11 citations · 19 across the 16 of their papers we have counts for
10 papers · 1 filter
Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
Zhi Cao, Cong Zhang, Yaoxin Wu +2
The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assig…
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…
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…
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…