GreenLane Logistics
AI-powered route optimization cutting fuel costs by 31%
Duration
12 weeks
Team
3 engineers
Year
2024
GreenLane operated a fleet of 340 delivery vehicles across the Southeastern US. Their dispatchers manually planned routes each morning — a process that took 3 hours and consistently produced suboptimal paths, burning excess fuel and missing delivery windows.
Route optimization at this scale is an NP-hard problem. Off-the-shelf solutions either cost $200K+ annually or couldn't handle their specific constraints: mixed vehicle types, driver shift preferences, real-time traffic, and customer-specific delivery windows down to 30-minute slots.
Built a custom optimization engine combining OR-Tools with a fine-tuned ML model trained on 18 months of historical delivery data
Created a dispatcher dashboard that generates optimized routes in under 90 seconds for the entire fleet
Integrated real-time traffic data and weather forecasts for dynamic re-routing throughout the day
Developed a driver mobile app with turn-by-turn navigation and proof-of-delivery capture
The numbers tell the story.
Annual fuel spend reduced by $1.2M across the fleet
Morning route planning went from hours to minutes
Up from 78% before the system was deployed
“The ROI was clear within the first month. We saved more on fuel than the entire project cost.”
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