Can I read GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems on EtoBox?

GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems by Wang, Zhenwei; Bai, Ruibin; Khan, Fazlullah; Ozcan, Ender; Zhang, Tiehua is a scholarly article available to read on EtoBox.

What is GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems about?

Learning-based methods have become increasingly popular for solving vehicle routing problems due to their near-optimal performance and fast inference speed. Among them, the combination of deep reinforcement learning and graph representation allows for the abstraction of node topology structures and features in an encoder-decoder style. Such an approach makes it possible to solve routing problems end-to-end without needing complicated heuristic operators designed by domain experts. Existing research studies have been focusing on novel encoding and decoding structures via various neural network models to enhance the node embedding representation. Despite the sophisticated approaches applied, there is a noticeable lack of consideration for the graph-theoretic properties inherent to routing problems. Moreover, the potential ramifications of inter-nodal interactions on the decision-making efficacy of the models have not been adequately explored. To bridge this gap, we propose an adaptive Graph Attention Sampling with the Edges Fusion framework (GASE),where nodes' embedding is determined through attention calculation from certain highly correlated neighbourhoods and edges, utilizing a fi

Author

Wang, Zhenwei; Bai, Ruibin; Khan, Fazlullah; Ozcan, Ender; Zhang, Tiehua

Published

2024

Language

EN