Operations

Route optimization

Computing the best sequence and assignment of stops across a fleet against live constraints: time windows, vehicle capacity, cost, and traffic. Solved once at plan time it decays by mid-morning, so Nash re-solves it continuously as the day changes.

Where this fits

The day-to-day vocabulary of running deliveries. Routes, waves, drops, manifests, ETAs, SLAs. These are the units operators reason in, and the inputs every downstream model needs.

Related terms

Delivery costs: The all-in cost to move one delivery from origin to destination, including labor, fuel, vehicle, packaging, exception-handling overhead, and any per-job carrier fee. The denominator behind every cost-per-stop and cost-per-order target. Compresses with route density, batching, and carrier mix.
Continuous re-optimization: Re-solving the plan as the day unfolds instead of freezing it at dispatch. A cancelled order, a late driver, or a new pickup reshapes the affected routes in place, so the plan tracks reality rather than the morning's assumptions.
Delivery success rate: The share of orders that complete on the first attempt, on time, with valid POD, and without exception. The cleanest single metric for whether dispatch is working. Above 99% is best-in-class; most operations sit lower.
Geofencing: A virtual boundary drawn around a place. Crossing it fires an event: arrival at a stop, entry to a zone, a status change, a customer notification. The trigger that turns raw location into an operational signal.
Manual Intervention: Any action a human takes in the dispatch loop that automation didn't make on its own: re-routing a stuck order, calling a driver, escalating an exception, manually reassigning capacity. Manual-intervention rate is a closely-watched KPI for autonomic systems; the goal is for it to drop toward zero as the system learns the operation.