Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2026
Abstract
Recent advances in Neural Combinatorial Optimization (NCO) methods have significantly improved the capability of neural solvers to handle synthetic routing instances. Nonetheless, existing neural solvers typically struggle to generalize effectively from synthetic, uniformly-distributed training data to real-world VRP scenarios, including widely recognized benchmark instances from TSPLib and CVRPLib. To bridge this generalization gap, we present Evolutionary Realistic Instance Synthesis (EvoReal), which leverages an evolutionary module guided by large language models (LLMs) to generate synthetic instances characterized by diverse and realistic structural patterns. Specifically, the evolutionary module produces synthetic instances whose structural attributes statistically mimics those observed in authentic real-world instances. Subsequently, pre-trained NCO models are progressively refined, firstly aligning them with these structurally enriched synthetic distributions and then further adapting them through direct fine-tuning on actual benchmark instances. Extensive experimental evaluations demonstrate that EvoReal markedly improves the generalization capabilities of state-of-the-art neural solvers, yielding a notable reduced performance gap compared to the optimal solutions on the TSPLib (1.05%) and CVRPLib (2.71%) benchmarks across a broad spectrum of problem scales.
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Sustainability
Publication
Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence (AAAI‑26), Singapore, January 20-27
Volume
40
First Page
36591
Last Page
36599
ISBN
9781577359067
Identifier
10.1609/aaai.v40i43.40982
Publisher
AAAI Press
City or Country
Singapore
Citation
ZHU, Jianghan; WU, Yaoxin; LIN, Zhuoyi; ZHANG, Zhengyuan; YIN, Haiyan; CAO, Zhiguang; JAYAVELU, Senthilnath; and LI, Xiaoli.
Bridging synthetic and real routing problems via LLM-guided instance generation and progressive adaptation. (2026). Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence (AAAI‑26), Singapore, January 20-27. 40, 36591-36599.
Available at: https://ink.library.smu.edu.sg/sis_research/11391
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1609/aaai.v40i43.40982
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons