Advances in Best-Worst Method

Proceedings of the Second International Workshop on Best-Worst Method (BWM2021)
1. Auflage 2022
ISBN: 978-3-030-89794-9
Verlag: Springer International Publishing

Buch, Englisch, 288 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 617 g

Reihe: Lecture Notes in Operations Research

This book presents recent advances in the theory and application of the Best-Worst Method (BWM). It includes selected papers from the Second International Workshop on Best-Worst Method (BWM2021), held in Delft, The Netherlands from 10-11 June, 2021, and provides valuable insights on why and how to use BWM in a diverse range 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, and complete information vs incomplete and uncertain situations. The papers gathered here will benefit academics and practitioners who are involved in multi-criteria decision-making and decision analysis.

Zielgruppe

Research

Autoren/Hrsg.

Rezaei, Jafar

Mohammadi, Majid

Brunelli, Matteo

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Einkauf, Logistik, Supply-Chain-Management
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Operations Research

Inhaltsverzeichnis

Preface.- The balancing role of Best and Worst in Best-Worst Method.- Hierarchical evaluation of criteria and alternatives within BWM: A Mon-te Carlo approach.- A two-step Best-Worst Method (BWM) and K-Means clustering recom-mender system framework.- A linguistic 2-tuple Best-Worst Method.- How does the entrepreneurship ecosystem contribute to the perfor-mance of entrepreneurial start-up firms?.- A multi-attribute decision-making to sustainable construction material selection: A Bayesian BWM-SAW hybrid model.- Risk assessment of passenger flow in an urban rail transit system: indi-cators, application, and analysis.- Bridge infrastructure resilience analysis against seismic hazard using Best-Worst Method.- A value-focused approach for the design of innovative logistics con-cepts: the case of off-peak pickup and delivery in the air cargo industry.- An Innovative digital maturity assessment model for smart cities.- Determining the importance of barriers to IoT implementation using Bayesian Best-Worst Method.- Assessment of environmental performance criteria in textile industry using the Best-Worst Method.

Über Autor(innen)

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 PhD at the same university. He has a background in Operations Research, and has published in several peer-reviewed journals. He is the 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 PhD 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 PhD at Delft University of Technology, The Netherlands, 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.