Stochastic capacitated dispersion problems in disaster preparedness for mass casualty incident

Abstract

Recent disasters that cause mass casualty incidents --such as the 2020 Beirut explosion or the 2023 Turkey-Syria earthquake-- have shown that critical facilities, which are meant to help the casualties, could be damaged by them. Therefore, it is critical to increase the resiliency of critical facilities by dispersing their locations. This paper proposes a stochastic capacitated dispersion model that considers a scenario in which a disaster can cause mass casualty incidents and damage critical facilities. The extent of damage is modeled as a function of the distance between the site locations of the facility and the epicenter of the disaster, as well as the level of severity of the disaster. The model incorporates a chance constraint to account for supply uncertainty. To solve this stochastic optimisation problem, we propose a new simheuristic algorithm that combines simulation and heuristic optimisation. Experiments show that our algorithm produces solutions that match the quality of the best deterministic solutions reported in the literature. In the stochastic disaster scenario, our algorithm produces solutions that can meet demand 90\% of the time, while deterministic solutions fail to meet this demand.

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ORCID for B. Stephan Onggo: orcid.org/0000-0001-5899-304X

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