158 citations · 231 across the 7 of their papers we have counts for
9 papers
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 --…
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
Rongkai Zhang, Cong Zhang, Zhiguang Cao +5
We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP wi…
Learning to Solve Routing Problems via Distributionally Robust Optimization
Yuan Jiang, Yaoxin Wu, Zhiguang Cao +1
Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. I…
Learning Large Neighborhood Search Policy for Integer Programming
Yaoxin Wu, Wen Song, Zhiguang Cao +1
We propose a deep reinforcement learning (RL) method to learn large neighborhood search (LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator…
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem
Liang Xin, Wen Song, Zhiguang Cao +1
We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specif…
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning
Jingwen Li, Liang Xin, Zhiguang Cao +3
Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for v…