Dynamic Route Optimization: Build an AI Co-Pilot for Your Dispatchers

Most small fleets rely on the routing module included with their Transportation Management System (TMS) or a telematics platform like Samsara. These tools are excellent for planning daily routes based on historical data and static constraints. They schedule a day's multi-stop route efficiently. However, they are not designed for continuous, real-time re-optimization across an entire fleet.

Consider a 20-truck local delivery company when a major accident blocks a highway and a priority customer calls with an unscheduled pickup. The dispatcher has three trucks nearby. The TMS can't answer: which driver is best positioned, considering the new traffic, their remaining stops, their current load, and their Hours of Service (HOS)? The dispatcher must manually check each driver's location, open a separate traffic app, call the drivers, and make a gut-feel decision in 15 minutes under pressure.

The structural issue is that off-the-shelf software solves the static Vehicle Routing Problem (VRP) once per day. The systems lack the architecture to continuously ingest multiple real-time data streams (traffic, weather, new orders) and resolve a dynamic VRP every few minutes. Their data models are fixed, so adding a unique business constraint, like a customer who only accepts deliveries between 2:00 PM and 4:00 PM, is often impossible.

The first step is a discovery and data audit. Syntora would map your current dispatch workflow and inventory the APIs available from your existing TMS and telematics providers. We identify the key constraints that drive decisions: delivery time windows, vehicle capacities, HOS rules, and driver qualifications. This audit results in a technical specification document you approve before any code is written.

The technical approach would be a serverless system built with Python and deployed on AWS Lambda. A FastAPI service would serve as the core engine. This service would poll your telematics API for vehicle locations every 60 seconds and ingest real-time data from weather and traffic APIs. Using Google's OR-Tools library, the system would re-solve the fleet-wide routing problem every 5 minutes, generating updated ETAs and route sequences. All data would be stored in a Supabase Postgres database you control.

The delivered system acts as a co-pilot for your dispatcher. When a new order arrives, the system presents a ranked list of the top 3 drivers best suited for the job, complete with the impact on their existing routes. This information appears in a simple web interface or can be pushed directly into your TMS. You receive the full Python source code, a runbook for maintenance, and complete control over the AWS and Supabase accounts, which typically cost under $50 per month to operate.