Improving the Asymmetric TSP by Considering Graph Structure
This work addresses optimization challenges in constraint programming for combinatorial problems like TSP, though it appears incremental in nature.
The authors tackled the asymmetric Traveling Salesman Problem by developing new implied propagators based on graph properties, which improved robustness on pathological instances and outperformed current state-of-the-art results.
Recent works on cost based relaxations have improved Constraint Programming (CP) models for the Traveling Salesman Problem (TSP). We provide a short survey over solving asymmetric TSP with CP. Then, we suggest new implied propagators based on general graph properties. We experimentally show that such implied propagators bring robustness to pathological instances and highlight the fact that graph structure can significantly improve search heuristics behavior. Finally, we show that our approach outperforms current state of the art results.