论文标题
可证明液体医疗氧分配问题的高质量解决方案
Provably High-Quality Solutions for the Liquid Medical Oxygen Allocation Problem
论文作者
论文摘要
氧气是一种挽救生命的必不可少的医学，用于各种医疗保健的多种指示。在Covid-19大流行期间，由于许多患者的肺部感染发生，对液体医学氧（LMO）的需求显着增加。但是，许多国家和地区尚未为这种现象的出现做好准备，LMO的供应有限导致许多地区的使用需求不满意。在本文中，我们制定了一个线性编程模型，其目的是在供应和运输能力的限制下最大程度地减少需求的需求。决策变量是使用特定数量的车辆在每个时间间隔将多少LMO从一个地方转移到另一个地方。将多个存储点添加到网络中，以提供更灵活的分配策略。所提出的模型是在印度实施的，其中包括现实世界中的LMO供求数据作为案例研究。与手动设计的分配策略相比，提出的模型成功地减少了不满意的需求。
Oxygen is an essential life-saving medicine used in several indications at all levels of healthcare. During the COVID-19 pandemic, the demand for liquid medical oxygen (LMO) has increased significantly due to the occurrence of lung infections in many patients. However, many countries and regions are not prepared for the emergence of this phenomenon, and the limited supply of LMO has resulted in unsatisfied usage needs in many regions. In this paper, we formulated a linear programming model with the objective to minimize the unsatisfied demand given the constraints of supply and transportation capacity. The decision variables are how much LMO should be transferred from a place to another at each time interval using a specific number of vehicles. Multiple storage points are added into the network to allow for more flexible allocation strategies. The proposed model is implemented in India with real-world LMO supply and demand data as a case study. Compared to the manually designed allocation strategy, the proposed model successfully reduces the unsatisfied demand.
