Solutions / Route optimization

Delivery route optimization

Daily routes built from real addresses, time windows, vehicle limits and how long each stop actually takes — planned in minutes instead of an hour of manual sequencing.

Logistics Optimization Ops
Planned in minutes
not an hour on a map
Constraints respected
windows, capacity, skills
Real stop durations
learned from your history
Who it is for

Delivery and field-service operations planning dozens of stops a day, where a dispatcher still sequences them by hand and by memory.

Short answer

Every morning the day’s orders become routes that respect delivery windows, vehicle capacity, driver shifts and required skills, using service times learned from your own history rather than a flat guess. The dispatcher keeps control — routes can be edited, locked and re-optimized — and the plan reaches drivers with navigation and ETAs attached.

The problem

Routes are built by one experienced dispatcher every morning, and nobody else can do it.

01

Manual planning cannot search the alternatives

A person sequences a reasonable route, not the best one, and cannot compare twenty variants under a deadline. The gap between reasonable and optimal is fuel, hours and missed windows.

02

Flat service times make every plan wrong

Assuming ten minutes at every stop is wrong in both directions: the plan slips by mid-morning and the promised windows go with it.

03

Replanning after a change is the real cost

A cancellation, an urgent order or a broken vehicle means rebuilding by hand at the worst possible moment, so most teams just absorb the inefficiency instead.

How it works

From trigger to result, step by step.

01

Clean the addresses first

Geocoding, validation and fixing the entries that resolve to the wrong place. Optimization on bad coordinates produces a confident plan that sends a van to the wrong street.

02

Learn how long stops really take

Service time by customer type, order size, floor, access and time of day, from your own completed deliveries. This single input decides whether the plan survives contact with the morning.

03

Encode the constraints that actually bind

Delivery windows, vehicle capacity and dimensions, driver shifts and breaks, required skills or certifications, zone restrictions. A plan that ignores one of these is not a plan.

04

Optimize, then let the dispatcher decide

Routes are proposed with the cost and the trade-offs visible; stops can be moved, pinned or excluded and the rest re-optimized around them. The dispatcher stays in charge and keeps the local knowledge in play.

05

Push to drivers and measure the plan against reality

Routes reach the driver app with navigation and ETAs; planned versus actual is reviewed weekly, and the service-time model corrects itself with each week of deliveries.

Before / after

What changes on the ground.

Today, by hand
×One dispatcher sequencing routes on a map
×Ten minutes assumed at every stop
×Replanning by hand after every change
×No comparison between planned and actual
With the automation running
Routes planned in minutes with constraints respected
Service times learned from your own deliveries
One-click re-optimization around locked stops
Weekly planned-versus-actual review that improves the model
What you get

Delivered, not demoed.

Address cleaning and geocoding across your delivery base
A service-time model built from your completed deliveries
An optimizer encoding your windows, capacity, shifts and skills
A dispatcher view with manual overrides and re-optimization
Driver delivery with navigation, ETAs and planned-versus-actual reporting
Built with

We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.

Python OR-Tools PostGIS PostgreSQL Metabase Google Maps n8n 1C
Time to production6–10 weeks
Build priceFixed price
First stepFree mini-audit
Honest limits

When this is not the right solution.

·With one vehicle and a handful of stops, a dispatcher with local knowledge will match any optimizer and cost nothing.
·If your addresses are unreliable and nobody will fix them, the outputs will be confidently wrong. Address quality is the prerequisite, not a detail.
·If drivers will not follow the plan, the savings never materialize. That is an operational change, and it has to be agreed before the software is built.

Questions we get about this one

It depends far more on your stop density and window tightness than on the algorithm. We measure your current routes first and report the gap, rather than quoting a percentage that came from someone else’s operation.

Bring us the process that hurts.

The mini-audit is free: we take your version of this process apart and tell you plainly whether automating it pays. If it does, you get a scope and a fixed price.