Delivery Route Optimization
A dispatcher planning next-day delivery routes by hand, or using a basic mapping tool, tends to optimize for the shortest total driving distance because that's what's visible on a map. But the shortest route isn't the deliverable one if it ignores the customer's 2-4pm delivery window, exceeds what the vehicle can actually carry, or runs a driver past their legal hours before the last stop. Building routes that satisfy all of those constraints simultaneously, across dozens of stops and multiple vehicles, is a genuinely hard combinatorial problem — one that gets worse, not better, the more a dispatcher tries to solve it by eye under time pressure every morning before the trucks need to leave.
STARTING PRICE
From €799
Complex tier · Multi-system orchestration, custom logic, and higher-volume or higher-risk processing.
Get a quote →Saves roughly 1-2 hrs/day for a dispatch team, plus reduced missed-window incidents.
How the automation works
We generate delivery routes that optimize for real operational constraints together, not distance alone: each stop's committed time window, each vehicle's capacity and what's already loaded, each driver's remaining legal hours, and known access restrictions like loading-dock hours or no-truck zones. The optimizer treats these as hard constraints the route must satisfy, then minimizes distance and time within that feasible set, rather than optimizing purely on distance and hoping the constraints happen to work out. When a route genuinely can't fit every stop within its constraints — too many time-windowed deliveries for the available vehicles — the system surfaces which stops don't fit rather than silently producing an infeasible route dispatch would discover was broken only once a driver was already behind schedule.
Process flow
- 01
Stops for the day collected trigger
Delivery stops for the planning window are pulled in automatically with address, committed time window, and package size and weight.
- 02
Apply vehicle and driver constraints ai
Each vehicle's capacity and each driver's remaining legal hours are applied as hard constraints the route plan must respect, not optimized around after the fact.
- 03
Optimize within constraints ai
Routes are built to satisfy time windows, capacity and access restrictions (loading docks, no-truck zones) first, then minimized for distance and time within that feasible set.
- 04
Assign stops to vehicles ai
Stops are allocated across available vehicles to balance load and route length, rather than filling one vehicle to capacity before considering the next.
- 05
Surface infeasible stops output
Stops that can't be fit into any route within their time window given available vehicles and drivers are surfaced explicitly for a dispatcher decision, not silently dropped or forced into an unrealistic route.
- 06
Dispatch to driver apps integration
Finalized routes are pushed directly to driver navigation and delivery apps, with stop sequence and time windows visible for the day.
Inputs
- Delivery stop list with addresses and time windows
- Vehicle capacity and current load
- Driver schedules and remaining legal hours
- Access restrictions (loading dock hours, delivery zone rules)
Outputs
- Optimized multi-stop route per vehicle
- Vehicle and driver assignment plan
- Infeasible-stop exception list
- Dispatched route pushed to driver app
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Optimizing purely for shortest total distance while ignoring delivery time windows produces a route that looks efficient on paper but fails in practice — a customer's committed 2-4pm window is a hard constraint, and a route that arrives outside it isn't actually a valid solution regardless of how short the driving distance is.
- Vehicle capacity constraints need to reflect what's realistically loadable, not just theoretical volume — irregular package shapes, load sequencing for last-in-first-out unloading, and weight distribution can all make a route infeasible even when total volume fits on paper.
- Ignoring driver hours-of-service limits when building a route can produce a plan that's mathematically optimal but illegal or unsafe to actually drive — remaining legal hours need to be a hard constraint on route length and stop count, not an afterthought checked once the route is already built.
- When there are genuinely more time-windowed stops than available vehicles can serve, forcing every stop into a route anyway just pushes the failure downstream to a driver who falls behind schedule mid-route — the optimizer needs to surface infeasible stops explicitly so a dispatcher can add a vehicle, adjust a window, or reschedule, rather than pretend the constraint doesn't exist.
Frequently asked questions
How does it handle a customer's specific delivery time window?
Time windows are treated as hard constraints the route must satisfy, not a preference weighed against distance — a stop that can't be reached within its window on any feasible route is flagged rather than scheduled outside it.
Can it plan routes across multiple vehicles at once?
Yes — stops are allocated across all available vehicles for the day, balancing capacity and route length rather than optimizing one vehicle's route in isolation.
What happens if there are more deliveries than vehicles can handle within their windows?
The optimizer surfaces which stops don't fit given current vehicles and drivers, so a dispatcher can add capacity, adjust a window with the customer, or reschedule rather than dispatching an infeasible route.
Does it account for driver working-hour limits?
Yes — remaining legal driving hours are applied as a hard constraint on route length, so a plan won't schedule a driver past their limit to hit the last stop.