Enhancing critical network infrastructure resilience through optimal post-disruption maintenance and routing decisions

Critical network infrastructures such as power grids, telecommunications, and transportation are considered critical for both the development and functioning of modern-day societies. Due to the complexity and interconnectedness of these network infrastructures, potential stress and disaster events can have serious impacts and cause wide-spread disruption and network components failure. Developing a post-disruption restoration plan is crucial in improving network resilience and rebounding a network back to a nominal or target functioning state. This paper deals with the problem of enhancing network resilience in the aftermath of random stress events. To restore the network to nominal functioning, maintenance actions are performed on its components. To optimize the network resilience after a stress event, a Mixed Integer Non-Linear optimization model is developed. This model aims to ensure efficient restoration, while minimizing the impact of disruptions on critical infrastructures’ performance. Unlike many existing network resiliency optimization models, the present model considers traveling time and allows joint decisions related to maintenance level selection, and repair crews’ maintenance tasks assignment and routing. Efficient solution methods are implemented and compared to solve the resulting optimization problem. Several numerical experiments are conducted to demonstrate key characteristics of the proposed optimization model and its ability to provide optimal and valid maintenance, repairpersons assignment and routing decisions for real-world critical network infrastructures.