Ahmed Laatabi, Benoit Gaudou, Chihab Hanachi, Patricia Stolf, & Sébastien Truptil. (2022). Coupling Agent-based Simulation with Optimization to Enhance Population Sheltering. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 116–132). Tarbes, France.
Abstract: Population sheltering is a recurrent problem in crisis management that requires addressing two aspects: evacuating vulnerable people using emergency vehicles and regulating movements of pedestrians and individual vehicles towards shelters. While these aspects have received considerable attention in modeling and simulation literature, very few approaches consider them simultaneously. In this paper, we argue that Agent-Based Modeling and Simulation (ABMS) and Optimization are two complementary approaches that can address the problem of sheltering globally and efficiently and be the basis of coherent frameworks for decision- and policy-making. Optimization can build efficient sheltering plans, and ABMS can explore what-if scenarios and use geospatial data to display results within a realistic environment. To illustrate the benefits of a framework based on this coupling approach, we simulate actual flash flood scenarios using real-world data from the city of Trèbes in South France. Local authorities may use the developed tools to plan and decide on sheltering strategies, notably, when and how to evacuate depending on available time and resources.
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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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Ben Ortiz, Laura Kahn, Marc Bosch, Philip Bogden, Viveca Pavon-Harr, Onur Savas, et al. (2020). Improving Community Resiliency and Emergency Response With Artificial Intelligence. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 35–41). Blacksburg, VA (USA): Virginia Tech.
Abstract: New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multi-pronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information, the more effective and timelier their response will be and the greater the benefit to affected populations. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first. Even though our system could be used in a number of use cases where people are forced from one location to another, we demonstrate the feasibility of our system for the use case of Hurricane Florence in Lumberton, a town of 21,000 inhabitants that is 79 miles northwest of Wilmington, North Carolina.
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Cedric Papion. (2018). Water supply network resilience in the Wellington Region. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 263–271). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Wellington sits across an active seismic fault line and depends on remote sources for its water supply. With widespread damage expected after a large earthquake, it may be months before a minimal water supply is restored to residents, and even longer before it reaches the tap. This paper presents a recent study undertaken to identify network vulnerabilities and take water supply resilience to the next level. The study presented a possible timeline for repairs to the bulk network and restoration of supply to each suburb's reservoir. This highlighted the most critical areas where an alternative supply or storage was needed. The study also considered how to get the water to the customers after the reticulation network had been damaged. The strategy considered by Wellington Water was to develop a seismically-resilient skeleton network connecting reservoirs and key distribution points. A notable innovation was the use of algorithms to determine optimal locations for public tap stands and identify the most cost-effective critical pipe network where strengthening upgrades needed to be focused. The aspects of the project concerning its significance for the region, the overall resilience strategy and the pipeline resilience engineering were presented at the Institute of Public Works Engineering Australasia (IPWEA) and Water NZ conferences in 2017. While this paper touches on these subjects, its main focus is on the use of geospatial information for earthquake preparedness and resilience planning.
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Claudio Arbib, Davide Arcelli, Julie Dugdale, Mahyar Tourchi Moghaddam, & Henry Muccini. (2019). Real-time Emergency Response through Performant IoT Architectures. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: This paper describes the design of an Internet of Things (IoT) system for building evacuation. There are two main
design decisions for such systems: i) specifying the platform on which the IoT intelligent components should be
located; and ii) establishing the level of collaboration among the components. For safety-critical systems, such as
evacuation, real-time performance and evacuation time are critical. The approach aims to minimize computational
and evacuation delays and uses Queuing Network (QN) models. The approach was tested, by computer simulation,
on a real exhibition venue in Alan Turing Building, Italy, that has 34 sets of IoT sensors and actuators. Experiments
were performed that tested the effect of segmenting the physical space into different sized virtual cubes. Experiments
were also conducted concerning the distribution of the software architecture. The results show that using centralized
architectural pattern with a segmentation of the space into large cubes is the only feasible solution.
