Earthquake preparedness based on reliability and relief of the emergency supply network: Pre-storage and pre-matching
Abstract The efficiency of post-disaster relief operations is impacted by the design of the emergency supply networks in the earthquake preparedness phase. This paper proposes a bi-objective mixed integer programming model to improve the preparedness of the emergency supply network. The maximization of connectivity reliability and relief is taken as the objective function as the design perspectives were inclined towards prioritizing human survival over cost. The number and location of storage points, the preset level and the pre-matching of demand nodes with their supply storage points are determined based on this. Then, applicability is evaluated through numerical examples, and critical parameters are demonstrated by sensitivity analysis. Finally, practicality is evaluated through practical examples. The results demonstrate that the method in this paper designs an emergency supply network with high reliability and relief level. The reliability of the supply network is improved by 42.3% and the rescue level reaches 96.6% compared with the pre-optimization period. Results may provide theoretical support for urban pre-earthquake preparedness and can help traffic planners and local governments formulate better earthquake prevention strategies.
Authors
- Hongjun Cui (ORCID: https://orcid.org/0000-0003-4004-6460)
- Minqing Zhu (ORCID: https://orcid.org/0000-0001-6979-2024)
- Yue Su (ORCID: https://orcid.org/0000-0003-1456-749X)
- Lijun Liu (ORCID: https://orcid.org/0000-0003-4385-8472)
- Weimin Li (ORCID: https://orcid.org/0000-0001-6531-173X)
- Xiushi Dong
- Jianling Gu
Institutions
- Hebei University of Technology (CN)
- HBIS (China) (CN)
Publication Details
- Journal
- Transportation Safety and Environment
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1093/tse/tdae021
- Citations
- 3
- Primary Topic
- Facility Location and Emergency Management
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Natural Science Foundation of China
- Department of Science and Technology, Hubei Provincial People's Government