FiLMMeD augments a Transformer with FiLM to create one model for 24 MDVRP variants, adds curriculum learning for multi-depot constraints, shows preference optimization outperforming RL in MTL, and outperforms baselines on experiments including 8 new formulations.
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COAgents introduces a cooperative multi-agent system with a partial search graph to guide intensification and diversification in vehicle routing problems, achieving new state-of-the-art results among learning-based methods on VRPTW benchmarks.
citing papers explorer
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FiLMMeD: Feature-wise Linear Modulation for Cross-Problem Multi-Depot Vehicle Routing
FiLMMeD augments a Transformer with FiLM to create one model for 24 MDVRP variants, adds curriculum learning for multi-depot constraints, shows preference optimization outperforming RL in MTL, and outperforms baselines on experiments including 8 new formulations.
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COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space
COAgents introduces a cooperative multi-agent system with a partial search graph to guide intensification and diversification in vehicle routing problems, achieving new state-of-the-art results among learning-based methods on VRPTW benchmarks.