275 citations · 1.2k across the 15 of their papers we have counts for
15 papers
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
Jianan Zhou, Yaoxin Wu, Wen Song +2
Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained an…
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling
Jianan Zhou, Yaoxin Wu, Zhiguang Cao +3
Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multi…
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Cong Zhang, Zhiguang Cao, Wen Song +2
Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far fro…
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…
Efficient Neural Neighborhood Search for Pickup and Delivery Problems
Yining Ma, Jingwen Li, Zhiguang Cao +4
We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the van…
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…
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem
Jingwen Li, Yining Ma, Ruize Gao +4
Existing deep reinforcement learning (DRL) based methods for solving the capacitated vehicle routing problem (CVRP) intrinsically cope with homogeneous vehicle fleet, in which the…
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Yining Ma, Jingwen Li, Zhiguang Cao +4
Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP be…
Dual Aspect Self-Attention based on Transformer for Remaining Useful Life Prediction
Zhizheng Zhang, Wen Song, Qiqiang Li
Remaining useful life prediction (RUL) is one of the key technologies of condition-based maintenance, which is important to maintain the reliability and safety of industrial equipm…
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…
Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems
Wen Song, Zhiguang Cao, Jie Zhang +1
Backtracking search algorithms are often used to solve the Constraint Satisfaction Problem (CSP). The efficiency of backtracking search depends greatly on the variable ordering heu…
Learning Improvement Heuristics for Solving Routing Problems
Yaoxin Wu, Wen Song, Zhiguang Cao +2
Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have…