Coordinated restoration of interdependent critical infrastructures: A novel distributed decision-making mechanism integrating optimization and reinforcement learning

Abstract

The proper functioning of any society heavily depends on its critical infrastructures (CIs), such as power grids, road networks, and water and waste-water systems. These infrastructures consist of facilities spread across a community to provide essential services to its residents. Their spatial expansion and functional interdependencies make them highly vulnerable against natural/manmade disasters. Functional interdependencies mean that the functionality of components in one CI relies on the services provided by others. These features, combined with decentralized decision-making structure of CIs and the stochastic nature of post-disaster environments, highly complicate the optimization process for restoring CIs damaged in disasters. Optimizing CI restorations is critical to maximizing the post-disaster resilience of communities.

In this paper, we integrate and leverage Reinforcement Learning (RL) and optimization strengths to design a novel distributed modeling and solution approach for advancing the restoration process for interdependent CIs after disasters. The proposed approach (1) bridges the gap between integrative and distinct decision-making, enabling coordinated restoration planning for CIs within a decentralized decision-making context; (2) handles post-disaster uncertainties (e.g., uncertainty in recovery times of disrupted components); (3) generates adaptive solutions that cope with post-disaster dynamics (e.g., varying numbers of recovery teams); and (4) is flexible enough to handle several restoration decisions (e.g., restoration scheduling and resource allocation) simultaneously.

To evaluate its performance, we focus on restoring the road and power CIs in Sioux Falls, South Dakota, disrupted by several tornado scenarios. The numerical results show that coordinated policies in the restoration process of interdependent CIs consistently yield higher service for the community. The overperformance of the coordinated restoration policies can be as high as 27.9 %. The impact of coordination is more significant in severe disasters with higher disruptions and in the absence of efficient recovery resources.