61 citations · 212 across the 25 of their papers we have counts for
8 papers · 1 filter
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,…
Enhancing the Cross-Size Generalization for Solving Vehicle Routing Problems via Continual Learning
Jingwen Li, Zhiguang Cao, Yaoxin Wu +1
Exploring machine learning techniques for addressing vehicle routing problems has attracted considerable research attention. To achieve decent and efficient solutions, existing dee…
A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
Mingfeng Fan, Jiaqi Cheng, Yaoxin Wu +4
In recent years, deep reinforcement learning (DRL) has gained traction for solving the NP-hard traveling salesman problem (TSP). However, limited attention has been given to the cl…
Large Language Models as End-to-end Combinatorial Optimization Solvers
Xia Jiang, Yaoxin Wu, Minshuo Li +2
Combinatorial optimization (CO) problems, central to decision-making scenarios like logistics and manufacturing, are traditionally solved using problem-specific algorithms requirin…
EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems
Dian Meng, Zhiguang Cao, Yaoxin Wu +3
Recent neural heuristics for the Vehicle Routing Problem (VRP) primarily rely on node coordinates as input, which may be less effective in practical scenarios where real cost metri…
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation
Mingfeng Fan, Jianan Zhou, Yifeng Zhang +3
Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs) by decomposing them into multipl…
Generalizable Heuristic Generation Through LLMs with Meta-Optimization
Yiding Shi, Jianan Zhou, Wen Song +4
Heuristic design with large language models (LLMs) has emerged as a promising approach for tackling combinatorial optimization problems (COPs). However, existing approaches often r…
Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
Qi Li, Zhiguang Cao, Yining Ma +2
Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution…