We compare stochastic programming and robust optimization decision models for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. In our models, the first stage captures the deployment of a fixed quantity of flood mitigation resources, and the second stage captures the operation of a potentially degraded power grid with the primary goal of minimizing load shed. To model grid operation, we introduce adaptations of the direct current (DC) and linear programming alternating current (LPAC) power flow approximation models that feature relatively complete recourse by way of an indicator variable. We apply our models to a pair of geographically realistic flooding case studies, one based on Hurricane Harvey and the other on Tropical Storm Imelda. We investigate the effect of the mitigation budget, the choice of power flow model, and the uncertainty perspective on the optimal mitigation strategy. Our results indicate the mitigation budget and uncertainty perspective are impactful, whereas choosing between the DC and LPAC power flow models is of little to no consequence. To validate our models, we assess the performance of the mitigation solutions they prescribe in an alternating current (AC) power flow model. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the Energy Institute, The University of Texas at Austin. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0125 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0125 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Oil and gas companies are drilling and developing fields in the Arctic Ocean, which is an environment with ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize locations and quantities for the docking stations and the AUVs, as well as the AUV scheduling policies, to maximize security of the platform. We model the system using a multistage stochastic facility location problem to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and two efficient online scheduling heuristics provide lower bounds and upper bounds for the multistage model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to two-dimensional radial detection.