Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

Yongqiang Zhang, Zhuang Hu (), Min Zhang, Wenting Ba and Ying Wang
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Yongqiang Zhang: College of Automobile and Transport Engineering, Nanjing Forestry University, Nanjing 210037, China
Zhuang Hu: Gosuncn Technology Group Co., Ltd., Guangzhou 510663, China
Min Zhang: School of Transportation, Southeast University, Nanjing 211189, China
Wenting Ba: College of Automobile and Transport Engineering, Nanjing Forestry University, Nanjing 210037, China
Ying Wang: College of Automobile and Transport Engineering, Nanjing Forestry University, Nanjing 210037, China

IJERPH, 2022, vol. 19, issue 16, 1-11

Abstract: Western China is a sparsely populated area with dispersed transportation infrastructure, making it challenging to meet people’s accessibility and mobility requirements. Rescue efficiency in sparse networks is severely hampered by the difficulty rescue teams have in getting to the scene of abrupt traffic accidents. This paper develops a layout optimization model for multiple rescue points to address this issue, suggests an improved particle swarm algorithm by including a variation that can reduce optimization time and avoid the disadvantage of precocity, and designs a MATLAB program using an adaptive variation algorithm. The proposed approach increases the effectiveness of rescue in a sparse network and optimizes the distribution of emergency resources.

Keywords: sparse network; resource allocation; emergency rescue; particle swarm optimization; MATLAB (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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