INTRODUCING A NEW MODEL FOR LOCATING THE LOCATION OF FIREFIGHTING FORCES BASED ON FUZZY REGION AND NONDOMINATED SORTING GENETIC ALGORITHM

Details

Hits: 1147

Volume 5 (2), December 2022, Pages 193-211

Saeed Hassani¹, Mohammad Tahghighi Sharabyan¹ and Zahra Tayyebi Qasabeh²

¹Islamic Azad University, Zanjan, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

²Payame Noor University of Guilan, Guilan, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

The establishment of fire stations is considered an essential part of the security of any city. At the time of an accident, the location of fire stations is essential for timely and quick relief. The delay in providing aid causes irreparable damage to the life and property of the city's people, and the correct location of fire stations can prevent such incidents from happening, which is necessary to achieve this goal. It is systematic and integrated based on a suitable model. Therefore, in this research, a suitable model for locating the position of firefighting forces based on fuzzy logic and mutated genetic algorithm is proposed, which has two objective functions: one for optimizing the urban coverage and the other for optimizing Building the number of fires stations. The goal is to deploy stations in such a way as to create maximum urban coverage, and on the other hand, considering the cost of deploying each station, the method seeks to reduce the number of stations. The criteria needed for the stations' location have been examined, including the distance from the existing fire station. S the distance from the areas at risk of earthquakes, the high population density, the density of wooden buildings, the proximity to the roads—the main and density of hazardous materials facilities., the data set of fire stations in Istanbul city was used, to check the results and simulation in this research. This data set contains two parts, one of which contains information about the location of the stations, which has 124 data, and the other contains related information to the areas where the fire occurred and has 107 data. In this research, five scenarios were set, the first scenario of two parameters, the second scenario of three parameters, the third, fourth, and fifth scenarios of four parameters and their influence on the choice of the parent were investigated, and the results showed that the best solution is It is obtained that both goals have the same weight in the scenarios. It happens when the number of stations reaches the desired level. In fact, by increasing the number of stations to the appropriate size, the urban coverage amount reached the desired results.

Keywords:

Location Location, Deployment of Fire Brigades, Fuzzy Logic, Mutated Genetic Algorithm.

DOI: https://doi.org/10.32010/26166127.2022.5.2.193.211

Reference 

Dey, A., Heger, A., & England, D. (2021). Urban Fire Station Location Planning: A Systematic Approach using Predicted Demand and Service Quality Index. arXiv preprint arXiv:2109.02160

Han, B., Hu, M., Zheng, J., & Tang, T. (2021). Site selection of fire stations in large cities based on actual spatiotemporal demands: A case study of nanjing city. ISPRS International Journal of Geo-Information, 10(8), 542.

Hou, G., Li, Q., Song, Z., & Zhang, H. (2021). Optimal fire station locations for historic wood building areas considering individual fire spread patterns and different fire risks. Case Studies in Thermal Engineering, 28, 101548.

Khanahmadi, M., Arabi, M., Vafaienejad, A., & Rezaiean, H. (2014). Locate fire stations Using Fuzzy Logic and AHP integration in GIS environment (Case Study: District 1 District 10 of Tehran). Scientific-Research Quarterly of Geographical Data (SEPEHR), 23(89), 88-98.

Nyimbili, P. H., & Erden, T. (2020). GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul, Turkey. Socio-Economic Planning Sciences, 71, 100860.

Oh, J. Y., Hessami, A., & Yang, H. J. (2019). Minimizing response time with optimal fire station allocation. Stud Eng Technol, 6(1), 47-58.

Seshadri, A. (2006). A fast elitist multiobjective genetic algorithm: NSGA-II. MATLAB Central, 182.

Suzuki, T., & Satoh, E. (2020). An analysis on the optimum location of fire department based on ambulance dispatch situation—A case study in Utsunomiya City. Japan Architectural Review, 3(2), 241-255.

Wang, W. (2019). Site Selection of Fire Stations in Cities Based on Geographic Information System and Fuzzy Analytic Hierarchy Process. Ingénierie des Systèmes d'Information, 24(6).

Wang, W., Xu, Z., Sun, D., & Lan, T. (2021). Spatial optimization of mega-city fire stations based on multi-source geospatial data: A case study in Beijing. ISPRS International Journal of Geo-Information, 10(5), 282.

Yao, J., Zhang, X., & Murray, A. T. (2019). Location optimization of urban fire stations: Access and service coverage. Computers, Environment and Urban Systems, 73, 184-190.