Optimization / Interactive Demo

Vehicle Routing Optimization

Last-mile routing: an OR-Tools model against a transparent greedy baseline, on one shared scenario.

Python OR-Tools CVRPTW Decision Support
12 / 12Orders served · both methods
64.4 kmOptimized · greedy 74.5 km
−13.5%Distance per served order

Bundled 12-customer scenario · straight-line distance matrix · one verified offline run

Show the trade-off, not just the winning number.

Both methods served every order, so the distances above are comparable. When coverage differs, the interface says so instead of reporting a percentage — partial service and failure stay visible.

Fleet
One depot · fixed homogeneous fleet
Hard constraints
Capacity · service time · delivery windows · shift length
Objective
Coverage first, then distance
  • Built-in example, or a seeded reproducible scenario
  • Fleet size, capacity, shift, speed, solver budget
  • Greedy vs OR-Tools routes on an interactive map
  • Coverage, distance, travel time, utilization, per-route detail
  • Upload a coordinate CSV — nothing is stored

Synthetic data, straight-line distance, homogeneous fleet. No order management, driver application, persistent database, or production routing SLA. A road-network edition could connect to a self-hosted OSRM instance.