强化学习
计算机科学
车辆路径问题
回程(电信)
水准点(测量)
启发式
马尔可夫决策过程
人工神经网络
人工智能
一般化
深度学习
布线(电子设计自动化)
机器学习
数学优化
马尔可夫过程
计算机网络
数学
数学分析
统计
大地测量学
基站
地理
作者
Conghui Wang,Zhiguang Cao,Yaoxin Wu,Long Teng,Guohua Wu
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-15
标识
DOI:10.1109/tnnls.2024.3371781
摘要
The vehicle routing problem with backhauls (VRPBs) is a challenging problem commonly studied in computer science and operations research. Featured by linehaul (or delivery) and backhaul (or pickup) customers, the VRPB has broad applications in real-world logistics. In this article, we propose a neural heuristic based on deep reinforcement learning (DRL) to solve the traditional and improved VRPB variants, with an encoder-decoder structured policy network trained to sequentially construct the routes for vehicles. Specifically, we first describe the VRPB based on a graph and cast the solution construction as a Markov decision process (MDP). Then, to identify the relationship among the nodes (i.e., linehaul and backhaul customers, and the depot), we design a two-stage attention-based encoder, including a self-attention and a heterogeneous attention for each stage, which could yield more informative representations of the nodes so as to deliver high-quality solutions. The evaluation on the two VRPB variants reveals that, our neural heuristic performs favorably against both the conventional and neural heuristic baselines on randomly generated instances and benchmark instances. Moreover, the trained policy network exhibits a desirable capability of generalization to various problem sizes and distributions.
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