1. Proceedings
  2. I3M
  3. 2021
  4. HMS
  5. 10.46354/i3m.2021.hms.006

Conceptual development of a probabilistic graphical framework for assessing port resilience 

  • a Katherine Smith , 
  • b Rafael Diaz,
  • c Yuzhong Shen, 
  • d Francesco Longo 
  • a,b Virginia Modeling, Analysis & Simulation Center, Old Dominion University, 1030 University Blvd, Suffolk, VA, 23435, United States of America
  • a, c  Department of Computational Modeling and Simulation Engineering, Old Dominion University, 1300 Engineering & Computational Sciences Building, Norfolk, VA, 23529, United States of America
  • d  Mechanical Department, University of Calabria, Via Pietro Bucci, 87036 Arcavacata, Rende CS, Italy
Cite as
Smith K., Diaz R., Shen Y., Longo F. (2021). Conceptual development of a probabilistic graphical framework for assessing port resilience . Proceedings of the 23rd International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation(HMS 2021), pp. 45-52. DOI:https://doi.org/10.46354/i3m.2021.hms.006

Abstract

Technological advances such as cyber physical systems and autonomous vehicles combined with increased disruptions including the Covid-19 pandemic and coastal natural disasters have heightened the importance of port risk analysis methodologies and frameworks that can accurately quantify and optimize resilience. This work presents the conceptual development of a novel combination of analysis methodologies linking a probabilistic graphic approach on a network of risk events with a functional dependency approach on a system network. Key advantages of these two methodologies are the ability to model and learn causal interactions rather than simply correlations and a high level of computational efficiency. This combination of robustness and flexibility offers the ability to quickly analyze multiple port configurations in order to invest in efforts that maximize the resilience-cost ratio. In addition, the methodology opens the door for real-time anomaly detection and causal analysis in order to enhance efforts in the protecting against attacks on infrastructure and in particular cyber physical systems.

References

  1. Diaz, R., Smith, K., Acero, B., Longo, F., & Padovano, A. (2021). Developing an Artificial Intelligence Framework to Assess Shipbuilding and Repair Sub-Tier Supply Chains Risk. Procedia Computer Science, 180, 996-1002.
  2. Diaz, R., Smith, K., Landaeta, R., & Padovano, A. (2020). Shipbuilding supply chain framework and digital transformation: a project portfolios risk evaluation. Procedia Manufacturing, 42, 173-180.
  3. Domonoske, C. (2021). If World's Battery Supply Doesn't Scale Up, Automakers Will Be In Trouble. Morning Edition. Retrieved from https://www.npr.org/2021/04/08/985253463/if-worlds-battery-supply-doesnt-scale-up-automakers-will-be-in-trouble
  4. Dowell, S., Sigmon, K., & Livingston, W. (2012). Transportation Challenges in the Hampton Roads, VA, Region. Retrieved from
  5. Garvey, P. R., & Pinto, C. A. (2009). Introduction to functional dependency network analysis. Paper presented at the Second International Symposium on Engineering Systems, , MIT, Cambridge, Massachusetts. 
  6. Ghahramani, Z. (1997). Learning dynamic Bayesian networks. Paper presented at the International School on Neural Networks, Initiated by IIASS and EMFCSC. 
  7. Gong, L., Xiao, Y.-b., Jiang, C., Zheng, S., & Fu, X. (2020). Seaport investments in capacity and natural disaster prevention. Transportation research part D: transport and environment, 85, 102367.
  8. Guariniello, C., & DeLaurentis, D. (2017). Supporting design via the System Operational Dependency Analysis methodology. Research in Engineering Design, 28, 53-69.
    doi:10.1007/s00163-016-0229-0
  9. Guo, H., & Hsu, W. (2002). A survey of algorithms for real-time Bayesian network inference. Paper presented at the Join Workshop on Real Time Decision Support and Diagnosis Systems.
  10. Henry, D., & Ramirez-Marquez, J. E. (2012). Generic metrics and quantitative approaches for system resilience as a function of time. Reliability Engineering & System Safety, 99, 114-122.
  11. Hosseini, S., Al Khaled, A., & Sarder, M. (2016). A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer. Journal of Manufacturing Systems, 41, 211-227.
  12. Hosseini, S., & Barker, K. (2016). Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports. Computers & Industrial Engineering, 93, 252-266.
  13. Hosseini, S., & Ivanov, D. (2020). Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review. Expert systems with applications, 161, 113649.
  14. Hosseini, S., Ivanov, D., & Blackhurst, J. (2020). Conceptualization and measurement of supply chain resilience in an open-system context. IEEE Transactions on Engineering Management.
  15. Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307.
  16. Jiang, P., & Haimes, Y. Y. (2004). Risk management for Leontief‐based interdependent systems. Risk Analysis: An International Journal, 24(5), 1215-1229.
  17. Justice, V., Bhaskar, P., Pateman, H., Cain, P., & Cahoon, S. (2016). US container port resilience in a complex and dynamic world. Maritime Policy & Management, 43(2), 179-191.
  18. Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society: Series B (Methodological), 50(2), 157-194.
  19. Leontief, W. W. (1951). The structure of American economy, 1919-1939: an empirical application of equilibrium analysis. Retrieved from
  20. Nicoletti, L., Chiurco, A., Arango, C., & Diaz, R. (2014). Hybrid approach for container terminals performances evaluation and analysis. International Journal of Simulation and Process Modelling, 9(1-2), 104-112.
  21. Okuyama, Y., & Santos, J. R. (2014). Disaster impact and input–output analysis. Economic Systems Research, 26(1), 1-12.
  22. Pant, R., Barker, K., Ramirez-Marquez, J. E., & Rocco, C. M. (2014). Stochastic measures of resilience and their application to container terminals. Computers & Industrial Engineering, 70, 183-194.
  23. Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward Causal Representation Learning. Proceedings of the IEEE.
  24. 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
  25. Pant, R., Barker, K., Ramirez-Marquez, J. E., & Rocco, C. M. (2014). Stochastic measures of resilience and their application to container terminals. Computers & Industrial Engineering, 70, 183-194.
  26. The Port of Virginia. (2017). NIT South Animation: YouTube. United Nations. (2020). Review
    of Maritime Transport (U. N. Pubblications Ed.). New York, NY. 
  27. Verschuur, J., Koks, E., & Hall, J. (2020). Port disruptions due to natural disasters: Insights into port and logistics resilience. Transportation research part D: transport and environment, 85, 102393.
  28. Wu, D., & Mochizuki, T. (2021). Why Shortages of a $1 Chip Sparked Crisis in Global Economy. Bloomberg Technology. Retrieved from https://www.bloomberg.com/news/articles/2021-04-05/why-shortages-of-a-1-chip-sparked-crisis-in-the-global-economy