AI Routing in Logistics
Industry
Logistics
Client
Private client
Team
PILOT
Year
2021
We were hired to build an intelligent routing system that eliminates the empty truck problem and optimizes freight efficiency in real-time.
Challenge
Empty or underutilized truck journeys affected 30-40% of freight operations industry-wide, causing wasted fuel, increased emissions, and reduced cost-effectiveness.
Trucks frequently carried partial loads or returned empty, creating massive inefficiencies that existing static route planning tools couldn't solve dynamically.
Approach
Collected and preprocessed over 15,000 logistics entries from Cargo.lt and Trans.eu, including distances, routes, pricing, and journey times data.
Implemented Ant Colony Optimization (ACO) algorithm mimicking ant foraging behavior to identify shortest and most efficient routes using simulated pheromones to represent optimal paths.
Built dynamic load management system that simulates thousands of paths in real-time, adjusting to route changes and load capacities to minimize empty or partially filled backhauls.
Applied Bayesian hyper-parameter optimization to handle extensive datasets while maintaining real-time route adaptability and high responsiveness.
Developed custom API integration with client's logistics platform for seamless data exchange and up-to-date route adjustments based on live load availability and truck locations.
Architecture/Backend
Python data processing pipeline, REST API integration framework, real-time data ingestion
AI/ML models
Ant Colony optimization, Bayesian h.p. optimization, dynamic route optimization
Infrastructure/Deployment
Real-time processing on scalable cloud-based systems with API-based integration
Key results
Significant cost reductions achieved by minimizing empty backhauls and optimizing truck space utilization across freight operations.
Improved environmental impact through reduced fuel consumption and emissions from fewer empty truck journeys.
Enhanced logistical efficiency via dynamic load management that responds to real-time fluctuations in truck availability and load capacity.
Built scalable system architecture designed for expansion as logistics needs evolve, positioning as long-term solution.
