用神经网络反向拆解再重建，提升车辆路径问题求解质量
Neural Deconstruction Search for Vehicle Routing Problems
- 先拆解后重建：神经策略反向拆解解，再由贪心算法重构建
- 在3类路径问题上达到或超过顶尖运筹学方法性能
- 适合需要高质量解且不依赖手工设计的场景
自回归构造方法通过逐步生成获得高质量车辆路径解，接近手工设计的运筹学技术。本文挑战这一序列构造范式，提出一种迭代搜索框架：由神经策略对解进行反向拆解，并与简单贪心插入算法协作重构。该方法在三种不同规模的复杂车辆路径问题上表现优异，性能达到或超越当前最先进的运筹学方法。
原文摘要 · Abstract (English)
Autoregressive construction approaches generate solutions to vehicle routing problems in a step-by-step fashion, leading to high-quality solutions that are nearing the performance achieved by handcrafted operations research techniques. In this work, we challenge the conventional paradigm of sequential solution construction and introduce an iterative search framework where solutions are instead deconstructed by a neural policy. Throughout the search, the neural policy collaborates with a simple greedy insertion algorithm to rebuild the deconstructed solutions. Our approach matches or surpasses the performance of state-of-the-art operations research methods across three challenging vehicle routing problems of various problem sizes.
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