11 citations · 12 across the 6 of their papers we have counts for
6 papers
AutoSND: From Execution Evidence to Structural Policies for Automated Network Dismantling Heuristic Discovery
Zhijing Hu, Changjun Fan, Yufan Deng +1
Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness and computational efficienc…
PyVRP: LLM-Driven Metacognitive Heuristic Evolution for Hybrid Genetic Search in Vehicle Routing Problems
Manuj Malik, Jianan Zhou, Shashank Reddy Chirra +1
Designing high-performing metaheuristics for NP-hard combinatorial optimization problems, such as the Vehicle Routing Problem (VRP), remains a significant challenge, often requirin…
Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
Jiaqi Wang, Zhiguang Cao, Peng Zhao +4
The rise of smart manufacturing under Industry 4.0 introduces mass customization and dynamic production, demanding more advanced and flexible scheduling techniques. The flexible jo…
Rethinking Efficiency in Neural Combinatorial Optimization: Batched Preference Optimization with Mamba
Zhenxing Xu, Zeyuan Ma, Weidong Bao +4
We study efficiency as a first-class objective in Neural Combinatorial Optimization (NCO) and present ECO, an efficient learning framework that combines batched preference optimiza…
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
Haoran Ye, Jiarui Wang, Zhiguang Cao +5
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design au…
GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
Haoran Ye, Jiarui Wang, Helan Liang +3
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Globa…