Optimal Spatiotemporal Resource Allocation in Public Health and Renewable Energy

We study five specific resource allocation problems — three motivated by applications in public health and two by applications in renewable energy. First, we describe a general method for optimizing pharmacy-based distribution of antivirals during an influenza pandemic, to maximize overall access for a user-specified target population. Second, we consider allocation of vaccine doses to population priority groups in an influenza pandemic. Since the spatial distribution of vaccination locations, many of which are pharmacies, and personnel might not be equal across different geographic regions, a simple pro-rata policy could provide unequal geographic coverage to population target groups. Third, we seek to answer the following question: during an influenza pandemic, in what volume, and to which locations, should antivirals be released to achieve the most benefit? Next, we study the problem of scheduling a hybrid wind generator-conventional generator system to make it dispatchable, with the aim of profit maximization. Our model ensures that with high probability we satisfy the day-ahead energy-supply promise that we make, using the combined output of the conventional and wind generators. Fifth, we study a pumped hydroelectric storage system with the goal of maximizing expected profit in an intraday market that requires bidding. By releasing water from the upper to the lower reservoir, through a hydroelectric turbine, water is used to produce electrical power, which is sold to generate revenue.