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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Hess, C., Müller, A.-G., Dörner, K. F., & Vigo, D. (2024). Waste collection routing: a survey on problems and methods. Central European Journal of Operations Research, 32(2), 399-434. https://doi.org/10.1007/s10100-023-00892-y
Ferdowsi, A., & Dehghan Chenary, M. (2024). Gain and Pain in Graph Partitioning: Finding Accurate Communities in Complex Networks. Algorithms, 17(6), Artikel 226. https://www.mdpi.com/1999-4893/17/6/226
Goodarzian, F., Ghasemi, P., Appolloni, A., Ali, I., & Cardenas Barron, L. (2024). Supply chain network design based on Big Data Analytics: heuristic-simulation method in a pharmaceutical case study. Production Planning & Control, 1-21. https://doi.org/10.1080/09537287.2024.2344729
Ahmadi, H., Jahangoshai Rezaee, M., & Ghasemi, P. (2024). Data-driven modeling using system dynamics simulation to provide relief in earthquake based on different scenarios. Environmental Science and Pollution Research, 31(24), 35266-35282. https://doi.org/10.1007/s11356-024-33490-9
Scherr, Y. O., Gansterer, M., & Hartl, R. F. (2024). Request acceptance with overbooking in dynamic and collaborative vehicle routing. European Journal of Operational Research, 314(2), 612-629. https://doi.org/10.1016/j.ejor.2023.10.014
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