Overview
What this challenge is about.
Implement tabu search for a 800-order grocery VRPTW, benchmark against OR-Tools, and write a decision memo. You get a verifiable certificate.
The scenario
The scale-up (around 200 people, around 25,000 deliveries per day across Lisbon and Porto) currently spends about 12 minutes solving the daily plan and an OR shop minute saved is roughly EUR 30,000 per year in fleet utilization at current contribution margins.
The Brief
What you'll do, and what you'll demonstrate.
Build a tabu-search VRPTW solver and quantify whether it beats Google OR-Tools on the operations team's real instance distribution at a 60-second budget.
Earning criteria — what you'll demonstrate
- Implement tabu search with neighborhoods, aspiration, and tenure scheduling
- Apply metaheuristics to a constrained real-world routing problem
- Benchmark a custom solver against an industrial baseline
- Reason about the maintain-vs-replace trade-off for OR tooling
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Evolutionary Computation and Metaheuristic Search
Master · Machine Learning
Strong alignment
This challenge maps to Evolutionary Computation and Metaheuristic Search at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Tabu Search
Apply tabu search to solve real industry problems and demonstrate production-level capability.
- Metaheuristics
Apply metaheuristics to solve real industry problems and demonstrate production-level capability.
- Vehicle Routing
Apply vehicle routing to solve real industry problems and demonstrate production-level capability.
- Optimization
Apply optimization to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Applied AI Scientist
Building a custom metaheuristic for a real operations problem and quantifying it against a commercial baseline is core applied-AI-scientist work at any logistics or delivery firm.
This challenge sharpens
- tabu-search
- vehicle-routing
- optimization
Data Scientist
Per-instance gap analysis and a maintain-vs-replace memo are exactly the deliverables a data scientist ships to an operations leadership team.
This challenge sharpens
- benchmarking
- optimization
- python
Machine Learning Engineer
Writing tested, profiled Python that respects a runtime budget is the MLE craft of shipping algorithms that survive production.
This challenge sharpens
- python
- benchmarking
- metaheuristics