Description
This book provides a comprehensive and practical guide to Multi-Criteria Decision-Making (MCDM) methods, with a strong focus on their industrial applications for solving complex real-world challenges. It bridges the gap between theoretical foundations and practical implementation, offering clear, actionable insights for professionals and researchers in engineering, operations management, and business analytics. By combining rigorous methodology with practical relevance, the book equips readers to make informed decisions in environments where trade-offs between cost, quality, and sustainability are critical.
Exploring advanced techniques such as AHP, TOPSIS, DEMATEL, and PROMETHEE, the book situates MCDM within industrial contexts like supply chain optimization, sustainability assessment, and risk management. It addresses the growing need for structured decision-making frameworks in data-driven settings and integrates Industry 4.0 technologies, including AI and IoT, to enable dynamic and adaptive strategies. Through real-world case studies from manufacturing, logistics, and energy sectors, it demonstrates how MCDM enhances transparency, resilience, and adaptability, making it an essential resource for academics, practitioners, and graduate students seeking to master decision analysis in modern industrial landscapes.
Multi-Objective Decision Making: Optimizing in a Complex World.- Pairwise Comparison Methods.- Mathematical Programming-Based Techniques In MCDM.- Distance-based techniques in MCDM.- Multi Criteria Decision Making Problems in Excel.
Alireza Amirteimoori is a full professor in Applied Mathematics & Operations Research Group at Istinye University, Türkiye. He completed his Ph. D degree in Islamic Azad University, Tehran, Iran. His research interests lie in the broad area of performance management with special emphasis on the quantitative methods of performance measurement, and especially those based on the broad set of methods known as Data Envelopment Analysis (DEA). Amirteimoori's papers appear in high-impact journals such as Applied Mathematics and Computation, Journal of the operations research society of Japan, Journal of Applied Mathematics.
Mansour Soufi is an assistant professor of industrial management at Islamic Azad University of Rasht, Iran. He completed his Ph. D degree at Tehran University, Iran. His research interests lie in the broad area of performance management, decision analysis, risk management, and quality control. Mansour published more than 5 books and 50 scientific papers in high-level journals.
