Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search
This is an incremental improvement for researchers and practitioners in combinatorial optimization, particularly for problems with tight constraints like vehicle routing.
The paper tackles the problem of improving Large Neighborhood Search (LNS) for combinatorial optimization by proposing a neural network heuristic called Dynamic Partial Removal, which uses a Hierarchical Recurrent Graph Convolutional Network to adaptively destroy and repair solutions. The results show it outperforms traditional LNS heuristics on vehicle routing problems, though no specific numerical gains are provided.
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}.