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Abstract
A cyber-physical supply network is composed of an undirected cyber supply network and a directed physical supply network. Such interdependence among firms increases efficiency but creates more vulnerabilities. The adverse effects of any failure can be amplified and propagated throughout the network. This paper aimed at investigating the robustness of the cyber-physical supply network against cascading failures. Considering that the cascading failure is triggered by overloading in the cyber supply network and is provoked by underload in the physical supply network, a realistic cascading model for cyber-physical supply networks is proposed. We conducted a numerical simulation under cyber node and physical node failure with varying parameters. The simulation results demonstrated that there are critical thresholds for both firm’s capacities, which can determine whether capacity expansion is helpful; there is also a cascade window for network load distribution, which can determine the cascading failures occurrence and scale. Our work may be beneficial for developing cascade control and defense strategies in cyber-physical supply networks.
Highlights
Considering that the cascading failure is triggered by overloading in the cyber supply network and provoked by underloading in the physical supply network, this paper tried to answer the following questions: RQ1: How can we find a certain region of the parameter space where cascade failures occur under cyber node failure?
We used the ratio of the survival nodes to measure the robustness of cyber-physical supply networks [41]: R=
We studied the robustness of the cyber-physical supply networks against cascading failures
Summary
The physical and digital worlds are becoming continuously more intertwined, bringing about cyber-physical supply networks with emergent interactions [1]. The physical supply network depends on the cyber supply network for its control, and the cyber supply network depends on the physical network for flow information. While the management of cyber-physical supply networks is a challenging problem [2], these interdependent systems tend to be fragile against failures, hazards, and attacks [3]. A failure of firms in one network results in a failure of dependent firms in other networks, which may induce further damage to the first network and so on [4]. The failures can trigger multiple parts of supply networks, influencing the performance and viability of the components of the entire network [5]
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