Inbunden, Engelska, 2021
Advances in Best-Worst Method
Proceedings of the Second International Workshop on Best-Worst Method (BWM2021)
Av Jafar Rezaei, Matteo Brunelli, Majid Mohammadi
Del i serien Lecture Notes in Operations Research
2221 kr
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Beskrivning
Produktinformation
- Utgivningsdatum: 2021-11-11
- Mått: 155 x 235 x 22 mm
- Vikt: 565 g
- Format: Inbunden
- Språk: Engelska
- Serie: Lecture Notes in Operations Research
- Antal sidor: 288
- Förlag: Springer Nature Switzerland AG
- ISBN: 9783030897949
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Mer om författaren
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.
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