1. Proceedings
  2. I3M
  3. 2022
  4. HMS
  5. 10.46354/i3m.2022.hms.002

Definition and Detection of Hypervulnerabilities using a Framework for Assessing Port Resilience 

  • a Katherine Smith , 
  • b Rafael Diaz, 
  • cYuzhong Shen, 
  • d Francesco Longo 
  • a Virginia Modeling, Analysis & Simulation Center, Old Dominion University, 1030 University Blvd, Suffolk, VA, 23435, United States of America
  • b Department of Computational Modeling and Simulation Engineering, Old Dominion University, 1300 Engineering & Computational Sciences Building, Norfolk, VA, 23529, United States of America
  • c Mechanical Department, University of Calabria, Via Pietro Bucci, 87036 Arcavacata, Rende CS, Italy
Cite as
Smith K., Diaz R., Shen Y., and Longo F. (2022).,Definition and Detection of Hypervulnerabilities using a Framework for Assessing Port Resilience. Proceedings of the 24th International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation(HMS 2022). , 002 . DOI: https://doi.org/10.46354/i3m.2022.hms.002

Abstract

Long term plans for maritime ports are identifying investments that will increase their capacity while decreasing their environmental footprint and operating costs. These changes are leading to increases in complexity at a time when leaner practices are driving investments to become more strategic. As such, this work proposes a generalized definition that allows a system or entity to be classified as exceedingly vulnerable by comparing it to other entities. This definition is developed from a set of definitions gathered from disparate fields. From this definition grounded in theory, the initial rules for complex system implementation are developed and demonstrated on both a small conceptual example and a port example. Finally, conclusions and directions for future work are provided.

References

  1. Blackhurst, J., Craighead, C. W., Elkins, D., & Handfield, R. B. (2005). An empirically derived agenda of critical research issues for managing supply-chain disruptions. International Journal of Production Research, 43(19), 4067-4081.
  2. Brown, B. (2019). Courage and vulnerability part i: Denitions and myths. Retrieved from https://brenebrown.com/wp-content/uploads/2019/08/Integration-Ideas_Courage-and-Vulnerability-Part-1-Definitions-and-Myths.pdf
  3. Burton, C. G. (2015). A validation of metrics for community resilience to natural hazards and disasters using the recovery from Hurricane Katrina as a case study. Annals of the Association of American Geographers, 105(1), 67-86.
  4. Calatayud, A., Mangan, J., & Palacin, R. (2017). Vulnerability of international freight flows to shipping network disruptions: A multiplex network perspective. Transportation Research Part E: Logistics and Transportation Review, 108, 195-208.
  5. Charrahy, Z., Yaghoobi‐Ershadi, M. R., Shirzadi, M. R., Akhavan, A. A., Rassi, Y., Hosseini, S. Z., . . . Hanafi‐Bojd, A. A. (2021). Climate change and its effect on the vulnerability to zoonotic cutaneous leishmaniasis in Iran. Transboundary and emerging diseases.
  6. Easley, R., Katsikides, N., Kucharek, K., Shamo, D., & Tiedeman, J. (2017). Freight performance measure primer. Retrieved from
  7. Garvey, P. R., & Pinto, C. A. (2009). Introduction to functional dependency network analysis. Paper presented at the The MITRE Corporation and Old Dominion, Second International Symposium on Engineering Systems, MIT, Cambridge, Massachusetts.
  8. Guariniello, C., & DeLaurentis, D. (2017). Supporting design via the system operational dependency analysis methodology. Research in Engineering Design, 28(1), 53-69.
  9. Havlin, S., Kenett, D., Bashan, A., Gao, J., & Stanley, H. (2014). Vulnerability of network of networks. The European Physical Journal Special Topics, 223(11), 2087-2106.
  10. Leontief, W. (1951). The Structure of american Economy, 1919-1939: An Empirical Application of Equilibrium Analysis: Oxford Univ Press.
  11. Li, Y., Chen, K., Collignon, S., & Ivanov, D. (2021). Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability. European Journal of Operational Research, 291(3), 1117-1131.
  12. Mustafa, D., Ahmed, S., Saroch, E., & Bell, H. (2011). Pinning down vulnerability: from narratives to numbers. Disasters, 35(1), 62-86.
  13. Oxford English Dictionary. (Ed.) (2021) Oxford English Dictionary.
  14. Pandit, P. S., Doyle, M. M., Smart, K. M., Young, C. C., Drape, G. W., & Johnson, C. K. (2018). Predicting wildlife reservoirs and global vulnerability to zoonotic Flaviviruses. Nature communications, 9(1), 1-10.
  15. Platto, S., Wang, Y., Zhou, J., & Carafoli, E. (2021). History of the COVID-19 pandemic: Origin, explosion, worldwide spreading. Biochemical and biophysical research communications, 538, 14-23.
  16. Ross, R., McEvilley, M., & Oren, J. (2016). Systems security engineering: Considerations for a multidisciplinary approach in the engineering of trustworthy secure systems. Retrieved from
  17. Smith, K., Diaz, R., & Shen, Y. (2022). Development of a framework to support informed shipbuilding based on supply chain disruptions. Procedia Computer Science, 200, 1093-1102.
  18. Smith, K., Diaz, R., Shen, Y., & Longo, F. (2021). Conceptual development of a probabilistic graphical framework for assessing port resilience. Paper presented at the 23rd International Conference on Harbor, Maritime and Multimodal Logistics Modeling and Simulation (HMS 2021) Online. https://www.proceedings.com/61129.html
  19. Tate, E. (2012). Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analysis. Natural Hazards, 63(2), 325-347.
  20. The Port of Virginia. (2016). 2065 Master Plan. Retrieved from https://www.portofvirginia.com/wp-content/uploads/2016/02/TPOV-master-plan-2065-final-020316.pdf
  21. Thekdi, S. A., & Santos, J. R. (2016). Supply chain vulnerability analysis using scenario‐based input‐output modeling: Application to port operations. Risk Analysis, 36(5), 1025-1039.
  22. United Nations. (1976). Port performance indicators. Retrieved from https://unctad.org/en/PublicationsLibrary/tdbc4d131sup1rev1_en.pdf
  23. United Nations Offce for Disaster Risk Reduction. (2021). Vulnerability. Retrieved from https : / / www .undrr.org/terminology/vulnerability
  24. Varma, A. (2008). Measurement Sources for Freight Performance Measures and Indicators. Retrieved from
  25. Wagner, S. M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph theory. International Journal of Production Economics, 126(1), 121-129.