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Maximum flow-based resilience analysis: From component to system.

Chong Jin¹, Ruiying Li^1,2, Rui Kang^1,2

¹School of Reliability and Systems Engineering, Beihang University, Beijing, China.

|May 26, 2017

Summary

This summary is machine-generated.

System resilience, the ability to withstand disruptions, is crucial for preventing economic and societal losses. This study introduces analytic models to quantify and compare system resilience, finding parallel systems enhance resilience with more components, unlike series systems.

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Area of Science:

Engineering
Network Science
Systems Analysis

Background:

System disruptions can lead to significant economic and societal losses.
Resilience, defined as the ability to withstand and recover from disruptions, is a critical design consideration.
Existing methods for quantifying system resilience are limited.

Purpose of the Study:

To develop analytic maximum flow-based resilience models for series and parallel systems.
To quantitatively evaluate and compare the resilience of different system structures.
To analyze the impact of component redundancy on overall system resilience.

Main Methods:

Development of analytic maximum flow-based resilience models for series and parallel systems.
Utilizing Zobel's resilience measure for quantitative analysis.
Employing a Monte Carlo-based simulation method for verification and network analysis.

Main Results:

Analytic models provide quantitative evaluation of system resilience.
For identical components, parallel system resilience increases with component count, while series system resilience remains constant.
Networked systems with redundant performance are generally more resilient, but effectiveness depends on redundancy placement.

Conclusions:

The developed analytic models effectively quantify and compare system resilience.
System topology and component redundancy significantly influence resilience.
Strategic placement of redundant capacity is essential for maximizing system resilience.