  This article demonstrates the effectiveness of employing a deep learning
model in an optimization pipeline. Specifically, in a generic exact algorithm
for a NP problem, multiple heuristic criteria are usually used to guide the
search of the optimum within the set of all feasible solutions. In this
context, neural networks can be leveraged to rapidly acquire valuable
information, enabling the identification of a more expedient path in this vast
space. So, after the explanation of the tackled traveling salesman problem, the
implemented branch and bound for its classical resolution is described. This
algorithm is then compared with its hybrid version termed "graph convolutional
branch and bound" that integrates the previous branch and bound with a graph
convolutional neural network. The empirical results obtained highlight the
efficacy of this approach, leading to conclusive findings and suggesting
potential directions for future research.
