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[2602.16012] Towards Efficient Constraint Handling in Neural Solvers for Routing Problems
Summary
The paper presents Construct-and-Refine (CaR), a novel framework for efficiently handling constraints in neural solvers for routing problems, demonstrating superior feasibility and solution quality compared to existing methods.
Why It Matters
As neural solvers become increasingly important in solving complex routing problems, this research addresses a critical gap in handling hard constraints effectively. The proposed CaR framework not only enhances computational efficiency but also broadens the applicability of neural solvers in real-world scenarios, making it a significant advancement in the field of artificial intelligence and optimization.
Key Takeaways
- CaR framework introduces a new approach to constraint handling in neural routing solvers.
- The framework allows for efficient generation of high-quality solutions with fewer computational steps.
- CaR utilizes a shared representation to facilitate knowledge transfer across different solver paradigms.
- Evaluation shows that CaR outperforms both classical and state-of-the-art neural solvers in feasibility and solution quality.
- This research could lead to more effective applications of AI in complex routing scenarios.
Computer Science > Artificial Intelligence arXiv:2602.16012 (cs) [Submitted on 17 Feb 2026] Title:Towards Efficient Constraint Handling in Neural Solvers for Routing Problems Authors:Jieyi Bi, Zhiguang Cao, Jianan Zhou, Wen Song, Yaoxin Wu, Jie Zhang, Yining Ma, Cathy Wu View a PDF of the paper titled Towards Efficient Constraint Handling in Neural Solvers for Routing Problems, by Jieyi Bi and 7 other authors View PDF HTML (experimental) Abstract:Neural solvers have achieved impressive progress in addressing simple routing problems, particularly excelling in computational efficiency. However, their advantages under complex constraints remain nascent, for which current constraint-handling schemes via feasibility masking or implicit feasibility awareness can be inefficient or inapplicable for hard constraints. In this paper, we present Construct-and-Refine (CaR), the first general and efficient constraint-handling framework for neural routing solvers based on explicit learning-based feasibility refinement. Unlike prior construction-search hybrids that target reducing optimality gaps through heavy improvements yet still struggle with hard constraints, CaR achieves efficient constraint handling by designing a joint training framework that guides the construction module to generate diverse and high-quality solutions well-suited for a lightweight improvement process, e.g., 10 steps versus 5k steps in prior work. Moreover, CaR presents the first use of construction-improvement-sh...
