This paper presents a two-phase mathematical framework for efficient power network damage assessment using unmanned aerial vehicle (UAV). In the first phase, a two-stage stochastic integer programming optimization model is presented for damage assessment in which the first stage determines the optimal UAV locations anticipating an arrival of an extreme weather event, and the second stage is to adjust the UAV locations, if necessary, when the arrival time of the predicted extreme weather becomes closer with updated information. UAV paths to scan the power network are generated in the second phase to minimize operating costs and final damage assessment completion time of the UAVs. Computational techniques are developed to help reduce the solution time. Numerical experiments show that the proposed stochastic model outperforms the deterministic counterpart in terms of the total UAV pre-positioning setup cost. Additionally, sensitivity analysis discovered the relations among damage assessment time, UAV pre-positioning setup cost, and operating cost.

We describe a mathematical model for UAV aided security operations in the oil and gas industry. Operating UAVs can provide seamless awareness on possible emergency situations such as oil spills, shipping incidents, industrial accidents, acts of terrorism, and so on. The primary goal of this model is to generate an optimal UAV operational schedule to meet surveillance needs in the areas of interest in each time period. The performance of these UAVs depends on the risk assessment on spatio-and-temporal characteristics of threats, specifications of available UAVs, and decision makers’ critical information requirements. The models are designed to provide insights into issues associated with designing and operating UAVs for strengthened maritime and port security