LaF-MCTS uses LLM-assisted flexible MCTS with a three-tier hierarchy, semantic pruning, and branch regrowth to automatically compose decomposition-enhanced CVRP solvers that outperform state-of-the-art methods on CVRPLib benchmarks.
New benchmark instances for the capacitated ve- hicle routing problem.European Journal of Operational Research, 257(3):845–858
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Introduces MnLP, a training method adding multi-step node prediction supervision to improve neural policies for vehicle routing problems.
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Automated Large-scale CVRP Solver Design via LLM-assisted Flexible MCTS
LaF-MCTS uses LLM-assisted flexible MCTS with a three-tier hierarchy, semantic pruning, and branch regrowth to automatically compose decomposition-enhanced CVRP solvers that outperform state-of-the-art methods on CVRPLib benchmarks.
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Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction
Introduces MnLP, a training method adding multi-step node prediction supervision to improve neural policies for vehicle routing problems.