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José Miguel Castillo, Starr Roxanne Hiltz, & Murray Turoff. (2012). Monte Carlo and decision making support in crisis management. 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: Simulation is an interdisciplinary science applicable to many branches of knowledge. One field in which simulation is relevant is decision making support (DMS), in which we use computers to run models of real or possible scenarios in order to evaluate alternative actions before carrying them out. We will obtain a useful simulation system only when the model (engine of the simulation process) has been made accurately to represent reality. Thus it is necessary to use a methodology that helps us to construct a simulation system. This paper presents some classifications of simulation systems and an introduction to the Monte Carlo method, with the objective of creating a framework of application of this method for the construction of simulation systems for decision making support in crisis management. One area of applicability is scenario-based simulations for training for cross-national teams to cooperate in large scale disasters. The final aim of this research will be the recommendation of standards and methodologies to build simulation systems in crisis management, specifically in decision making support. © 2012 ISCRAM.
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Beate Rottkemper, & Kathrin Fischer. (2013). Decision making in humanitarian logistics – A multi-objective optimization model for relocating relief goods during disaster recovery operations. 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. 647–657). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Disaster recovery operations rarely proceed smoothly and disruptions often require the redistribution of relief items. Such a redistribution has to be carried out taking into account both the current disruption and the uncertainty regarding possible future incidents in the respective area. As decisions have to be made fast in humanitarian operations, extensive optimization runs cannot be conducted in such a situation. Nevertheless, sensible decisions should be made to ensure an efficient redistribution, considering not only satisfaction of needs but also operational costs, as the budget is usually scarce in the recovery phase of a disaster. In this work, different scenarios are generated and then solved with a multiobjective optimization model to explore possible developments. By evaluating the results of these scenarios, decision rules are identified which can support the decision maker in the actual disaster situation in making fast, but nevertheless well-founded, decisions.
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Huizhang Shen, & Jidi Zhao. (2010). Decision-making support based on the combination of CBR and logic reasoning. 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: In recent years, various crises arise frequently and cause tremendous economic and life losses. Meanwhile, current emergency decision models and decision support systems still need further improvement. This paper first proposes a new emergency decision model based on the combination of a new case retrieval algorithm for Case-Based Reasoning (CBR) and logic reasoning, and then address a sample flood disaster emergency decision process to explain the application of the model in practice.
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Yaping Ma, Hui Zhang, Tao Chen, & Rui Yang. (2015). Decentralized Evacuation System Based on Occupants Distribution and Building Information. 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: Effective evacuation is critical for safety of occupants. The exiting evacuation systems lack flexibility and don?t consider the distribution of occupants. It is possible to direct occupants to danger areas or cause congestion in certain areas. In this paper, a decentralized evacuation system is proposed to compute the safest path in real time. The system is composed of fire detection sensors, zone controllers, elevator sensors, human tracking and monitoring systems and dynamic egress signs. All devices are placed at the predetermined locations based on integrated design of the building. The entire building is divided into many basic zones which are operating quite independently, and global information is communicated to neighboring zones and consequently to entire network by zone controllers. The system acts in decentralized fashion. The elevator and dynamic factors are considered in guidance system. Simulations are performed to determine the advantage of the system.
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Shengcheng Yuan, Ma Ma, H. Zhang, & Yi Liu. (2013). An urban traffic evacuation model with decision-making capability. 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. 317–321). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Traffic evacuation is one of the most challenging problems in a mega city due to crowded road conditions. This study focuses on developing a traffic evacuation model with decision-making capability. The model basically consists of two modules. The first one is a decision-making support module which runs very fast and provides short-forecast. The second one is a simulation module, which is used for simulating real evacuation process and for overall performance evaluation with vehicle tracking model. The first module can be considered as a “local” module as only partial information, such as traffic information in certain junctions is available. The second module can be considered as a global module which provides traffic directions for junction, and effective using of road-nets. With integration of two modules, overall system optimization may be achieved. Simulation cases are given for model validation and results are satisfied.
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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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