A risk function for the stochastic modeling of electric capacity expansion

We present a stochastic optimization model for planning capacity expansion under capacity deterioration and demand uncertainty. The paper focuses on the electric sector, although the methodology can be used in other applications. The goals of the model are deciding which energy types must be installed, and when. Another goal is providing an initial generation plan for short periods of the planning horizon that might be adequately modified in real time assuming penalties in the operation cost. Uncertainty is modeled under the assumption that the demand is a random vector. The cost of the risk associated with decisions that may need some tuning in the future is included in the objective function. The proposed scheme to solve the nonlinear stochastic optimization model is Generalized Benders' decomposition. We also exploit the Benders' subproblem structure to solve it efficiently. Computational results for moderate‐size problems are presented along with comparison to a general‐purpose nonlinear optimization package. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:662–683, 2001     

Extending the MAD portfolio optimization model to incorporate downside risk aversion

A mathematical model of portfolio optimization is usually represented as a bicriteria optimization problem where a reasonable tradeoff between expected rate of return and risk is sought. In a classical Markowitz model, the risk is measured by a variance, thus resulting in a quadratic programming model. As an alternative, the MAD model was developed by Konno and Yamazaki, where risk is measured by (mean) absolute deviation instead of a variance. The MAD model is computationally attractive, since it is easily transformed into a linear programming problem. An extension to the MAD model proposed in this paper allows us to measure risk using downside deviations, with the ability to penalize larger downside deviations. Hence, it provides for better modeling of risk averse preferences. The resulting m‐MAD model generates efficient solutions with respect to second degree stochastic dominance, while at the same time preserving the simplicity and linearity of the original MAD model. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 185–200, 2001     

C++ Builder面向对象编程在多路串行通信中的应用

以模拟导航信号源软件开发为例 ,从面向对象分析设计方法、多线程方法、异常处理等几方面介绍了C ++Builder面向对象编程在多通道实时串行通信系统中应用的一种方法     

不同热解温度石英杂化酚醛复合材料渗透率测试

为得到不同温度下石英杂化酚醛材料的渗透率,自主搭建了材料气体渗透率测试平台,提出基于Darcy定律的复合材料气体渗透率的测试方法。对不同温度下石英杂化材料进行研究,测量渗透过程中试验件上下表面气体压力变化和流过试验件的气体流量,进而得到复合材料的渗透率。结果表明:该实验平台可以用来测量复杂孔隙复合材料的渗透率。石英杂化酚醛材料渗透率与热解温度呈正相关,热解温度越高,复合材料的渗透率越大,材料渗透率和热解温度满足关系式K=9.5×10-15T-6.32×10-12。673 K热解温度下,复合材料渗透率为10-13 m²量级;873 K和1 073 K热解温度下,其渗透率在10-12 m²量级。本试验结果丰富了该类树脂基复合材料的基础物性数据,为材料渗透和热质扩散特性分析提供了依据。     

Testing of software with an operational profile

One of the important features of any software system is its operational profile. This is simply the set of all operations that a software is designed to perform and the occurence probabilities of these operations. We present a new model on optimal software testing such that testing is done sequentially using a set of test cases. There may be failures due to the operations in each of these cases. The model parameters, consisting of testing costs and failure rates, all depend on the cases used and the operations performed. Our aim is to find the optimal testing durations in all of the cases in order to minimize the total expected cost. This problem leads to interesting decision models involving nonlinear programming formulations that possess explicit analytical solutions under reasonable assumptions. © 2000 John Wiley & Sons, Inc., Naval Research Logistics 47: 620–634, 2000     

Scheduling stochastic jobs with asymmetric earliness and tardiness penalties

We consider a stochastic counterpart of the well-known earliness-tardiness scheduling problem with a common due date, in which n stochastic jobs are to be processed on a single machine. The processing times of the jobs are independent and normally distributed random variables with known means and known variances that are proportional to the means. The due dates of the jobs are random variables following a common probability distribution. The objective is to minimize the expectation of a weighted combination of the earliness penalty, the tardiness penalty, and the flow-time penalty. One of our main results is that an optimal sequence for the problem must be V-shaped with respect to the mean processing times. Other characterizations of the optimal solution are also established. Two algorithms are proposed, which can generate optimal or near-optimal solutions in pseudopolynomial time. The proposed algorithms are also extended to problems where processing times do not satisfy the assumption in the model above, and are evaluated when processing times follow different probability distributions, including general normal (without the proportional relation between variances and means), uniform, Laplace, and exponential. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44, 531–557, 1997.     

一种基于神经网络的磁性目标定位方法

磁性目标定位问题可归结为一类非线性规划问题的求解 .该问题的最大特点就是其目标函数的计算过程极为繁琐 .而目标函数计算的快慢对磁定位的实时性有很大影响 .在详细研究了目标函数之后 ,给出了目标函数的一种神经网络结构实现 .由于该网络结构易于并行计算和VLSI实现 ,从而可使磁定位的实时性得到改善 .     

Shortest path interdiction problem with arc improvement recourse: A multiobjective approach

We consider the shortest path interdiction problem involving two agents, a leader and a follower, playing a Stackelberg game. The leader seeks to maximize the follower's minimum costs by interdicting certain arcs, thus increasing the travel time of those arcs. The follower may improve the network after the interdiction by lowering the costs of some arcs, subject to a cardinality budget restriction on arc improvements. The leader and the follower are both aware of all problem data, with the exception that the leader is unaware of the follower's improvement budget. The effectiveness of an interdiction action is given by the length of a shortest path after arc costs are adjusted by both the interdiction and improvement. We propose a multiobjective optimization model for this problem, with each objective corresponding to a different possible improvement budget value. We provide mathematical optimization techniques to generate a complete set of strategies that are Pareto‐optimal. Additionally, for the special case of series‐parallel graphs, we provide a dynamic‐programming algorithm for generating all Pareto‐optimal solutions.     

异构混合存储的软硬件协同数据放置策略

分析比较当前大数据中心典型混合存储架构,针对其不能综合运用存储管理系统和存储设备优势的问题,提出软硬件协同的数据放置策略,同时考虑软件层混合存储管理系统和硬件层混合存储设备的特点,根据应用特性为数据选择合适的存储管理系统和设备。面向不同应用场景,提出运行前规划存储路径的静态放置模式和运行中规划存储路径的动态放置模式。基于存储管理系统和设备性能参数建模,采用仿真方法实现各数据放置策略,并运行实际应用中的三类负载进行实验测试,结果表明采用软硬件协同的数据放置策略相比只考虑存储管理系统和存储设备的数据放置策略,性能可以提高近30%。     

From Westphalian Failure to Heterarchic Governance in MENA: The Case of Syria

Abstract

The problematic export of the Westphalian system to MENA is examined, taking Syria as exemplar. The export model is juxtaposed to actual non-lineal trajectories, semi-sovereignty and hybrid or failing states. This is manifested in post-uprising Syria in failing statehood, fragmented and overlapping governance, permeable and collapsing borders, the loss of sovereignty to trans-state movements, “competitive regime-building” between the Asad regime and jihadist warlords, and “competitive interventionism” by external powers filling the governance vacuum with their own proxies. The result is heterarchic zones of limited statehood in which state sovereignty is contested by both international (supra-state) penetration and sub-state fragmentation.