Suchergebnisse

AbstractAs climate change threatens to cause increasingly frequent and severe natural disasters, decisionmakers must consider costly investments to enhance the resilience of critical infrastructures. Evaluating these potential resilience improvements using traditional cost‐benefit analysis (CBA) approaches is often problematic because disasters are stochastic and can destroy even hardened infrastructure, meaning that the lifetimes of investments are themselves uncertain. In this article, we develop a novel Markov decision process (MDP) model for CBA of infrastructure resilience upgrades that offer prevention (reduce the probability of a disaster) and/or protection (mitigate the cost of a disaster) benefits. Stochastic features of the model include disaster occurrences and whether or not a disaster terminates the effective life of an earlier resilience upgrade. From our MDP model, we derive analytical expressions for the decisionmaker's willingness to pay (WTP) to enhance infrastructure resilience, and conduct a comparative static analysis to investigate how the WTP varies with the fundamental parameters of the problem. Following this theoretical portion of the article, we demonstrate the applicability of our MDP framework to real‐world decision making by applying it to two case studies of electric utility infrastructure hardening. The first case study considers elevating a flood‐prone substation and the second assesses upgrading transmission structures to withstand high winds. Results from these two case studies show that assumptions about the value of lost load during power outages and the distribution of customer types significantly influence the WTP for the resilience upgrades and are material to the decisions of whether or not to implement them.

AbstractImported agricultural pests can cause substantial damage to agriculture, food security, and ecosystems. In the United States, the Agricultural Quarantine Inspection Monitoring (AQIM) program conducts random sampling to estimate the probabilities that cargo and passengers arriving at ports of entry carry pests. Assessing these risks accurately is critical to enable effective policies and operational procedures. This study introduces a pathway‐level analysis with an objective function aligned with AQIM's goal, offering a new perspective compared to the current container‐by‐container approach, which relies on heuristics to set inspection rates. We formulate an optimization model that minimizes the mean squared error of the probability estimates that AQIM obtains. The central decision‐making tradeoff that the model explores is whether it is preferable to sample more arriving containers (and fewer boxes per container) or more boxes per container (and fewer containers), given limited resources. We first derive an analytical solution for the optimal sampling strategy by leveraging several approximations. Then, we apply our model to a numerical case study of maritime cargo sampling at the Port of Long Beach. Across a wide range of parameter settings, the optimal strategy samples more containers (but fewer boxes per container) than the current AQIM protocol. The difference between the two strategies and the accuracy improvement with the optimal approach are larger if the pest statuses of boxes in the same container are more strongly correlated. We recommend that AQIM record box‐level (beyond only container‐level) inspection data, which could be used to estimate this correlation and other model parameters.