Operations Research & Simulation
Help companies figure out the best way to use their resources—whether that's scheduling shifts, routing trucks, or testing "what if" scenarios before making big changes.
Model the future, optimize the present, and turn complex decisions into measurable outcomes—through simulation, optimization, and AI.
Start a conversationThree disciplines. One goal: making complex systems work better.
Help companies figure out the best way to use their resources—whether that's scheduling shifts, routing trucks, or testing "what if" scenarios before making big changes.
Build custom AI tools that actually fit real workflows—from forecasting demand to automating repetitive tasks with machine learning and large language models.
Turn messy data into clear answers. Interactive dashboards, statistical analysis, and system integrations that help teams make better decisions faster.
Real challenges, measurable outcomes.
Built an interactive online platform to train thousands of decision-makers and practitioners for pilgrimages with over a million participants. Evidence-based training for safety, efficiency, and preparedness.
With desior GmbHSimulated months of SKU flows across 1M+ items to optimize size limits, assignment rules, and storage strategies. Simulation-based parameters outperformed Excel planning for smoother operations from day one.
With Planningio GmbHModeled how a million fans would move through Doha across all transport modes with complex decision models. Identified critical bottlenecks and tested interventions to ensure smooth crowd flow during the tournament.
With desior GmbHRedesigned the periodic delivery routes for over 100 libraries. The optimized solution eliminated the need for one vehicle while maintaining service levels.
With Attalo GmbHOptimized schedules for mobile libraries covering 1,000+ stops across more than five depots. Balanced visit frequency, driver shifts, and vehicle capacity into one coherent plan.
Developed an algorithm that reduced split shipments by 80% in theoretical evaluations for an e-commerce retailer. Smarter inventory placement means fewer boxes, lower shipping costs, happier customers.
Redesigned district boundaries and dispatching rules for German and Belgian police forces. Simulation-tested changes showed improved response times and coverage, with results under discussion for adoption.
Built AI tutors for university courses that guide students through problems step by step—without giving away answers. Improved understanding and engagement while maintaining academic integrity.
Developing intelligent routing for demand-responsive transport in rural areas. Flexible scheduling that adapts to actual passenger needs instead of empty fixed routes.
A collaborative approach from first conversation to lasting results.
Interview stakeholders and the people closest to the challenge. Map out how things work, what data exists, and where it sits in silos. Clarify what you're really trying to solve as what's wanted and what's needed aren't always the same.
Structure everything logically. Test and discuss assumptions, pinpoint where improvements have the largest impact, and identify what data is missing and should be collected. Revisit: is what we're building still what's needed?
Start with quick wins and a working prototype, whether optimization algorithm, a simulation model, a training platform, or an AI system. Discuss what works, adjust course, and iterate based on the feedback and the first outcome.
Hand over results that stand on their own, whether that's tools, dashboards, or recommendations your team can act on. Documentation and hands-on training where needed, so the solution outlasts the project.
These interactive demos illustrate some simulation and optimization concepts I use in projects. They’re simplified on purpose—just enough to show how the underlying methods behave. Choose which demo to explore below:
A depot serves customers with optimized delivery routes. Click anywhere to add a new customer (up to 24) and the 3-opt algorithm iteratively improves the route. Watch as vehicles travel the optimized path. Use the reset button to start over.
Agents move through the space using a social-force model. Tap to place obstacle blocks. As local density rises, agents shift from teal to orange and slow down. Use the reset button to clear obstacles. Note, dense crowd scenarios can be resource-intensive.
Hands-on courses in programming, optimization, and AI—at universities and for corporate teams.
Need something specific? I design workshops around your team's challenges—from mathematical optimization and simulation to AI implementation and data analysis. Tailored to your skill level and goals.
All courses focus on practical skills: real problems, real code, and results you can apply right away.

Photo: Mirjam Kilter
I focus on complex operations where off-the-shelf software falls short. The kind of problems that need someone to dig into the details, connect the dots, and build something that actually holds up in practice—not just in theory.
My toolkit covers mathematical optimization (JuMP, Gurobi, HiGHS, OR-Tools), custom simulation models—both agent-based and discrete-event—and AI solutions including LLM integration and RAG systems. I work primarily in Python and Julia, with PostgreSQL/PostGIS for geospatial data and Docker for deployment. Results are delivered as interactive dashboards, web applications, or reports—built faster with AI-assisted tools like Claude Code and OpenCode.
Based in Hamburg, I work across logistics, public safety, and transportation. For larger projects, I tap into a network of specialists through Hamburg Analytics to bring the right expertise on board quickly. I also continue research part-time at the University of Hamburg and teach programming and optimization at universities and in corporate workshops.
Whether you have a specific challenge or just want to explore what's possible—reach out.