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Dynamic vehicle routing and task allocation

Route2 is an innovative engine based on artificial intelligence dedicated to creating delivery schedules and planning routes for vehicles based on orders. Route2 provides dynamic optimization of transport routes, capable of solving complex vehicle routing problems in minutes. Optimal routes determined by the Route2 module are, above all, minimized total transport costs while maintaining a certain level of efficiency, but also saving time needed for planning and ensuring on-time delivery.

Route2 provides measurable benefits

Automate and accelerate the route planning process in your company. Minimize total transportation costs while maintaining delivery timeliness.

Up to 23% less

savings in operating costs (OpEx)

Up to 20% less

reduction in mileage and service time without delivery delays.

Up to 20% more

increase in vehicle space utilization.

Up to 10% less

reduction in the required fleet size.

Up to 100%

automation of route planning and fleet management processes

Up to 25% less

optimization of capital expenditures (CapEx), including effective fleet electrification, ESG standards, and new operational strategies.

Route2 Functionalities

Route2 is the future of dynamic vehicle routing. Leveraging advanced AI, Route2 transforms fleet efficiency with real-time adaptability, consistently delivering optimal routing solutions to mitigate delays and maximize fleet potential. It dynamically manages transport systems, adjusting routes based on parameters like new and canceled orders or changing traffic conditions. Key functionalities of the Route2 module include:

Dynamic route planning considering unpredictability in transportation

Static assignment: Orders are allocated once for a set time period (e.g., 15 minutes, one hour, one day), without updates. This limits fleet efficiency as it doesn’t adapt to new orders or cancellations. Static assignments are ideal for fleet operators who receive transportation orders with a time lag (e.g., once a day) with the need for delivery in the next time interval (the next day). Dynamic assignment: Routes and assignments can be re-optimized based on new orders and road conditions. This solution is beneficial for real-time order processing, it can increase fleet efficiency by up to 23% (23% lower operational cost for a given pool of orders).

Optimization for one or many distribution centers and transshipment hubs

A multimodal system enables significant operational savings, especially for companies handling orders over long distances with potential distribution centers along the way (first mile → hub-to-hub travel → last mile). By considering transshipments in a single optimization, we can better adjust vehicle sizes to transportation needs and increase vehicle utilization efficiency. Transshipment points and distribution centers can be defined by the operator using dedicated tools for the Route2 module. By optimizing services in the multimodal system, our solution ensures coordination of transportation tasks, taking into account factors such as time required for transshipment and delays.

Handling Parameters such as Time Windows, Priorities, and Employee Schedules

Time windows refer to a time interval during which a transportation order can be picked up or delivered, beyond which the operator will be penalized or may not have the possibility to fulfill the order. Meanwhile, priorities determine which order brings greater profit or has a higher priority for servicing in a specific time window. Order priorities are combined with time windows to enable management of vehicles and orders that may have conflicting service times or to enforce the algorithm to choose the most cost-effective solution. In the Route2 module, the fleet operator can define necessary vehicle and driver breaks (as well as their locations). Based on the defined break schedules, the Route2 module will plan the order of service for orders to ensure the break schedules are fulfilled.

Predictive routing for fleet rebalancing, including charging

Route2 includes a built-in module for learning transport patterns and their effectiveness. The algorithm’s self-learning capabilities enable the optimization of fleet utilization for better future balancing, such as the algorithm knowing that peak deliveries from point A are expected at 11am, so it positions vehicles at point A before orders appear. Similarly, vehicle loading and servicing are planned outside of peak fleet utilization hours. Battery charging optimization can occur in reactive mode (charging because the battery has low charge) and proactive mode (charging because the vehicle is close to the charging station and doesn’t have priority orders to fulfill in the near future).

These are not all functionalities, click and see more Route2 functionalities

Do you want to streamline your transportation processes?

At Aleet, we start by understanding the operational model of your transport business to customize our software to meet your company’s needs.