Advances in Best-Worst Method / Lecture Notes in Operations Research (PDF)
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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.
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.
- 2023, 1st ed. 2023, 177 Seiten, Englisch
- Herausgegeben: Jafar Rezaei, Matteo Brunelli, Majid Mohammadi
- Verlag: Springer International Publishing
- ISBN-10: 3031248163
- ISBN-13: 9783031248160
- Erscheinungsdatum: 31.01.2023
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- Grösse: 10 MB
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