Comparing Metaheuristic Optimization Algorithms for Ambulance Allocation: An Experimental Simulation Study

Schjølberg, Magnus Eide; Bekkevold, Nicklas Paus; Sánchez-Díaz, Xavier; Mengshoel, Ole Jakob

doi:10.1145/3583131.3590345

Cited by 2 publications

(1 citation statement)

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“…In this work, the behavior of the NGSA-II algorithm for finding the temporary locations of emergency vehicles is studied, which is supported by the results of Schjølberg et al [17] and Saeidian et al [18]. They found in their experiments that for the problem of finding the location of emergency vehicles, the Genetic Algorithm achieves better results when comparing different metaheuristics, achieving a better response time, which is more pronounced in scenarios with a high demand for emergency vehicles.…”

Section: Related Worksupporting

confidence: 66%

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medina-Perez,

Guzmán,

Saldana-Perez

et al. 2024

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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

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Section: Related Worksupporting

confidence: 66%