158 citations · 231 across the 5 of their papers we have counts for
7 papers
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 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…
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems
Liang Xin, Wen Song, Zhiguang Cao +1
We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM)…
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Cong Zhang, Wen Song, Zhiguang Cao +3
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myria…