  The 2021 NeurIPS Machine Learning for Combinatorial Optimization (ML4CO)
competition was designed with the goal of improving state-of-the-art
combinatorial optimization solvers by replacing key heuristic components with
machine learning models. The competition's main scientific question was the
following: is machine learning a viable option for improving traditional
combinatorial optimization solvers on specific problem distributions, when
historical data is available? This was motivated by the fact that in many
practical scenarios, the data changes only slightly between the repetitions of
a combinatorial optimization problem, and this is an area where machine
learning models are particularly powerful at. This paper summarizes the
solution and lessons learned by the Huawei EI-OROAS team in the dual task of
the competition. The submission of our team achieved the second place in the
final ranking, with a very close distance to the first spot. In addition, our
solution was ranked first consistently for several weekly leaderboard updates
before the final evaluation. We provide insights gained from a large number of
experiments, and argue that a simple Graph Convolutional Neural Network (GCNNs)
can achieve state-of-the-art results if trained and tuned properly.
