George H. Bressler, Murray E. Jennex, & Eric G. Frost. (2012). X24 Mexico: Stronger together. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Can populations self-organize a crisis response? This is a work in progress report on Exercise 24, X24, Mexico, a follow up to the first two exercises, X24 and X24 Europe The X24 exercises used a variety of free and low-cost social media and web 2.0 tools to organize, plan, and manage local and international expertise and organizations in the response to a preset disaster scenario. The first X24 focused on Southern California, while the second X24, X24 Europe, focused on the Balkan area of Eastern Europe. These exercises involved over 12,500 participants for X24 while X24 Europe had over 49,000 participants. This paper presents an overview of the recently completed X24 Mexico exercise, as well as the preliminary results. © 2012 ISCRAM.
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Cendrella Chahine, Thierry Vidal, Mohamad El Falou, & François Pérès. (2022). Multi-Agent Dynamic Planning Architectures for Crisis Rescue Plans. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 243–255). Tarbes, France.
Abstract: We are interested in rescue management in crises such as in terrorist attacks. Today, there are emergency plans that take into account all the stakeholders involved in a crisis depending on the event type, magnitude and place. Unfortunately, they do not anticipate the evolution of the crisis situation such as traffic and hospital overcrowding. In addition, decisions are taken after the information has been passed from the operational level to higher levels. This work focuses on the operational level of the emergency plan. What will happen if the actors at this level, can make certain decisions without escalating the information to higher levels? To answer this question, a multi-agent dynamic planning approach is proposed and it will be tested in two different architectures in order to see how much autonomy can be given to an agent and how they coordinate to save the victims.
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Trishan R. De Lanerolle, Ralph A. Morelli, Norman Danner, Danny Krizanc, Gary Parker, & Ozgur Izmirli. (2008). Creating an academic community to build Humanitarian FOSS: A progress report. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 337–341). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper describes The Humanitarian FOSS Project, a National Science Foundation funded effort to help revitalize undergraduate computing education by getting students and faculty involved in building open source software that benefits the community.
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Gerhard Rauchecker, & Guido Schryen. (2018). Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 69–82). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system, which we develop in joint cooperation with a disaster management authority and a software development company.
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Haya Aldossary, & Graham Coates. (2021). Multi-objective Optimization for Coordinating Emergency Resources in Multiple Mass Casualty Incidents. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 1015–1027). Blacksburg, VA (USA): Virginia Tech.
Abstract: Effective co-ordination between resource-constrained emergency services during multiple mass casualty incidents (MCIs) plays a significant role in the response phase. In such a case, the co-ordination problem needs to be solved, namely the allocation of responders-to-incidents, responders-to-casualties, vehicles to travel to casualties at incidents and transport casualties to hospitals, and task assignment to responders and vehicles. A Neighborhood Search Algorithm (NSA) is employed to solve the co-ordination problem with the aim of reducing the suffering of casualties, with varying injuries and health classifications. An application of the NSA is enabled using a hypothetical case study of MCIs including three scenarios in a major urban area of the UK. The experiments conducted show the effectiveness of using different approaches to generate an initial response plan, and the performance of the NSA in developing a final optimized plan.
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Linda Plotnick, Elizabeth Avery Gomez, Connie White, & Murray Turoff. (2007). Furthering development of a unified emergency scale using Thurstone's Law of Comparative Judgment: A progress report. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 411–418). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In disasters, local civilians on or near the scene, are often first to respond and give aid. Therefore, the public needs to be well-informed with accurate, time critical information. However, a primary information source is event-specific scales that are inconsistent in their categorization and measurement, adding confusion to public responsiveness. These scales are not extendable to new emergencies in a changing world. We argue for development of a unified emergency scale to facilitate communication and understanding. This scale will inform local communities with regional community-specific information, and will be extendable for further use by professional responders. Research in progress elicited 15 dimensions of an emergency using a Delphi-like process and then ranked the dimensions by importance utilizing Thurstone's Law of Comparative Judgment. Contributions of this paper are to highlight the need for an unequivocal, unified scale and further its development.
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Adriaan Ter Mors, Xiaoyu Mao, Nicola Roos, Cees Witteveen, & Alfons H. Salden. (2007). Multi-agent system support for scheduling aircraft de-icing. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 467–478). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Results from disaster research suggest that methods for coordination between individual emergency responders and organizations should recognize the independence and autonomy of these actors. These actor features are key factors in effective adaptation and improvisation of response to emergency situations which are inherently uncertain. Autonomy and adaptability are also well-known aspects of a multi-agent system (MAS). In this paper we present two MAS strategies that can effectively handle aircraft deicing incidents. These MAS strategies help improve to prevent and reduce e.g. airplane delays at deicing stations due to changing weather conditions or incidents at the station, where aircraft agents adopting pre-made plans that would act on behalf of aircraft pilots or companies, would only create havoc. Herein each agent using its own decision mechanism deliberates about the uncertainty in the problem domain and the preferences (or priorities) of the agents. Furthermore, taking both these issues into account each proposed MAS strategy outperforms a naive first-come, first-served coordination strategy. The simulation results help pilots and companies taking decisions with respect to the scheduling of the aircraft for deicing when unexpected incidents occur: they provide insights in the impacts and means for robust selection of incident-specific strategies on e.g. deicing station delays of (individual) aircraft.
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David J. Wald. (2013). Adding secondary hazard and ground-truth observations to PAGER's loss modeling. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 586–591). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: A rapid, holistic view of earthquake disasters begins with earthquake location and magnitude, alerted by seismic networks. The initial source characteristics, along with any available ground-shaking observations, can be used to rapidly estimate the shaking extent, its severity (e.g., ShakeMap), and its likely impact to society, for example, employing the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response, or PAGER, system. When serous impacts are likely, PAGER's impact-based alerts can, in turn, begin the process of primary response at the local, national, or international level, and the process of reconnaissance via social media, the mainstream media, scientific analyses, and remotely-sensed and ground-truth observations. In this work-in-progress report, we describe our initial efforts to incorporate event-specific ground-truth observations and model secondary ground-failure hazards back into the loss-modeling domain in order to provide a more holistic view each earthquake disaster.
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Felix Wex, Guido Schryen, & Dirk Neumann. (2012). Operational emergency response under informational uncertainty: A fuzzy optimization model for scheduling and allocating rescue units. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Coordination deficiencies have been identified after the March 2011 earthquakes in Japan in terms of scheduling and allocation of resources, with time pressure, resource shortages, and especially informational uncertainty being main challenges. We suggest a decision support model that accounts for these challenges by drawing on fuzzy set theory and fuzzy optimization. Based on requirements from practice and the findings of our literature review, the decision model considers the following premises: incidents and rescue units are spatially distributed, rescue units possess specific capabilities, processing is non-preemptive, and informational uncertainty through linguistic assessments is predominant when on-site units vaguely report about incidents and their attributes, or system reports are not exact. We also suggest a Monte Carlo-based heuristic solution procedure and conduct a computational evaluation of different scenarios. We benchmark the results of our heuristic with results yielded through applying a greedy approach. The results indicate that using our Monte Carlo simulation to solve the decision support model inspired by fuzzy set theory can substantially reduce the overall harm. © 2012 ISCRAM.
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Duncan T. Wilson, Glenn I. Hawe, Graham Coates, & Roger S. Crouch. (2013). Scheduling response operations under transport network disruptions. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 683–687). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Modeling the complex decision problems faced in the coordination of disaster response as a scheduling problem to be solved using an optimization algorithm has the potential to deliver efficient and effective support to decision makers. However, much of the utility of such a model lies in its ability to accurately predict the outcome of any proposed solution. The stochastic nature of the disaster response environment can make such prediction difficult. In this paper we examine the effect of unknown disruptions to the road transport network on the utility of a disaster response scheduling model. The effects of several levels of disruption are measured empirically and the potential of using real-time information to revise model parameters, and thereby improve predictive performance, is evaluated.
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