MS 17

Resilience and Reliability Modelling of Critical Assets in the Age of Disasters & Pandemics

Organizers

Ashraf Labib, ashraf.labibnull@port.ac.uk

Faculty of Business & Law, Operations & Systems Management, University of Portsmouth, United Kingdom

Dylan Jones, Dylan.jonesnull@port.ac.uk

School of Mathematics, University of Portsmouth, United Kingdom

Akilu Kaltungo, akilu.kaltungonull@manchester.ac.uk

Department of Mechanical, Aerospace & Civil Engineering, University of Manchester, United Kingdom

Ahmed Noaman Karar, Ahmed.Kararnull@myport.ac.uk

Faculty of Business & Law, Operations & Systems Management, University of Portsmouth, United Kingdom

Abstract

Disasters and major failures have taught us a need for a paradigm shift from efficiency-based decision making to resilience-based. Such shift has a direct impact on asset and supply chain management as it leads to a shift from just-in-time to a just-in-case mind set. Another paradigm shift is to move from seeking solutions through optimisation approaches to embracing uncertainty and the generation of what –if scenarios through hybrid modelling, with pros and cons in the strategic decision-making process.

Resilience as a conceptual idea is profound and considered to have a key role in dealing with disasters such as pandemics. However, there is little research on modelling resilience and integrating it with other approaches in order to systematise its operation (Labib, 2021). The concept of the resilience triangle originated from the work of Bruneau et al. (2003), and then mathematically modelled by Ayyub (2014), and recently extended and combined with Bowtie modelling and applied to managing pandemics by Labib (2021).

We encourage hybrid approaches in modelling, as well as application of advanced resilience and reliability modelling in innovative applications such as healthcare, search and rescue, asset management, and managing innovations and its barriers.

References

  1. Ayyub, B.M., 2014. Systems resilience for multihazard environments: Definition, metrics, and valuation for decision making. Risk Anal. 34 (2), 340–355.
  2. Bruneau, M., Chang, S.E., Eguchi, R.T., Lee, G.C., O’Rourke, T.D., Reinhorn, A.M., Shinozuka, M., Tierney, K., Wallace, W.A., von Winterfeldt, D., 2003. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq Spectra 19, 733–752.
  3. Labib, A. (2021). Towards a new approach for managing pandemics: Hybrid resilience and bowtie modelling. Safety Science, 139, 105274.