Abstract: Disasters trigger the need for relief items. The flows of relief items to the beneficiaries in the disaster area are determined by humanitarian logistics networks. The setup and operations of such networks can be supported by employing Operations Research models. Several models, based on different methodologies are available to support decision-making in the field of humanitarian logistics. This work-in-progress analysis suggests a structure of a methodological tool kit for humanitarian logistics. With its help, practitioners in the field of humanitarian logistics should be better equipped to find, compare, and apply available analytical models for their individual decision problem. To serve as an illustration, one OR model is presented in detail according to the proposed structure of the methodological tool kit.

Abstract: The US has not yet developed adequate models for estimating hazard generated needs, the necessary first step for developing useful decision support systems needed to estimate the capability and capacity of the response forces required. Modeling and technology required to support the decisions made by humanitarian relief organizations requires scenario driven catastrophic planning. This paper documents the lack of effective decision support tools and systems for humanitarian aid and describes the current state of models and methods used for determination of hazard generated needs. The paper discusses work performed on a catastrophic earthquake preparedness project. It outlines how the results of this project will be used to advance the modeling and decision support capabilities of federal, state and local disaster planners and emergency responders.

Abstract: Despite intense research on decision-making in action, we still know little about when decision-makers rely on deliberate vs. intuitive decision-making in decision situations under complexity and uncertainty. This paper studies decision-making modes (deliberate vs. intuitive) in complex task environments contingent on perceived complexity, experience, and decision style preference. We find that relatively inexperienced decision-makers respond to increases in subjective complexity with an increase in deliberation and tend to follow their decision style preference. Experienced decision-makers are less guided by their decision preference and respond to increases in subjective complexity only minimally. Our paper contributes to a developing stream of research linking decision-making with intra-personal and environmental properties and fosters our understanding of the conditions under which decision-makers rely on intuitive vs. deliberate decision modes. In doing so, we go one step further towards a comprehensive theory of decision-making in action.

Abstract: Emergency is a threatening condition that requires urgent action, an effective response and within an emergency scenario there may be risks for responders, as well as for those affected. Response time is crucial for affected individuals and environments to be addressed on their needs. In this context, the goal of this work is to support the agents involved in the emergency response, through an application-supported collaborative solution using drones. This solution aims to collect information from the worked emergency scenario, so that, through the collaboration of specialists, there is a greater support for the decision-making made by the responsible agents within this scenario, causing it to occur in a shorter time, thus speeding up the response to the emergency. In this work, the aim was to validate with experts from the Rio de Janeiro Firefighters, who already work with drones, by evaluating the utility of the solution in real scenarios.

Abstract: Natural and man-made hazards may affect industrial production sites by both direct losses (due to physical damage to assets and buildings) and indirect losses (production losses). Indirect losses, e.g. from production downtimes, can exceed direct losses multiple times. Thus, the vulnerability of industrial sectors to indirect losses is an important component of risk and its determination is an important part within risk analysis. In this paper a conceptual indicator framework is presented which allows to assess the indirect vulnerability of industrial sectors to different types of disasters in a quantitative manner. The results are useful for information sharing and decision making in crisis management and emergency planning (mitigation measures, business continuity planning), since the developed indicator system helps to take the complex phenomenon of industrial vulnerability and the underlying interdependencies into account. Besides the identification and conceptual motivation of the indicators, methodical aspects such as standardization, weighting and aggregation are addressed.

Abstract: Several recent studies point the way to enabling emergency response managers to be able to find relevant posts and incorporate them into their sensemaking and decision making processes. Among the approaches that have improved the ability to find the most relevant information are the social conventions of creating topic groups and tags and of “retweeting;” the use of trained volunteers to filter and summarize posts for responders; automated notifications of trending topics; natural language processing of posts; techniques for identifying posts from the disaster site; and the use of GIS and crisis maps to visually represent the distribution of incidents.

Abstract: During a disaster, health and medical decision makers need accurate, timely information. However, it is seldom readily available to the right decision makers, at the right time. Quite a number of databases currently exist with information about health and medical organizations which decision makers need during a disaster. Some of these databases have functions that facilitate decision-making and communication before, during and after a disaster. In theory, linking several existing databases will supply this information. Also, other functions can be provided in one package for incident management and monitoring of the preparedness capacity of a State's health and medical systems. But, this has not happened yet in Florida. This research assessed the different users needs, defined the information required to make good decisions and is testing a pilot decision support system of linked databases.

Abstract: The damage caused by recent events in Japan in 2011 and USA in 2012 highlighted the need to adopt measures to increase the resilience of communities against extreme events and disasters. In addition to the conventional and official information that is necessary for adaptation to disasters, recently, common citizens residents in the affected areas also began contributing with voluntary qualified and updated information. In this context, this work-in-progress presents an approach that uses voluntary information – Also known by VGI (Volunteered Geographic Information) – As a data source for Spatial Decision Support Systems (SDSS) in order to assist the decision-making in disaster management. Our approach consists of a framework that integrates voluntary and conventional data, a SDSS and processes and methods for decision-making. As a result, it is expected that this approach will assist official organizations in disaster management by providing mechanisms and information.

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.

Abstract: The paper provides an overview of past, present and future development in the program to implement a Flood Forecasting and Decision-Support System (DSS) for the SAIH network in some Spanish basins. These tools represent a significant advance by embedding the decision-making components for management of hydraulic infrastructure into the flood forecasting and flood early warning procedures. The DSS has been implemented based on an open-shell platform for integrating various data sources and different simulation models. So far, it covers the Segura, Jucar, Tajo, Duero and Miño-Sil basins, which represent 42% of Spanish territory. Special attention is paid to the decision-support for the operation of the 66 major reservoirs as a fundamental part of flood management.

Abstract: Emerging threats provide motivation to develop new methods for preparing the next generation of crisis responders. Bayesian theory shifts reasoning toward a probabilistic, epistemic paradigm, giving rise to Evans' revised heuristic-analytic theory. Researchers at The Pennsylvania State University use scenario-based training and the analytic decision game (ADG) to blend and implement these processes as foundational pedagogy for engaging, educating and training medical students as crisis responders and critical thinkers. The ADG scenarios vary by content and level of expertise, lending themselves readily adaptable to both crisis response preparation and the development of clinical reasoning. The ADG creates a virtual crisis requiring participants to engage in scenario management as role-players. For the past two years, medical students from the Penn State College of Medicine, in their first year of training, have participated in the ADG Lights Out scenario, testing community preparation and resilience after a wide-spread and months-long power outage.

Abstract: In this paper, we propose the use of an ontology-based and semantic technologies approach to improving shared situation awareness amongst teams dealing with emergency situations. We have also identified that shared and team situation awareness tends to be viewed only in terms of cooperative task completion and so we have tried to describe their important relationship with team decision making. The applicability of our approaches is demonstrated by a case study of mass evacuation in the case of a tsunami event. We show how ontology can be used to represent context-based situations and how the axioms and rules can improve team situation awareness.