  This paper presents a novel neural network design that learns the heuristic
for Large Neighborhood Search (LNS). LNS consists of a destroy operator and a
repair operator that specify a way to carry out the neighborhood search to
solve the Combinatorial Optimization problems. The proposed approach in this
paper applies a Hierarchical Recurrent Graph Convolutional Network (HRGCN) as a
LNS heuristic, namely Dynamic Partial Removal, with the advantage of adaptive
destruction and the potential to search across a large scale, as well as the
context-awareness in both spatial and temporal perspective. This model is
generalized as an efficient heuristic approach to different combinatorial
optimization problems, especially to the problems with relatively tight
constraints. We apply this model to vehicle routing problem (VRP) in this paper
as an example. The experimental results show that this approach outperforms the
traditional LNS heuristics on the same problem as well. The source code is
available at
\href{https://github.com/water-mirror/DPR}{https://github.com/water-mirror/DPR}.
