Modeling and managing resilience and risk for interdependent networks

Abstract

Catastrophic events have the potential to cause major disruptions to interdependent networks such as supply chains, critical infrastructures and other networks that are vital for the functioning of our society. Addressing the resilience and risk of compromised networks is challenging due to a variety of factors. First, these networks are becoming increasingly interdependent, such that network recovery is contingent upon recovery in connected networks. Additionally, interdependent networks may have multiple functions, system users, owners, and stakeholders. There is a need for data-informed decision-making models that address resilience and risk for these interdependent infrastructure networks. We address these issues by: 1) Modeling interdependencies among infrastructure networks using a multi-layer interdependent network framework, 2) Quantifying the relationship between network resilience and the inter-network connection structure, and 3) Supporting management and strategic decision-making for investment in inter-network connection structures, supporting risk mitigation needs and strategies. Our paper combines network optimization and network science methods to produce a unified framework to analyze resilience. We apply the methods of this paper to the interdependent infrastructure network of Puerto Rico following the 2017 Hurricane Maria. This paper supports decision-making for data-informed resilience management for interdependent infrastructure networks.