AI Delivery Route Optimization: More Customers, Fewer Kilometres
For distribution companies, food delivery, courier services, and anyone running customer deliveries, route optimization is synonymous with cost reduction and capacity growth.
Why Manual Planning Falls Short
A dispatcher with 10 years of experience plans routes intuitively — quickly, but suboptimally. The problems:
- Delivery windows (customer wants delivery between 10:00 – 12:00) make routes more complex
- Different vehicle capacities — which load goes on which vehicle?
- Dynamic traffic conditions — congestion, road closures, weather
- Last-minute stops added — the entire plan needs to be recalculated
AI calculates the optimal solution for 50 stops and 5 vehicles in under 30 seconds. A dispatcher would need an hour for the same task.
How AI Optimization Works
Input: Orders and Constraints
For each order the system knows:
- Delivery address + GPS coordinates
- Required delivery window (if applicable)
- Shipment weight and volume
- Special requirements (refrigerated, fragile, oversized)
For each vehicle:
- Capacity (kg, m³)
- Type (refrigerated, flatbed, van)
- Start and end location (depot)
- Driver’s permitted working hours
Output: Optimal Routes per Vehicle
Each driver receives in the mobile app:
- Ordered stop list with addresses
- Estimated arrival time at each customer
- Navigation with live updates
- Delivery instructions (keypad code, reception, warehouse)
Live Tracking and Reallocation
The dispatcher sees all vehicles on the map in real time:
- Where each driver is
- How many stops have been completed
- Estimated time to complete the route
- Delays and their causes
If a driver falls behind, AI suggests reallocating stops to another vehicle.
Typical Results
| Metric | Before AI | After AI |
|---|---|---|
| Average km/vehicle/day | 210 km | 155 km |
| Stops per vehicle/day | 18 | 24 |
| Fuel cost per month (5 vehicles) | €4,200 | €3,100 |
| Dispatcher planning time | 1.5 h/day | 15 min/day |
