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.
So far, edge-finding is the only one major filtering algorithm for unary resource constraint with time complexity O(nlog n). This paper proposes O(nlog n) versions of another two filtering algorithms: not-first not-last and propagation of detectable precedences. These two algorithms can be used together with the edge-finding to further improve the filtering. This paper also propose new O(nlog n) implementation of fail detection (overload checking).