  The ML-Constructive heuristic is a recently presented method and the first
hybrid method capable of scaling up to real scale traveling salesman problems.
It combines machine learning techniques and classic optimization techniques. In
this paper we present improvements to the computational weight of the original
deep learning model. In addition, as simpler models reduce the execution time,
the possibility of adding a local-search phase is explored to further improve
performance. Experimental results corroborate the quality of the proposed
improvements.
