• Computer Science > Artificial Intelligence [Submitted on 17 Feb 2026] Title:Towards Efficient Constraint Handling in Neural Solvers for Routing Problems 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-shared representation, enabling potential knowledge sharing across paradigms by unifying the encoder, especially in more complex constrained scenarios. • We evaluate CaR on typical hard routing constraints to showcase its broader applicability.

Article Summaries:

  • Computer Science > Artificial Intelligence [Submitted on 17 Feb 2026] Title:Towards Efficient Constraint Handling in Neural Solvers for Routing Problems 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-R

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