A Supervised Machine Learning Approach for the Vehicle Routing Problem
conference paper
This paper expands on previous machine learning techniques applied to combinatorial optimisation problems, to approximately solve the capacitated vehicle routing problem (VRP). We leverage the versatility of graph neural networks (GNNs) and extend the application of graph convolutional neural networks, previously used for the Travelling Salesman Problem, to address the VRP. Our model employs a supervised learning technique, utilising solved instances from the OR-Tools solver for training. It learns to provide probabilistic representations of the VRP, generating final VRP tours via non-autoregressive decoding with beam search. This work shows that despite that reinforcement learning based autoregressive approaches have better performance, GNNs show great promise to solve complex optimisation problems, providing a valuable foundation for further refinement and study.
TNO Identifier
992891
Source title
ICORES 2024 13th International Conference on Operational Research and Enterprise Systems Rome, 24-26 february 2024
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