Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2025
Abstract
The Vehicle Routing Problem (VRP) is a critical combinatorial optimization problem with wide-reaching real-world applications, particularly in logistics, transportation. While neural network-based VRP solvers have shown impressive results on test instances similar to training data, their performance often degrades when faced with varying scales and unseen distributions, limiting their practical applicability. To overcome these limitations, we introduce DGL (Dynamic Global-Local Information Aggregation), a novel model that combines global and local information to effectively solve VRPs. DGL dynamically adjusts local node selections within a localized range, capturing local invariance across problems of different scales and distributions, thereby enhancing generalization. At the same time, DGL integrates global context into the decision-making process, providing richer information for more informed decisions. Additionally, we propose a replacement-based self-improvement learning framework that leverages data augmentation and random replacement techniques, further enhancing DGL's robustness. Extensive experiments on synthetic datasets, benchmark datasets, and real-world country map instances demonstrate that DGL achieves state-of-the-art performance, particularly in generalizing to large-scale VRPs and real-world scenarios. These results showcase DGL's effectiveness in solving complex, realistic optimization challenges and highlight its potential for practical applications.
Keywords
vehicle routing problem, global-local information aggregation, neural combinatorial optimization, generalization, self-improvement learning, data augmentation, large-scale VRP, deep reinforcement learning, routing optimization, scalable solvers
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
First Page
8669
Last Page
8677
Identifier
10.24963/ijcai.2025/964
City or Country
Montreal, Canada
Citation
XIAO, Yubin; WU, Yuesong; CAO, Rui; WANG, Di; CAO, Zhiguang; WU, Xuan; ZHAO, Peng; LI, Yuanshu; ZHOU, You; and JIANG, Yuan.
DGL: Dynamic global-local information aggregation for scalable VRP generalization with self-improvement learning. (2025). Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. 8669-8677.
Available at: https://ink.library.smu.edu.sg/sis_research/10559
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.24963/ijcai.2025/964