Route Optimization with AI: Algorithms and Real Results

The problem has a name: VRP

The Vehicle Routing Problem (VRP) is one of the most studied combinatorial optimization problems in computer science. The basic version: given a depot, a fleet of vehicles, and a set of customers, find the routes that minimize total distance traveled. The real version: add time windows, load capacity, vehicle types, driver restrictions, emission zones, customer preferences, and traffic that varies by hour.

It is NP-hard. No algorithm finds the optimal solution in reasonable time for problems with more than a few dozen stops. What does exist are algorithms that find very good solutions in seconds. And “very good” in logistics means millions of euros in annual difference.

Which algorithms work in production

Academic research on VRP fills libraries. In production, three algorithm families dominate:

Metaheuristics. Algorithms like Adaptive Large Neighborhood Search (ALNS) and modified genetic algorithms. They start from an initial solution (often generated by a simple heuristic like nearest neighbor) and iteratively improve it by destroying and reconstructing parts of the solution. Google OR-Tools implements several of these metaheuristics and is open source. In our experience, OR-Tools solves problems of 500-1,000 stops in under 30 seconds with results within 5% of the theoretical optimum.

Constraint programming. Instead of searching for the best solution, you define the space of valid solutions through constraints (time window, capacity, driving time) and search for the best within that space. OptaPlanner (Java) and Google CP-SAT are the reference tools. Particularly useful when constraints are complex or changing.

ML models for prediction. They do not solve VRP directly but feed better data into the algorithms. A model that predicts travel time between two points considering time of day, day of week, and weather conditions produces better routes than using static Google Maps distances. We have trained traffic prediction models with XGBoost on historical fleet GPS data that reduce travel time estimation error from 22% (generic Google Maps) to 8%.

Results we have measured

Numbers from real projects with distribution companies in Spain:

Food distribution (Andalusia). 45 vehicles, 380 daily deliveries, 2-hour time windows. Before: manual planning by the traffic manager, 6 hours of daily work. After: automatic optimization in 4 minutes, 30-minute human review. Result: 12% fewer kilometers, 18% less total route time, 8% fuel cost reduction. Positive ROI in 4 months.

Last-mile parcel delivery (Madrid). 120 drivers, 2,400 daily deliveries, high traffic variability. The key was traffic prediction: the model learns that the M-30 highway at 8:30 AM doubles the travel time versus 10:00 AM and adjusts routes accordingly. Result: 9% more deliveries per driver per day, 15% reduction in failed deliveries due to arriving outside time windows.

Industrial distribution (Levante region). 22 vehicles with weight constraints (up to 24 tons), clients with loading docks requiring advance booking. Here constraint programming was critical: the algorithm manages dock appointments as hard constraints that cannot be violated. Result: 7% kilometer reduction, but the primary saving was in appointment non-compliance penalties (85% reduction).

What AI does not solve

Route optimization with AI is not magic. There are real limitations:

Input data. If your delivery addresses have errors, if you do not record customer time windows, if you do not know the actual weight of each shipment, the algorithm optimizes garbage. 40% of the effort in these projects goes to cleaning and structuring input data.

Real-time exceptions. A vehicle breaks down, a customer cancels, a road is closed. Algorithms can reoptimize, but they need real-time data and an operational process feeding them. Without vehicle GPS and a driver communication system, reoptimization is theoretical.

Human factor. Drivers know their routes. They know the alley on X street cannot fit a 3-axle truck even though Google Maps says it can. They know customer Y opens half an hour early on Tuesdays. That tacit knowledge is not in any dataset, and the best implementations incorporate it: the driver can flag constraints that the algorithm respects.

Route optimization is not an AI project. It is an operations project that uses AI as a tool. The distinction matters, because success depends not on the algorithm but on data quality, integration with existing systems, and adoption by the traffic management team. For a broader view of logistics automation with AI, see our comprehensive logistics automation guide. And to understand how generative AI complements these optimization algorithms, our article on generative AI in logistics covers prediction and autonomous decision-making.