Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2026
Abstract
We propose a novel approach to Vehicle Routing Problems (VRPs) that integrates the strengths of neural spatial embeddings with the semantic understanding of large language models (LLMs). While traditional neural methods excel in structured routing, they typically ''feel'' constraints through rigid mechanisms like feasibility masking, limiting their expressiveness. Conversely, LLMs demonstrate robust semantic capabilities but lack the spatial reasoning essential for precise routing optimization. To address these gaps, we introduce a hierarchical, multi-scale fusion architecture that integrates LLM-derived semantics with spatial routing features. Our contributions include: (1) a scale-aware decomposition aligning LLM features with spatial representations, (2) a bidirectional cross-modal attention module enabling interaction between linguistic and spatial domains, and (3) a progressive refinement pathway ensuring semantic and spatial fidelity. Through theoretical analysis, we prove the convergence properties of our fusion mechanism and its effectiveness in preserving critical information from both modalities. Evaluations across 16 VRP variants demonstrate competitive performance, highlighting the benefits of embedding semantic understanding into VRP optimization.
Keywords
Large Language Model, Vehicle Routing Problem, Combinatorial Optimization, Learning to Optimize
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Sustainability
Publication
AAMAS 2026: Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems, Paphos, Cyprus, May 25-29
First Page
3633
Last Page
3635
ISBN
9798400723179
Identifier
10.65109/ISOA2063
Publisher
ACM
City or Country
New York
Citation
MALIK, Manuj; ZHOU, Jianan; JIN, Yan; and CAO, Zhiguang.
LLMAide: Language-assisted neural solver for vehicle routing problems. (2026). AAMAS 2026: Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems, Paphos, Cyprus, May 25-29. 3633-3635.
Available at: https://ink.library.smu.edu.sg/sis_research/11393
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/10.65109/ISOA2063
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
Comments
Extended Abstract