AI glossary · Basic · Classic ML, recsys & search · Reinforcement learning · Logistics & supply chain
Route optimization
Route optimization is the task of finding the trip order and routes that minimize total travel distance, time or cost, while taking into account multiple delivery locations, vehicles and time constraints together.
About this term
- Level
- Basic · Technical terms you often meet in AI news
- Category
- Industry AI
- Industries
- Logistics & supply chain
- Added
- 2026-10-10
How it works
The inputs are constraints such as delivery locations, the number of vehicles and their load capacity, the arrival time windows customers want, and drivers' working hours. Because the number of possibilities grows explosively, checking every route is impossible, so mathematical optimization techniques or heuristic algorithms that quickly find a 'good enough' answer are used. On top of this, machine learning predicts travel times for each segment based on real traffic conditions, improving the accuracy of the calculation. If an accident or an extra order comes up during a run, the route is recalculated and the updated route is sent to the driver.
Why it matters
Transportation, especially the final delivery leg to the customer's door, makes up a large share of logistics costs. Even small reductions in routes cut fuel costs, the number of vehicles and driver working hours together. The rate of meeting the time windows promised to customers rises, which also improves service quality. Shorter travel distances also mean lower carbon emissions, linking it to environmental goals.
Things to know
The computationally optimal route is not always the best one on the ground. Information such as buildings where parking is hard, elevator waits, or shortcuts only the driver knows is not well captured in the data. If a route that only minimizes the numbers forces an unreasonable schedule on drivers, it creates safety and labor-condition problems, so rules on breaks and working hours must be included as constraints. And if the demand forecast is wrong, even the vehicle allocation goes off, so route optimization only works when it runs in tandem with demand forecasting.
Example
Every morning, a parcel delivery company receives that day's volume and addresses, divides vehicles by region, and automatically generates the stop order for each vehicle. The driver app shows the set order and estimated arrival times, and if roads are congested or an urgent order comes in, the remaining route is recalculated. For services where refrigerated vehicles and time-window promises matter, such as grocery delivery, these conditions are also included when planning routes.