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20052023
most citedHeterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning

158 citations · 674 across the 63 of their papers we have counts for

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5 papers · 1 filter

cs.AI2023★ 48 cited

Neural Airport Ground Handling

Yaoxin Wu, Jianan Zhou, Yunwen Xia +3

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics o…

cs.AI2023★ 61 cited

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…

cs.AI2022★ 3 cited

A Coalition Formation Game Approach for Personalized Federated Learning

Leijie Wu, Song Guo, Yaohong Ding +2

Facing the challenge of statistical diversity in client local data distribution, personalized federated learning (PFL) has become a growing research hotspot. Although the state-of-…

cs.AI2021★ 11 cited

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…

cs.AI2021★ 45 cited

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…