Optimize Rwanda Drone Delivery Speed

I’m examining how Rwanda’s pioneering drone-delivery network can move even faster by sharpening the delivery-algorithm layer. My sole focus is on the technological side—specifically the mathematics, data structures and decision logic that pick, sequence and adjust each flight path in real time.

What I need from you is a compact but rigorous study that shows, in measurable terms, how alternative or refined algorithms could cut overall delivery time. Feel free to draw on graph-search techniques, heuristic or meta-heuristic optimisation, predictive analytics, or machine-learning-supported routing—whatever you judge most promising for Rwanda’s geography, air-corridor rules and demand patterns.

Deliverables
• Literature and market review of algorithms currently deployed (Zipline and any public/open-source equivalents)
• Simulation or analytical model that reflects Rwanda’s terrain, weather data, no-fly zones and service-point density
• Comparative benchmark of at least two alternative routing approaches against the baseline, focusing on total mission time and turnaround per sortie
• A concise technical report (≈15 pages) detailing methodology, results, and recommendations ready for peer review

Acceptance criteria
• All code or notebooks run reproducibly on Python or MATLAB with clearly documented dependencies
• Speed-improvement metrics expressed both as percentage gain and absolute minutes saved per delivery
• Clear linkage between simulation assumptions and real-world Rwandan constraints

If questions arise on data availability or regulatory airspace parameters, flag them early so we can secure the right datasets. I look forward to seeing the creative algorithmic paths you carve through Rwanda’s skies.