I'm just reading . The authors use a neural network to solve capacitated vehicle routing problems through iterative generation of tours by solving a price-collecting traveling salesman problem in the action selection step of the neural network.
In the conclusion they state that other promising approaches to use machine learning to help solve discrete optimization problems would be to learn the selection of local search operators.
The success of local search methods in tackling these problems suggests an orthogonal reinforcement learning approach, in which the action space is a set of cost-improving local moves, could be successful.
I was very surprised that this has not been studied before, since it seems kind of an obvious avenue to take (no need to encode constraints directly in the NN as it can be handled by the search operators). A quick search only turned up , which seems to generate initial solutions via reinforcement learning and then improves these solutions with local search.
Topics like learning to branch in/decompose mixed-integer programs have been studied since at least 2014 [3-5]. I would argue that those topics have much higher barriers of entry than learning search operator selection for VRPs.
Does anyone know of and can point me to research that studies learning the selection of local search operators (think relocate vs. swap)? Does not need to be vehicle routing. Delarue A., Anderson R., Tjandraatmadja C. (2020). Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing. https://arxiv.org/abs/2010.12001.
 Zhao, J., Mao, M., Zhao, X., & Zou, J. (2020). A hybrid of deep reinforcement learning and local search for the vehicle routing problems. IEEE Transactions on Intelligent Transportation Systems.
 He, H., Daume III, H., & Eisner, J. M. (2014). Learning to search in branch and bound algorithms. In Advances in neural information processing systems (pp. 3293-3301).
 Khalil, E. B., Le Bodic, P., Song, L., Nemhauser, G., & Dilkina, B. (2016). Learning to branch in mixed integer programming. In Thirtieth AAAI Conference on Artificial Intelligence.
 Kruber, M., Lübbecke, M. E., & Parmentier, A. (2017). Learning when to use a decomposition. In International Conference on AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (pp. 202-210). Springer, Cham.