Eva Regnier

Public Evacuation Decisions and Hurricane Track Uncertainty

Public officials with the authority to order hurricane evacuations face a difficult trade-off between risks to life and costly false alarms. Evacuation decisions must be made on the basis of imperfect information, in the form of forecasts. The quality of these decisions can be improved if they are also informed by measures of uncertainty about the forecast, including estimates of the value of waiting for updated, more accurate, forecasts. Using a stochastic model of storm motion derived from historic tracks, this paper explores the relationship between lead time and track uncertainty for Atlantic hurricanes and the implications of this relationship for evacuation decisions. Typical evacuation clearance times and track uncertainty imply that public officials who require no more than a 10% probability of failing to evacuate before a striking hurricane (a false negative) must accept that at least 76%---and for some locations over 90%---of evacuations will be false alarms. Reducing decision lead times from 72 to 48 hours for major population centers could save an average of hundreds of millions of dollars in evacuation costs annually, with substantial geographic variation in savings.

Replacement under ongoing technological progress

This paper evaluates the cost savings achievable in the infinite horizon single-asset replacement problem by considering technological progress affecting assets available after multiple future replacements and allowing variable service lives. Using a simple geometric model to specify asset costs, constant service lives are shown to be suboptimal. It is shown by example that common replacement decision methods yield higher service lives for the first asset and substantially different discounted total costs for the series of assets. This effect is illustrated numerically using automobile cost data.

A Dynamic Decision Model Applied to Hurricane Landfall

Abstract The decision to prepare for an oncoming hurricane is typically framed as a static cost:loss problem, based on a strike-probability forecast. The value of waiting for updated forecasts is therefore neglected. In this paper, the problem is reframed as a sequence of interrelated decisions that more accurately represents the situation faced by a decision maker monitoring an evolving tropical cyclone. A key feature of the decision model is that the decision maker explicitly anticipates and plans for future forecasts whose accuracy improves as lead time declines. A discrete Markov model of hurricane travel is derived from historical tropical cyclone tracks and combined with the dynamic decision model to estimate the additional value that can be extracted from existing forecasts by anticipating updated forecasts, rather than incurring an irreversible preparation cost based on the instantaneous strike probability. The value of anticipating forecasts depends on the specific alternatives and cost profile o...

Simulating pirate behavior to exploit environmental information

Recent years have seen an upsurge in piracy, particularly off the Horn of Africa. Piracy differs from other asymmetric threats, such as terrorism, in that it is economically motivated. Pirates operating off East Africa have threatened maritime safety and cost commercial shipping billions of dollars paid in ransom. Piracy in this region is conducted from small boats which can only survive for a few days away from their base of operations, have limited survival in severe weather, and cannot perform boarding operations in high wind or sea state conditions. In this study we use agent models and statistical design of experiments to gain insight into how meteorological and oceanographic forecasts can used to dynamically predict relative risks for commercial shipping.

A Value-Focused Approach to Energy Transformation in the United States Department of Defense

The United States Department of Defense (DoD) has identified its energy requirements as a key vulnerability and in recent years has taken substantial initiatives to improve its energy profile. As part of this process, DoD leaders have issued guidance documents outlining goals and objectives relating to energy. These documents are intended to inform many different decisions at strategic, managerial, and operational levels. They specify a wide range of objectives that overlap only partially, while identical terms appear in many documents, but with inconsistent definitions. In this paper, we review 44 strategic guidance documents and apply a value-focused thinking approach to identify and define explicitly a comprehensive set of common objectives for energy decisions in the DoD. The objectives and associated definitions are intended to facilitate horizontal and vertical communication within the DoD. In addition, the objectives we define suggest possible metrics that may be comparable across services and in some cases may be aggregated across organizational levels.

Complexity and Self-Sustainment in Disaster Response Supply Chains

Governmental organizations play a major role in disaster relief operations. Supply chains set up to respond to disasters differ dramatically in many dimensions that affect the cost of relief efforts. One factor that has been described recently is self-sustainment, which occurs when supplies consumed by intermediate stages of a supply chain must be provided via the chain itself because they are not locally available. This article applies the concept of self-sustainment to response supply chains. A mathematical model of a self-sustaining response supply chain is developed. Analysis of this model yields insights about the relationships and interactions among self-sustainment, speed of disaster onset, dispersion of impact, and the cost of the relief efforts.

The fuel multiplier in multi-stage supply chains

Fuel requirements on the battlefield impose direct costs associated with the resources necessary to transport the fuel and protect logistics assets, in addition to indirect energy security costs. Estimating the enterprise-wide demand for fuel associated with fuel consumption on the battlefield is a challenging, but necessary, step to making good decisions. This paper presents a modeling framework for estimating the enterprise-wide fuel requirements associated with a multistage fuel supply chain, demonstrating a multiplicative increase in fuel demand with additional stages, and examining the fuel impact of protecting the supply chain.

An application of the multiple knapsack problem: The self-sufficient marine

Self-Sufficiency (SS) is the ability to maintain capability without external support or aid. Operations in austere environments with limited functional infrastructure and logistical support, which are common in humanitarian assistance and disaster relief as well as military operations, must be self-sufficient. In this paper, we explore the challenges of SS in the United States Marine Corps (USMC). Marines engage in a wide variety of expeditionary operations, and must function without logistical support for long stretches of time. They face competing constraints, including the load that a squad can carry, mission requirements, resources required for sustainment, and the extent to which resources can be shared. We extend the knapsack problem in several ways to model a Marine squads decisions regarding what items to carry and how to distribute them. The Office of Naval Research found the models and the results to be significant as baseline analysis for the resource demands of a self-sufficient squad. Though the data and scenarios are USMC-specific, the challenges of SS can be found in any expeditionary undertakings or operations in austere environments.

User Focus and Simulation Improve Predictions of Piracy Risk

Piracy is an increasingly costly and violent threat to commercial shipping and other vessels off the Horn of Africa. However, because pirates operate in small vessels that cannot navigate or attack in high seas or winds, pirate activity is highly sensitive to environmental conditions. The US Naval Oceanographic Office provides an operational forecast of the pirate threat; counterpiracy forces use this forecast to allocate their efforts over several million square miles. The most recent version uses simulation to model the effects of pirate behavior in interaction with winds, waves, and currents over time to forecast the geographic distribution of the pirate threat. As part of the development of the pirate behavior model, one author traveled to Bahrain to interview counter-piracy forces. We then used carefully designed simulation experiments to identify the variables that are most influential in determining the distribution of predicted pirate activity. The results confirmed the importance of elements of the pirate behavior model that were derived from our operator interviews, informed decisions regarding operational settings for key parameters, and generated insights to guide future updates to the model and intelligence-gathering efforts. The resulting model uses our recommendations, including alternate pirate search patterns. It has been operational since March 2011 and is briefed daily to the senior leadership of US Naval Forces Central Command.

Probability Forecasts Made at Multiple Lead Times

Many probability forecasts are revised as new information becomes available, generating a time series of forecasts for a single event. Although methods for evaluating probability forecasts have been extensively studied, they apply to a single forecast per event. This paper is the first to evaluate probability forecasts that are made—and therefore revised—at many lead times for a single event. I postulate a norm for multi-period probability-forecasting systems and derive properties that should hold regardless of the forecasting process. I use these properties to develop methods for evaluating a forecasting system based on a sample. I apply these methods to the National Hurricane Center’s wind-speed probability forecasts and to statistical election forecasts, finding evidence that both can be improved using the current set of predictors. This paper was accepted by Manel Baucells, decision analysis.