带有疾病、分段常数变量的捕食-被捕食系统的稳定性和分支行为

本文研究一类带有疾病和分段常数变量的捕食-被捕食模型的稳定性和分支行为.首先通过计算得到捕食-被捕食模型对应的差分模型,利用线性稳定性理论讨论边界和正平衡点局部渐近稳定的充分条件.其次将食饵种群的出生率作为分支参数,使用分支理论研究差分模型在边界和正平衡点处产生鞍结点分支、翻转分支、Neimark-Sacker分支、Neimark-Sacker分支、鞍结点-Neimark-Sacker分支、鞍结点-翻转分支和翻转-Neimark-Sacker分支的充分条件.最后数值模拟验证理论分析的正确性,并展示模型复杂的动力学性态.     

多周期多产品采购量分配优化模型

为了解决随机需求与价格折扣并存条件下的多周期多产品采购量分配问题,建立了相应的多目标混合整数随机规划模型.该模型的特点是:①模型的约束条件中兼具确定性和随机性;②通过累计需求和累计采购量表示多周期的库存持有成本;③通过约束条件方程式准确地表现随机需求和价格折扣两大假设条件.针对该模型的特殊结构,提出了一种适用的求解策略:首先,通过把机会约束转化为确定性等价类,从而将多目标混合整数随机规划模型转化为确定型多目标混合整数规划模型;然后,采用目标规划法求得问题的满意解.此外,通过应用算例说明了模型的有效性和可行性.     

一类捕食与被捕食LV模型的扩散性质

本文证明了一类带有扩散的捕食与被捕食Ｌｏｔｋａ－Ｖｏｌｔｅｒｒａ模型的如下性质：当该模型存在正平衡点时,它的一切正解是强持续生存的;当扩散率较小时,该系统的正平衡点是稳定的;当扩散率增大且位于某一开区间内变化时,该系统的正平衡点是不稳定的,而且分支出唯一的小振幅空间周期解;当扩散率继续增大时,该系统的正平衡点又变为稳定的．     

带有区间型三角模糊数的多目标规划模型算法

给出了模糊多目标线性规划模型的一种有效算法,其中目标函数和约束条件中的系数都是区间型三角模糊数.首先,通过引入区间型三角模糊数的截集,将模糊多目标函数转化成单目标函数.其次,引入用于比较两个三角模糊数的强占优可能性准则,将模型中的模糊约束条件转化为经典不等式组.然后,利用Matlab软件编程求解转化的经典单目标线性规划...     

具有时滞的生态流行病模型的稳定性和Hopf分支

该文考虑一类食饵染病的时滞捕食被捕食模型. 作者分析了系统的非负不变性, 边界平衡点的性质和全局稳定性. 证明了当时滞τ=τ\-1+τ\-2适当小时, 正平衡点是局部渐近稳定的,随着时滞的增加, 正平衡点由稳定变为不稳定, 系统在正平衡点附近发生Hopf分支.     

蛛网模型收敛的一些充要条件

蛛网模型刻画了某种商品在市场中的供求波动,是一种重要的典型动态经济学模型.本文对传统的蛛网模型加以改进,建立了需求函数和供给函数可以为线性函数或非线性函数的广义蛛网模型.并对其进行了动态分析与稳定性分析,得到了模型存在稳定均衡点的一些充要条件.     

基于截断δ-冲击模型的不完全维修更换策略

本文在截断δ-冲击模型的基础上,考虑了因系统劣化而导致的冲击失效门限值与维修时间的变化,扩充失效状态,从而提出一种新的截断δ-冲击模型。以最小费用为目标,稳态可用度为约束条件,建立N型更换策略的不完全维修更换策略模型,并给出三种常用冲击到达间隔分布的期望寿命。最后通过算例验证模型的有效性,并对参数进行灵敏度分析。     

基于直觉模糊集信息的双边匹配决策

研究了基于直觉模糊集信息的双边匹配问题。首先给出了直觉模糊集和双边匹配相关理论;接着描述了基于直觉模糊集信息的双边匹配问题;以每个主体满意程度最大为目标,考虑到一对一双边匹配约束条件,建立了多目标双边匹配模型;运用线性加权法将多目标双边匹配模型转化为单目标双边匹配模型;进而通过求解该模型获得"最佳"双边匹配。最后,供需匹配实例分析说明了所提方法的有效性。     

目标规划在离散型动态投入产出模型中的应用

以离散型动态投入产出模型为约束条件的主体、以决策部门所希望达到的种种目标为约束条件的附加部分,建立动态投入产出目标规划模型.通过求解动态投入产出目标规划模型而得到离散型动态投入产出模型的解.此解法与其它解法相比具有更大的实用价值.     

