In this paper, we investigate how to value an option to build a power plant when electricity demand fluctuates over time. Towards this aim, we first construct a transmission network, and obtain locational marginal prices for the network buses utilizing optimal power flow. Next, we construct a lattice model under the assumption that the demand fluctuation over time is represented by a geometric Brownian motion. Based on this demand lattice, we derive the economic consequences of costs to a bus with and without a power plant in a risk neutral world. These in turn will lead to the computation of the value of an option to build a power plant. This value of the option will be useful for the electric power planning as the bus with a higher value of this option indicates that the community in this bus is demonstrating a higher degree of potential need for such a power plant.

Porcine reproductive and respiratory syndrome (PRRS) is a disease caused by the PRRS virus (PRRSV) that has spread globally in the last 30 years and causes huge economic losses every year. This research aims to 1) investigate the relationship between the PRRSV detection in two age categories (wean-to-market and adult/sow farm), and 2) examine the extent to which the wean-to-market PRRSV positive rate forecasts the adult/sow farm PRRSV positive rate. The data we used are the PRRSV RNA detection results between 2007 and 2019 integrated by the US Swine Disease Reporting System project that represent 95% of all porcine submissions tested in the US National Animal Health Network. We first use statistical tools to investigate to what extent the increase in PRRSV positive submissions in the wean-to-market is related to the PRRSV increase in adult/sow farms. The statistical analysis confirms that an increase in the PRRSV positive rate of wean-to-market precedes the increase in the adult/sow farms to a large extent. Then we create the dynamic exponentially weighted moving average control charts to identify out-of-control points (i.e., signals) in the PRRSV rates for both wean-to-market and adult/sow farms. This control-chart-based analysis finds that 78% of PRRSV signals in the wean-to-market are followed by a PRRSV rate signal in the adult/sow farms within eight weeks. We expect that our findings will help the producers and veterinarians to justify and reinforce the implementation of bio-security and bio-contaminant practices to curb disease spread across farms.

Determining how to allocate resources in order to prevent and prepare for disruptions is a challenging task for homeland security officials. Disruptions are uncertain events with uncertain consequences. Resources that could be used to prepare for unlikely disruptions may be better used for other priorities. This chapter presents an optimization model to help homeland security officials determine how to allocate resources to prevent and prepare for multiple disruptions and how to allocate resources to respond to and recover from a disruption. In the resource allocation model, prevention reduces the probability of a disruption, and preparation and response both reduce the consequences of a disruption. The model is applied to the US Gulf Coast region and considers a Deepwater Horizon‐type oil spill and a hurricane similar to Hurricane Katrina.