A systematic review of optimization methods for recovery planning in cyber–physical infrastructure networks: Current state and future trends

Abstract

Cyber–Physical Systems are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging to cyber attacks. However, the development of effective strategies to restore disrupted cyber–physical infrastructure systems continues to be a major challenge. Even though there have been an increasing number of papers evaluating recovery planning in cyber–physical infrastructures networks, a comprehensive literature review focusing on mathematical modeling and optimization methods is still lacking. Thus, this study critically analyzes the literature on optimization techniques for recovery planning of cyber–physical infrastructure networks after a disruption, to synthesize key findings on the current methods in this domain. A total of 152 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, metaheuristic), objective function, and network size. Having identified the gaps, a set of future trends for both the methodology and application of optimization algorithms is presented. Overall, there is a need to shift toward scalable optimization solution algorithms, empowered by data-driven methods and machine learning algorithms, to provide reliable and computationally efficient decision-support systems for decision-makers and practitioners.