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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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Duncan T. Wilson, Glenn I. Hawe, Graham Coates, & Roger S. Crouch. (2012). Estimating the value of casualty health information to optimization-based decision support in response to major incidents. 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: In this paper we describe a work-in-progress decision support program designed for use in the response to major incidents in the UK. The proposed program is designed for use in a continuous fashion, where the updating of its model, the search for solutions to the model through an optimization algorithm, and the issuing of these solutions are carried out concurrently. The model facilitates the inclusion of dynamic and uncertain features of emergency response. The potential of such an approach to deliver high-quality response plans through enabling more accurate modeling is evaluated through focusing on the case of casualty health information. Computational experiments show there is significant value in monitoring the dynamic and uncertain health progression of casualties and updating the model accordingly. © 2012 ISCRAM.
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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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Felix Wex, Guido Schryen, & Dirk Neumann. (2011). Intelligent decision support for centralized coordination during Emergency Response. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Automated coordination is regarded as a novel approaches in Emergency Response Systems (ERS), and especially resource allocation has been understudied in former research. The contribution of this paper is the introduction of two variants of a novel resource allocation mechanism that provide decision support to the centralized Emergency Operations Center (EOC). Two quantitative models are computationally validated using real-time, data-driven, Monte-Carlo simulations promoting reliable propositions of distributed resource allocations and schedules. Various requirements are derived through a literature analysis. Comparative analyses attest that the Monte-Carlo approach outperforms a well-defined benchmark.
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Gary Bennett, Lili Yang, & Boyka Simeonova. (2017). A Heuristic Approach to Flood Evacuation Planning. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 380–388). Albi, France: Iscram.
Abstract: Flood evacuation planning models are an important tool used in preparation for flooding events. Authorities use the plans generated by flood evacuation models to evacuate the population as quickly as possible. Contemporary models consider the whole solution space and use a stochastic search to explore and produce solutions. The one issue with stochastic approaches is that they cannot guarantee the optimality of the solution and it is important that the plans be of a high quality. We present a heuristically driven flood evacuation planning model; the proposed heuristic is deterministic, which allows the model to avoid this problem. The determinism of the model means that the optimality of solutions found can be readily verified.
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Gary M. Fetter, Mauro Falasca, Christopher W. Zobel, & Terry R. Rakes. (2010). A multi-stage decision model for debris disposal operations. 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: As shown by Hurricane Katrina, disposing of disaster-generated debris can be quite challenging. Extraordinary amounts of debris far exceeding typical annual amounts of solid waste are almost instantaneously deposited across a widespread area. Although the locations and amounts of debris can be easily summarized looking back after recovery activities have been completed, they are uncertain and difficult at best to estimate as debris operations begin to unfold. Further complicating matters is that the capacity of cleanup resources, which is dependent upon available equipment, labor, and subcontractors, can fluctuate during on-going cleanup operations. As a result, debris coordinators often modify initial resource assignments as more accurate debris estimates and more stable resource capacities become known. In this research, we develop a computer-based decision support system that incorporates a multi-stage programming model to assist decision makers with allocating debris cleanup resources immediately following a crisis event and during ongoing operations as debris volumes and resource capacities become known with increasing certainty.
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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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Guido Bruinsma, & Robert De Hoog. (2006). Exploring protocols for multidisciplinary disaster response using adaptive workflow simulation. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 53–65). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: The unique and dynamic changing nature in which a disaster unfolds forces emergency personnel involved with the mitigation process to be greatly flexible in their implementation of protocols. In past disasters the incapability of the disaster organization to swiftly adjust the workflow to the changing circumstances, has resulted in unnecessary delays and errors in mitigation. Addressing this issue, we propose and demonstrate a method for simulating disasters for work and protocol optimization in disasters response (TAID), based on the BRAHMS multi-agent modeling and simulation language. Our hypothesis is that this low fidelity simulation environment can effectively simulate work practice in dynamic environments to rearrange workflow and protocols. The results from an initial test simulation of the Hercules disaster at Eindhoven airport in the Netherlands look promising for future and broader application of our disaster simulation method.
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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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Haya Aldossary, & Graham Coates. (2019). A Preliminary Optimisation-based Approach to Coordinate the Response of Ambulances in Mass Casualty Incidents. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Mass Casualty Incidents (MCIs) may occur with no notice and require a rapid response to manage the casualties and arrange their transportation to hospitals. MCIs may result in different numbers of casualties and fatalities. Further, response time can play a crucial role in reducing fatalities and protecting lives. This paper reports on a preliminary optimisation-based approach, termed MCIER, which has been developed to co-ordinate the response of ambulances to multiple MCIs. In this approach, a realistic representation of the road network is modelled for the geographical area of interest. Also, a Neighbourhood Search Algorithm (NSA) has been developed in order to find the optimum solution to the problem under consideration. A hypothetical case study of a MCI in Newcastle-upon-Tyne has been considered to investigate the effect on response time of the time of day, and day of week, on which the incident occurs.
