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Chinese Journal of Management Science ›› 2025, Vol. 33 ›› Issue (2): 131-140.doi: 10.16381/j.cnki.issn1003-207x.2022.0198

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Research on Distributed Robust Optimization Scheme of Emergency Blood Supply Network Configuration Based on Data Driven

Ling Zhang1(), Jinpeng Li1, Shengqun Chen2   

  1. 1.School of Economics and Management,Fuzhou University,Fuzhou 350108,China
    2.School of Information Engineering,Fujian Business University,Fuzhou 350102,China
  • Received:2022-01-28 Revised:2022-05-04 Online:2025-02-25 Published:2025-03-06
  • Contact: Ling Zhang E-mail:zhangling@fzu.edu.cn

Abstract:

After the earthquake and other emergencies, emergency medical resources such as blood are needed for the treatment of the wounded. Reasonable blood supply and allocation is the premise and guarantee of medical treatment. In the configuration of blood supply network, it should not only improve the daily blood supply efficiency and avoid excessive blood collection, but also considers the impact of post disaster emergencies to reduce casualties caused by insufficient blood supply in this study. In order to minimize the pre-disaster operation cost and ensure the smooth progress of post disaster safety rescue, considering the two different blood demand characteristics before and after the disaster, a multi cycle distributed robust optimization model of emergency blood supply network and configuration based on data-driven is established. With the historical data of the needs of past disasters, the blood needs are expressed in emergencies in the form of fuzzy sets, and the data-driven Rsome robust optimization third-party library in Python are used to solve the model. Finally, through the actual case analysis, the applicability of the decision-making model is verified, which shows that the model can provide decision support for decision-makers in the process of considering the emergency blood supply network and configuration of emergencies.

Key words: emergency medical resources, blood supply network, distributionally robust optimization, uncertain demand

CLC Number: