  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.
