Intelligent Decision Support for Centralized Coordination during Emergency Response

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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