Greedy Delivery-Slot Assignment for a Munich Grocery Startup
Overview
What this challenge is about.
Build a greedy slot-assignment algorithm for a Munich grocery delivery startup and validate it against a manual baseline. Earn a verifiable certificate.
The scenario
The company is a venture-backed grocery-delivery startup operating across Munich's urban zones, where four-hour delivery windows and a fleet of around 120 part-time riders make slot assignment a daily bottleneck for its small dispatch team.
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 while never violating rider working-hour constraints.
Earning criteria — what you'll demonstrate
- Design a greedy heuristic with a defensible priority ordering for a constrained assignment problem.
- Translate real-world scheduling constraints (shifts, maximum working hours, slot windows) into explicit code checks.
- Define fair, well-specified benchmark metrics and compare a heuristic against a human baseline.
- Reason about and communicate the failure modes of a greedy approach versus full optimization.
- Structure a technical design proposal that ties every claim to observable evidence in the data.
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Greedy Algorithms
Apply greedy algorithms to solve real industry problems and demonstrate production-level capability.
- Algorithm Analysis
Apply algorithm analysis to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Constraint Handling
Apply constraint handling to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Data Structures
Apply data structures to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Backend / Algorithms Engineer
Building a constrained assignment heuristic and proving it against real data mirrors the daily work of backend engineers who turn business rules into efficient, correct scheduling and matching services that run in production.
This challenge sharpens
- greedy-algorithms
- constraint-handling
- python
Logistics Optimization Engineer
Comparing a heuristic to a baseline and naming when it breaks down is how optimization engineers decide between fast heuristics and full solvers for routing and slotting in delivery operations.
This challenge sharpens
- algorithm-analysis
- benchmarking
- greedy-algorithms
Data / Decision Engineer
Defining fair metrics and validating an algorithm against historical decisions builds the evaluation and data-structuring discipline decision engineers use to ship data-driven systems that stakeholders trust.
This challenge sharpens
- benchmarking
- data-structures
- constraint-handling