Welcome to the Department of Business Analytics and Decision Making
The Department of Business Decisions and Analytics is dedicated to high-quality research in quantitative economics and decision support. Building upon a data-driven and optimization-oriented perspective, we develop models and solve complex problems in today’s rapidly changing business environment. We are committed to research-based and competent teaching and try to convey a deep understanding of methods and real-world problems.
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Florio, A. M., Gendreau, M., Hartl, R. F., Minner, S., & Vidal, T. (2023). Recent advances in vehicle routing with stochastic demands: Bayesian learning for correlated demands and elementary branch-price-and-cut. European Journal of Operational Research, 306(3), 1081-1093. https://doi.org/10.1016/j.ejor.2022.10.045
Alarcon Ortega, E. J., & Dörner, K. F. (2023). A sampling-based matheuristic for the continuous-time stochastic inventory routing problem with time-windows. Computers & Operations Research, 152, Artikel 106129. https://doi.org/10.1016/j.cor.2022.106129
Knyazev, D. (2023). How to fight corruption: Carrots and sticks. Economic Inquiry, 61(2), 413-429. https://doi.org/10.1111/ecin.13125
Seidl, A., & Wrzaczek, S. (2023). Opening the source code: The threat of forking. Journal of Dynamics and Games, 10(2), 121-150. https://doi.org/10.3934/jdg.2022010
Guo, S., & Vetschera, R. (2023). Preference Reversals in Dynamic Decision-making Under Uncertainty Based on Regret Theory. Managerial and Decision Economics, 44(3), 1720-1731. https://doi.org/10.1002/mde.3778
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