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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Momenitabar, M., Ebrahimi, Z. D., & Ghasemi, P. (2022). Designing a sustainable bioethanol supply chain network: A combination of machine learning and meta-heuristic algorithms. Industrial Crops and Products, 189, Artikel 115848. https://doi.org/10.1016/j.indcrop.2022.115848
Novák, A. J., & Feichtinger, G. (2022). Accumulation and obsolescence of research knowledge. Central European Journal of Operations Research, 30(4), 1151-1166. https://doi.org/10.1007/s10100-021-00755-4
Braune, R. (2022). Packing-based branch-and-bound for discrete malleable task scheduling. Journal of Scheduling, 25(6), 675-704. https://doi.org/10.1007/s10951-022-00750-w
Braune, R., Gutjahr, W., & Vogl, P. (2022). Stochastic radiotherapy appointment scheduling. Central European Journal of Operations Research, 30(4), 1239-1277. https://doi.org/10.1007/s10100-021-00762-5
Kusoncum, C., Sethanan, K., Hartl, R. F., & Jamrus, T. (2022). Modified differential evolution and heuristic algorithms for dump tippler machine allocation in a typical sugar mill in Thailand. Operational research, 22(5), 5863-5895. https://doi.org/10.1007/s12351-020-00597-z
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