  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.
