Deep Learning-based Resource Allocation for Infrastructure Resilience

TL;DR

This paper presents a deep learning approach to optimize resource allocation for rapid infrastructure recovery after disasters, using data-driven models to approximate complex restoration strategies and improve community resilience.

Contribution

It introduces a novel deep neural network method to estimate near-optimal restoration sequences for interdependent networks, addressing NP-complete challenges.

Findings

Restoration sequences are nearly optimal despite NP-completeness.

Models effectively balance resource use and recovery time.

Method applied successfully to real-world Shelby County infrastructure.

Abstract

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.