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Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs
Created by
Haebom
Author
Filip Rydin, Attila Lischka, Jiaming Wu, Morteza Haghir Chehreghani, Bal azs Kulcs ar
Outline
This paper proposes two graph neural network-based methods to solve multi-objective routing problems in multi-graph environments. The first method operates directly on the multi-graph, while the second simplifies the multi-graph using a learned pruning strategy before performing routing. Experiments on a variety of problems and graph distributions demonstrate that the proposed models outperform powerful heuristic and neural network-based models.
Takeaways, Limitations
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Takeaways:
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We present a novel approach to solving multi-objective routing problems in multi-graph environments.
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We propose two different model structures to address different aspects of the problem.
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Demonstrated competitive performance compared to powerful heuristic and neural network-based models.
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Limitations:
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Lack of information about the specific model structure or details of the paper.
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Further research is needed on applicability in real-world environments.
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Lack of clear information about the specific objectives of multi-goal routing (e.g., minimum cost, maximum efficiency, etc.).
