Advances in Best-Worst Method - Proceedings of the Fourth International Workshop on Best-Worst Method (BWM2023)

This proceedings book contains selected papers from the Fourth International Workshop on Best-Worst Method (BWM2023), held in Delft, the Netherlands, from 8 to 9 June 2023.
It presents recent advancements in theory and applications of the Best-Worst Method (BWM). It provides valuable insights on why and how to use BWM in a diverse set of applications including health, energy, supply chain management, and engineering. The book highlights the use of BWM in different settings including single decision-making vs group decision-making, full information vs incomplete and uncertain situations. Academics and practitioners who are involved in multi-criteria decision-making and decision analysis benefit from the papers published in this book.



·         Jafar Rezaei is Associate Professor and Head of the Transport and Logistics Section at the Department of Engineering Systems and Services, Faculty of Technology, Policy, and Management, Delft University of Technology, the Netherlands. He completed his Ph.D. at the same university. He has a background in operations research and has published in several peer-reviewed journals. He is Editor-in-Chief of Journal of Supply Chain Management Science and serves as Editorial Board Member for several scientific journals. In 2015, he developed the Best-Worst Method (BWM). His main research interests are in multi-criteria decision-making and its applications in different fields.
 
·         Matteo Brunelli is Associate Professor of Mathematical Methods at the Department of Industrial Engineering, University of Trento, Italy. He received his Bachelor and Master degrees from the University of Trento, Italy, and his Ph.D. from Åbo Akademi University, Finland. He spent five years as Postdoctoral Researcher at Aalto University, Finland. His research interests include decision analysis, preference modelling, mathematical representations of uncertainty, and fuzzy sets.
 
Majid Mohammadi is Postdoctoral Researcher at Vrije Universiteit Amsterdam (VU), the Netherlands. Prior to joining VU, he pursued postdoctoral research at Eindhoven University of Technology and completed his Ph.D. at Delft University of Technology, earning a cum laude, the highest distinction in the Dutch academic system. His research interests are in methodological contributions to various domains such as multi-criteria decision-making, machine and deep learning, Bayesian statistics, and statistical learning theory.