紧急输送策略的线性规划模型

以美国南弗洛里达州递避飓风袭击为例建立了紧急输送情况下的线性规划模型。其中具体建立了以SCEPD(美国南弗洛里达州紧急情况预防部门)提出的公路反向，地区分块撤离，使用小路等三个最受人们关注的策略的模型。模型的灵敏度分析能很好地解释建立更多的临时避难所、限制车辆等策略对撤离的影响。在相近的环境下，中模型解得的结果很接近官方预测的结果，并且本的模型能对更广泛的情况进行分析．可给SCEPD提供一定参考。     

Risk models with dependence between claim occurrences and severities for Atlantic hurricanes

In the line of Cossette et al. (2003), we adapt and refine known Markovian-type risk models of Asmussen (1989) and Lu and Li (2005) to a hurricane risk context. These models are supported by the findings that El Niño/Southern Oscillation (as well as other natural phenomena) influence both the number of hurricanes and their strength. Hurricane risk is thus broken into three components: frequency, intensity and damage where the first two depend on the state of the Markov chain and intensity influences the amount of damage to an individual building. The proposed models are estimated with Florida hurricane data and several risk measures are computed over a fictitious portfolio.     

Un modèle mathématique de cyclone

The source waves are some particular solutions to genuinely non linear hyperbolic systems with a source term, whose propagation velocity is a constant determined by the roots of this source term. We propose a system of 2 equations on a 2 dimension space, which model the velocity field of the atmosphere near the ground. The source term is made of three parts: a given pressure gradient, a friction or aspiration effect and the Coriolis force. In the case where these parameters are constant, we build a solution which is a constant outside a circular crown. The internal circle represents the eye's wall of the hurricane and corresponds to a share shock wave. The external circle is a set generating the bifurcation which actually models the hurricane. To cite this article: A.-Y. LeRoux et al., C. R. Acad. Sci. Paris, Ser. I 339 (2004).     

On Dynamic Generalized Linear Models with Applications

In this paper we combine the idea of ‘power steady model’, ‘discount factor’ and ‘power prior’, for a general class of filter model, more specifically within a class of dynamic generalized linear models (DGLM). We show an optimality property for our proposed method and present the particle filter algorithm for DGLM as an alternative to Markov chain Monte Carlo method. We also present two applications; one on dynamic Poisson models for hurricane count data in Atlantic ocean and the another on the dynamic Poisson regression model for longitudinal count data.     

Response of a tethered aerostat to simulated turbulence

Aerostats are lighter-than-air vehicles tethered to the ground by a cable and used for broadcasting, communications, surveillance, and drug interdiction. The dynamic response of tethered aerostats subject to extreme atmospheric turbulence often dictates survivability. This paper develops a theoretical model that predicts the planar response of a tethered aerostat subject to atmospheric turbulence and simulates the response to 1000 simulated hurricane scale turbulent time histories. The aerostat dynamic model assumes the aerostat hull to be a rigid body with non-linear fluid loading, instantaneous weathervaning for planar response, and a continuous tether. Galerkin’s method discretizes the coupled aerostat and tether partial differential equations to produce a non-linear initial value problem that is integrated numerically given initial conditions and wind inputs. The proper orthogonal decomposition theorem generates, based on Hurricane Georges wind data, turbulent time histories that possess the sequential behavior of actual turbulence, are spectrally accurate, and have non-Gaussian density functions. The generated turbulent time histories are simulated to predict the aerostat response to severe turbulence. The resulting probability distributions for the aerostat position, pitch angle, and confluence point tension predict the aerostat behavior in high gust environments. The dynamic results can be up to twice as large as a static analysis indicating the importance of dynamics in aerostat modeling. The results uncover a worst case wind input consisting of a two-pulse vertical gust.     

