论文标题
动态部分删除：用于大型邻里搜索的神经网络启发式
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search
论文作者
论文摘要
本文介绍了一种新型的神经网络设计，该设计学习了大型邻里搜索（LNS）的启发式方法。 LNS由销毁操作员和维修操作员组成，该操作员指定了一种进行邻里搜索以解决组合优化问题的方法。本文提出的方法应用了分层复发图卷积网络（HRGCN）作为LNS启发式，即动态的部分删除，具有自适应破坏的优势，并有可能在空间和时间观点中遍及大规模搜索以及跨越大规模的搜索。该模型被推广为用于不同组合优化问题的有效启发式方法，尤其是针对相对严格的问题。我们将此模型应用于本文中的车辆路由问题（VRP）。实验结果表明，这种方法在同一问题上的表现也优于传统的LNS启发式方法。源代码可在\ href {https://github.com/water-mirror/dpr} {https://github.com/water-mirror/dpr}中获得。
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}.
