9 papers
Autoresearch in Mixed-Integer Linear and Nonlinear Programming
Yuwei Gu, Yaoxin Wu, Tong Guo +2
Despite recent progress in autoresearch, applying it to practical operations research problems, typically formulated as NP-hard mixed-integer linear or nonlinear programs (MILPs or…
LaT: LLM-as-Trainer for Multi-Task Vehicle Routing Solvers
Yang Wang, Ya-Hui Jia, Wei-Neng Chen +3
Multi-task neural solvers aim to handle multiple Vehicle Routing Problem (VRP) variants within a unified model, avoiding separate training for each constraint combination. However,…
AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network
Bolin Shen, Ziwei Huang, Zhiguang Cao +1
The Traveling Salesman Problem (TSP) is a cornerstone of combinatorial optimization and arises in many practical scenarios. Although graph-based learning approaches have been explo…
Vision-Assisted Foundation Model for Solving Multi-Task Vehicle Routing Problems
Shuangchun Gui, Zhiguang Cao, Wen Song +1
Multi-task vehicle routing problems play a critical role in enhancing efficiency across various industries and service sectors. These problems consist of multiple variants that opt…
Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty
Tianjue Lin, Jianan Zhou, Jieyi Bi +4
Two-Stage Robust Optimization (2RO) with discrete uncertainty is challenging, often rendering exact solutions prohibitive. Scenario reduction alleviates this issue by selecting a s…
RADAR: Learning to Route with Asymmetry-aware DistAnce Representations
Hang Yi, Ziwei Huang, Yining Ma +1
Recent neural solvers have achieved strong performance on vehicle routing problems (VRPs), yet they mainly assume symmetric Euclidean distances, restricting applicability to real-w…
Chain-of-Context Learning: Dynamic Constraint Understanding for Multi-Task VRPs
Shuangchun Gui, Suyu Liu, Xuehe Wang +1
Multi-task Vehicle Routing Problems (VRPs) aim to minimize routing costs while satisfying diverse constraints. Existing solvers typically adopt a unified reinforcement learning (RL…
DRAGON: LLM-Driven Decomposition and Reconstruction Agents for Large-Scale Combinatorial Optimization
Shengkai Chen, Zhiguang Cao, Jianan Zhou +5
Large Language Models (LLMs) have recently shown promise in addressing combinatorial optimization problems (COPs) through prompt-based strategies. However, their scalability and ge…
Bridging Synthetic and Real Routing Problems via LLM-Guided Instance Generation and Progressive Adaptation
Jianghan Zhu, Yaoxin Wu, Zhuoyi Lin +5
Recent advances in Neural Combinatorial Optimization (NCO) methods have significantly improved the capability of neural solvers to handle synthetic routing instances. Nonetheless,…