Optimization of the distribution of hospital supplies during the Covid-19 pandemic under uncertainties

Abstract

The novel coronavirus pandemic, a biological disaster, has increased the demand for medical supplies. In response, humanitarian logistics has become an important component in disaster management efforts, essential to relieving the suffering of those affected. The unpredictable nature of such crises makes planning these operations a challenge. In this context, mathematical models are crucial tools that support decision-making processes, ensuring effective logistics responses in disaster scenarios. This paper introduces a robust mathematical model designed to optimize the distribution of hospital supplies in scenarios with varying demand. The model serves as a strategic decision support tool by integrating facility location and vehicle allocation, incorporating parameters such as facility opening costs, transportation, travel times, urgency levels, fleet heterogeneity, and the optimal number of trips. Real-world data from five municipalities in Rio de Janeiro, Brazil, were used to validate the model during the Covid-19 pandemic. Computational experiments demonstrated that the robust model effectively balances costs and logistics performance, with total costs increasing by up to 42.7% in medium demand scenarios and decreasing by up to 15.4% in high demand scenarios, depending on the probability of occurrence and risk aversion. The model presents a conservative solution that accommodates different demand scenarios and provides better performance compared to deterministic solutions obtained from the average demand.