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.
An agentic framework with llms for solving complex vehicle routing problems
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cs.AI 2years
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Simulator-constrained LLM recovers 94-99% of MILP optimal NPV for mine scheduling while scaling linearly.
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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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Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling
Simulator-constrained LLM recovers 94-99% of MILP optimal NPV for mine scheduling while scaling linearly.