Abdelgawad, A. A. (2023). An Updated System Dynamics Model for Analysing the Cascading Effects of Critical Infrastructure Failures. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 595–608). Omaha, USA: University of Nebraska at Omaha.
Abstract: Aiming at examining the cascading effects of the failure of Critical Infrastructure (CI), this work-in-progress research introduces an improved System Dynamics model. We represent an improvement over the previous models aimed at studying CIs interdependencies and their cascading effects. Our model builds on earlier models and corrects their flaws. In addition to introducing structural enhancements, the improvements include using unpublished data, a fresh look at a previously collected dataset and employing a new data processing to address and resolve some longstanding issues. The dataset was fed to an optimisation model to produce a new dataset used in our model. The structure of our SD model, its dataset and the data processing techniques we employed to create this dataset are all described in the study. Although the model has passed the fundamental validation criteria, more validation testing and scenario exploration are yet to be conducted.
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Aarland, M., Radianti, J., & Gjøsæter, T. (2023). Using System Dynamics to Simulate Trust in Digital Supply Chains. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 516–529). Omaha, USA: University of Nebraska at Omaha.
Abstract: The power industry is outsourcing and digitalising their services to provide better, faster, and more reliable supply of electric power to the society. As a result, critical infrastructure increases in complexity and tight couplings between multiple suppliers and systems in digital supply chains. It also introduces new risks and challenges that are difficult to manage for critical infrastructure owners. To address the vulnerability in digital supply chains, we have developed a system dynamics model that represent important challenges to manage cybersecurity in digital supply chains, based on input from an expert group in the power industry. The system dynamics model illustrates how trust in suppliers as well as the need for control play important roles in outsourcing. Scenarios were developed and simulated.
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Tobias Andersson Granberg, Sara Erlander, David Fredman, Lovisa Olovsson, & Emma Persson. (2022). Predicting Volunteer Travel Time to Emergencies. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 44–54). Tarbes, France.
Abstract: A model is developed, which can predict the travel time for volunteers that are dispatched as first responders to emergencies. Specifically, the case of lay responders to out of hospital cardiac arrest is studied. Positions from historical responses is used to estimate the real response times, which are used to train and evaluate the new travel time model. The new model considers the road network and the transport mode most likely used by the volunteers. The results for the new model are compared to a model used in an existing volunteer initiative. They show that the new model can make better predictions in 59.7% of the cases. This can be used directly as a base for improving the travel time estimates in existing volunteer initiatives, and to improve the input data to the continuously evolving volunteer resource management systems.
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Ahmed T. Elsergany, Amy L. Griffin, Paul Tranter, & Sameer Alam. (2014). Descriptive and Geographical Analysis of Flood Disaster Evacuation Modelling. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings ? 11th International Conference on Information Systems for Crisis Response and Management (pp. 55–59). University Park, PA: The Pennsylvania State University.
Abstract: The planning of evacuation operations for a riverine flood disaster is vital for minimizing their negative impacts on human lives. This paper aims to develop a systematic method to model and plan evacuation trip generation and distribution for riverine floods. To achieve this aim, it adapts the transportation or Hitchcock problem, an operations research technique employed in conventional four-stage transportation modeling, and that is used to plan and model transport in normal situations, so that it is appropriate for flood disaster situations focusing on the first two stages. Concentrating on pre-flood hazard planning, our evacuation modelling considers two types of flood disaster data environments: certain environs, in which all decision variables are known, and uncertain environs, when probabilities of decision variables are considered in the evacuation plans.
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Shengcheng Yuan, Yi Liu, Gangqiao Wang, Hongshen Sun, & H. Zhang. (2014). A dynamic-data-driven driving variability modeling and simulation for emergency evacuation. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 70–74). University Park, PA: The Pennsylvania State University.
Abstract: This paper presents a dynamic data driven approach of describing driving variability in microscopic traffic simulations for both normal and emergency situations. A four-layer DGIT (Decision, Games, Individual and Transform) framework provides the capability of describing the driving variability among different scenarios, vehicles, time and models. A four-step CCAR (Capture, Calibration, Analysis and Refactor) procedure captures the driving behaviors from mass real-time data to calibrate and analyze the driving variability. Combining the DGIT framework and the CCAR procedure, the system can carry out adaptive simulation in both normal and emergency situations, so that be able to provide more accurate prediction of traffic scenarios and help for decision-making support. A preliminary experiment is performed on a major urban road, and the results verified the feasibility and capability of providing prediction and decision-making support.
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