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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, Guido Schryen, Dirk Neumann
Copyright: © 2012 |Volume: 4 |Issue: 3 |Pages: 19
ISSN: 1937-9390|EISSN: 1937-9420|EISBN13: 9781466612839|DOI: 10.4018/jiscrm.2012070103
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MLA

Wex, Felix, et al. "A Fuzzy Decision Support Model for Natural Disaster Response under Informational Uncertainty." IJISCRAM vol.4, no.3 2012: pp.23-41. http://doi.org/10.4018/jiscrm.2012070103

APA

Wex, F., Schryen, G., & Neumann, D. (2012). A Fuzzy Decision Support Model for Natural Disaster Response under Informational Uncertainty. International Journal of Information Systems for Crisis Response and Management (IJISCRAM), 4(3), 23-41. http://doi.org/10.4018/jiscrm.2012070103

Chicago

Wex, Felix, Guido Schryen, and Dirk Neumann. "A Fuzzy Decision Support Model for Natural Disaster Response under Informational Uncertainty," International Journal of Information Systems for Crisis Response and Management (IJISCRAM) 4, no.3: 23-41. http://doi.org/10.4018/jiscrm.2012070103

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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. 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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