Solving the Team Orienteering Problem with Transformers

Description

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.

For questions about this multi-agent system, please contact Daniel Fuertes at This email address is being protected from spambots. You need JavaScript enabled to view it..

Download

Click here to download the code.

Citation

D. Fuertes, C. R. del Blanco, F. Jaureguizar, N. García, Solving the Team Orienteering Problem with Transformers, under review.