Volume 13 Issue 5
Oct.  2022
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Yanyan Wang, Baiqing Sun. Multiperiod Equitable and Efficient Allocation Strategy of Emergency Resources Under Uncertainty[J]. International Journal of Disaster Risk Science, 2022, 13(5): 778-792. doi: 10.1007/s13753-022-00437-y
Citation: Yanyan Wang, Baiqing Sun. Multiperiod Equitable and Efficient Allocation Strategy of Emergency Resources Under Uncertainty[J]. International Journal of Disaster Risk Science, 2022, 13(5): 778-792. doi: 10.1007/s13753-022-00437-y

Multiperiod Equitable and Efficient Allocation Strategy of Emergency Resources Under Uncertainty

doi: 10.1007/s13753-022-00437-y

This work was supported by the National Social Science Foundation of China (Grant No. 21CGL044).

  • Available Online: 2022-11-01
  • Equitable and efficient allocation of emergency resources is critical to ensure the success of relief efforts. The challenge comes largely from two aspects:the resources available for allocation are usually limited in quantity, especially in the early period of emergency response; and a large amount of uncertain information in the relief process affects the decision making of resource allocation. A multiperiod allocation model of emergency resources that takes into account both efficiency and equity based on uncertain disaster information is proposed. Interval number and triangular fuzzy number are introduced to describe the different sources of uncertainty (for example, demand, transportation time, and maximum transport amount), and the loss caused by unmet demand is used to quantify equity. Then, the deterministic transformation method of uncertain parameters is designed and the linear weighted sum method is applied to solve the proposed model. Finally, a computational case based on the 2017 Jiuzhaigou earthquake in Sichuan Province, China was conducted to validate the proposed model. The results show that the proposed model is feasible in the multiperiod allocation of emergency resources among multi-disaster sites, and the findings can help emergency managers to allocate emergency resources more scientifically, equitably, and effectively under uncertainty.
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