Advances in Best-Worst Method

Proceedings of the Fourth International Workshop on Best-Worst Method (BWM2023)
2023
ISBN: 978-3-031-40327-9
Verlag: Springer Nature Switzerland

Buch, Englisch, 247 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 559 g

Reihe: Lecture Notes in Operations Research

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.

Zielgruppe

Research

Autoren/Hrsg.

Rezaei, Jafar

Mohammadi, Majid

Brunelli, Matteo

Fachgebiete

• Mathematik | Informatik Mathematik Operations Research
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Produktionsmanagement, Qualitätskontrolle

Inhaltsverzeichnis

Probabilistic group decision-making using BWT.- Robust stakeholder-based group-decision making framework: the Multi-Actor Multi-Criteria Analysis (MAMCA) with the integration of Best-Worst Method (BWM).- A consistent and consensual best-worst method and its application to salespersons’ performance evaluation problem.- Which Prioritization Method is Better for Deriving Priority from Best-Worst Preferences? A Theoretical and Experimental Analysis.- A hesitant multiplicative best-worst method for multiple criteria decision making.- Industry 4.0 and green entrepreneurship for environmental sustainability: Exploring barriers from an Indian SME Perspective.- Supplier selection for the oil industry using a combined BWM & F-VIKOR, case study: National Iranian South Oil Company.- Assessing smartness of an automotive industry: Importance-Performance Analysis.- Determining the criterion weights for the selection of volunteers in humanitarian organizations by the Best-Worst Method.- Emergency service quality assessment using SERVQUAL and BWM.- Avalanche risk analysis by a combined Geographic Information System and Bayesian Best-Worst Method.- Snow Avalanche Hazard Prediction Using the Best-Worst Method – Case Study: the Šar Mountains, Serbia.- Assessment of renewable energy development strategies with BWM-Grey TOPSIS.

Über Autor(innen)

·         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 ofTrento, 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.