基于深度学习的基础设施弹性资源分配
Deep Learning-based Resource Allocation for Infrastructure Resilience
摘要：
从优化的角度来看，资源分配是解决停电和交通堵塞等应用中常见的限制因素的研究基石之一。在本文中，我们采取了数据驱动的方法来估计一个最佳的节点恢复序列，立即恢复的基础设施网络后，自然灾害，如地震。我们从TD-INDP生成数据，TD-INDP是一种用于相互依赖网络的最佳恢复策略的高保真模拟器，并采用深度神经网络来近似这些策略。尽管事实上，根本的问题是NP-完全的，恢复序列通过我们的方法得到的观察是接近最优的。此外，通过训练多个模型-所谓的估计器-各种资源可用性水平，我们提出的方法平衡了资源利用率和恢复时间之间的权衡。决策者可以使用我们训练的模型在突发事件发生后更有效地分配资源，从而提高社区的复原力。除了他们的预测能力，这种训练有素的估计解开不同的节点功能之间的相互依赖性的影响，在恢复策略。我们通过田纳西州谢尔比县的现实世界相互依赖的基础设施来展示我们的方法。
英文摘要：
From an optimization point of view, resource allocation is one of the cornerstones of research for addressing limiting factors commonly arising in applications such as power outages and traffic jams. In this paper, we take a data-driven approach to estimate an optimal nodal restoration sequence for immediate recovery of the infrastructure networks after natural disasters such as earthquakes. We generate data from td-INDP, a high-fidelity simulator of optimal restoration strategies for interdependent networks, and employ deep neural networks to approximate those strategies. Despite the fact that the underlying problem is NP-complete, the restoration sequences obtained by our method are observed to be nearly optimal. In addition, by training multiple models---the so-called estimators---for a variety of resource availability levels, our proposed method balances a trade-off between resource utilization and restoration time. Decision-makers can use our trained models to allocate resources more efficiently after contingencies, and in turn, improve the community resilience. Besides their predictive power, such trained estimators unravel the effect of interdependencies among different nodal functionalities in the restoration strategies. We showcase our methodology by the real-world interdependent infrastructure of Shelby County, TN.
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