  ALNS is a popular metaheuristic with renowned efficiency in solving
combinatorial optimisation problems. However, despite 16 years of intensive
research into ALNS, whether the embedded adaptive layer can efficiently select
operators to improve the incumbent remains an open question. In this work, we
formulate the choice of operators as a Markov Decision Process, and propose a
practical approach based on Deep Reinforcement Learning and Graph Neural
Networks. The results show that our proposed method achieves better performance
than the classic ALNS adaptive layer due to the choice of operator being
conditioned on the current solution. We also discuss important considerations
such as the size of the operator portfolio and the impact of the choice of
operator scales. Notably, our approach can also save significant time and
labour costs for handcrafting problem-specific operator portfolios.