Forecast-driven model for prepositioning supplies in preparation for a foreseen hurricane

In this paper, we present a forecast-driven dynamic model for prepositioning relief items in preparation for a foreseen hurricane. Our model uses forecast advisories issued by the National Hurricane Center (NHC), which are issued every 6?h. Every time a new advisory is issued with updated information, our model determines the amount and location of units to be prepositioned and it also re-prepositions already prepositioned units. The model also determines the best time for starting the prepositioning activities. Our approach uses a combination of Decision Theory and stochastic programming. The outcomes of our model are presented in a way that could be easily understood by humanitarian practitioners who are ultimately the ones who would use and apply our model.     

Equivalent and Nonequivalent Barotropic Modes for Rotating Stratified Flows

All the possible equivalent barotropic (EB) laminar solutions are firstly explored,and all the possible non-EB elliptic circulations and hyperbolic laminar modes of rotating stratified fluids are disco...     

Spatial Regression Models for Extremes

Meteorological data are often recorded at a number of spatial locations. This gives rise to the possibility of pooling data through a spatial model to overcome some of the limitations imposed on an extreme value analysis by a lack of information. In this paper we develop a spatial model for extremes based on a standard representation for site-wise extremal behavior, combined with a spatial latent process for parameter variation over the region. A smooth, but possibly non-linear, spatial structure is an intrinsic feature of the model, and difficulties in computation are solved using Markov chain Monte Carlo inference. A simulation study is carried out to illustrate the potential gain in efficiency achieved by the spatial model. Finally, the model is applied to data generated from a climatological model in order to characterize the hurricane climate of the Gulf and Atlantic coasts of the United States.     

Coupling input–output analysis with multiobjective linear programming models for the study of economy–energy–environment–social (E3S) trade-offs: a review

Private enterprises, such as Wal-Mart and Home Depot, have implemented effective relief efforts in responding to Hurricane Katrina in 2005. From the perspective of operation research, the factors of this success have been addressed as quick response, pre-position and logistic expertise. However, the waste made by FEMA for stockpiling food and ice in anticipation of a busy hurricane season in 2006 and several other cases indicate otherwise. In this paper, we introduce the private enterprise into a relief supply chain and propose an outsourcing framework to uncover the reason of Wal-Mart’s success and provide explanations to the counter-cases. Our study shows that the effective relief effort carried out by private enterprises in responding to Hurricane Katrina in 2005 attributes to the combination of proactive response method and logistic expertise. Our findings advocate adopting different strategies to deal with different types of relief supplies. Specifically, the proactive insourcing strategy is good enough for a relief supply chain involving only imperishable relief supplies. For perishable goods, the proactive outsourcing strategy can make a relief supply more efficient.     

Hospital capacity,capability, and emergency preparedness

Disasters often result in shifts in hospital capacity utilization. Emergency preparedness plans must recognize capacity at the service-line level. This information can provide an additional level of detail to better design response activities and develop cost-effective disaster response plans. We model a possible preparedness plan for Florida hospitals in the case of a major disaster. We model a hurricane event because, in addition to its similarity to other disasters, it provides enough warning for substantive preparation activities. Following Johansen, we measure capacity in a frontier setting using data envelopment analysis. We also use a criterion of economic capability to ensure that a Pareto Optimal situation can be maintained. Information on hospital capacity, patient characteristics of inpatient discharges, and financial performance was merged to perform this study. Our findings suggest there is not enough excess capacity for some specialized services in Florida. However, possible evacuation policies can still be derived from our findings satisfying medical and economic capabilities.     

国际天然气价格驱动因素的结构性变化

页岩气开采技术的大规模应用增加了非常规天然气供应潜力,对国际天然气市场供需格局产生了较大冲击。本文利用带结构断点的协整检验、时变系数模型和条件误差修正模型等方法系统地研究了天然气价格与原油价格的动态关系,以及库存、天气和投机等短期因素对气价变化的影响。结果显示,国际天然气价格与原油价格间的协整关系在2005年飓风季与2008年金融危机期间发生了结构性变化,而且原油价格对天然气价格的影响强度呈现倒U型结构。此外,极端天气、突发性事件及投机等短期因素对气价存在显著的短期影响。不过,随着天然气供应出现过剩局面,天然气价格对这些短期因素的敏感性已大幅降低。     

