Advances in Best-Worst Method

Proceedings of the Fifth International Workshop on Best-Worst Method (BWM2024)
2025
ISBN: 978-3-031-76765-4
Verlag: Springer

Buch, Englisch, 197 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 481 g

Reihe: Lecture Notes in Operations Research

This proceedings book contains selected papers from the Fifth International Workshop on Best-Worst Method (BWM2024), held in Delft, the Netherlands, from 13 to 14 June 2024. It presents recent advancements in theory and applications of the Best-Worst Method. 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 could benefit from the papers published in this proceeding.

Zielgruppe

Research

Autoren/Hrsg.

Rezaei, Jafar

Brunelli, Matteo

Mohammadi, Majid

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung

Inhaltsverzeichnis

Analyzing Swiss Energy Policy through a Fuzzy BWM-PROMETHEE Approach: A Socio-Political Multi-Criteria Decision Analysis.- A synergistic integration between large language models and the Best-Worst Method.- A Decision Support Tool for Stakeholder Engagement in Sustainable Land Management using the WEFE Nexus: A simulation for the Aral Sea Basin stakeholders.- Multi-Criteria Decision Making for Ranking Innovation Levels of G8 Countries with Extended GII: An Integrated Bayesian BWM and TOPSIS Method-ology.- How AI Transforms Barriers to Organic Arable Farming Adoption.- Exploring the Horizon of Industry 5.0: A Multifaceted Socio-Economic Transformation Towards a Sustainable and Inclusive Industrial Evolution.- Geospatial modeling of suitable sites for solar power plants based on GIS and BWM: A case study of the city of Kraljevo, Serbia.- Prioritizing the Product Features for Wearable Airbag Design using the Best-Worst Method.- Bayesian Best-Worst Method Application for Assessing the Potential Effecting Areas of Climate Change: A Case Study in Turkey.- Integrated Approach for Mobile Sales App Feature Classification: Kano Model and BBWM Perspective.

Über Autor(innen)

Jafar Rezaei is Associate Professor 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 Mult-Criteria Decision Analysis 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's and Master's 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.