61 citations · 212 across the 25 of their papers we have counts for
5 papers · 1 filter
Learning to Handle Complex Constraints for Vehicle Routing Problems
Jieyi Bi, Yining Ma, Jianan Zhou +4
Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feas…
Collaboration! Towards Robust Neural Methods for Routing Problems
Jianan Zhou, Yaoxin Wu, Zhiguang Cao +3
Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues --…
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Jianan Zhou, Zhiguang Cao, Yaoxin Wu +4
Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, mak…
Cross-Problem Learning for Solving Vehicle Routing Problems
Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou +4
Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP va…
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Cong Zhang, Zhiguang Cao, Yaoxin Wu +2
Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningf…