I help organizations turn complex planning decisions into optimization models: from a first pilot that tests the idea, to production models your team runs every day.
Optimization studies for grids, storage and renewables.
Production schedules and staff rosters that respect every rule.
Fleet routes that cut distance, time and cost.
Where to locate facilities and how goods should flow.
Better use of staff, rooms and supplies in care delivery.
Fix models that are infeasible, wrong or too slow.
One well-defined problem, your data, and a working model with a short report. The fastest way to test whether optimization pays off for you.
An independent review of an existing model or codebase: what is wrong, why, and how to fix or speed it up.
Regular support for an internal team that builds and maintains optimization models.
Hands-on workshops in mathematical modeling, Pyomo and OR-Tools, built around your own problems.
We discuss the decision you need to make, the data you have and what a good result looks like.
A written proposal with scope, deliverables, timeline and price.
The model is built and validated against your rules and data, with regular check-ins.
Documented code, a results report and a walkthrough, so your team can run and extend the model.
Models are written in Python using Pyomo, Google OR-Tools, CP-SAT, GAMS, HiGHS, Gurobi, IPOPT. See selected projects for worked examples.
Documented Python code for the model, a short report explaining the results and assumptions, and a walkthrough session so your team can run and extend it.
Usually not. Most models run well with free open-source solvers such as HiGHS, CBC, IPOPT or Google OR-Tools. If you already license Gurobi or CPLEX, the model can use it.
After a short discovery call I send a written proposal with a fixed scope, deliverables, timeline and price, so there are no open-ended costs.
Yes. Data is used only for the project, and I am happy to work under a non-disclosure agreement. An anonymized sample is often enough to start.
