on Multi-Attribute Decision-Making Methods, Zolghadr-Asli Babak, Bozorg-Haddad Omid, Loбiciga Hugo A.

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 methods implementation in various fields
• Provides a detailed yet concise description of each methods theoretical foundation
• Maps each methods 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.