Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909601781252096 |
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| author | Oishi, Koshi Hashizume, Yota Jimbo, Tomohiko Kaji, Hirotaka Kashima, Kenji |
| author_facet | Oishi, Koshi Hashizume, Yota Jimbo, Tomohiko Kaji, Hirotaka Kashima, Kenji |
| contents | Transport systems on networks are crucial in various applications, but face a significant risk of being adversely affected by unforeseen circumstances such as disasters. The application of entropy-regularized optimal transport (OT) on graph structures has been investigated to enhance the robustness of transport on such networks. In this study, we propose an imitation-regularized OT (I-OT) that mathematically incorporates prior knowledge into the robustness of OT. This method is expected to enhance interpretability by integrating human insights into robustness and to accelerate practical applications. Furthermore, we mathematically verify the robustness of I-OT and discuss how these robustness properties relate to real-world applications. The effectiveness of this method is validated through a logistics simulation using automotive parts data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_17967 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning Oishi, Koshi Hashizume, Yota Jimbo, Tomohiko Kaji, Hirotaka Kashima, Kenji Machine Learning Systems and Control Transport systems on networks are crucial in various applications, but face a significant risk of being adversely affected by unforeseen circumstances such as disasters. The application of entropy-regularized optimal transport (OT) on graph structures has been investigated to enhance the robustness of transport on such networks. In this study, we propose an imitation-regularized OT (I-OT) that mathematically incorporates prior knowledge into the robustness of OT. This method is expected to enhance interpretability by integrating human insights into robustness and to accelerate practical applications. Furthermore, we mathematically verify the robustness of I-OT and discuss how these robustness properties relate to real-world applications. The effectiveness of this method is validated through a logistics simulation using automotive parts data. |
| title | Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2402.17967 |