How to optimize delivery routes: a practical guide for fleets

Every morning, in hundreds of warehouses, someone opens a spreadsheet and starts building the day’s delivery routes. They know the customers by heart, know which roads to avoid, remember who needs their goods before 10am. It works — but it’s slow, hard to reproduce and almost impossible to verify: nobody really knows whether those routes are the most efficient possible, or whether every day burns avoidable kilometres and hours.

Optimizing delivery routes means moving from relying on experience alone to actually measuring. This guide covers what that means in practice, which constraints genuinely matter, and how to move from manual planning to a system that computes optimal routes in minutes.

What does optimizing delivery routes mean

Optimizing delivery routes means assigning stops to vehicles and ordering them in the sequence that minimizes an objective — usually distance, time or cost — while respecting every operational constraint. It’s not simply “finding the shortest path between two points”: it’s solving simultaneously which vehicle serves which customers, in what order, and at what time, across dozens or hundreds of stops.

It’s a mathematical problem known as the Vehicle Routing Problem (VRP). Its difficulty grows explosively with the number of stops: with just 20 deliveries the possible combinations already exceed what a person can evaluate by hand. That’s why manual optimization, beyond a certain scale, never produces the best solution — it produces an acceptable one, which is a different thing.

The constraints that actually matter

The difference between a theoretical path and a route that works lies entirely in the constraints. Serious optimization considers them all at once:

• Time windows: the customer only receives between 8 and 12, or has several slots in the day.
• Vehicle capacity: weight, volume, number of parcels, refrigerated compartments.
• Multiple warehouses: departures and returns from different depots.
• Priorities and mandatory sequences: urgent deliveries, pickups before drop-offs, fixed points.
• Urban restrictions: low-emission zones (LEZ/ZTL), time-based access limits.
• Driving and rest times: for fleets subject to the EU Mobility Package, routes must respect the limits of Regulation 561/2006.
• Available resources: vehicles and drivers actually on duty that day, owner-operators included.

Ignoring even one of these constraints produces routes that look great on paper but fail in reality — which is exactly why generic mapping tools fall short in real distribution.

Manual planning vs automated optimization

The point isn’t to replace the planner: it’s to free them from the mechanical work of building routes by hand, so they can focus on exceptions and data. It’s the shift we describe in moving from Excel to automated route planning.

How to optimize delivery routes in 6 steps

1. Collect stop data: addresses, time windows, quantities, any notes. A perfect format isn’t needed — modern systems clean raw data.
2. Define the real constraints: vehicle capacity, depots, priorities, urban restrictions, driving times.
3. Set the objective: minimize distance, total time, number of vehicles or cost. These are different objectives and lead to different routes.
4. Run the optimization: the engine computes the best assignment and sequence within the constraints.
5. Simulate and compare: try alternative scenarios (one more vehicle, a different window) and compare the impact on km and cost before deciding.
6. Execute and measure: send the routes to drivers, track progress and compare planned vs actual to improve the next round.

The last step closes the loop: without measuring the real result, optimization stays a theoretical exercise.

What to look for in route optimization software

Not all software is equal. For real-world distribution, the criteria that make the difference are:

• Raw data import: being able to upload your Excel file as-is, without reformatting, lowers the adoption barrier.
• Industry-specific constraints: grocery, pharma, food & beverage have different rules; the software should adapt to you, not the other way round.
• Urban awareness: LEZ/ZTL must enter the calculation, not be patched in by hand afterwards.
• Scenario simulation: being able to evaluate decisions (one more vehicle, a new client) before making them.
• Execution and proof of delivery: driver app and digital POD to close the cycle.

It’s exactly the approach of OptivoRoute, our route optimization software: upload the Excel, the AI builds the routes within your real constraints, and you execute them with the driver app.

How much do you actually save

Industry benchmarks are concrete: route optimization typically cuts 10-20% of kilometres and 10-15% of fuel, and brings planning from half a day to a few minutes. On a 20-vehicle fleet that’s tens of thousands of euros a year in avoidable inefficiency. In its first optimization tests, a customer like Bonduelle reduced the vehicles needed by two trucks a day.

To quantify the return on your own case, see our deep dive on the ROI of route optimization software.

Frequently asked questions