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Hossein Baharmand, & Tina Comes. (2015). A Framework for Shelter Location Decisions by Ant Colony Optimization. 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: Earthquakes frequently destroy the homes and livelihoods of thousands. One of the most important concerns after an earthquake is to find a safe shelter for the affected people. Because of large numbers of potential locations, the multitude of constraints (e.g. access to infrastructures; security); and the uncertainty prevailing (e.g., number of places required) the identification of optimal shelter locations is a complex problem. Nevertheless, rapidly locating shelters and transferring the affected people to the nearest shelters are high priority in crisis situations. In this paper, we develop a framework based on Ant Colony Optimization (ACO) to support decisions-makers in the response phase. Using the same framework, we also derive recommendations for urban planning in the preparedness phase. We demonstrate our method with a case focusing on the city of Kerman, in Iran.
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Hussain Aziz Saleh. (2005). Dynamic optimisation of the use of space technology for rapid disaster response and management. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 139–141). Brussels: Royal Flemish Academy of Belgium.
Abstract: Modern space and information technologies provide valuable tools for the solution of many real-world problems in fields of managing effects of natural and man-made disasters, geomatic engineering, etc. Therefore, the need to develop and optimise the use of these technologies in an efficient manner is necessary for providing reliable solutions. This paper aims to develop powerful optimisation algorithms extending current highly successful ideas of artificial intelligence for developing of the disaster warning network which is a system of satellites and ground stations for providing real time early warning of the impact of the disaster and minimise its effects (e.g., earthquakes, landslides, floods, volcanoes, etc). Such intelligent algorithms can provide a degree of functionality and flexibility suitable both for constructing high-accuracy models and in monitoring their behaviour in real time.
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Jutta Hild, Jonathan Ott, Yvonne Fischer, & Christian Glökler. (2010). Markov based decision support for cost-optimal response in security management. 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 this contribution, we introduce a prototype of a decision support tool for cost-optimal response in security management. The threat situation of a closed infrastructure, exposed to multiple threats, and the corresponding response actions are modeled by a continuous-time Markov decision process (CMDP). Since the CMDP cannot be solved exactly for large infrastructures, the response actions are determined from a heuristic, based on an index rule. The decision support tool's user interface displays the infrastructure's current threat state and proposes the heuristic response actions to the decision maker. In this way, global situation awareness can be enhanced and the decision maker is able to initiate an almost cost-optimal response action in short time.
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Kostas Kolomvatsos, Kakia Panagidi, & Stathes Hadjiefthymiades. (2013). Optimal spatial partitioning for resource allocation. 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. 747–757). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Spatial partitioning consists of the problem of finding the best segmentation of an area under specific conditions. The final goal is to identify parts of the area where a number of resources could be allocated. Such cases are common in disaster management scenarios. In this paper, we consider such a scenario and propose a methodology for the resource allocation for emergency response. We utilize an intelligent technique that is based on the Particle Swarm Optimization algorithm. We define the problem by giving specific formulations and describe the proposed algorithm. Moreover, we provide a method for separating the area into cells and describe a technique for calculating cell weights based on the underlying spatial data. Finally, we present a case study for allocating a number of ambulances and give numerical results concerning the run time and the total coverage of the examined area.
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Kui Wang, Jose Marti, Ming Bai, & K.D. Srivastava. (2012). Optimal decision maker algorithm for disaster response management with I2Sim applications. 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: Disaster response management has become an important area of research in recent years, with authorities spending more resources in the area. Infrastructure resource interdependencies are key critical points for a system to operate optimally. After a disaster occurs, infrastructures would have sustained certain degrees of damage, the allocation of limited resources to maximize human survival becomes a top priority. The I2Sim (Infrastructures Interdependencies Simulator) research group at the University of British Columbia (UBC) has developed a software simulation toolbox to help authorities plan for disaster responses. This paper presents an optimization decision algorithm based on Lagrange multipliers, which provides the theoretical basis for I2Sim software decision maker layer. There is a simple scenario of three hospitals constructed with the I2Sim toolbox to illustrate the interdependencies of water and electricity. © 2012 ISCRAM.
