Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems

Hansen Wang, Zefang Zong, Tong Xia, Shuyu Luo, Meng Zheng, Depeng Jin, Yong Li

Keywords: vehicle routing problem, reinforcement learning, optimization

Abstract:

The large-scale vehicle routing problems are defined based on the classical VRPs with thousands of customers. It is of great importance to find an efficient and high-quality solution for real-world applications. However, existing algorithms for VRPs including non-learning heuristics and RL-based methods, only perform well on small-scale instances with usually no more than a hundred customers. They are unable to solve large-scale VRPs due to either high computation cost or explosive solution space that results in model divergence.
Inspired by the classical idea of Divide-and-Conquer, we present a novel Rewriting-by-Generating(RBG) framework with hierarchical RL agents to solve large-scale VRPs. RBG consists of a rewriter agent that refines the customer division globally and an elementary generator to infer regional solutions locally. Extensive experiments demonstrate the effectiveness and efficiency of our proposed RBG framework. It outperforms LKH3, the state-of-the-art method for CVRPs, by $2.43\%$ when customer number $N=2000$ and shortens the inference time by about 100 times.

One-sentence Summary: We propose a novel Rewriting by Generating (RBG) framework based on hierarchical RL agents to solve large-scale vehicle routing problem with usually more than a thousand customers..

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