GSOLU
Logistics & Supply Chain· Urban + regional delivery network

Choreographing Vehicles, Drivers, Stops, and Time - A Real-Time Logistics Operating System

A constraint-aware, traffic-intelligent route optimization and fleet coordination platform that turned manual delivery planning into structured, calm, real-time logistics infrastructure.

Built for an operator running multiple vehicles, multiple drivers and thousands of daily stops, this platform replaced whiteboards, phone calls and intuition with a real-time logistics operating system. Routes optimize themselves, traffic and access constraints are respected automatically, every product is barcoded from pickup to dropoff, and every stakeholder - from dispatcher to driver to end customer - sees exactly what they need, exactly when they need it.

Client
Multi-vehicle distribution operator
Project
Route optimization & fleet operations platform
Size
Multiple vehicles · multiple drivers · thousands of stops
dispatch / live-routes
123456789
9 stops · ETA
14:32
0
Route planning time
0
Failed deliveries
0
Stops handled per day
0
Customer ETA latency
Stack
LaravelPostgreSQLPostGISRedisNext.jsReact NativeCustom routing engineTraffic API integrationConstraint-aware schedulingBarcode scanningRealtime fleet dashboardsCustomer tracking pages
Overview
3 sections
The problem

Before us

Multiple vehicles. Multiple drivers. Hundreds of stops a day, each with its own delivery window, priority, access constraint and load profile. Before our platform, the operations team coordinated all of it through a fragile mix of spreadsheets, phone calls, paper run-sheets and senior-dispatcher memory. Routes were planned by hand. Sequencing was driven by intuition. Restricted zones were remembered, not enforced. Traffic was a daily surprise. Customers had no idea when their delivery would arrive - and neither, often, did the dispatcher.

Our solution

What we built

We built a real-time logistics operating system that thinks in vehicles, drivers, stops, time windows, traffic conditions and access constraints simultaneously. Routes are optimized in seconds instead of hours. Restricted zones, congestion, priority and capacity are respected at the planning layer. Drivers move through their day with a calm mobile experience. Every product is barcoded from pickup to dropoff. And every customer receives a live, accurate arrival window without a single phone call.

The result

Four months later

Manual planning collapsed from hours to minutes. Routes became measurably tighter. Failed deliveries dropped. Drivers stopped wasting fuel and frustration on bad sequencing. Dispatchers stopped firefighting and started orchestrating. Customers stopped calling to ask when their order would arrive - because the platform was already telling them.

The Challenge

A logistics operation that everyone knew was being run on heroics

Walk into any traditional delivery operation at six in the morning and you will see the same thing: a senior dispatcher hunched over a printout, a phone in one hand, a marker in the other, mentally re-sequencing a day that hasn't even started yet. Vehicles, drivers, stops, time windows, restricted streets, customer requests - all of it held together by experience, instinct, and the willingness of a few exceptional people to carry the entire operation in their head. It works. Until volume rises, traffic shifts, or the dispatcher has a bad morning.

Why manual planning breaks at scale

You cannot reason your way through a thousand simultaneous constraints

Even a small delivery operation faces a problem that mathematics has been arguing about for decades. Once you cross a certain number of vehicles and stops, the number of possible plans explodes. A human dispatcher can build *a* plan. They cannot build the *best* plan. And the difference between a good plan and a bad plan, multiplied across hundreds of stops a day, hundreds of operating days a year, is the difference between a healthy logistics business and one that quietly bleeds margin into the streets it drives.

Operational complexity

Logistics is many problems pretending to be one

Each vehicle has a different load capacity. Each driver has a different start time and shift length. Each stop has a delivery window, a priority, sometimes a restricted vehicle requirement. Some streets are closed to large vehicles. Some neighbourhoods turn into parking lots at predictable hours. Some customers absolutely must be visited before noon. Some deliveries can wait. Some can't. The platform had to think about all of it, simultaneously, every time the day reshuffled.

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