In this paper, we propose and study a framework for disaster housing logistics planning under demand uncertainty. Specifically, we utilize the two-stage chance-constrained stochastic programming models to achieve the balance between logistics operational cost and demand fulfillment especially towards extreme disaster scenarios. To do so, we incorporate two operational modalities, one for the ordinary modality and the other for the emergency modality, and the emergency modality is only allowed to be activated for a certain percentage of scenarios that is specified by the decision maker among all scenarios. The set of scenarios is generated according to a regression model for characterizing the disaster housing demand based on a selected number of independent variables, which is trained offline from historical data. Our preliminary numerical results based on a case study on Hurricane Ian have shown the effectiveness of the proposed approach compared to some standard benchmark approaches and provided managerial insights in disaster housing logistics planning.

Power grids are vulnerable to wildfire propagation which can trigger extended power loss by failing transmission lines. During dry seasons, environmental conditions increase the risk of wildfires, exposing power grids to failure conditions. Simultaneously, power system operation can induce the ignition and spread of ongoing wildfires. In this work, we propose a multi-stage optimization model to switch transmission lines dynamically to adapt the transmission network topology to respond to the wildfire while considering the trade-off between operational performance and wildfire propagation. The proposed formulation is a multi-stage model with decision-dependent probabilities. We developed a stochastic nested decomposition algorithm to solve this model and present a case study using a 33-bus transmission system.

For more than a century, election officials across the United States have inspected voting machines before elections using a procedure called Logic and Accuracy Testing (LAT). This procedure consists of election officials casting a test deck of ballots into each voting machine and confirming the machine produces the expected vote total for each candidate. In this work, we bring a scientific perspective to LAT by introducing the first formal approach to designing test decks with rigorous security guarantees. Specifically, we propose using robust optimization to find test decks that are guaranteed to detect any voting machine misconfiguration that would cause votes to be swapped across candidates. Out of all the test decks with this security guarantee, the robust optimization problem yields the test deck with the minimum number of ballots, thereby minimizing implementation costs for election officials. To facilitate deployment at scale, we developed a practical exact algorithm for solving our robust optimization problems based on mixed-integer optimization and the cutting plane method. In partnership with the Michigan Bureau of Elections, we retrospectively applied our robust optimization approach to all 6928 ballot styles from Michigan's November 2022 general election; this retrospective study reveals that the test decks with rigorous security guarantees obtained by our approach require, on average, only 1.2\% more ballots than current practice. Our robust optimization approach has since been piloted in real-world elections by the Michigan Bureau of Elections as a low-cost way to improve election security and increase public trust in democratic institutions.