Continuous Move Optimization

The core algorithmic logic that strings together multiple loads into a single, unbroken tour. Instead of treating each shipment as an isolated transaction, the engine solves a traveling salesman problem (TSP) variant with strict time windows. It evaluates all possible load sequences to find the path that minimizes the gap between a drop-off and the next pick-up. The system must account for hours-of-service (HOS) regulations, ensuring the chained route is legally drivable without violating mandatory rest periods. Effective chaining converts a truck from a single-use asset into a continuously utilized resource, directly attacking the industry-standard 20-30% empty mile rate.

A specialized sub-routine focused exclusively on reducing non-revenue miles between loads. The engine calculates the deadhead ratio—the distance traveled empty divided by the distance traveled loaded—for every potential load combination. It applies a penalty cost to empty repositioning in the objective function, forcing the optimizer to favor load pairs with minimal geographical separation. Advanced implementations use predictive heatmaps of future load availability to determine if a longer deadhead now positions the truck in a high-demand zone for a premium backhaul later, balancing immediate cost against strategic network repositioning.

The constraint solver that ensures every chained load is operationally feasible. Each shipment carries a strict appointment window—a hard time range for pick-up and delivery. The engine must verify that the drive time between a delivery and the next pick-up, plus buffer for traffic and loading, fits within the gap. A single violation breaks the chain. The solver uses constraint propagation to prune invalid sequences early, dramatically reducing the search space. It also accounts for detention risk at facilities known for chronic delays, inserting protective buffers to prevent cascading schedule failures across the entire tour.

The economic objective function that drives tour construction. Rather than simply minimizing cost, the optimizer seeks to maximize revenue per mile (RPM) across the entire trip. It evaluates each candidate load's rate against the total miles required to service it, including the deadhead to reach it. A high-paying load that requires a long empty repositioning may be rejected in favor of a slightly lower-paying load that fits seamlessly into the existing route. This holistic view prevents the common brokerage pitfall of booking a single profitable load that strands a truck in a freight-deficit zone, destroying utilization for the following days.

An advanced continuous move strategy that coordinates multiple trucks and trailers to eliminate driver downtime. When hours-of-service limits prevent a single driver from completing a long chain, the engine orchestrates a trailer swap at a designated meet point. One driver drops a loaded trailer, and a fresh driver hooks it to continue the journey immediately. This decouples the tractor from the trailer's journey, enabling true 24/7 asset utilization. The optimizer must solve the complex spatio-temporal matching problem of aligning two drivers' schedules, locations, and available hours at a safe, legal exchange point.

The strategic extension of continuous moves beyond a single day into a multi-day trip plan that spans a driver's entire work week. The engine forecasts load availability 3-7 days into the future using probabilistic demand models and constructs a complete tour that returns the driver home by a specified date. This provides carriers with revenue certainty and eliminates the daily scramble for the next load. The planner must balance long-term utilization against the uncertainty of future spot rates, often locking in a mix of contract and predicted spot loads to create a stable, high-RPM itinerary.