Advances in Best-Worst Method

This book presents recent advances in the theory and application of the Best-Worst Method (BWM). It includes selected papers from the Third International Workshop on Best-Worst Method (BWM2022), held in Delft, the Netherlands, from 9 to 10 June 2022. The book provides valuable insights on why and how to use BWM in a diverse range of applications including health, energy, supply chain management, and engineering. Moreover, it highlights the use of BWM in different settings including individual decision-making vs group decision-making, and with complete information vs incomplete and uncertain information. Academics and practitioners whose work involves multi-criteria decision-making and decision analysis will particularly benefit from the papers gathered here.

Presents recent advancements in Best-Worst Method Provides valuable insights on applications in a wide range of fields Covers both theoretical and practical perspectives

Auteur

Jafar Rezaei is an 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 an 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 an 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 a Postdoctoral Researcher at Aalto University, Finland. His research interests include decision analysis, preference modelling, mathematical representations of uncertainty, and fuzzy sets. Majid Mohammadi is a 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.