Details
| Original language | English |
|---|---|
| Article number | 020901 |
| Number of pages | 11 |
| Journal | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering |
| Volume | 6 |
| Issue number | 2 |
| Early online date | 27 Mar 2020 |
| Publication status | Published - Jun 2020 |
Abstract
Complex systems-such as gas turbines, industrial plants, and infrastructure networks- are of paramount importance to modern societies. However, these systems are subject to various threats. Novel research does not only focus on monitoring and improving the robustness and reliability of systems but also focus on their recovery from adverse events. The concept of resilience encompasses these developments. Appropriate quantitative measures of resilience can support decision-makers seeking to improve or to design complex systems. In this paper, we develop comprehensive and widely adaptable instruments for resilience-based decision-making. Integrating an appropriate resilience metric together with a suitable systemic risk measure, we design numerically efficient tools aiding decision-makers in balancing different resilience-enhancing investments. The approach allows for a direct comparison between failure prevention arrangements and recovery improvement procedures, leading to optimal tradeoffs with respect to the resilience of a system. In addition, the method is capable of dealing with the monetary aspects involved in the decision-making process. Finally, a grid search algorithm for systemic risk measures significantly reduces the computational effort. In order to demonstrate its wide applicability, the suggested decision-making procedure is applied to a functional model of a multistage axial compressor, and to the U-Bahn and S-Bahn system of Germany's capital Berlin.
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
- Engineering(all)
- Mechanical Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, Vol. 6, No. 2, 020901, 06.2020.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Resilience decision-making for complex systems
AU - Salomon, Julian
AU - Broggi, Matteo
AU - Kruse, Sebastian
AU - Weber, Stefan
AU - Beer, Michael
N1 - Funding information: The authors kindly thank the German Research Foundation (DFG) for the financial support to accomplish the research project D5 “Resilience Based Decision Criteria for Optimal Regeneration” within the Collaborative Research Center (CRC) 871— Regeneration of Complex Capital Goods.
PY - 2020/6
Y1 - 2020/6
N2 - Complex systems-such as gas turbines, industrial plants, and infrastructure networks- are of paramount importance to modern societies. However, these systems are subject to various threats. Novel research does not only focus on monitoring and improving the robustness and reliability of systems but also focus on their recovery from adverse events. The concept of resilience encompasses these developments. Appropriate quantitative measures of resilience can support decision-makers seeking to improve or to design complex systems. In this paper, we develop comprehensive and widely adaptable instruments for resilience-based decision-making. Integrating an appropriate resilience metric together with a suitable systemic risk measure, we design numerically efficient tools aiding decision-makers in balancing different resilience-enhancing investments. The approach allows for a direct comparison between failure prevention arrangements and recovery improvement procedures, leading to optimal tradeoffs with respect to the resilience of a system. In addition, the method is capable of dealing with the monetary aspects involved in the decision-making process. Finally, a grid search algorithm for systemic risk measures significantly reduces the computational effort. In order to demonstrate its wide applicability, the suggested decision-making procedure is applied to a functional model of a multistage axial compressor, and to the U-Bahn and S-Bahn system of Germany's capital Berlin.
AB - Complex systems-such as gas turbines, industrial plants, and infrastructure networks- are of paramount importance to modern societies. However, these systems are subject to various threats. Novel research does not only focus on monitoring and improving the robustness and reliability of systems but also focus on their recovery from adverse events. The concept of resilience encompasses these developments. Appropriate quantitative measures of resilience can support decision-makers seeking to improve or to design complex systems. In this paper, we develop comprehensive and widely adaptable instruments for resilience-based decision-making. Integrating an appropriate resilience metric together with a suitable systemic risk measure, we design numerically efficient tools aiding decision-makers in balancing different resilience-enhancing investments. The approach allows for a direct comparison between failure prevention arrangements and recovery improvement procedures, leading to optimal tradeoffs with respect to the resilience of a system. In addition, the method is capable of dealing with the monetary aspects involved in the decision-making process. Finally, a grid search algorithm for systemic risk measures significantly reduces the computational effort. In order to demonstrate its wide applicability, the suggested decision-making procedure is applied to a functional model of a multistage axial compressor, and to the U-Bahn and S-Bahn system of Germany's capital Berlin.
UR - http://www.scopus.com/inward/record.url?scp=85089181310&partnerID=8YFLogxK
U2 - 10.1115/1.4044907
DO - 10.1115/1.4044907
M3 - Article
AN - SCOPUS:85089181310
VL - 6
JO - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
JF - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
SN - 2332-9017
IS - 2
M1 - 020901
ER -