Route optimization — how automatic tour planning really works
Route optimization computes from many stops, vehicles, time windows and skills the shortest or fastest order — automatically, in seconds instead of hours. This page explains the Vehicle Routing Problem (VRP), shows typical constraints, why manual planning becomes exponentially expensive beyond 5 vehicles, and how Trailo solves it in practice.
No credit card · Pre-filled sample data · Cancel anytime
What is route optimization?
Route optimization is the mathematical problem of finding the best tour allocation from a set of stops and vehicles — defined by criteria like shortest distance, shortest time, minimum cost or maximum stops per day. Mathematically it is a variant of the Traveling Salesman Problem, known as the Vehicle Routing Problem (VRP).
The task is NP-hard: for 10 stops there are already 3.6 million possible orderings, for 15 stops over 1.3 billion. With constraints like time windows, driver skills and multi-depot, the solution space grows exponentially. Modern optimizers solve this in seconds with solver heuristics and — at Trailo additionally — machine clustering to sensibly pre-bundle stop groups.
Four constraints any serious route optimization must master
Without these four, every theory falls apart in practice.
Time windows per stop
Medical practices are only reachable Tuesdays 8–12. Private customers want the 2 PM slot. If an optimizer doesn't understand time windows, it plans the theoretically shortest tour — which in practice fails at closed doors.
Multi-depot & start/end per driver
Driver A starts in north Cologne, driver B in south, both finish at home. Multi-depot logic assigns stops to the nearest start — that often halves total kilometers compared to a single-depot assumption.
Skill matching & fixed-driver assignment
Not every technician can service every piece of equipment. Not every driver has keys to every site. Route optimization has to treat skill and customer bindings as hard constraints — otherwise theoretically optimal tours appear that nobody is allowed to drive.
Breaks, shift lengths & stop durations
An 8-hour shift with a 30-minute break and a 15-minute load-stop duration per address sets tight limits. Real optimization respects working-time rules and individual stop durations — otherwise the tour collapses in practice.
See how Trailo applies these constraints to a real tour — 14-day trial, ready in 5 minutes with sample data.
Manual planning vs. automatic optimization
Four typical effects when the fleet grows past 5 vehicles.
Complexity explodes
5 vehicles × 8 stops × time windows + skill constraints = more permutations than a human can grasp in an hour. Manual planning typically ends with "good but not optimal" tours.
Re-optimization on emergencies
Emergency at 10:00 → re-sort five tours. Automatically in seconds, manually 15–30 minutes — per emergency, per day.
Recurring contracts don't scale
Spreading quarterly, monthly and half-yearly contracts across weeks, dodging emergencies, planning breaks — manually starting over every month.
Knowledge leaves with the dispatcher
The experienced dispatcher knows the city, the customers, the soft spots. If they're away or sick, planning collapses. Codified knowledge in software doesn't vanish when someone is missing.
Trailo starts at €39/vehicle/month (yearly billing). All plans monthly cancelable.
Frequently asked questions about route optimization
What is route optimization?
Route optimization automatically computes the shortest or fastest tour order from multiple stops, vehicles, time windows and driver skills. Mathematically it is a Vehicle Routing Problem (VRP) — an extension of the classical Traveling Salesman Problem with constraints like capacity, time windows and multi-depot.
From what point does automatic route optimization pay off?
For one vehicle on a linear tour, a maps app solves the problem well enough. As soon as multiple vehicles, time windows or mandatory orderings come in, manual optimization becomes exponentially expensive. Practically: from 5 vehicles or 30 stops, automatic optimization pays off.
Which constraints can modern route optimization handle?
Time windows per stop, breaks per driver, capacity limits (volume, weight), skill matching (which technician can do what), multi-depot start/end, fixed-driver assignment per regular customer, mandatory orderings, stop duration per job.
Does Trailo use real AI for route optimization?
Trailo uses an established VRP approach with modern solver heuristics plus machine clustering (k-means) for stop bundling per tour. "AI-powered" refers to the combination — from purely algorithmic optimization to learning components such as the planned AI chat interface (Coming Soon).
How much time does automatic route optimization save?
Up to 90 % planning time per day, up to 20 % more stops per day, up to 15 % less fuel cost compared to manual planning (established VRP approach with modern solver heuristics). Results vary by operation and route structure.
Where do I find route optimization in Trailo?
Route optimization is a core function in all plans — already from Starter. You set up stops and vehicles, click "Plan Day", and Trailo generates the optimized tour in seconds. More on all features on the route planning software overview.
Start today — try in 5 minutes
14 days free. Pre-filled sample data. No credit card. Cancel anytime.