Prescriptive Analytics

Within this field the goal is to build automated decisions decision systems. We combine AI-methods such as reinforcement learning with mathematical optimization to derive effective operational decision strategies. The developed methods enable dynamic, real-time decision making under incomplete information. Furthermore, we employ contextual optimization to integrate look-ahead predictions and optimization models.

Contacts:

Jun.-Prof. Dr. Kai Heinrich, Prof. Dr. Janis Neufeld, Prof. Dr. Elmar Lukas, Prof. Dr. Marlin Ulmer

We work on projects including:

• Dynamic pricing and demand management in mobility and transportation platforms
• Fleet allocation and workforce scheduling in transportation and mobility services
• Inventory management to balance reliable supply with transportation and holding cost
• Trip planning and routing to ensure fast journeys that respect individual preferences
• Solving the inventory routing problem for ATM replenishment
• Data-driven discovery of exercise strategies for American options
• Image-based trading agents via explainable reinforcement learning
• Explainable reinforcement learning for solving the dynamic ambulance relocation and dispatching problem

Publications:

• Cuellar-Usaquén, D., Ulmer, M.W., Antons, O., & Arlinghaus, J.C. (2025). Dynamic multi-period recycling collection routing with uncertain material quality. OR Spectrum, 47, 699-742. https://doi.org/10.1007/s00291-025-00808-z
• Hildebrandt, F.D., Lesjak, Ž., Strauss, A., & Ulmer, M.W. (2025). Integrated Fleet and Demand Control for On-Demand Meal Delivery Platforms. Management Science. https://doi.org/10.1287/mnsc.2022.02039
• Cuellar-Usaquén, D., Ulmer, M.W., Gomez, C., & Álvarez-Martínez, D. (2024). Adaptive stochastic lookahead policies for dynamic multi-period purchasing and inventory routing. European Journal of Operational Research, 318(3), 1028-1041. https://doi.org/10.1016/j.ejor.2024.06.011
• Horstmannshoff, T., Ehmke, J.F., & Ulmer, M.W. (2025). Dynamic learning-based search for multi-criteria itinerary planning. Omega, 129, 103159. https://doi.org/10.1016/j.omega.2024.103159
• Ulmer, M.W., Erera, A., & Savelsbergh, M. (2022). Dynamic service area sizing in urban delivery. OR Spectrum, 44, 763-793. https://doi.org/10.1007/s00291-022-00666-z
• Ulmer, M.W. (2020). Dynamic Pricing and Routing for Same-Day Delivery. Transportation Science, 54(4), 1016-1033. https://doi.org/10.1287/trsc.2019.0958
• Ulmer, M.W., & Savelsbergh, M. (2020). Workforce Scheduling in the Era of Crowdsourced Delivery. Transportation Science, 54(4), 1113-1133. https://doi.org/10.1287/trsc.2020.0977