Route OptimizationSolutions
Replace hand-drawn delivery runs with routes that balance distance, time windows and vehicle limits, and let dispatchers tweak them when reality intervenes.
Route Optimization Solutions: what the work involves
Many fleets still plan routes from a driver's memory or a dispatcher's whiteboard. Vehicles cross paths, one rider is overloaded while another finishes early, customers with delivery windows are visited late, and fuel disappears on avoidable kilometres. In congested cities, where a drive of five kilometres can take forty minutes, plans that ignore travel time are fiction.
DevKey builds route planning around the actual constraints of your operation: depots, vehicle types and capacities, driver shifts, customer time windows, service durations and priority orders. A solver generates candidate plans that cut travel and balance workloads, using road-network travel times, with realistic traffic assumptions where data allows. Dispatchers see the routes on a map, drag stops between vehicles, and re-optimise after changes. Drivers receive the sequence on their phones, and completed trips feed back to improve estimates.
Core features
Constraint-aware solver
Time windows, capacity, vehicle type, driver hours and priority stops are modelled together, rather than optimising distance alone.
Road-network travel times
Distances and durations come from a routing engine, not straight lines, so estimated arrival times match what drivers experience.
Dispatcher map interface
Plans appear on a map with stops and loads, and dispatchers can reassign, reorder or lock stops before dispatch.
Re-optimisation during the day
New orders, cancellations and delays trigger a revised plan for remaining stops without scrambling completed work.
Driver app and proof of delivery
Drivers receive the sequence, navigate, capture signatures or photos and update statuses, which also supply data on actual service times.
Performance reporting
Planned and actual distance, time, stops per route and lateness are compared, showing where assumptions need adjusting.
What we get right before launch
Optimal on paper, unusable in practice
Drivers know about blocked lanes and customers who never answer the door. We include their feedback, allow manual locks and overrides, and tune the planner until routes are respected, not ignored.
Data quality of addresses
Poor geocoding sends vehicles to the wrong lane. We validate and correct coordinates, let drivers pin locations after a delivery, and store those corrections for the next visit.
Maps and traffic costs
Commercial map and traffic services charge by usage, and open data varies in quality. We compare options such as OpenStreetMap-based engines and paid APIs, and show the trade-off in accuracy and cost.
Tools and technology
- Google OR-Tools
- OSRM and Valhalla
- Google Maps Platform
- Python
- FastAPI
- PostgreSQL with PostGIS
- React
- Flutter
- Docker
Common questions, answered
How much can route optimisation save?
That depends on how efficient your current planning is, so we avoid promising a number. We simulate your past days with the planner, compare distance and time, and share the result before you commit.
Does it handle traffic in cities like Karachi and Lahore?
We can use historical or live traffic data where available, which improves estimates, but congestion remains unpredictable. Plans include buffers, and re-optimisation during the day handles drift.
Can it work with our own riders and vehicles?
Yes. Vehicles, riders, shift hours and capacities are configured in the system, including bikes, vans and trucks with different limits. Mixed fleets are normal.
Do drivers need a smartphone?
A basic Android phone is enough for the driver app. For drivers without smartphones, dispatchers can send printed or messaged route sheets, though this loses live status updates.
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Ready to start your Route Optimization Solutions project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.