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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References
-
Baek J, Alhindi TJ, Jeong YS, Jeong MK, Seo S, Kang J, Shim W, Heo Y (2021) Real-time fire detection system based on dynamic time warping of multichannel sensor networks. Fire Saf J 123:103364. https://doi.org/10.1016/j.firesaf.2021.103364
-
Bispo R, Vieira FG, Bachir N, Espadinha-Cruz P, Lopes JP, Penha A, Marques FJ, Grilo A (2023) Spatial modelling and mapping of urban fire occurrence in Portugal. Fire Saf J 138:103802. https://doi.org/10.1016/j.firesaf.2023.103802
-
Chen M, Wang K, Dong X, Li H (2020) Emergency rescue capability evaluation on urban fire stations in China. Process Saf Environ Prot 135:59–69. https://doi.org/10.1016/j.psep.2019.12.028
-
Chen J, Cui G, Shen S (2023) A polymorphic firefly algorithm with self-adaptation strategy for process system heat integration. Case Stud Therm Eng 1(47):103116. https://doi.org/10.1016/j.csite.2023.103116
-
De Wit RA, Helsloot I (2021) Public perception in regard to fire service in the Netherlands. Fire Saf J 122:103343. https://doi.org/10.1016/j.firesaf.2021.103343
-
Galko I, Kuffa R, Magdolenová P, Svetlik J, Veľas A (2021) RFID tags at the operation of fire stations. Transp Res Proc 55:941–948. https://doi.org/10.1016/j.trpro.2021.07.062
-
Habibi R (2020) Modern middle-class housing in Tehran: reproduction of an archetype: episodes of urbanism 1945–1979, vol 21. Brill
-
Intini P, Ronchi E, Gwynne S, Pel A (2019) Traffic modeling for wildland–urban interface fire evacuation. J Transp Eng, Part A: Syst 145(3):04019002. https://doi.org/10.1061/JTEPBS.0000221
-
Jatana N, Suri B (2020) Particle swarm and genetic algorithm applied to mutation testing for test data generation: a comparative evaluation. J King Saud Univ-Comput Inf Sci 32(4):514–521. https://doi.org/10.1016/j.jksuci.2019.05.004
-
Kheirdast A, Damirchi ES, Grjibovski AM, Khodabandehlou E, Pireinaladin S (2022) Performance appraisal of Tehran firefighting stations in attempted and threating suicide with national and global standards (the first 6 months of 2018). Iran J Public Health 51(4):946
-
Kheirdast A, Jozi SA, Rezaian S, Tehrani M (2023). Fire management in the fire department and safety services of Tehran with a high reliability approach (a case study of the stations covered by the 19th district of Tehran Municipality), [in Persian]. Doctoral thesis, Islamic Azad University, Tehran, Iran. p 204–217
-
Kim JH, Park YJ, Yi CY, Lee DE (2023) Stochastic flame locating method hybridizing Kalman filter and deep neural network for rapid fire response at construction sites. J Build Eng 1(66):105967
-
Li P, Yang Y, Zhao W, Zhang M (2021) Evaluation of image fire detection algorithms based on image complexity. Fire Saf J 121:103306. https://doi.org/10.1016/j.firesaf.2021.103306
-
Liaw HJ, Liu CC, Wan JF, Tzou TL (2023) Process safety management lessons learned from a fire and explosion accident caused by a liquefied petroleum gas leak in an aromatics reforming unit in Taiwan. J Loss Prev Process Ind 83:105058. https://doi.org/10.1016/j.jlp.2023.105058
-
Liu D, Xu Z, Yan L, Fan C (2020) Dynamic estimation system for fire station service areas based on travel time data. Fire Saf J 118:103238. https://doi.org/10.1016/j.firesaf.2020.103238
-
Mohammadi S, Hejazi SR (2023) Using particle swarm optimization and genetic algorithms for optimal control of non-linear fractional-order chaotic system of cancer cells. Math Comput Simul 206:538–560. https://doi.org/10.1016/j.matcom.2022.11.023
-
Nayak J, Swapnarekha H, Naik B, Dhiman G, Vimal S (2023) 25 years of particle swarm optimization: flourishing voyage of two decades. Arch Comput Methods Eng 30(3):1663–1725. https://doi.org/10.1007/s11831-022-09849-x
-
NFPA N. 1710 Standard for the Organization and Deployment of Fire Suppression Operations, Emergency Medical Operations, and Special Operations to the Public by Career Fire Departments. www.nfpa.org/disclaimers.
-
Nyimbili PH, Erden T (2020) GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul, Turkey. Socio-Econ Plann Sci 71:100860. https://doi.org/10.1016/j.seps.2020.100860
-
Penney G, Habibi D, Cattani M (2020) RUIM–A fire safety engineering model for rural urban interface firefighter taskforce deployment. Fire Saf J 113:102986. https://doi.org/10.1016/j.firesaf.2020.102986
-
Qu N, Li Z, Li X, Zhang S, Zheng T (2022) Multi-parameter fire detection method based on feature depth extraction and stacking ensemble learning model. Fire Saf J 128:103541. https://doi.org/10.1016/j.firesaf.2022.103541
-
Shabir S, Singla R (2016) A comparative study of genetic algorithm and the particle swarm optimization. Int. J. Electr. Eng. 9(2):215–23
-
Shahparvari S, Fadaki M, Chhetri P (2020) Spatial accessibility of fire stations for enhancing operational response in Melbourne. Fire Saf J 117:103149. https://doi.org/10.1016/j.firesaf.2020.103149
-
Wihartiko FD, Wijayanti H, Virgantari F (2018) Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem. IOP Conf Ser: Mater Sci Eng 332:012020. https://doi.org/10.1088/1757-899X/332/1/012020
-
Xu Z, Liu D, Yan L (2021) Evaluating spatial configuration of fire stations based on real-time traffic. Case Stud Therm Eng 25:100957. https://doi.org/10.1016/j.csite.2021.100957
-
Xu ZD, Liu X, Xu W, Sun B, Liu X, Xu D (2023) Flame propagation characteristics of gas explosions in utility tunnels considering spatial obstacles. J Pipeline Syst Eng Pract 14(1):04022066. https://doi.org/10.1061/JPSEA2.PSENG-1397
-
Zou P, Jiao J, Zhou F (2023) A twofold update quantum-inspired genetic algorithm for efficient combinatorial optimal decisions in engineering system design and operations. J Eng Des 34(4):271–293. https://doi.org/10.1080/09544828.2023.2188394
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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