The research of the chair focuses on the intersection of data analytics and operations research, with a particular emphasis on applications in supply chain management and logistics. The research activity of the chair extends beyond developing solution methods based on the traditional predict-then-optimize paradigm. The chair also explores how machine learning can be embedded within heuristic algorithms to enhance their performance by improving solution quality and reducing computational effort. Through this integrated perspective, the chair's work supports the development of data-driven decision methodologies that connect prediction, decision-making, and computational performance.

The research focus lies on:

• Logistics and Mobility
• Production and Supply Chain Management
• Machine Learning
• Heuristic Optimization
• Stochastic Optimization