Overview
What this challenge is about.
Receive 14 days of order data (around 4,800 orders) with timestamps, addresses (already geocoded), and slot preferences, plus rider rosters with shift starts, ends, and max working hours per day. Implement a greedy algorithm in Python that: (1) sorts incoming orders by a defensible priority (e.g., narrowest slot preference first), (2) assigns each to the rider with earliest available slot that fits constraints, (3) handles the 'no rider fits' case explicitly. Compare against the manual baseline on slot-fill rate, customer-preferred-slot match rate, and rider-utilization variance. Deliver the code, a 4-page analysis, and a discussion of when greedy will break down.
The Brief
What you'll do, and what you'll demonstrate.
Replace manual delivery-slot assignment with a greedy algorithm that matches or beats human dispatchers on slot-fill rate across 14 days of historical orders.
Earning criteria — what you'll demonstrate
- Apply greedy-algorithm design to a constrained real-world problem
- Choose and justify a priority ordering
- Identify when a greedy heuristic is provably good and when it isn't
- Benchmark a heuristic against a human baseline honestly
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Product Manager
Technical product managers in operations-heavy products need to read and reason about heuristics like this to prioritize the right solver upgrade.
This challenge sharpens
- algorithm-analysis
- benchmarking
- constraint-handling