Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm
As a high-frequency disaster with potentially devastating consequences, urban fires not only threaten the lives of city residents but can also lead to severe property losses, especially for hazardous chemical leaking scenarios. Quick and scientific decision-making regarding resource allocation durin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/8/1/27 |
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| author | Xiaolei Zhang Kaigong Zhao Shang Gao Changming Li |
| author_facet | Xiaolei Zhang Kaigong Zhao Shang Gao Changming Li |
| author_sort | Xiaolei Zhang |
| collection | DOAJ |
| description | As a high-frequency disaster with potentially devastating consequences, urban fires not only threaten the lives of city residents but can also lead to severe property losses, especially for hazardous chemical leaking scenarios. Quick and scientific decision-making regarding resource allocation during urban fire emergency responses is crucial for reducing disaster damages. Based on several key factors such as the number of trapped individuals and hazardous chemical leaks during the early stages of an incident, an emergency weight system for resource allocation is proposed to effectively address complex situations. In addition, a multi-objective optimization model is built to achieve the shortest response time for emergency rescue teams and the lowest cost for material transportation. Additionally, a pre-allocated bee swarm algorithm is introduced to mitigate the issue of local incident points being unable to participate in rescue due to low weights, and a comparison of traditional genetic algorithms and particle swarm optimization algorithms is conducted. Experiments conducted in a virtual urban fire scenario validate the effectiveness of the proposed model. The results demonstrate that the proposed model can effectively achieve the dual goals of minimizing transportation time and costs. Furthermore, the bee swarm algorithm exhibits advantages in convergence speed, allowing for the faster identification of ideal solutions, thereby providing a scientific basis for the rapid allocation of resources in urban fire emergency rescues. |
| format | Article |
| id | doaj-art-fd2d353763904b5db7695df7be6a0f0b |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-fd2d353763904b5db7695df7be6a0f0b2025-01-24T13:32:20ZengMDPI AGFire2571-62552025-01-01812710.3390/fire8010027Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm AlgorithmXiaolei Zhang0Kaigong Zhao1Shang Gao2Changming Li3School of Emergency Management and Safety Engineering, China University of Mining and Technology, Beijing 100083, ChinaSchool of Civil and Resources Engineering, University of Science and Technology of Beijing, Beijing 100083, ChinaSchool of Emergency Management and Safety Engineering, China University of Mining and Technology, Beijing 100083, ChinaSchool of Emergency Management and Safety Engineering, China University of Mining and Technology, Beijing 100083, ChinaAs a high-frequency disaster with potentially devastating consequences, urban fires not only threaten the lives of city residents but can also lead to severe property losses, especially for hazardous chemical leaking scenarios. Quick and scientific decision-making regarding resource allocation during urban fire emergency responses is crucial for reducing disaster damages. Based on several key factors such as the number of trapped individuals and hazardous chemical leaks during the early stages of an incident, an emergency weight system for resource allocation is proposed to effectively address complex situations. In addition, a multi-objective optimization model is built to achieve the shortest response time for emergency rescue teams and the lowest cost for material transportation. Additionally, a pre-allocated bee swarm algorithm is introduced to mitigate the issue of local incident points being unable to participate in rescue due to low weights, and a comparison of traditional genetic algorithms and particle swarm optimization algorithms is conducted. Experiments conducted in a virtual urban fire scenario validate the effectiveness of the proposed model. The results demonstrate that the proposed model can effectively achieve the dual goals of minimizing transportation time and costs. Furthermore, the bee swarm algorithm exhibits advantages in convergence speed, allowing for the faster identification of ideal solutions, thereby providing a scientific basis for the rapid allocation of resources in urban fire emergency rescues.https://www.mdpi.com/2571-6255/8/1/27urban fire emergency responsehazardous chemical leakageemergency weightmulti-objective optimization modelbee swarm algorithm |
| spellingShingle | Xiaolei Zhang Kaigong Zhao Shang Gao Changming Li Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm Fire urban fire emergency response hazardous chemical leakage emergency weight multi-objective optimization model bee swarm algorithm |
| title | Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm |
| title_full | Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm |
| title_fullStr | Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm |
| title_full_unstemmed | Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm |
| title_short | Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm |
| title_sort | optimization of urban fire emergency resource allocation based on pre allocated swarm algorithm |
| topic | urban fire emergency response hazardous chemical leakage emergency weight multi-objective optimization model bee swarm algorithm |
| url | https://www.mdpi.com/2571-6255/8/1/27 |
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