Optimizing Emergency Response Unit Location using Genetic Algorithm for Better Response Efficiency

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@InProceedings{Padmalatha:2025:ICMCSI,

• author = "E Padmalatha and Brahmakanti Suma Geethika and Shravani Saiba",
• title = "Optimizing Emergency Response Unit Location using Genetic Algorithm for Better Response Efficiency",
• booktitle = "2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)",
• year = "2025",
• pages = "1577--1582",
• month = jan,
• keywords = "genetic algorithms, genetic programming, Costs, Scalability, Urban areas, Emergency services, Time factors, Resource management, Informatics, Mobile computing, Emergency Medical Services, Emergency Medical Dispatch, Dynamic Optimisation",
• DOI = "doi:10.1109/ICMCSI64620.2025.10883515",
• abstract = "Optimising the location of an Emergency Response Unit (ERU) is crucial for reducing response times and enhancing emergency services. It uses a Genetic Algorithm (GA) to find the optimal ERU location within a lOxlO km city grid, where each cell has a known emergency frequency. The GA minimises total response time using a cost function (response time). The GA applies truncation selection, one-point crossover, and swap mutation operators to iteratively find the best location. Additionally, it addresses maximizing coverage, minimizing Emergency Medical Services(EMS) construction costs, and optimising service region ratios. A modular Genetic Programming Hyper Heuristic framework is also used to refine Emergency Medical Dispatch (EMD) decision-making processes.",
• notes = "Also known as \cite{10883515}",

}

Genetic Programming entries for E Padmalatha Brahmakanti Suma Geethika Shravani Saiba

Citations