OD-DEAL: Dynamic Expert-Guided Adversarial Learning with Online Decomposition for Scalable Capacitated Vehicle Routing

• URL: http://arxiv.org/abs/2602.00488v2
• Date: Tue, 03 Feb 2026 02:29:48 GMT
• Title: OD-DEAL: Dynamic Expert-Guided Adversarial Learning with Online Decomposition for Scalable Capacitated Vehicle Routing
• Abstract summary: OD-BCC is a massive learning framework that integrates hybrid genetic search (HGS) and online barycenter clustering (conquer) decomposition.<n> OD- trains graph attention network (GAT)-based generative policy through a minimax game.<n>This enables sub-second, neural-quality inference required for dynamic large-scale deployment.
• Score: 2.7864713441393474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Solving large-scale capacitated vehicle routing problems (CVRP) is hindered by the high complexity of heuristics and the limited generalization of neural solvers on massive graphs. We propose OD-DEAL, an adversarial learning framework that tightly integrates hybrid genetic search (HGS) and online barycenter clustering (BCC) decomposition, and leverages high-fidelity knowledge distillation to transfer expert heuristic behavior. OD-DEAL trains a graph attention network (GAT)-based generative policy through a minimax game, in which divide-and-conquer strategies from a hybrid expert are distilled into dense surrogate rewards. This enables high-quality, clustering-free inference on large-scale instances. Empirical results demonstrate that OD-DEAL achieves state-of-the-art (SOTA) real-time CVRP performance, solving 10000-node instances with near-constant neural scaling. This uniquely enables the sub-second, heuristic-quality inference required for dynamic large-scale deployment.

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