Quantum mechanics

Algorithm

Process (computing)

Engineering

Reliability engineering

Event (particle physics)

Theoretical computer science

Rank (graph theory)

Random graph

Operating system

Power (physics)

Component (thermodynamics)

Data mining

Computer science

Risk analysis (engineering)

Business

Thermodynamics

Physics

Ranking (information retrieval)

Graph

Mathematics

Reliability (semiconductor)

Artificial intelligence

Giant component

Measure (data warehouse)

Combinatorics

Resilience (materials science)

State (computer science)

Reliability Engineering & System Safety

Disruptive events such as natural disasters and human errors can have widespread adverse impacts on several networked infrastructures, affecting their functionalities and possibly resulting in large economic losses. It is, therefore, of great significance for these networks to exhibit resilience, defined as the ability of a network to recover from a disruptive event. Inspired by the measures of component importance used in reliability communities, this paper proposes a new resilience-based component importance ranking measure for multi-state networks from the perspective of a post-disaster restoration process. Considering the stochastic nature of disruptive events, the importance measure of each component is evaluated by finding the minimal recovery paths for various disruptive events, and it can be represented by a probability distribution. A stochastic ranking approach is implemented to identify the importance rank of each component in a network. Compared to existing methods, the proposed importance measure not only takes the multi-state characteristics of a network and its components into account but also quantifies the impact of both capacity improvement and recovery time of a component on network resilience. The proposed importance measure is exemplified through case studies in the Seervada Park road network.

A new resilience-based component importance measure for multi-state networks