Logistics / Transportation6 weeks
AI-Driven Dynamic Route & Fleet Optimization
Client: MetroFlow Couriers
Optimize routes in real time considering traffic, weather, and order changes for entire fleets.
The Challenge
MetroFlow Couriers needed to optimize delivery routes in real-time to reduce fuel costs and delivery times, while dynamically adapting to traffic, weather, and last-minute order changes. The challenge was to continuously replan efficient routes for entire fleets.
Key Challenges:
- Reduce fuel costs while maintaining delivery efficiency
- Optimize routes in real-time considering traffic and weather conditions
- Adapt to last-minute order changes without disrupting entire fleet operations
- Improve on-time delivery rates across large fleet operations
Without dynamic routing, the company faced high fuel costs, delayed deliveries, inefficient route planning, and poor customer satisfaction.
Our Solution
- Combinatorial optimization engine processing GPS, traffic, and order data
- Reinforcement learning models continuously learning optimal routing strategies
- Real-time route replanning adapting to changing conditions
- Geospatial API integration for accurate traffic and weather data
- Fleet-wide optimization considering entire delivery network
- Automated dispatch system coordinating route assignments
Technology Stack
PythonOR-ToolsReinforcement LearningGeospatial APIsApache KafkaRedis
Client Testimonial
"Cut fuel consumption by 15% and achieved 98.7% on‑time deliveries."
— Logistics Manager, MetroFlow Couriers