Research on multi-objective hierarchical site selection coverage of fire station

PLoS One. 2024 Dec 23;19(12):e0309731. doi: 10.1371/journal.pone.0309731. eCollection 2024.

Abstract

The fire station location has essential theoretical and practical values, not only in terms of maintaining the safety of life and property, but also enriching the optimization theory of site selection problems. To study the multi-objective siting problem of fire stations, we firstly divided demand areas and fire stations into three levels to form a comprehensive hierarchical emergency coverage network covering fire risk areas. Secondly, the nodes of the original location of the fire station were added to the set of nodes of the planned construction of the fire station. By introducing the coverage attenuation function, the multi-objective level fire station location model covering the maximum fire risk value and the minimum construction cost of the fire station was established. Then, the epsilon constraint method was used to address the multi-objective model, followed by designing the genetic algorithm based on the problem characteristics for solutions. To validate the effectiveness of the proposed model and algorithm, numerical experiments were performed, which took an urban area in China as an example. The solutions indicated that although retaining more of the original fire stations reduced the siting costs, most strategies tended to fail to cover higher values of fire risk, and the service coverage increases with the number of new fire stations. Additionally, sensitivity analysis was conducted to explore the effect of different parameters on the maximum fire risk value that can be covered by a fire station. A compromise coordinated siting scheme with a hierarchy of fire stations can be obtained by solving the proposed model. It can provide decision support for related departments to develop optimal siting configurations.

MeSH terms

  • Algorithms*
  • China
  • Fires*
  • Models, Theoretical*

Grants and funding

This research is funded by National Key Research and Development Program of China (No. 2020YFC1512505). The funders provided financial support and data resources on the research elements of this paper.