Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA.
Department of Computer Science, Stanford University, Stanford, CA, USA.
Sci Rep. 2021 Oct 4;11(1):19623. doi: 10.1038/s41598-021-98643-z.
One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe this evacuation process as a Capacitated Vehicle Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep Algorithm). A central question is whether Deep Reinforcement Learning provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We found that, in comparison to the Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with more efficient routing. However, the evacuation time saved by Deep Reinforcement Learning does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.
从 COVID-19 大流行中吸取的教训之一是保持社交距离的重要性，即使在飓风前疏散等具有挑战性的情况下也是如此。为了探索将社交距离与疏散行动相结合的意义，我们将此疏散过程描述为带容量约束的车辆路径问题 (CVRP)，并使用基于 DNN (深度神经网络)的解决方案 (深度强化学习) 和非 DNN 解决方案 (扫掠算法) 来解决它。一个核心问题是，深度强化学习是否能够提供足够的额外路由效率，以适应时间受限的疏散行动中增加的社交距离。我们发现，与扫掠算法相比，深度强化学习可以为决策者提供更有效的路线。然而，深度强化学习节省的疏散时间几乎无法弥补社交距离所需的额外时间，并且随着应急车辆容量接近每户人数，其优势就会消失。
