Encyclopedia of Multi-Attribute Decision Making (MADM) presents current methods in MADM in a simple way, including Sections on Weighting Methods, Extensions for the MADM Methods, Ranking Methods, and Outranking Methods. Each method chapter presents two numerical examples for each method, one simple example with less than six criteria and six alternatives, and one complex example with six or more criteria and six or more alternatives. In addition, most chapters are written by the original developers of the method, ensuring insight into and practical application of MADM. The book is also filled with over 200 full-color figures that illustrate methods and applications.

The book, in one volume, demystifies the complex world of MADM, blending theoretical concepts with hands-on practices and case studies. It bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of MADM.

Includes chapters on all the current Multi-Attribute Decision Making methods, including Sections on Weighting Methods, Extensions for the MADM Methods, Ranking Methods, and Outranking Methods
Presents a consistent structure, enabling easier learning of methods
Provides two numerical examples for each method, one simple example with less than six criteria and six alternatives, and one complex example with six or more criteria and six or more alternatives
Includes comprehensive coverage of balanced theory and practice, thus ensuring a strong theoretical foundation paired with actionable insights for real-world applications

About the Authors

Gholamreza Haseli is currently a Research Assistant at University College Dublin, Ireland. Concurrently, he is a Ph.D. Candidate in Industrial Engineering at Tecnologico de Monterrey, Mexico. Recently, he introduced the Base-Criterion Method (BCM) and the HECON methods for multi-attribute decision-making. Gholamreza Haseli serves as a reliable group decision-making framework designer for analysis in the VOTE-TRA Project, funded by the Science Foundation Ireland through the Digital Voting Hub for Sustainable Urban Transport System (22/NCF/DR/11309). His research interests encompass decision science, fuzzy sets, fuzzy numbers, multi-criteria decision-making, waste management, and transportation.

Dr. Muhammet Deveci is an Associate Professor at the Department of Industrial Engineering in the Turkish Naval Academy, National Defense University, Istanbul, Turkey, and he is Honorary Senior Research Fellow with the Bartlett School of Sustainable Construction, University College London, UK. Dr. Deveci is also a Visiting Professor at Royal School of Mines in the Imperial College London, London, UK. He worked as a Visiting Researcher and Postdoctoral Researcher, in 2014-2015 and 2018–2019, respectively, with the School of Computer Science, University of Nottingham, Nottingham, U.K. Dr Deveci is an outstanding researcher and has published over 240 papers in journals, as well as more than 25 contributions in International Conferences related to his areas. Dr. Deveci received the 100th-anniversary award for his worldwide scientific achievements from the Scientific and Technological Research Council of Turkey (TUBITAK).

Dr Deveci has also been engaged with the wider community providing academic service through chairing/organising conferences, streams, tutorials, reviewing papers, and acting as Editorial Board Member of journals including IEEE Transactions on Intelligent Vehicles (T-IV), IEEE Transactions on Emerging Topics in Computational Intelligence, Information Sciences, Applied Soft Computing, Engineering Applications of Artificial Intelligence, and Artificial Intelligence Review.

Dr Deveci is an internationally recognized scientist in intelligent decision support systems underpinned by computational intelligence, particularly uncertainty handling, fuzzy systems, combinatorial optimization, and multicriteria decision making. His research and development activities are multidisciplinary and lie at the interface of operational research, computer science and artificial intelligence. He has been tackling challenging real-world problems without stripping off their complexities, including climate change, renewable energy, sustainable transport, and urban mobility.

Prof. Mostafa Hajiaghaei-Keshteli is an Associate Professor at the Department of Industrial Engineering, Tecnol�gico de Monterrey, which is one of the most prestigious universities for industry and supply chain in Mexico. He is also a Co-founder and Senior Researcher at the Center of Sustainable Smart Logistics (CLIS), established in 2022, to offer sustainable logistics solutions to the public and private sector in Mexico and North America. He has developed famous metaheuristic algorithms, including algorithms such as Keshtel Alg., Red Deer Alg., Tree Growth Alg., and the Social Engineering Optimizer. He is also an editorial member in some top Q1 journals at Elsevier, such as Applied Soft Computing (ASOC), Engineering Applications of Artificial Intelligence (EAAI), and Expert Systems with Applications (ESWA). He has a solid industrial background, both in managerial and consulting sectors, in different industries such as agriculture, food, automotive, transportation, and engine industries.

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