TY - CHAP
T1 - Assessment and Optimization of System Resilience
T2 - A Critical Review
AU - Savachkin, Alex
AU - Ardila-Rueda, Weimar
AU - Romero-Rodriguez, Daniel
AU - Hua, Kaixun
AU - Cruz, Jose Navarro De La
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Efficiency-driven design of modern engineering systems, such as critical infrastructure, cyber-physical systems, power grids, and enterprise networks, has left them exposed to increasingly risk-encumbered environments. Disruptions triggered by natural disasters, process hazards, human errors, and cyber attacks have elevated the risk exposure of engineering systems and revealed the need for systems that can offer higher resilience to hedge against adverse events. Resilience refers to a system’s ability to withstand and mitigate the impact of disruptions and return to normal operating conditions. The existing literature on system resilience and related quality attributes (e.g., reliability, availability, robustness, etc.) reveals that there is no uniform definition of resilience or a consistent way to measure it. In this paper, we provide a critical review of the existing models on resilience assessment and optimization. We identify important areas of potential contributions for measuring and optimizing resilience for complex systems operating in dynamic, uncertain environments. We also highlight the broader impacts of proposed research strategies.
AB - Efficiency-driven design of modern engineering systems, such as critical infrastructure, cyber-physical systems, power grids, and enterprise networks, has left them exposed to increasingly risk-encumbered environments. Disruptions triggered by natural disasters, process hazards, human errors, and cyber attacks have elevated the risk exposure of engineering systems and revealed the need for systems that can offer higher resilience to hedge against adverse events. Resilience refers to a system’s ability to withstand and mitigate the impact of disruptions and return to normal operating conditions. The existing literature on system resilience and related quality attributes (e.g., reliability, availability, robustness, etc.) reveals that there is no uniform definition of resilience or a consistent way to measure it. In this paper, we provide a critical review of the existing models on resilience assessment and optimization. We identify important areas of potential contributions for measuring and optimizing resilience for complex systems operating in dynamic, uncertain environments. We also highlight the broader impacts of proposed research strategies.
KW - Disruption management
KW - Dynamic optimization
KW - Literature review
KW - Resilience assessment
KW - Risk analysis
KW - System resilience
UR - https://www.scopus.com/pages/publications/105003560077
U2 - 10.1007/978-3-031-76440-0_20
DO - 10.1007/978-3-031-76440-0_20
M3 - Chapter
T3 - Studies in Big Data
SP - 251
EP - 260
BT - Studies in Big Data
PB - Springer Science and Business Media Deutschland GmbH
ER -