Abstract
Fire safety is very important. Efficient facility planning is required to protect against destruction and reduce the risk of damage to facilities, and loss of lives, In this study, we propose an urban-rural maximal covering location model with the configuration of different types of areas for optimizing fire response units. Since the basic covering location model tends to cover more in a high population density area, with a limitation of resources a low population density area may leave as an unserved area. The objective is to maximize the demand that can be covered in standard response time. The GIS is utilized for managing the data and classifying the service area. A real-world case study is presented. The results show that solving problems with the proposed model can achieve full coverage with less number of facilities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Church, R., ReVelle, C.: The maximal covering location problem. Pap. Reg. Sci. Assoc. 32, 101–118 (1974)
Pirkkul, H., Schilling, D.: The maximal covering location problem with capacities on total workload. Manage. Sci. 37(2), 233–248 (1991)
Black, B., Meamon, B.: Facility location in humanitarian relief. Int. J. Log. Res. Appl. 11(2), 101–121 (2008)
Chanta, S., Sangsawang, O.: Shelter-site selection during flood disaster. Lect. Notes Manag. Sci. 4, 282–288 (2012)
Alzahrani, A., Hanbali, A.: Maximum coverage location model for fire stations with top corporate risk locations. Int. J. Ind. Eng. Oper. Manag. 3(2), 58–74 (2021)
Srianan, T., Sangsawang, O.: Path-relinking for fire station location. In: Vasant, P., Zelinka, I., Weber, G.W. (eds.) Intelligent Computing & Optimization. ICO 2018. Advances in Intelligent Systems and Computing, vol. 866. Springer, Cham (2019)
U.S. Department of Agriculture, Economic Research Service, Rural Classification. Accessible at https://www.ers.usda.gov
Wang, J., Zhou, J.: Spatial evaluation of the accessibility of public service facilities in Shanghai: a community differentiation perspective. PLoS ONE 17(5), e0268862 (2022)
Chanta, S., Mayorga, M., McLay, L.: Improving emergency service in rural areas: a bi-objective covering location model for EMS systems. Ann. Oper. Res. 221, 133–159 (2014)
Karim, A., Awawdeh, M.: Integrating GIS accessibility and location-allocation models with multicriteria decision analysis for evaluating quality of life in Buraidah City, KSA. Sustainability 12, 1412 (2020)
Luo, W., Yao, J., Mitchell, R., Zhang, X., Li, W.: Locating emergency services to reduce urban-rural inequalities. Socio-Econ. Plann. Sci. 84, 101416 (2022)
Liu, H., Li, Y., Hou, B., Zhao, S.: Optimizing the location of park-and-ride facilities in suburban and urban areas considering the characteristics of coverage requirements. Sustainability 14, 1502 (2022)
Farahani, R., Fallah, S., Ruiz, R., Hosseini, S., Asgari, N.: OR models in urban service facility location: a critical review of applications and future developments. Eur. J. Oper. Res. 276, 1–27 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chanta, S., Sangsawang, O. (2023). Optimizing Fire Response Unit Location for Urban-Rural Area. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-031-50158-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-50158-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50157-9
Online ISBN: 978-3-031-50158-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)