Heuristic algorithms based on the isochron analysis for dynamic relocation of medical emergency vehicles

Karpova, Yulia; Villa, Fulgencia; Vallada, Eva; Vecina, Miguel Ángel

doi:10.1016/j.eswa.2022.118773

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…Finally, one of the works that is most closely related to the present proposal is described in [25]. Karpova et al presented a technique for dynamically relocating ambulances that are available at a specific time, attempting to maintain complete coverage that adapts when an ambulance is occupied.…”

Section: Related Workmentioning

confidence: 99%

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 Workmentioning

confidence: 99%

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medina-Perez,

Guzmán,

Saldana-Perez

et al. 2024

Information

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“…They showed that the proposed model could reduce the operational costs of EMS centers by shortening the time needed to satisfy EMS demands in Beirut. Karpova et al 14 presented an innovative model for the dynamic re-location of ambulances. Isochron overlap analysis was used to determine location scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

Khalilzadeh,

Bahari

2023

Health�Serv�Insights

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To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.

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Section: A Heuristic Algorithmsmentioning

confidence: 99%

“…In AI, heuristics are employed when it is not feasible to to solve a specific problem using a sequential algorithm [12]. Since heuristics prioritise speed rather than accuracy, they are commonly integrated with optimization algorithms to enhance outcomes [12]. In deep neural networks, successive iterations are interrelated, and at each level, decisions are made regarding which paths to pursue or discard based on their closeness to the desired solution.…”

Section: A Heuristic Algorithmsmentioning

confidence: 99%

Artificial Intelligence: Using BLAST Algorithm for DNA Classification of Thicket Vegetation

Gundu

2023

iCARTi

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Artificial Intelligence (AI) has revolutionized molecular ecology by enhancing DNA classification methods. This study explores the application of the Basic Local Alignment Search Tool (BLAST) algorithm, an AI-powered bioinformatics tool, for DNA classification within the unique ecosystem of Eastern Cape Thickets. Our focus was on leveraging BLAST's capabilities to identify and characterize plant species based on genetic sequences. We collected plant samples from diverse locations within the Eastern Cape Thickets and employed advanced DNA extraction and sequencing techniques to acquire a comprehensive dataset. BLAST analysis played a central role in comparing our sequences with the GenBank database to determine species identities and potential functions. The BLAST algorithm demonstrated remarkable efficiency in identifying known species, confirming their presence in the thicket ecosystem. Notably, BLAST's utility extended beyond species confirmation, as it unveiled sequences with limited matches, suggesting the existence of novel genetic elements specific to the region. By harnessing AI, BLAST facilitated accurate species classification and functional annotation. Our study showcases the potency of AI-driven DNA classification using the BLAST algorithm in elucidating the genetic makeup of the Eastern Cape Thickets. The algorithm's proficiency in decoding intricate genetic information, elucidating functional attributes, and predicting potential adaptations underscores its pivotal role in modern molecular ecology research. These findings contribute to the broader understanding of genetic diversity within this ecosystem and highlight the transformative impact of AI-driven bioinformatics tools on ecological studies and conservation strategies.

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Heuristic algorithms based on the isochron analysis for dynamic relocation of medical emergency vehicles

Cited by 9 publications

References 37 publications

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

Artificial Intelligence: Using BLAST Algorithm for DNA Classification of Thicket Vegetation

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