Different optimization approaches, one shared planning goal.

Why is one method not enough?

Routing problems behave differently depending on size, constraint types and solving time. A method strong on one problem family may be weaker on another, so many methods are used side by side in the literature and in practice.

PyVRP

PyVRP is an open-source vehicle routing solver that handles many variants, including capacities, time windows and multiple depots. Its core is written in C++, it is used from Python and it is based on hybrid genetic search.

PyVRP is a third-party project independent of Trackinia and belongs to its own developers.

Google OR-Tools

OR-Tools is Google’s open-source optimization toolkit. Its routing module supports constraints such as capacities, time windows, multiple depots and penalties for dropped stops, and uses local-search-based heuristics.

OR-Tools is also a third-party project independent of Trackinia.

A shared planning goal

Whichever approach is used, the planner sees the same thing: orders, vehicles, drivers and constraints are given in one problem definition, and the result is presented as routes, timing, cost items and unplanned tasks.

This page does not claim that engines run at the same time, are selected automatically or that one method beats another. The quality of a result always depends on the structure of the problem and the solving time.