Prepositioning supplies in preparation for disasters

In this paper, we examine the decision of where to preposition supplies in preparation for a disaster, such as a hurricane or terrorist attack, and how much to preposition at a location. If supplies are located closer to the disaster, it can allow for faster delivery of supplies after the disaster. As a result of being closer, though, the supplies may be in a risky location if the disaster occurs. Considering these risks, we derive equations for determining the optimal stocking quantity and the total expected costs associated with delivering to a demand point from a supply point. We provide a sensitivity analysis to show how different parameters impact stocking levels and costs. We show how our cost model can be used to select the single best supply point location from a discrete set of choices and how it can be embedded within existing location algorithms to choose multiple supply points. Our computational experiments involve a variety of relationships between distance and risk and show how these can impact location decisions and stocking levels.     

New analytical and CWENO numerical solutions for axisymmetric horizontal flows with heat sources

Some new nonlinear analytical solutions are found for axisymmetric horizontal flows dominated by strong heat sources. These flows are common in multiscale atmospheric and oceanic flows such as hurricane embryos and ocean gyres. The analytical solutions are illustrated with several examples. The proposed exact solutions provide analytical support for previous numerical observations and can be also used as benchmark problems for validating numerical models. A central weighted essentially non-oscillatory (CWENO) reconstruction is also employed for numerical simulation of the corresponding integro-differential equations. Due to the use of the same polynomial reconstruction for all derivatives and integral terms, the balance between those terms is well preserved, and the method can precisely reproduce the exact solutions, which are hard to capture by traditional upwind schemes. The developed analytical solutions were employed to evaluate the performance of the numerical method, which showed an excellent performance of the numerical model in terms of numerical diffusion and oscillation.     

基于不确定需求的灾后应急救灾网络规划模型与算法

突发事件发生后,快速应急响应的第一步是启动应急救灾网络,合理配置应急救灾资源,以保证救灾过程顺利进行,提高救援效率。本文以台风灾害为背景,建立二阶段应急救灾网络的混合整数规划模型,解决台风灾害的灾后应急救灾网络的规划与设计问题。在求解模型时,考虑需求信息的分布难以确定,并且在一定范围内变动的特点,利用鲁棒优化的方法处理不确定性需求,从而得到合理的临时救灾中心选址以及应急资源配置信息。数值试验表明,建立的模型是实际可行的,而且算法也是有效的。     

A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management

Hurricane forecasts are intended to convey information that is useful in helping individuals and organizations make decisions. For example, decisions include whether a mandatory evacuation should be issued, where emergency evacuation shelters should be located, and what are the appropriate quantities of emergency supplies that should be stockpiled at various locations. This paper incorporates one of the National Hurricane Center's official prediction models into a Bayesian decision framework to address complex decisions made in response to an observed tropical cyclone. The Bayesian decision process accounts for the trade-off between improving forecast accuracy and deteriorating cost efficiency (with respect to implementing a decision) as the storm evolves, which is characteristic of the above-mentioned decisions. The specific application addressed in this paper is a single-supplier, multi-retailer supply chain system in which demand at each retailer location is a random variable that is affected by the trajectory of an observed hurricane. The solution methodology is illustrated through numerical examples, and the benefit of the proposed approach compared to a traditional approach is discussed.     

An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis

For accurately and efficiently estimating the time-dependent failure probability (TDFP) of the structure, a novel adaptive multiple-Kriging-surrogate method is proposed. In the proposed method, the multiple Kriging models with different regression trends (i.e., constant, linear and quadratic) are simultaneously constructed with the highest accuracy, on which the TDFP can be obtained. The multiple regression trends are adaptively selected based on the size of sample base, the maximum differences of multiple models and the global accuracy of multiple models. After that, the most suitable multiple regression trends are identified. The proposed method can avoid man-made subjectivity for regression trend in general Kriging surrogate method. Furthermore, better accuracy and efficiency will be obtained by the proposed multiple surrogates than just using a fixed regression model for some engineering applications. Five examples involving four applications with explicit performance function and one tone arch bridge under hurricane load example with implicit performance function are introduced to illustrate the effectiveness of the proposed method for estimating TDFP.     