蛛网模型的分析性质

首先分析了一种只是纯粹考虑了数学意义的蛛网模型的稳定点求法,并指出其不妥之处在于没有考虑到经济情况的证明问题,这样使利用这类蛛网模型解决的问题缺乏有效的支撑.因此给出综合考虑了数学和经济意义下的蛛网模型的稳定点存在的证明,再应用常微分方程的特征值求法对收敛型的蛛网模型的稳定点求法进行三种情况的分别讨论,最后给出了差分方程形式下的蛛网模型的稳定点的特征值求法并猜想蛛网模型是否具有Markov Proc.性质.     

Robust strategies for natural gas procurement

In order to serve their customers, natural gas local distribution companies (LDCs) can select from a variety of financial and non-financial contracts. The present paper is concerned with the choice of an appropriate portfolio of natural gas purchases that would allow a LDC to satisfy its demand with a minimum tradeoff between cost and risk, while taking into account risk associated with modeling error. We propose two types of strategies for natural gas procurement. Dynamic strategies model the procurement problem as a mean-risk stochastic program with various risk measures. Naive strategies hedge a fixed fraction of winter demand. The hedge is allocated equally between storage, futures and options. We propose a simulation framework to evaluate the proposed strategies and show that: (i) when the appropriate model for spot prices and its derivatives is used, dynamic strategies provide cheaper gas with low risk compared to naive strategies. (ii) In the presence of a modeling error, dynamic strategies are unable to control the variance of the procurement cost though they provide cheaper cost on average. Based on these results, we define robust strategies as convex combinations of dynamic and naive strategies. The weight of each strategy represents the fraction of demand to be satisfied following this strategy. A mean–variance problem is then solved to obtain optimal weights and construct an efficient frontier of robust strategies that take advantage of the diversification effect.     

基于设备状态监控的单级备件库存系统补货策略研究

本文研究一个周期性订货的多设备同备件库存系统,将备件库存策略与设备状态监控相结合,讨论了存在设备状态监控情形下的备件库存策略。针对设备状态自然腐蚀过程和人 为修复过程的复合过程,运用一个新的马尔科夫概率转移矩阵对设备需求概率进行刻画,并在此基础上给出静态订货模型和状态监控下的动态订货模型的最优订货策略。通过对比以上两种订货策略优缺点,本文提出一种新的启发式订货策略: 基于关键状态的订货策略模型。该策略可以有效降低对全部设备实行动态监控的信息成本,且成本节省优于静态订货策略,对于企业的现实问题有着较好的指导意义。     

Optimal mean–variance efficiency of a family with life insurance under inflation risk

We study an optimization problem of a family under mean–variance efficiency. The market consists of cash, a zero-coupon bond, an inflation-indexed zero-coupon bond, a stock, life insurance and income-replacement insurance. The instantaneous interest rate is modeled as the Cox–Ingersoll–Ross (CIR) model, and we use a generalized Black–Scholes model to characterize the stock and labor income. We also take into account the inflation risk and consider our problem in the real market. The goal of the family is to maximize the mean of the surplus wealth at the retirement or death of the breadwinner and minimize its variance by finding a portfolio selection. The efficient frontier and optimal strategies are derived through the dynamic programming method and the technique of solving associated nonlinear HJB equations. We also present a numerical illustration to explore the impact of economical parameters on the efficient frontier.     

Interior-Point Solver for Large-Scale Quadratic Programming Problems with Bound Constraints

In this paper, we present an interior-point algorithm for large and sparse convex quadratic programming problems with bound constraints. The algorithm is based on the potential reduction method and the use of iterative techniques to solve the linear system arising at each iteration. The global convergence properties of the potential reduction method are reassessed in order to take into account the inexact solution of the inner system. We describe the iterative solver, based on the conjugate gradient method with a limited-memory incomplete Cholesky factorization as preconditioner. Furthermore, we discuss some adaptive strategies for the fill-in and accuracy requirements that we use in solving the linear systems in order to avoid unnecessary inner iterations when the iterates are far from the solution. Finally, we present the results of numerical experiments carried out to verify the effectiveness of the proposed strategies. We consider randomly generated sparse problems without a special structure. Also, we compare the proposed algorithm with the MOSEK solver. Research partially supported by the MIUR FIRB Project RBNE01WBBB “Large-Scale Nonlinear Optimization.”     

