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在医疗和后勤不确定性下,针对脏分配问题的新两阶段网络数据包裹分析模型:一个真实案例研究
Farhad Hamidzadeh1, Mir Saman Pishvaee2, Naeme Zarrinpoor3
1School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran. farhad.hamidzadeh@gmail.com.
Health care management science
|October 1, 2024
概括
这项研究引入了一种用于分配的新方法,提高了效率,并对器官-患者对进行排名. 这种新的方法增强了器官移植,特别是在COVID-19流行病等不确定的时期.
科学领域:
- 医疗保健系统 医疗保健系统
- 运营研究 运营研究
- 移植医学 移植医学
背景情况:
- 器官移植因供需不平衡而面临挑战,导致患者死亡率.
- 移植是最常见的手术,分配是一个关键的决策点.
- 医疗和后勤数据的不确定性,加剧了COVID-19大流行,复杂的器官分配.
研究的目的:
- 开发一种新的方法来评估和排名的器官-病人对分配.
- 为了解决在不确定性和COVID-19流行病下脏分配的复杂性.
- 提高器官移植供应链的效率.
主要方法:
- 使用了双阶段网络数据包裹分析 (DEA).
- 基于信誉的综合机会约束编程 (CCP) 用于处理不确定性.
- 开发了一种新的两阶段模糊网络数据包裹分析 (TSFNDEA) 方法.
主要成果:
- 该TSFNDEA方法有效地评估效率,并对器官-患者对进行排名.
- 该方法考虑了分配系统中的医疗和后勤因素.
- 使用真实案例研究的验证算法证实了该方法的适用性和有效性.
结论:
- 开发的TSFNDEA方法的性能优于现有的脏分配系统.
- 这种方法在不确定性下提供了独特的效率分解能力.
- 该方法为脏分配提供了一个强大的框架,可以适应其他网络结构.
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