comparing-the-performance-of-genetic-algorithm-and-particle-swarm-optimization-algorithm-in-allocating-and-scheduling-fire-stations-–-springer

Comparing the performance of genetic algorithm and particle swarm optimization algorithm in allocating and scheduling fire stations – Springer

Abstract

Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSOA) have positive effects on the allocation and scheduling of the stations, this research seeks to find which one of these two methods is more appropriate to shorten the time to reach fire/incident site in the Region 19 of Tehran. This is an applied type of research. Data analysis was carried out using NFPA standards and MATLAB software. The statistical population includes 8 fire stations and 250 personnel of the stations, and sampling volume was obtained using Morgan’s table (n = 148). In order to efficiently assign and schedule fire stations to arrive at the site, a linear numerical programming model was presented with the aim of minimizing the arrival time and taking into account the effect of firemen’s fatigue (α = 0.1). Findings of the research showed that the operation processing time (of fire extinguishing) had a normal distribution with a mean of 40 min and a variance of 10 min, independent of the severity of the incident. Also, fatigue coefficient was calculated 0.1 by analyzing the sensitivity of the solution time of the algorithm with changes [0–1]. Initial standard travel time, with an average speed of 47 km/h and a density factor of 1.24, was 5min:20s. Solving the problem in large and small dimensions showed that the initial power effect of each fire station is 0.36 according to the fatigue level of the forces. Based on the obtained results, GA performs better in terms of problem solution time, and the improved PSOA also has higher quality answers.

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Acknowledgements

Here I would like to thank all the people who cooperated in writing the article and patiently answered the questions and questionnaires.

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Authors and Affiliations

  1. Faculty of Marine Sciences and Technology, Department of Environmental Management, North Tehran Branch, Islamic Azad University, Tehran, Iran

    A. Kheirdast

  2. Department of Environment, North Tehran Branch, Islamic Azad University, Tehran, Iran

    S. A. Jozi

  3. Department of Environment, Shahrood Branch, Islamic Azad University, Shahrood, Iran

    S. Rezaian

  4. Department of Environment, North Tehran Banch, Islamic Azad University, Tehran, Iran

    M. M. E. Tehrani

Contributions

Seyed Ali Jozi: methodology and conclusions. Afrasyab Kheirdast: data analysis, genealogical method and research background. Sahar Rezaian: preparing the introduction and analyzing the results and findings. Mahnaz Mirza Ebrahim Tehrani: working method, discussion and conclusion.

Corresponding author

Correspondence to S. A. Jozi.

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Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Editorial responsibility: Samareh Mirkia.

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Cite this article

Kheirdast, A., Jozi, S.A., Rezaian, S. et al. Comparing the performance of genetic algorithm and particle swarm optimization algorithm in allocating and scheduling fire stations. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05839-7

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  • DOI: https://doi.org/10.1007/s13762-024-05839-7

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