This book offers a comprehensive, mathematically grounded introduction to multi-attribute decision-making (MADM) for undergraduate, graduate, and doctoral students, as well as researchers in engineering, management, economics, and biology. It bridges theory and practice, covering fundamental concepts, advanced methods, and MATLAB implementations. Topics include critical, subjective, objective, and comprehensive weighting methods, ranking techniques, and their integration for solving complex decision problems. The book addresses challenges such as ranking abnormalities, complexity, and…mehr
This book offers a comprehensive, mathematically grounded introduction to multi-attribute decision-making (MADM) for undergraduate, graduate, and doctoral students, as well as researchers in engineering, management, economics, and biology. It bridges theory and practice, covering fundamental concepts, advanced methods, and MATLAB implementations. Topics include critical, subjective, objective, and comprehensive weighting methods, ranking techniques, and their integration for solving complex decision problems. The book addresses challenges such as ranking abnormalities, complexity, and applicability in emerging applications. It also explores fuzzy MADM for handling uncertainty and non-sharp boundaries, highlighting recent research directions and open challenges. Filling a gap in the literature, this book serves as a valuable resource for academics and professionals seeking to apply MADM in dynamic, interdisciplinary contexts.
Ashok Kumar Yadav holds a B.Tech. from AKTU Lucknow, an M.Tech. and Ph.D. from JNU New Delhi, India, and has over seven years of teaching and research experience. He is an assistant professor in Information Technology at Rajkiya Engineering College, Azamgarh, India, and formerly taught at the University of Delhi. His research interests include MADM, blockchain, machine learning, and mobility management in mobile communication. He has authored publications in Elsevier, Springer, World Scientific, and Taylor & Francis journals, co-authored Mastering Disruptive Technologies, holds six SCIE papers, two Scopus publications, and six patents. He actively reviews for international journals, serves on conference committees. Ali Ahmadian is a senior research scientist at Mediterranea University of Reggio Calabria, Italy, a visiting professor at Istanbul Okan University, Turkey, and former fellow researcher at the Institute of IR 4.0, National University of Malaysia. He earned his Ph.D. in 2014 as the best postgraduate student at Universiti Putra Malaysia (UPM) and later served as a postdoctoral fellow and fellow researcher at UPM. His research focuses on computational methods and models in computer science, biology, physics, and engineering using fuzzy and fractional calculus, with applications in nano-communication networks, drug delivery, nanofluids, AI, and more. He has secured 18 national and international grants, published over 350 papers in leading journals, and edited seven books. Ajay Pratap is an assistant professor in Computer Science and Engineering at IIT (BHU), Varanasi, India. He earned his Ph.D. from IIT Patna (2018) and completed postdoctoral research at Missouri University of Science and Technology, USA (2018–2019). His research covers cyber-physical systems, statistical learning, algorithm design for next-generation wireless networks, mobile computing, applied graph theory, and game theory. His current focus includes HetNet, small cells, fog computing, IoT, smart healthcare, and D2D communication in 5G and beyond. He has published in top journals and received multiple awards, including Best Paper Awards, an NSF Travel Grant (IEEE SmartComp, USA), and the Young Engineer Award in Computer Engineering from the Institution of Engineers (India) in 2023.
Inhaltsangabe
Chapter 1. Introduction to Decision Theory. Chapter 2. Normalization Strategies. Chapter 3. Subjective Weigh Methods. Chapter 4. Objective Weight Methods. Chapter 5. Integrated Weight Methods. Chapter 6. Multi Attribute Decision Making Models. Chapter 7. Challenges in MADM Models. Chapter 8. Applications of MADM.
