TY - GEN
T1 - Resilience analysis and allocation for complex systems using Bayesian Network
AU - Yodo, Nita
AU - Wang, Pingfeng
N1 - This research is partially supported by National Science Foundation through Faculty Early Career Development (CAREER) award (CMMI-1351414) and the Award (CMMI-1200597), and by the Department of Transportation through University Transportation Center (UTC) Program.
PY - 2015
Y1 - 2015
N2 - The concept of resilience has been explored in diverse disciplines. However, there are only a few which focus on how to quantitatively measure engineering resilience and allocate resilience in engineering system design. This paper is dedicated to exploring the gap between quantitative and qualitative assessments of engineering resilience in the domain of designing complex engineered systems, thus optimally allocating resilience into subsystems and components level in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian Network is employed as a quantitative tool for the assessment and analysis of engineering resilience for complex systems. One industrial-based case study, a supply chain system, is employed to demonstrate the proposed approach. The proposed resilience quantification and allocation approach using Bayesian Networks would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.
AB - The concept of resilience has been explored in diverse disciplines. However, there are only a few which focus on how to quantitatively measure engineering resilience and allocate resilience in engineering system design. This paper is dedicated to exploring the gap between quantitative and qualitative assessments of engineering resilience in the domain of designing complex engineered systems, thus optimally allocating resilience into subsystems and components level in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian Network is employed as a quantitative tool for the assessment and analysis of engineering resilience for complex systems. One industrial-based case study, a supply chain system, is employed to demonstrate the proposed approach. The proposed resilience quantification and allocation approach using Bayesian Networks would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.
UR - https://www.scopus.com/pages/publications/84979084435
UR - https://www.scopus.com/pages/publications/84979084435#tab=citedBy
U2 - 10.1115/DETC201546999
DO - 10.1115/DETC201546999
M3 - Conference contribution
AN - SCOPUS:84979084435
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 41st Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015
Y2 - 2 August 2015 through 5 August 2015
ER -