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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Goodarzian, F., Ghasemi, P., & Gunasekaren, A. (2023). A fuzzy sustainable model for COVID-19 medical waste supply chain network. Fuzzy Optimization and Decision Making. https://doi.org/10.1007/s10700-023-09412-8
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, [100236]. https://doi.org/10.1016/j.dajour.2023.100236
Zeren, D., Kocatürk, F., & Toparli, M. B. (2023). Combined material model to predict flow curves of cold forging raw materials having high strain hardening exponent. in L. Madej, M. Sitko, & K. Perzynsk (Hrsg.), Material Forming - The 26th International ESAFORM Conference on Material Forming – ESAFORM 2023 (Band 28, S. 1503-1510) https://doi.org/10.21741/9781644902479-162
Matsatsinis, N., & Vetschera, R. (2023). Editorial for the Feature Cluster on OR and Analytics in the Era of Digital Transformation – Selected papers from EURO 2021. European Journal of Operational Research, 306(3), 999-1000. https://doi.org/10.1016/j.ejor.2022.11.026
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
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