����fMRI���ݵ�ȫ�Զ�̬�������������������Ե��о�

There're about 10^{11} neurons in the human brain.Through the synaptic junction, neurons have formed a highly complex network.And it is really important to figure out the information expressed in the network, which will contribute to the resolution of the prevention and diagnosis of cognitive disorder of human beings. This paper uses the schizophrenia and healthy controlled subjects' fMRI data to construct the brain network model, in order to explores abnormal topological properties of schizophrenics' brain network based on graph theory. When studying the human brain network information traditionally by the basement of graph theory, it's all assure that the human brain network model has invariance, so it takes the whole period of time series data in constructing human network model, which is a kind static network. However, it's hard to ensure this because of the nonstationarity of fMRI functional time series data. Thus, when constructing human brain network model, we should take its time-variation into consideration, then construct a dynamic brain network. We can explore the brain network information better. In this research, we segment the time series data, using time windows, to constructing dynamic brain network model, then analyze it combined with the knowledge of graph theory, thereby reducing effects that the nonstationarity of fMRI functional time series data will have. Comparing dynamic brain network of the schizophrenic patients with normal controls subjects' in different level, the results show that there are difference in single node property, group network property of schizophrenic patients and normal control subjects' whole brain dynamic functional connectivity network. The discovery of these difference in network topological properties has provide new clues for the further study on the pathological mechanism of schizophrenia.     

Portfolio symmetry and momentum

This paper presents a novel theoretical framework to model the evolution of a dynamic portfolio (i.e., a portfolio whose weights vary over time), considering a given investment policy. The framework is based on graph theory and the quantum probability. Embedding the dynamics of a portfolio into a graph, each node of the graph representing a plausible portfolio, we provide the probabilities for a dynamic portfolio to lie on different nodes of the graph, characterizing its optimality in terms of returns. The framework embeds cross-sectional phenomena, such as the momentum effect, in stochastic processes, using portfolios instead of individual stocks. We apply our methodology to an investment policy similar to the momentum strategy of Jegadeesh and Titman (1993). We find that the strategy symmetry is a source of momentum.     

基于主被动合作博弈的医患关系研究

针对我国医患关系日趋紧张,医患矛盾逐渐凸显这一问题,在对中国近五年医患事件进行分析的基础上,分别从信任程度、信息不对称程度、诊疗代价等多个视角出发,利用演化博弈理论和复制动态方程,构建了医患双方合作策略选择演化博弈模型,探究了影响医患关系演化的相关因素。研究表明:从长期来看,医患关系博弈系统存在两种演化路径:主动合作和被动合作;增大医患双方的信任程度,减小医患双方的信息不对称程度,降低医患双方合作成本,增大以政府为主导的仲裁机构对医患双方的奖励和惩罚都将会促使医患双方由被动合作转向主动合作。     

Hot mudflow prediction area model and simulation based on Cellular Automata for LUSI mud plume at Sidoarjo in East Java

We propose a new hot mudflow prediction model based on Cellular Automata (CA). Using our CA prediction model, we present simulations of the LUSI hot mudflow in the Sidoarjo disaster area. Our CA method to predict mudflow is based on a fluid dynamic model, because hot mudflow characteristics are similar to fluid. The CA model also takes into consideration landscape data, including features such as dikes and buildings. The Moore neighborhood model is adopted for CA to take into account the relationship between the cell of interest and the surrounding cells. A Gaussian interpolation is used to approximate the behavior of the hot mudflow over landscape features. We evaluated the prediction accuracy of our CA model, by comparing results from the CA model with remote sensing satellite data from the disaster areas and measurements of the mudflow disaster area. Simulation results of the LUSI hot mudflow show relatively good prediction accuracy in comparison with conventional models. Therefore, we conclude that the CA model will be valuable for predictions pertaining to hot mudflow in future disasters of a similar nature.     

Exercising flexible load contracts: Two simple strategies

A flexible load contract is a type of swing option where the holder has the right to receive a given quantity of electricity within a specified period, at a fixed maximum effect (delivery rate). The contract is flexible, in the sense that delivery (the take hours) is called one day in advance. We investigate two simple strategies for exercising flexible load contracts, where both use price information from the forward market. For 10 contracts traded in the period 1997–2001, we calculate the performance of the two strategies and compare with the reported performance of one complex dynamic programming approach as well as the actual results obtained by three anonymous market participants. The comparison indicates that our simple computer‐efficient strategies perform better on average and produce more stable results. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimal evacuation planning using staging and routing

Evacuation is an important disaster management tool. Evacuating a large region by automobile (the most commonly used mode) is a difficult task, especially as high levels of traffic congestion often form. This paper studies the use of demand-based strategies, specifically, the staging and routing of evacuees. These strategies attempt to manage demand in order to reduce or eliminate congestion. A strategic mixed-integer programming planning model that accounts for evacuation dynamics and congestion is used to study these strategies. The strategies adopted incorporate different evacuee types based on destination requirements and shelter capacity restrictions. The main objective studied is to minimize the network clearance time. We examine the structure of optimal strategies, yielding insights into the use of staging and routing in evacuation management. These insights are then used to develop effective solution procedures. To demonstrate the efficacy of the proposed solution technique, we provide computational experience using a large realistic example based on Virginia Beach.