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中国管理科学 ›› 2025, Vol. 33 ›› Issue (2): 131-140.doi: 10.16381/j.cnki.issn1003-207x.2022.0198cstr: 32146.14.j.cnki.issn1003-207x.2022.0198

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基于数据驱动的应急血液供应网络配置分布式鲁棒优化方案研究

张玲1(), 李锦棚1, 陈圣群2   

  1. 1.福州大学经济与管理学院,福建 福州 350108
    2.福建商学院信息工程学院,福建 福州 350102
  • 收稿日期:2022-01-28 修回日期:2022-05-04 出版日期:2025-02-25 发布日期:2025-03-06
  • 通讯作者: 张玲 E-mail:zhangling@fzu.edu.cn
  • 基金资助:
    福建省社会科学研究基地重大项目(FJ2023JDZ018)

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

摘要:

地震等突发事件发生后,需要血液等应急医疗资源对伤员进行救治,合理的血液配置是开展医疗救治工作的前提和保障。在血液供应网络配置中,不仅要提高日常供血效率,避免过量采血,还需要考虑灾后突发事件的影响,减少人员因供血不足而导致的伤亡。为了保障灾前运营成本最小化和灾后安全救援能够顺利进行,本文在考虑灾前和灾后具有两种不同血液需求特性的情况下,建立基于数据驱动的应急血液供应网络与配置的多周期分布式鲁棒优化模型。寻找过去灾害发生时需求的历史数据,将突发事件中的血液需求表示成模糊集形式,并利用python中基于数据驱动的Rsome鲁棒优化第三方库求解模型。最终通过实际案例分析,验证了该决策模式的适用性,表明所建立的模型能够为决策者在考虑突发事件的应急血液供应网络与配置过程中提供决策支持。

关键词: 应急医疗资源, 血液供应网络, 分布式鲁棒优化, 不确定需求

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

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