This paper investigates the efficiency of traditional portfolio optimization models when the returns of financial assets are highly volatile, e.g., in financial crises periods. We also develop alternative optimization models that combine the mean absolute deviation (MAD) and the conditional value at risk (CVaR), attempting to mitigate inefficient, low return and/or high-risk, portfolios. Three methodologies for estimating the probability of the asset's historical returns are also compared. By using historical data on the Brazilian stock market between 2004 and 2013, we analyze the efficiency of the proposed approaches. Our results show that the traditional models provide portfolios with higher returns, but our propose model are able to generate lower risk portfolios, which might be more attractive in volatile markets. In addition, we find that models that do not use equiprobable scenarios produce better results in terms of return and risk.

We introduce a two-stage stochastic program to handle typical disaster preparedness activities under uncertainty from a multi-agency perspective. The model explicitly takes into account the number of people without healthcare attention, relief aid, and shelter support. We build a function that represents the total number of people at risk of not receiving proper humanitarian assistance using a bi-objective approach in which expected logistics costs are also minimized.The benefit of our approach is assessed through real flood cases in Mexico in which GIS analysis was used to enhance data gathering and to provide risk maps that could be potentially used by policy-makers in practical settings. The overall results suggest that sheltering decisions have to be closely coordinated with the management of material and human resources to avoid an increased number of people deprived of attention and relief aid.The Pareto Frontier also reveals that some solutions exhibit a quite interesting trade-off, e.g., it