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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Mlekusch, J., & Hartl, R. (2025). The Dual-Resource-Constrained Re-entrant Flexible Flow shop A Constraint Programming approach and a Hybrid Genetic Algorithm. International Journal of Production Research, 63(5), 1803-1824. https://doi.org/10.1080/00207543.2024.2392198
Dehghan Chenary, M., Ferdowsi, A., & Hartl, R. (2024). A Pareto-Based Clustering Approach for Solving a Bi-Objective Mobile Hub Location Problem with Congestion. Logistics, 8(4), Artikel 130. https://doi.org/10.3390/logistics8040130
Horstmannshoff, T., Ehmke, J. F., & Ulmer, M. (2024). Dynamic learning-based search for multi-criteria itinerary planning. Omega, 129, Artikel 103159. https://doi.org/10.1016/j.omega.2024.103159
Müller, A.-G., & Müller, D. (2024). Problem size reduction methods for large CVRPs. Computers & Operations Research, 172, Artikel 106820. https://doi.org/10.1016/j.cor.2024.106820
Razavian, B., Fayyaz, M., Ghasemi, P., Ozkul, S., & Babaee Tirkolaee, E. (2024). Addressing barriers to big data implementation in sustainable smart cities: Improved zero-sum grey game and grey best-worst method. Journal of Innovation & Knowledge, 9(4), Artikel 100593. https://doi.org/10.1016/j.jik.2024.100593
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