Practicable robust stochastic optimization under divergence measures with an application to equitable humanitarian response planning.

We seek to provide practicable approximations of the two-stage robust stochastic optimization model when its ambiguity set is constructed with an -divergence radius. These models are known to be numerically challenging to various degrees, depending on the choice of the -divergence function. The numerical challenges are even more pronounced under mixed-integer first-stage decisions.

Brazilian disaster datasets and real-world instances for optimization and machine learning.

We present comprehensive datasets of Brazilian disasters from January 2003 to February 2021 as well as real-world optimization instances built up from these data. The data were gathered through a series of open available reports obtained from different government and institutional sources. Afterwards, data consolidation and summarization were carried out using Excel and Python.

An optimization model for planning testing and control strategies to limit the spread of a pandemic - The case of COVID-19.

The global health crisis caused by the coronavirus SARS-CoV-2 has highlighted the importance of efficient disease detection and control strategies for minimizing the number of infections and deaths in the population and halting the spread of the pandemic. Countries have shown different preparedness levels for promptly implementing disease detection strategies, via mass testing and isolation of identified cases, which led to a largely varying impact of the outbreak on the populations and health-care systems. In this paper, we propose a new pandemic resource allocation model for allocating limited disease detection and control resources, in particular testing capacities, in order to limit the spread of a pandemic.