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This paper presents a novel neural network design that learns the heuristicfor Large Neighborhood Search (LNS). LNS consists of a destroy operator and arepair operator that specify a way to carry out the neighborhood search tosolve the Combinatorial Optimization problems. The proposed approach in thispaper applies a Hierarchical Recurrent Graph Convolutional Network (HRGCN) as aLNS heuristic, namely Dynamic Partial Removal, with the advantage of adaptivedestruction and the potential to search across a large scale, as well as thecontext-awareness in both spatial and temporal perspective. This model isgeneralized as an efficient heuristic approach to different combinatorialoptimization problems, especially to the problems with relatively tightconstraints. We apply this model to vehicle routing problem (VRP) in this paperas an example. The experimental results show that this approach outperforms thetraditional LNS heuristics on the same problem as well. The source code isavailable at\href{https://github.com/water-mirror/DPR}{https://github.com/water-mirror/DPR}.

Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search