  Route planning for a fleet of vehicles is an important task in applications
such as package delivery, surveillance, or transportation. This problem is
usually modeled as a Combinatorial Optimization problem named as Team
Orienteering Problem. The most popular Team Orienteering Problem solvers are
mainly based on either linear programming, which provides accurate solutions by
employing a large computation time that grows with the size of the problem, or
heuristic methods, which usually find suboptimal solutions in a shorter amount
of time. In this paper, a multi-agent route planning system capable of solving
the Team Orienteering Problem in a very fast and accurate manner is presented.
The proposed system is based on a centralized Transformer neural network that
can learn to encode the scenario (modeled as a graph) and the context of the
agents to provide fast and accurate solutions. Several experiments have been
performed to demonstrate that the presented system can outperform most of the
state-of-the-art works in terms of computation speed. In addition, the code is
publicly available at http://gti.ssr.upm.es/data.
