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Research Article
Mar 27, 2020

Resilience Decision-Making for Complex Systems

Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 6, Issue 2

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. This article is available in the ASME Digital Collection at https://doi.org/10.1115/1.4044907.

Information & Authors

Information

Published In

Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 6Issue 2June 2020

History

Received: Jan 16, 2019
Revision received: Jul 16, 2019
Published online: Mar 27, 2020
Published in print: Jun 1, 2020

Authors

Affiliations

Julian Salomon [email protected]
Institute for Risk and Reliability, Leibniz Universität Hannover, Callinstraße 34, Hannover 30167, Germany e-mail: [email protected]
Matteo Broggi [email protected]
Institute for Risk and Reliability, Leibniz Universität Hannover, Callinstraße 34, Hannover 30167, Germany e-mail: [email protected]
Sebastian Kruse [email protected]
Institute for Risk and Uncertainty, University of Liverpool, Peach Street, Liverpool L69 7ZF, UK e-mail: [email protected]
Stefan Weber [email protected]
Institute of Probability and Statistics, Leibniz Universität Hannover, Welfengarten 1, Hannover 30167, Germany e-mail: [email protected]
Michael Beer [email protected]
Institute for Risk and Reliability, Leibniz Universität Hannover, Callinstraße 34, Hannover 30167, Germany; Institute for Risk and Uncertainty, University of Liverpool, Peach Street, Liverpool L69 7ZF, UK; International Joint Research Center for Engineering Reliability and Stochastic Mechanics (ERSM), Tongji University, Shanghai 200092, China e-mail: [email protected]

Funding Information

German Research Foundationhttp://dx.doi.org/10.13039/501100001659: Sonderforschungsbereich 871

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