Industrial Decision Analysis
Theory and Applications of MCDM Methods
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Beschreibung
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.
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.
Serves as a key reference for students and professionals in multi-criteria decision making Offers practical tools for real-world evaluations across diverse industries Features numerous step-by-step methods and algorithms for fast, effective learning
Autor*in
Alireza Amirteimoori
Themen in »Industrial Decision Analysis«
Multi-Criteria Decision Making Multi-Attribute Decision Making Multi-Objective Decision Making Pairwise Comparison Methods Mathematical Programming-Based Techniques In MCDM Distance-based techniques in MCDM MCDM Problems in Excel Preference Ranking Organization Method for Enrichment Evaluation Elimination and Choice Expressing Reality Technique for Order Preference by Similarity to Ideal Solution Industrial Applications AHP TOPSIS Supply Chain Optimization Sustainability