TL;DR
LMask is a learning framework that uses lazy masking and backtracking to efficiently generate feasible solutions for complex constrained routing problems, achieving state-of-the-art results.
Contribution
It introduces LazyMask decoding with backtracking and refinement embedding, providing theoretical guarantees and improved performance on constrained routing benchmarks.
Findings
LMask achieves higher feasibility rates than existing neural methods.
LMask produces better solution quality on TSPTW and TSPDL.
The approach reduces sampling costs through backtracking budget and penalty mechanisms.
Abstract
Routing problems are canonical combinatorial optimization tasks with wide-ranging applications in logistics, transportation, and supply chain management. However, solving these problems becomes significantly more challenging when complex constraints are involved. In this paper, we propose LMask, a novel learning framework that utilizes dynamic masking to generate high-quality feasible solutions for constrained routing problems. LMask introduces the LazyMask decoding method, which lazily refines feasibility masks with the backtracking mechanism. In addition, it employs the refinement intensity embedding to encode the search trace into the model, mitigating representation ambiguities induced by backtracking. To further reduce sampling cost, LMask sets a backtracking budget during decoding, while constraint violations are penalized in the loss function during training to counteract…
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