Optimization is not shortest path
Drawing a line between two points is navigation. Route optimization is deciding which stops go on which vehicle, in what order, across a whole fleet, under a stack of competing constraints. The hard part is not distance — it is doing it for hundreds of stops and dozens of vehicles fast enough to act on before dispatch.
The constraints that make it hard
- Time windows: Customers, stores, and depots each accept deliveries only within set hours.
- Vehicle capacity: Weight, volume, and pallet limits differ per vehicle and cannot be exceeded.
- Driver rules: Shift length, breaks, and licensing constrain how long and where a driver can run.
- Service type: Refrigerated, oversized, or hazardous loads restrict which vehicle can carry what.
The trade-offs
There is rarely a single "best" route — there are routes optimized for different goals. Minimize total distance and you may miss time windows. Maximize stops-per-driver and you may blow out overtime. A good optimizer exposes the trade-off so the operator can weight cost against service instead of pretending only one answer exists.
Dense, logical routes don't just cut fuel — they lift deliveries-per-driver, which is the number that actually moves cost-per-delivery.
Garbage in, garbage out
Optimization is only as good as its inputs: accurate addresses and geocodes, real service times per stop, correct vehicle profiles, and live traffic where it matters. Feed it clean data and it clusters deliveries geographically, tightens the sequence, and reliably raises deliveries per driver. Feed it stale addresses and it confidently plans routes that fall apart on the road.