Univariate and bivariate GPD methods for predicting extreme wind storm losses

Wind storm and hurricane risks are attracting increased attention as a result of recent catastrophic events. The aim of this paper is to select, tailor, and develop extreme value methods for use in wind storm insurance. The methods are applied to the 1982-2005 losses for the largest Swedish insurance company, the Länsförsäkringar group. Both a univariate and a new bivariate Generalized Pareto Distribution (GPD) gave models which fitted the data well. The bivariate model led to lower estimates of risk, except for extreme cases, but taking statistical uncertainty into account the two models lead to qualitatively similar results. We believe that the bivariate model provided the most realistic picture of the real uncertainties. It additionally made it possible to explore the effects of changes in the insurance portfolio, and showed that loss distributions are rather insensitive to portfolio changes. We found a small trend in the sizes of small individual claims, but no other trends. Finally, we believe that companies should develop systematic ways of thinking about “not yet seen” disasters.     

Pricing catastrophe swaps: A contingent claims approach

In this paper, we comprehensively analyze the catastrophe (cat) swap, a financial instrument which has attracted little scholarly attention to date. We begin with a discussion of the typical contract design, the current state of the market, as well as major areas of application. Subsequently, a two-stage contingent claims pricing approach is proposed, which distinguishes between the main risk drivers ex-ante as well as during the loss reestimation phase and additionally incorporates counterparty default risk. Catastrophe occurrence is modeled as a doubly stochastic Poisson process (Cox process) with mean-reverting Ornstein-Uhlenbeck intensity. In addition, we fit various parametric distributions to normalized historical loss data for hurricanes and earthquakes in the US and find the heavy-tailed Burr distribution to be the most adequate representation for loss severities. Applying our pricing model to market quotes for hurricane and earthquake contracts, we derive implied Poisson intensities which are subsequently condensed into a common factor for each peril by means of exploratory factor analysis. Further examining the resulting factor scores, we show that a first order autoregressive process provides a good fit. Hence, its continuous-time limit, the Ornstein-Uhlenbeck process should be well suited to represent the dynamics of the Poisson intensity in a cat swap pricing model.     

Pricing catastrophe swaps: A contingent claims approach

In this paper, we comprehensively analyze the catastrophe (cat) swap, a financial instrument which has attracted little scholarly attention to date. We begin with a discussion of the typical contract design, the current state of the market, as well as major areas of application. Subsequently, a two-stage contingent claims pricing approach is proposed, which distinguishes between the main risk drivers ex-ante as well as during the loss reestimation phase and additionally incorporates counterparty default risk. Catastrophe occurrence is modeled as a doubly stochastic Poisson process (Cox process) with mean-reverting Ornstein–Uhlenbeck intensity. In addition, we fit various parametric distributions to normalized historical loss data for hurricanes and earthquakes in the US and find the heavy-tailed Burr distribution to be the most adequate representation for loss severities. Applying our pricing model to market quotes for hurricane and earthquake contracts, we derive implied Poisson intensities which are subsequently condensed into a common factor for each peril by means of exploratory factor analysis. Further examining the resulting factor scores, we show that a first order autoregressive process provides a good fit. Hence, its continuous-time limit, the Ornstein–Uhlenbeck process should be well suited to represent the dynamics of the Poisson intensity in a cat swap pricing model.     

Boundary conditions of the second-order differential equation and the Riccati equation

The conversion of a second-order linear ordinary differential equation with variable coefficients into a Riccati equation depends on whether the second-order problem is an initial-value or two-point boundary-value problem. The distinction is critical in determining the initial condition for the Riccati equation. If the second-order problem is an initial-value problem, the choice of the Riccati transformation depends on whether a zero initial condition for the function or its derivative is specified. If the problem is a two-point boundary-value problem, special methods must be introduced as described in the paper.