A Fuzzy Decision Support Model for Natural Disaster Response under Informational Uncertainty

A Fuzzy Decision Support Model for Natural Disaster Response under Informational Uncertainty

Felix Wex (Information Systems Research, University of Freiburg, Germany), Guido Schryen (Information Systems Research, University of Regensburg, Germany) and Dirk Neumann (Information Systems Research, University of Freiburg, Germany)
DOI: 10.4018/jiscrm.2012070103


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. The authors address this issue of operational emergency response in natural disaster management (NDM) by suggesting a decision support model and a Monte Carlo heuristic which account for these challenges by drawing on fuzzy set theory and fuzzy optimization. Deriving requirements for addressing NDM situations from both practice and literature, they propose a decision model that accounts for the following phenomena: (a) incidents and rescue units are spatially distributed, (b) rescue units possess specific capabilities, (c) processing is non-preemptive, and (d) informational uncertainty occurs due to vague and linguistic specifications of data. The authors computationally evaluate their heuristic and benchmark the results with current best practice solutions. The authors’ results indicate that applying the new heuristic can substantially reduce overall harm.
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Natural disasters, including earthquakes, tsunamis, floods, hurricanes, and volcanic eruptions, have caused tremendous harm and continue to threaten millions of humans and various infrastructure capabilities each year. Being consistent with the terminology of the International Federation of Red Cross and Red Crescent Societies (IFRC) and the U.S. Federal Emergency Management Agency (FEMA), we use the term “disaster” in the following sense (IFRC, n.d.): “A disaster is a sudden, calamitous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community’s or society’s ability to cope using its own resources.” In this study, we focus on disasters based on natural disasters, rather on technological, man-made, or attack-based disasters. In contrast to disasters of the latter types, their natural counterparts are not preventable. Thus, both the actions that need to be taken before, during and after disasters and the used data are different. For example, risk management of floods and hurricanes can draw on geological data while the risk management of nuclear attacks by terrorists cannot do so.

The coordination of resources during natural disasters is characterized by a high level of informational uncertainty due to the chaotic situation, severe resource shortages, and a high demand for timely information in the presence of the disruption of infrastructure support (Chen et al., 2008). The March 2011 earthquakes near the coast of Sendai, Japan manifested these presumptions, as did the management of the succeeding nuclear disaster (Krolicki, 2011). Emergency operations centers (EOC) were confronted with the partial breakdown of information systems and transportation infrastructure. Officials had to deal with numerous incidents where more than 27,000 people were found dead or missing and some 150,000 Japanese displaced (Sanders, 2011). Actions of local commanders and rescue teams were coined by a high degree of improvisation and decentralization. The involvement of numerous, international organizations with different disaster response policies, resources, and technological infrastructures as well as capabilities led to distributed planning and implementing of response actions (Chawla, 2011). Poor communication between geographically dispersed EOCs, a lack of clear command structure and accurate data, and an immense time pressure intensified the dilemma (Deutsche Presse-Agentur, 2011; Dmitracova, 2010). Even though resource scarcity can occur, we argue that the “appropriate allocation of [spatially distributed] resources is more important (…) [and] a problem of coordination” (Comfort et al., 2004; Klingner, 2011).

The above issues reveal that the allocation of rescue units to incidents remains a challenge in effectively utilizing available resources and designing Emergency Response Systems (ERS). In practice, as told by associates of the German Federal Agency of Technical Relief (THW), assignments and schedules for resources are still derived through the application of greedy policies: for example, based on a ranking of incidents in terms of destructiveness, the most severe incidents are sequentially handled by the closest, idle rescue units (also stated by Comfort, 1999). However, this straightforward – albeit in many cases common and favorable – rule ignores estimated processing times of incidents, which may significantly affect overall casualties and harm.

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