Applying Deep Reinforcement Learning to VRP and Its Extension
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- NOBUHARA Genya
- School of Engineering, The University of Tokyo
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- FUJII Hideki
- School of Engineering, The University of Tokyo
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- UCHIDA Hideaki
- School of Engineering, The University of Tokyo
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- YOSHIMURA Shinobu
- School of Engineering, The University of Tokyo
Bibliographic Information
- Other Title
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- VRPへの深層強化学習の適用と問題の拡張
Abstract
<p>In the current home medical care system, the matching of patients and doctors and the scheduling of medical care are done manually, which is inefficient for doctors. In order to make home medical care more general, scheduling must be more efficient and automated. The goal of this research is to develop an efficient algorithm that helps to create such a schedule. As a first step, the authors applied deep reinforcement learning to the vehicle routing problem (VRP), a problem for minimizing the travel costs of multiple vehicles that travel from a starting point to a demanded point with satisfying all demands. Then, the problem was extended to the scheduling problem for visiting patients by adding conditions specific to home medical care, such as time constraints for treating patients in their desired time frame and matching patients and doctors according to symptoms, gender, etc.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2021 (0), 3F1GS10i05-3F1GS10i05, 2021
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390851320454049920
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- NII Article ID
- 130008051867
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed