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Victor A. Bañuls, Murray Turoff, & Joaquin Lopez. (2010). Clustering scenarios using cross-impact analysis. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Scenarios are frequently used in Emergency Planning and Preparedness. These scenarios are developed based on the hypothesis of occurrence or not of significant events. This is a complex process because of the interrelations between events. This fact, along with the uncertainty about the occurrence or non-occurrence of the events, makes the scenario generation process a challenging issue for emergency managers. In this work a new step-by-step model for clustering scenarios via cross-impact is proposed. The authors. proposal adds tools for detecting critical events and graphical representation to the previous scenario-generation methods based on Cross-Impact Analysis. Moreover, it allows working with large sets of events without using great computational infrastructures. These contributions are expected to be useful for supporting the analysis of critical events and risk assessment tasks in Emergency Planning and Preparedness. Operational issues and practical implications of the model are discussed by means of an example.
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Douglas Alem, & Alistair Clark. (2015). Insights from two-stage stochastic programming in emergency logistics. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: This paper discusses the practical aspects and resulting insights of the results of a two-stage mathematical network flow model to help make the decisions required to get humanitarian aid quickly to needy recipients as part of a disaster relief operation. The aim of model is to plan where to best place aid inventory in preparation for possible disasters, and to make fast decisions about how best to channel aid to recipients as fast as possible. Humanitarian supply chains differ from commercial supply chains in their greater urgency of response and in the poor quality of data and increased uncertainty about important inputs such as transportation resources, aid availability, and the suddenness and degree of “demand”. The context is usually more chaotic with poor information feedback and a multiplicity of decision-makers in different aid organizations. The model attempts to handle this complexity by incorporating practical decisions, such as pre-allocation of emergency goods, transportation policy, fleet management and procurement, in an uncertainty environment featured by a scenario-based approach. Preliminary results based on the floods and landslides disaster of the Mountain Region of Rio de Janeiro state, Brazil, point to how to cope with these challenges by using the mathematical model.
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