At CORBA, we believe that academic research should make a difference beyond publications. In close collaboration with partners from business, government, and society, we apply cutting-edge Operations Research and Business Analytics methods to support data-driven decision-making. Our goal is to create sustainable value by linking economic success with social and environmental awareness.
Together with Solar Materials GmbH, we develop an end-to-end machine learning model to optimize recycling processes, maximize yield, and reduce waste within a circular economy framework.
In collaboration with Thyssenkrupp-Nucera, we evaluate AI-based approaches that support risk managers in assessing corporate resilience. Our goal is to identify information that signals shifts in risk trajectories and enables more informed decisions.
A collaboration with Siemens AG, we develop predictive and prescriptive methods to optimize ATM inventories and replenishment routes. By combining machine learning with optimization, we aim to improve decision quality beyond pure demand forecasting.
In collaboration with AOK Sachsen-Anhalt, we develop data-driven planning solutions that improve home healthcare nurses' working conditions by integrating their preferences into planning and reducing unnecessary travel. We also investigate how collaboration between care providers can further enhance efficiency and care quality.
This external PhD project, conducted in collaboration with Rolls-Royce Deutschland, focuses on optimizing complex production processes in the aerospace industry.
Together with partners like Deutsche Bahn and ÖBB, we develop AI-based optimization methods for energy-efficient and robust railway planning. Our solutions adapt to disruptions and make operations more sustainable, reliable, and transparent.
In collaboration with Sparkassen-Finanzgruppe, we explored the potential of IloT-driven digital financing, providing insights for banks to strategically navigate the evolving digital market.
From industry collaborations to scientific publications - explore our research output.
Last Modification: 03.12.2025 -
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