01 / Logistics
Optimizing a line-haul network
Applying operations research to improve line-haul planning, consolidate routes, and make a parcel network more efficient.
Read the Paack case studyOperations research · Optimization · Solver engineering · Metaheuristics
Better decisions, built on sound optimization.
I turn complex operational challenges into reliable optimization systems, combining mathematical modeling, algorithms, and solver-engineering depth.
Projects
From real-world constraints to decisions people can use.
A selection across logistics, healthcare, and solver integrations.
02 / Energy
Planning with uncertain energy forecasts
Combining probabilistic forecasts and optimization to support energy planning under uncertainty.
Read the energy-planning case study
03 / Healthcare
Improving home-care planning
Using optimization to help plan home-care services while balancing practical trade-offs in care delivery.
Read the home-care case study
04 / Radiotherapy
Optimizing radiotherapy schedules
PhD research on using logistical optimization to improve radiotherapy planning and reduce waiting times.
Read the NKI research storyExperience
Work across research, industry, and implementation.
I’m a PhD-trained operations research scientist and optimization engineer. I work from problem formulation through solver behavior and implementation, connecting rigorous models with the engineering needed to use them in practice. My experience spans logistics, healthcare, energy, and solver technology.
Career experience
FICO Xpress
2023–2026Senior Software Engineer · Solver Specialist
Worked across solver integrations, optimization support, technical training, notebooks, and AI-enabled resources for optimization users.
Paack
2021–2023Operations Research Scientist
Applied optimization to line-haul network planning and production last-mile routing.
Academic and research roles
2012–2021UPF · Netherlands Cancer Institute · University of Twente · INESC TEC
Developed optimization, simulation, and decision-support methods for healthcare, energy, scheduling, and resource-planning problems.
Expertise
Optimization from formulation to implementation.
I connect sound mathematical models with the solver, code, and operational context needed to make them useful.
01 / Methods
Formulating the decision
Linear, mixed-integer, and nonlinear optimization; constraint modeling; heuristics and metaheuristics; decomposition; stochastic and multi-objective optimization; simulation.
02 / Solver engineering
Make models robust and performant
Model validation, infeasibility and numerical diagnosis, performance tuning, solver API integration, and optimization pipelines built for real workflows.
03 / Engineering practice
Built to test, deploy, and scale
Testing and CI/CD · Version control with Git · Containerization with Docker · Cloud deployment on AWS and GCP.
Python and C/C++; Xpress, Gurobi, CPLEX, SCIP, and OR-Tools; plus modeling tools including Pyomo, PuLP, CVXPY, AMPL, and Mosel.
04 / Applications
Working with operational complexity
Logistics and routing; workforce and home-care scheduling; radiotherapy planning; electricity generation; and resource allocation.
05 / AI integration
Connect AI capabilities to optimization systems
LLM-assisted optimization and solver workflows, agent skills, Model Context Protocol (MCP) servers, tool calling, agentic systems, and AI plugins that connect these capabilities to optimization tools.
Strong expertise with Claude (Code), GitHub Copilot, and Codex.
The focus is hybrid OR + AI: use solver-backed evaluation, feasibility and correctness checks, and human oversight to keep AI-assisted decision systems reliable.
Blogs
Optimization and solver engineering, in practice.
Technical articles on optimization, solvers, uncertainty, and developer workflows.
Modeling tools
Build Optimization Models Like a Data Scientist
Enhanced Pandas support for building optimization models with familiar data-science workflows.
Solver fundamentals
Selecting the Right Optimization Solver for your Organization
A practical introduction to choosing a solver that fits your models, requirements, and organization.
Deployment
From Code to Container: Deploying Python Optimization Applications with Dockerfiles
How containerization can package Python optimization applications for more consistent deployment.
Explore more articles, integrations, and developer resources
Optimization and AI
Modeling tools and integrations
Developer tools and workflows
Community and learning
Publications
Research grounded in real decisions.
Peer-reviewed work on healthcare planning, radiotherapy, and energy systems.
Home-care planning · 2022
Coordinating home health and social care
A decision-support approach tested on a 790-job case study left 2–6 jobs unassigned (0.3–0.8%), while balancing service coverage and practical care-planning trade-offs.
Radiotherapy scheduling · 2021
Bringing optimization into clinical practice
Clinical comparisons reported a 51% reduction in session-start variability at one center, alongside fewer schedule gaps and machine switches.
Planning under uncertainty · 2018
Allocating radiotherapy staff under uncertain demand
In the model, the share meeting treatment-start targets rose from 91.3% to 97.9% for subacute patients and from 96.3% to 100% for regular patients; these are modeled, not claimed as realized operational outcomes.
View all nine peer-reviewed publications
- 2022
- 2021
- 2021
- 2020
- 2020
- 2019
- 2018
- 2016
- 2016
Contact
Let’s start a conversation.
For professional conversations, research discussions, and potential collaborations:
Prefer LinkedIn? Connect with me