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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Guo, S., & Vetschera, R. (2023). Preference Reversals in Dynamic Decision-making Under Uncertainty Based on Regret Theory. Managerial and Decision Economics, 44(3), 1720-1731. https://doi.org/10.1002/mde.3778
Goodarzian, F., Ghasemi, P., Gonzalez, E. DR. S., & Tirkolaee, E. B. (2023). A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms. Journal of Environmental Management, 328, [116892]. https://doi.org/10.1016/j.jenvman.2022.116892
Choukolaei, H. A., Ghasemi, P., & Goodarzian, F. (2023). Evaluating the efficiency of relief centers in disaster and epidemic conditions using multi-criteria decision-making methods and GIS: A case study. International Journal of Disaster Risk Reduction, 85, [103512]. https://doi.org/10.1016/j.ijdrr.2022.103512
Kia, R., Goodarzian, F., & Ghasemi, P. (2023). A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms. Socio-Economic Planning Sciences, 85, [101378]. https://doi.org/10.1016/j.seps.2022.101378
Heitmann, R. -J. O., Soeffker, N., Ulmer, M. W., & Mattfeld, D. C. (2023). Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services. EURO Journal on Transportation and Logistics, 12, [100104]. https://doi.org/10.1016/j.ejtl.2023.100104
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