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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yongqiang Zhang & Zhuang Hu & Min Zhang & Wenting Ba & Ying Wang, 2022. "Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization," IJERPH, MDPI, vol. 19(16), pages 1-11, August.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:10295-:d:891785
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    References listed on IDEAS

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    1. Robert Rijavec & Rok Marsetič & Irena Strnad, 2023. "Towards Optimal TMS Integration Scenario Based on HRM and SWOT Analysis," Sustainability, MDPI, vol. 15(13), pages 1-14, June.

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