RouteML

Fleet Route Optimization

Saving the client $1.8M annually in fuel and overtime costs.

23%Fuel cost reduction

31%More deliveries per route

200+Vehicles optimized

99.2%On-time delivery rate

The Challenge

Optimize delivery routes for a 200-vehicle fleet across 3 metro areas, accounting for real-time traffic, delivery windows, vehicle capacity, and driver hours-of-service constraints.

Our Approach

Hybrid approach combining OR-Tools for constraint satisfaction with a reinforcement learning agent for dynamic re-routing. Historical delivery data trained a demand forecasting model for proactive fleet positioning.

Tech Stack

OR-ToolsPPO (RL Agent)ProphetFastAPIRedisPostgreSQL

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