Firm-level behavior control after large-scale urban flooding using multi-agent deep reinforcement learning
Firm-level behavior control after large-scale urban flooding using multi-agent deep reinforcement learning
复制标题
使用多智能体深度强化学习进行大规模城市洪水后的企业级行为控制
DOI：
10.1145/3356470.3365529
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shibasaki Ryosuke
中科院分区：
文献类型：
--
作者：
Yang Shaofeng;Ogawa Yoshiki;Ikeuchi Koji;Akiyama Yuki;Shibasaki Ryosuke
随着自然灾害的规模化、多样化和频繁化，供应链中断造成的间接经济损失也越来越大。因此，灾后尽快恢复对企业来说非常重要。在本文中，我们使用强化学习来优化公司的行动策略，使其能够在大规模城市洪水后有效地恢复供应链中的企业间交易网络。该代理持有从企业间交易数据和洪水模拟分析数据获得的灾害和供应链信息，使我们能够通过使用高维数据构建详细的空间状态来创建具有详细城市基础设施信息的模拟。本文还提出了一种基于多智能体深度强化学习的企业行动策略，以优化企业在恢复过程中的行为。这项工作弥合了高维数据集输入和灾后行为之间的鸿沟，使人工智能体能够学习灾后采取的最佳行动。我们的结果如下。通过学习，代理可以在灾难发生后有效地恢复。公司倾向于首先获得替代业务合作伙伴，然后进行恢复工作和业务扩张。
With natural disasters have become large scale, diversified, and frequent, the indirect economic damage due to interruption of supply chain tends to be large. Therefore, it is important to recover as quickly as possible for companies after disasters. In this paper, we use reinforcement learning to optimize a company's action strategy so that it can efficiently recover the inter-firm transaction network in the supply chain after large-scale urban flooding. The agent holds information on disaster and supply chains obtained from inter-firm transaction data and flood simulation analysis data, enabling us to create a simulation with detailed urban infrastructure information by using the high-dimensional data to construct detailed spatial states. The paper also proposes an action policy for companies based on multi-agent deep reinforcement learning to optimize the behavior of companies in the recovery process. This work bridges the divide between high-dimensional data set input and post-disaster behaviors, enabling an artificial agent to learn the best action to take after a disaster. Our results are as follows. Through learning, agents can recover efficiently after a disaster. Companies tend to secure alternative business partners first and then perform recovery work and business expansion.
高知市南海海槽地震海啸影响的全国企业供应链网络中断估计
影响因子：
0.8
作者：
Yoshiki Ogawa;Yuki Akiyama;Muneta Yokomatsu;Yoshihide Sekimoto;and Ryosuke Shibasaki
通讯作者：
and Ryosuke Shibasaki
供应链中断的最佳风险缓解计划
DOI：
--
发表时间：
2015
期刊：
--
影响因子：
--
作者：
S. Ohmori;K. Yoshimoto
通讯作者：
K. Yoshimoto
影响因子：
--
作者：
K. Ikeuchi;Shigeo Ochi;Goro Yasuda;Jiro Okamura;Masashi Aono
通讯作者：
Masashi Aono