Solving large-scale routing optimization problems with networks and only networks

For the first time, a fully neural approach has been proposed, capable of solving the optimization problem of routes of extremely large dimensions ($\sim$5000 points) with real-world constraints such as cargo capacity, time windows, and delivery sequencing. The proposed solution allows for rapid suboptimal problem-solving for small and medium dimensions ($<$ 1000 points). Meanwhile, it outperforms heuristic approaches for tasks of extremely large dimensions ($>$ 1000 points), thereby representing a state-of-the-art (SotA) solution in the field of route optimization with real-world constraints and extremely large dimensions.