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Lili Yang, Qun Liu, Shuang-Hua Yang, & Dapeng Yu. (2015). Evacuation Planning with Flood Inundation as Inputs. 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: Recent flooding events happening in our city demonstrate frequency and severity of floods in the UK, highlighting the need to plan and prepare, and efficiently defend. Different from the numerous evacuation model and optimization algorithms, this paper aims to address flood evacuation planning with flood inundation as inputs. A dynamic flooding model and prediction to estimate the development of both surface water and flooding from rivers and watercourses has been fed into evacuation planning at various levels. A three-step approach is proposed. The first step is to identify assembly point designation. The second step is to find the candidate shortest path from each assembly point to all safe areas for all evacuees with consideration of possible inundation. The last step is to determine the optimal safe area for evacuees in the inundation area. The work presented in this paper has emphasized timing issue in evacuation planning. A case study is given to illustrate the use of the approach.
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Loïc Bidoux, Jean-Paul Pignon, & Frédérick Bénaben. (2014). A model driven system to support optimal collaborative processes design in crisis management. 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. 245–249). University Park, PA: The Pennsylvania State University.
Abstract: This paper presents a system dedicated to support crises managers that is focused on the collaboration issues of the actors involved in the response. Based on context knowledge, decision makers' objectives and responders' capabilities, the system designs in a semi-automatic way a set of collaborative process alternatives that can optimize coordination activities during an ongoing crisis resolution. The technical design of the system mixes optimization algorithms with inference of logical rules on an ontology. Candidate processes are evaluated through multi-criteria decision analysis and proposed to the decision-makers with associated key performance indicators to help them with their choice. The overall approach is model driven through a crisis meta-model and an axiomatic theory of crisis management.
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Ma Ma, Shengcheng Yuan, H. Zhang, & Yi Liu. (2013). Framework design for operational scenario-based emergency response system. 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. 332–337). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The present paper introduces a scenario-based framework design for connecting emergency response system with human behavior analysis and social information processing, which aims at improving its comprehensive capability in dealing with unexpected situations caused by physical, social and psychological factors during a crisis. The overall framework consists of four function modules: Scenario awareness, scenario analysis, scenario evolvement and scenario response. A detailed function design for each module is presented as well as the related methodologies used for integration of four modules. The contribution of this paper includes two aspects. One is realizing the integration of incident evolution, information-spreading and decision-making by taking account of physical, social and psychological effects during emergency. The other is improving the efficiency of decisionmaking through dynamic optimization process.
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Mauro Falasca, Christopher W. Zobel, & Gary M. Fetter. (2009). An optimization model for humanitarian relief volunteer management. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: One of the challenges of humanitarian organizations is that there exist limited decision technologies that fit their needs. It has also been pointed out that those organizations experience coordination difficulties with volunteers willing to help. The purpose of this paper is to help address those challenges through the development of a decision model to assist in the management of volunteers. While employee workforce management models have been the topic of extensive research over the past decades, no work has focused on the problem of managing humanitarian relief volunteers. In this paper, we discuss a series of principles from the field of volunteer management and develop a multi criteria optimization model to assist in the assignment of volunteers to tasks. We present an illustrative example and analyze a solution methodology where the decision maker exercises his/her preferences by trading-off conflicting objectives. Conclusions, limitations, and directions for future research are also discussed.
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Michael Morin, Irène Abi-Zeid, Claude-Guy Quimper, & Oscar Nilo. (2017). Decision Support for Search and Rescue Response Planning. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 973–984). Albi, France: Iscram.
Abstract: Planning, controlling and coordinating search and rescue operations is complex and time is crucial for survivors who must be found quickly. The search planning phase is especially important when the location of the incident is unknown. We propose, implement, solve, and evaluate mathematical models for the multiple rectangular search area problem. The objective is to define optimal or near-optimal feasible search areas for the available search and rescue units that maximize the probability of success. We compare our new model to an existing model on problem instances of realistic size. Our results show that we are able to generate, in a reasonable time, near optimal operationally feasible plans for searches conducted in vast open spaces. In an operational context, this research can increase the chances of finding s urvivors. Ultimately, as our models get implemented in the Canadian Coast Guard search planning tool, this can translate into more lives being saved.
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