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Replaced a third-party routing engine with a domain-specific solver that respected driver work-time rules and reduced cost-per-stop by 19%.
Every engagement is framed around the business situation, the constraint that made it hard, and the decision that turned delivery into a controlled path to value.
Situation
The off-the-shelf routing engine charged per stop, did not respect driver work-time regulations in the carrier’s market, and produced routes that drivers routinely ignored. Costs were rising faster than volume.
Constraint
The off-the-shelf routing engine charged per stop, did not respect driver work-time regulations in the carrier’s market, and produced routes that drivers routinely ignored. Costs were rising faster than volume.
Decision
Built a domain-specific routing solver because the commercial and regulatory constraints were the product, not edge cases.
Build
We built a vehicle-routing solver in Go using a metaheuristic appropriate to the problem size, with hard constraints encoded for legal work-time, vehicle weight, and customer time windows. Soft constraints captured the driver preferences that surfaced in the field interviews.
Routes were reviewed by depot managers before dispatch via an interface designed with them. Acceptance and override rates were captured per route and fed back into the solver weights weekly.
Outcome
Cost-per-stop dropped 19% in the first full quarter on the new engine.
On-time delivery rate improved by 8 percentage points.
What changed after launch
Depot managers could review, override, and improve routes, turning local knowledge into weekly optimization signals.
“The new routes respected the real world. Drivers trusted them because their constraints were finally represented.”
The work was not abstract modernization. It changed day-to-day behavior, ownership, and the evidence leaders used to make decisions.
Before
After
The delivery plan made the system boundary explicit, then used rehearsals, gates, and telemetry to optimize safely before launch.
Delivery architecture
Logistics control loop
Discover
We built a vehicle-routing solver in Go using a metaheuristic appropriate to the problem size, with hard constraints encoded for legal work-time, vehicle weight, and customer time windows. Soft constraints captured the driver preferences that surfaced in the field interviews.
Launch
Routes were reviewed by depot managers before dispatch via an interface designed with them. Acceptance and override rates were captured per route and fed back into the solver weights weekly.
We built a vehicle-routing solver in Go using a metaheuristic appropriate to the problem size, with hard constraints encoded for legal work-time, vehicle weight, and customer time windows. Soft constraints captured the driver preferences that surfaced in the field interviews.
Routes were reviewed by depot managers before dispatch via an interface designed with them. Acceptance and override rates were captured per route and fed back into the solver weights weekly.
After launch
Depot managers could review, override, and improve routes, turning local knowledge into weekly optimization signals.
A similar problem?
A senior engineer will follow up within one business day with an opinionated take on the shape of the work.
