Provably High-Quality Solutions for the Liquid Medical Oxygen Allocation Problem

TL;DR

This paper presents a linear programming model for optimizing liquid medical oxygen allocation to minimize unmet demand, demonstrated with real-world data in India, outperforming manual strategies.

Contribution

It introduces a novel LP-based approach with multiple storage points for flexible LMO allocation, validated on real-world data in India.

Findings

Reduces unsatisfied demand compared to manual strategies

Successfully applied to real-world Indian data

Provides a flexible allocation framework with storage points

Abstract

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.