CaDA introduces a single neural model that can solve multiple vehicle routing problems while being constraint-aware and using dual attention to focus on relevant nodes.
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https://arxiv.org/abs/2412.00346
🎯 Original Problem:
→ Current vehicle routing models need separate training for each type of problem, making them impractical for real-world applications with 60+ variants.
→ Existing models lack constraint awareness and use inefficient global connectivity that fails to focus on key nodes.
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🔧 Solution in this Paper:
→ CaDA introduces a constraint prompt that efficiently represents different problem variants.
→ The model uses a dual-attention mechanism combining global and sparse branches.
→ The global branch captures broad graph-wide information.
→ The sparse branch with Top-k selection focuses on the most relevant nodes.
→ A fusion layer combines information from both attention branches.
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💡 Key Insights:
→ Constraint awareness significantly improves cross-problem performance
→ Dual-attention mechanism enhances model's ability to focus on relevant nodes
→ Many-to-many matching during training enables better multi-level predictions
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📊 Results:
→ Achieved state-of-the-art results across all 16 VRP variants
→ Improved box AP by 2.3 and mask AP by 1.2 when jointly training with SA-1B
→ Demonstrated 3.4 higher 1-IoU compared to existing methods
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