论文标题
基于神经结构启发式的大型邻里搜索
Large Neighborhood Search based on Neural Construction Heuristics
论文作者
论文摘要
我们建议使用基于神经网络作为维修操作员的学习构建启发式启发式启发式启发式启发式方法，以解决时间窗口（VRPTW）解决车辆路由问题。我们的方法使用图形神经网络对问题进行编码并自动解票解码解决方案，并通过对施工任务进行加固学习的培训，而无需任何标签进行监督。神经维修操作员与本地搜索程序，启发式破坏操作员以及适用于少数人群的选择程序结合使用，以达到复杂的解决方案方法。关键思想是使用学习的模型重新构造部分破坏的解决方案，并通过破坏启发式方法（或随机策略本身）引入随机性，以有效地探索大型社区。
We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem with time windows (VRPTW). Our method uses graph neural networks to encode the problem and auto-regressively decodes a solution and is trained with reinforcement learning on the construction task without requiring any labels for supervision. The neural repair operator is combined with a local search routine, heuristic destruction operators and a selection procedure applied to a small population to arrive at a sophisticated solution approach. The key idea is to use the learned model to re-construct the partially destructed solution and to introduce randomness via the destruction heuristics (or the stochastic policy itself) to effectively explore a large neighborhood.
