VaP-CSMV uses a cross-semantic encoder and multi-view decoder to unify DRL solving of HFVRP variants, outperforming prior neural solvers while matching heuristics at much lower inference time and generalizing zero-shot to unseen scales.
Neural combinatorial optimization algorithms for solving vehicle routing problems: A comprehensive survey with perspectives
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The paper introduces the Compositional Geometry Routing Problem and proposes DiCon, a differential-attention plus double-level contrastive learning solver that reports strong performance and generalization on mixed-geometry routing instances.
A knowledge-embedded RL framework decomposes generalized CVRPs into route-first and cluster-second subproblems, using dynamic programming to guide the RL solver and a history-enhanced context module to handle partial observability, yielding better solutions than prior learning methods.
Presents MAEnvs4VRP, a modular PyTorch library providing unified multi-agent environments for multiple variants of vehicle routing problems following the AEC model.
citing papers explorer
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Vehicle-as-Prompt: A Unified Deep Reinforcement Learning Framework for Heterogeneous Fleet Vehicle Routing Problem
VaP-CSMV uses a cross-semantic encoder and multi-view decoder to unify DRL solving of HFVRP variants, outperforming prior neural solvers while matching heuristics at much lower inference time and generalizing zero-shot to unseen scales.
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Learning to Solve Compositional Geometry Routing Problems
The paper introduces the Compositional Geometry Routing Problem and proposes DiCon, a differential-attention plus double-level contrastive learning solver that reports strong performance and generalization on mixed-geometry routing instances.
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A Unified Knowledge Embedded Reinforcement Learning-based Framework for Generalized Capacitated Vehicle Routing Problems
A knowledge-embedded RL framework decomposes generalized CVRPs into route-first and cluster-second subproblems, using dynamic programming to guide the RL solver and a history-enhanced context module to handle partial observability, yielding better solutions than prior learning methods.
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Multi-Agent Environments for Vehicle Routing Problems
Presents MAEnvs4VRP, a modular PyTorch library providing unified multi-agent environments for multiple variants of vehicle routing problems following the AEC model.