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Time Granularity Impact on Propagation of Disruptions in a System-of-Systems Simulation of Infrastructure and Business Networks

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  • Mateusz Iwo Dubaniowski

    (ETH Zurich, Future Resilient Systems, Singapore-ETH Centre, Singapore 138602, Singapore)

  • Hans Rudolf Heinimann

    (ETH Zurich, Future Resilient Systems, Singapore-ETH Centre, Singapore 138602, Singapore)

Abstract

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

Suggested Citation

  • Mateusz Iwo Dubaniowski & Hans Rudolf Heinimann, 2021. "Time Granularity Impact on Propagation of Disruptions in a System-of-Systems Simulation of Infrastructure and Business Networks," IJERPH, MDPI, vol. 18(8), pages 1-24, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:3922-:d:532458
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    References listed on IDEAS

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    Cited by:

    1. Thomas J. Huggins & Lili Yang & Didier Sornette, 2021. "Introduction to the Special Issue on Cascading Disaster Modelling and Prevention," IJERPH, MDPI, vol. 18(9), pages 1-4, April.

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