Route Optimization

Route optimization is the automatic calculation of the order and path in which a set of vehicles visits a list of destinations, aiming to minimize a cost metric (distance, time, fuel or number of vehicles) while respecting real constraints such as delivery time windows, load capacity, driver shifts and customer priority. Instead of a planner building route sheets by hand, an optimization engine evaluates thousands of possible combinations and returns the best assignment.

Technically, it is a variant of the vehicle routing problem (VRP), a classic of operations research that is solved with heuristic algorithms because the number of combinations grows explosively with each new stop. In logistics practice, it feeds the daily planning of deliveries and of the last mile, and it is part of what Trazzo coordinates when it sequences the deliveries of a distribution operation.

The result is not just a sequence of stops: it includes estimated arrival times, the assignment of each order to a specific vehicle and, increasingly, real-time re-optimization when something unexpected comes up (a customer who was not there, traffic, or an urgent order that comes in mid-morning).

Why it matters in a real operation

In distribution, transportation is usually one of the heaviest costs in the chain, and much of that cost is decided before the truck leaves: in how the route sheets are built. Manual planning tends to overload the drivers who know their area best, leaves vehicles half empty and creates empty miles from back-and-forth trips that a person cannot see with 80 or 200 stops on the table. Route optimization attacks that point: it turns an operational constraint that is hard to reason through by hand into a repeatable calculation that is redone every morning in minutes.

The typical impact the industry reports runs along three lines: less distance traveled (less fuel and wear), more deliveries per shift with the same fleet, and better compliance with agreed windows, which improves metrics like OTIF. These improvements should be read as industry benchmarks, not as a guaranteed result: they depend on delivery density, the quality of the addresses and how tight the time windows are.

How it works, in plain terms

The engine starts from three inputs: the list of orders with their location (which requires good geocoding to turn each address into coordinates), the business constraints and an objective to minimize. With that, it solves, in order:

• Assignment: which orders go to which vehicle, respecting weight and volume capacity.
• Sequencing: in what order each vehicle visits its stops to shorten the total route.
• Scheduling: what time it expects to reach each point, to meet the windows and the driver's legal working hours.
• Recalculation: adjusting the plan when something changes during the day (cancellations, delays, new orders).

Many solutions use historical and live traffic models so the estimated times resemble the real world and not a straight line on the map.

A concrete example in Argentina

A beverage distributor in Greater Buenos Aires (AMBA) delivers to 150 kiosks and corner stores a day with six trucks. Without optimization, the supervisor builds the routes by fixed, inherited zones, and on Mondays (the peak day) some drivers finish at 7 p.m. while others are back by noon. With a routing engine that considers each store's receiving windows (many do not receive between 12 and 2 p.m.), each truck's capacity and traffic on the Panamericana highway, the route sheets are rebalanced: the work is spread evenly, deliveries are added without adding a vehicle, and the number of failed visits from arriving outside receiving hours goes down. The plan is tied to real-time tracking so the team can react if something falls behind.

Common mistakes

• Dirty addresses: if geocoding fails, the engine optimizes over the wrong points. The quality of the address data is the foundation of everything.
• Decorative constraints: loading time windows or capacities that are not real inflates the cost and makes the plan less credible to drivers.
• Blindly trusting the map: ignoring access points, one-way streets, closed zones or the real unloading time leads to plans that look great on screen but are impossible on the street.
• Not measuring planned against actual: without comparing the suggested route with what really happened, the system never learns or earns the team's trust.

How it differs from geocoding and tracking

These three concepts get confused because they coexist in the same delivery operation, but they solve different things:

Optimization is the decision layer: it uses the data that geocoding prepares and delivers a plan that tracking then monitors against the reality of the street.