Multi-view graph contrastive learning for solving vehicle routing problems
Recently, neural heuristics based on deep learning have reported encouraging results for solving vehicle routing problems (VRPs), especially on independent and identically distributed (i.i.d.) instances, e.g. uniform. However, in the presence of a distribution shift for the testing instances, their...
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sg-smu-ink.sis_research-91692023-09-26T10:35:02Z Multi-view graph contrastive learning for solving vehicle routing problems JIANG, Yuan CAO, Zhiguang WU, Yaoxin ZHANG, Jie Recently, neural heuristics based on deep learning have reported encouraging results for solving vehicle routing problems (VRPs), especially on independent and identically distributed (i.i.d.) instances, e.g. uniform. However, in the presence of a distribution shift for the testing instances, their performance becomes considerably inferior. In this paper, we propose a multi-view graph contrastive learning (MVGCL) approach to enhance the generalization across different distributions, which exploits a graph pattern learner in a self-supervised fashion to facilitate a neural heuristic equipped with an active search scheme. Specifically, our MVGCL first leverages graph contrastive learning to extract transferable patterns from VRP graphs to attain the generalizable multi-view (i.e. node and graph) representation. Then it adopts the learnt node embedding and graph embedding to assist the neural heuristic and the active search (during inference) for route construction, respectively. Extensive experiments on randomly generated VRP instances of various distributions, and the ones from TSPLib and CVRPLib show that our MVGCL is superior to the baselines in boosting the cross-distribution generalization performance. 2023-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8166 https://ink.library.smu.edu.sg/context/sis_research/article/9169/viewcontent/227_multi_view_graph_contrastive_l.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Graph embeddings Graph theory Vehicle routing Databases and Information Systems |
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Graph embeddings Graph theory Vehicle routing Databases and Information Systems JIANG, Yuan CAO, Zhiguang WU, Yaoxin ZHANG, Jie Multi-view graph contrastive learning for solving vehicle routing problems |
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Recently, neural heuristics based on deep learning have reported encouraging results for solving vehicle routing problems (VRPs), especially on independent and identically distributed (i.i.d.) instances, e.g. uniform. However, in the presence of a distribution shift for the testing instances, their performance becomes considerably inferior. In this paper, we propose a multi-view graph contrastive learning (MVGCL) approach to enhance the generalization across different distributions, which exploits a graph pattern learner in a self-supervised fashion to facilitate a neural heuristic equipped with an active search scheme. Specifically, our MVGCL first leverages graph contrastive learning to extract transferable patterns from VRP graphs to attain the generalizable multi-view (i.e. node and graph) representation. Then it adopts the learnt node embedding and graph embedding to assist the neural heuristic and the active search (during inference) for route construction, respectively. Extensive experiments on randomly generated VRP instances of various distributions, and the ones from TSPLib and CVRPLib show that our MVGCL is superior to the baselines in boosting the cross-distribution generalization performance. |
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JIANG, Yuan CAO, Zhiguang WU, Yaoxin ZHANG, Jie |
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JIANG, Yuan CAO, Zhiguang WU, Yaoxin ZHANG, Jie |
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JIANG, Yuan |
title |
Multi-view graph contrastive learning for solving vehicle routing problems |
title_short |
Multi-view graph contrastive learning for solving vehicle routing problems |
title_full |
Multi-view graph contrastive learning for solving vehicle routing problems |
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Multi-view graph contrastive learning for solving vehicle routing problems |
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Multi-view graph contrastive learning for solving vehicle routing problems |
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multi-view graph contrastive learning for solving vehicle routing problems |
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Institutional Knowledge at Singapore Management University |
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2023 |
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https://ink.library.smu.edu.sg/sis_research/8166 https://ink.library.smu.edu.sg/context/sis_research/article/9169/viewcontent/227_multi_view_graph_contrastive_l.pdf |
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