Welcome to
the Department of Business Decisions and Analytics
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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Kinast, A., Braune, R., Dörner, K. F., & Rinderle-Ma, S. (2023). Optimizing the Solution Quality of Metaheuristics Through Process Mining Based on Selected Problems from Operations Research. in C. Di Francescomarino, A. Burattin, C. Janiesch, & S. Sadiq (Hrsg.), Business Process Management Forum - BPM 2023 Forum, Proceedings (S. 232-248). Springer. https://doi.org/10.1007/978-3-031-41623-1_14
Ghasemi, P., & Pourghader Chobar, A. (2023). Design of an Optimal Robust Possibilistic Model in the Distribution Chain Network of Agricultural Products with High Perishability under Uncertainty. Sustainability, 15(15), Artikel 11669. https://doi.org/10.3390/su151511669
Ghasemi, P., Goodarzian, F., Simic, V., & Babaee Tirkolaee, E. (2023). A DEA-based simulation-optimisation approach to design a resilience plasma supply chain network: a case study of the COVID-19 outbreak. International Journal of Systems Science: Operations & Logistics, 10(1), Artikel 2224105. https://doi.org/10.1080/23302674.2023.2224105
Krebs, C., & Ehmke, J. F. (2023). Solution validator and visualizer for (combined) vehicle routing and container loading problems. Annals of Operations Research, 326(1), 561-579. https://doi.org/10.1007/s10479-023-05238-0
Momenitabar, M., Ebrahimi, Z. D., Bengtson, K., Helmi, W., & Ghasemi, P. (2023). An integrated machine learning and quantitative optimization method for designing sustainable bioethanol supply chain networks. Decision Analytics Journal, 7, Artikel 100236. https://doi.org/10.1016/j.dajour.2023.100236
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