Optimizing disaster relief: Real-time operational and tactical decision support

We introduce a real-time decision support system which uses optimization methods, simulation, and the judgement of the decision maker for operational assignment of units to tasks and for tactical allocation of units to task requirements. The system, named ARES for the Greek god of war, accommodates a high degree of detail in the logistics of unit movements during operations, yet separates the assignment and allocation activities in a fashion which naturally accommodates human intervention and judgement—ARES is designed to assist the decision maker, not to replace him. ARES is demonstrated with a hypothetical scenario constructed for 14 Engineering Battalions of the Hellenic Army which are assigned 20 tasks employing 25 resource types in repairing major damage to public works following a great earthquake. (This hypothetical data was prepared prior to the earthquake in Kalamata near Athens on 13 September, 1986, and exhibits uncanny, but coincidental, resemblance to that real situation.) ARES is designed for use in real time, and quick data preparation is aided by the provision from published sources of standard data for many foreseeable tasks; this data can be quickly accessed via visual icons on a computer screen and customized for the actual work at hand. © 1993 John Wiley & Sons, Inc.     

Real-time task scheduling with overheads considered

n periodic tasks are to be processed by a single machine, where each task i has a maximum request rate or periodicity F_i, a processing time E_i, a deadline D_i, relative to each request of task i, a task-request interrupt overhead I_i, and a task-independent scheduling overhead S. Two scheduling strategies are considered for sequencing the execution of an arbitrary arrangement of task requests in time: the preemptive and the nonpreemptive earliest-deadline algorithms. Necessary and sufficient conditions are derived for establishing whether a given set of tasks can be scheduled by each scheduling strategy. The conditions are given in the form of limited simulations of a small number of well-defined task-request arrangements. If all simulations succeed, the schedule is feasible for the given set of tasks. If any simulation fails, the schedule is infeasible. While interrupt handling and scheduling overheads can be handled by such simulations, context switching overhead resulting from preemption cannot. A counterexample illustrates how the simulations fail to uncover unschedulable task sets when context switching overhead is considered.     

An analytical evaluation of optimal solution value estimation procedures

The estimation of optimal solution values for large-scale optimization problems is studied. Optimal solution value estimators provide information about the deviation between the optimal solution and the heuristic solution. Some estimation techniques combine heuristic solutions with randomly generated solutions. In particular, we examine a class of jacknife-based estimators which incorporate any heuristic solution value with the two best randomly generated solution values. The primary contribution of this article is that we provide a framework to analytically evaluate a class of optimal solution value estimators. We present closed-form results on the relationship of heuristic performance, sample size, and the estimation errors for the case where the feasible solutions are uniformly distributed. In addition, we show how to compute the estimation errors for distributions other than uniform given a specific sample size. We use a triangular and an exponential distribution as examples of other distributions. A second major contribution of this article is that, to a large extent, our analytical results confirm previous computational results. In particular, the best estimator depends on how good the heuristic is, but seems to be independent of the underlying distribution of solution values. Furthermore, there is essentially an inverse relationship between the heuristic performance and the performance of any estimator. © 1994 John Wiley & Sons, Inc.     

The frequency assignment problem: A solution via nonlinear programming

This paper gives a mathematical programming model for the problem of assigning frequencies to nodes in a communications network. The objective is to select a frequency assignment which minimizes both cochannel and adjacent-channel interference. In addition, a design engineer has the option to designate key links in which the avoidance of jamming due to self interference is given a higher priority. The model has a nonconvex quadratic objective function, generalized upper-bounding constraints, and binary decision variables. We developed a special heuristic algorithm and software for this model and tested it on five test problems which were modifications of a real-world problem. Even though most of the test problems had over 600 binary variables, we were able to obtain a near optimum in less than 12 seconds of CPU time on a CDC Cyber-875.     

A new surrogate dual multiplier search procedure

Efficient computation of tight bounds is of primary concern in any branch-and-bound procedure for solving integer programming problems. Many successful branch-and-bound approaches use the linear programming relaxation for bounding purposes. Significant interest has been reported in Lagrangian and surrogate duals as alternative sources of bounds. The existence of efficient techniques such as subgradient search for solving Lagrangian duals has led to some very successful applications of Lagrangian duality in solving specially structured problems. While surrogate duals have been theoretically shown to provide stronger bounds, the difficulty of surrogate dual-multiplier search has discouraged their employment in solving integer programs. Based on the development of a new relationship between surrogate and Lagrangian duality, we suggest a new strategy for computing surrogate dual values. The proposed approach allows us to directly use established Lagrangian search methods for exploring surrogate dual multipliers. Computational experience with randomly generated capital budgeting problems validates the economic feasibility of the proposed ideas.     

Optimal Bayesian single-sampling attribute plans with modified beta prior distribution

In this article we study the properties of the optimal Bayesian single-sampling plans when the prior distribution of the lot fraction defective is modified Beta, which has been found useful in the analysis of inspection schemes for complex production systems. These properties are used to devise an improved and more efficient algorithm for the determination of the optimal sampling plans. Numerical examples are presented to illustrate the potential computational savings of the algorithm.     

基于核主成分分析的铁谱磨粒特征提取方法研究

针对铁谱分析的磨粒识别过程中存在原始磨粒特征描述指标参数多、非线性突出的问题,提出基于核主成分分析的磨粒特征提取方法,介绍该方法的原理与算法。结合某柴油发动机故障检测与分析系统中铁谱磨粒自动识别的应用实例,并与传统主成份分析方法进行对比分析,结果表明该方法在进行样本非线性特征参数指标综合以及特征维数压缩方面具有可行性和有效性。     

Challenging the Hegemon: Al Qaeda's Elevation of Asymmetric Insurgent Warfare Onto the Global Arena

This chapter examines how Al Qaeda, an asymmetric power, was able to become a global challenger to the existing international structure. Neither regional insurgency theories nor grand-scale global theories alone are adequate to explain Al Qaeda's ability. Rather, a modification of the seven dimensions of the Manwaring paradigm for successful insurgency combined with key concepts of global revolutionary or imperial powers formerly applied to states gives insight to this fundamental question. Al Qaeda's asymmetric global challenge is unique, and understanding the nature of the threat is key to finally defeating it.     

体外天线气动加热下三维瞬态温度场建模与仿真

利用工程上常用的突起物干扰系数法,建立了体外天线气动加热工程计算方法,结合成熟的商业软件,采用二次开发技术,建立了突起物三维瞬态温度场计算模型.讨论了飞行条件下外露天线2种不同形式对天线接插件温度的影响,计算结果表明:为保证天线正常工作的温度环境,避免高热流的冲击,在天线设计时需考虑防热措施.     

基于UML的作战系统描述方法

在仿真的具体实现过程中,系统描述是其中的关键步骤.分析了利用统一建模语言UML对系统进行描述的方法和步骤,并以炮兵群(团)作战系统为例,给出了UML中的用例图、类图、活动图以及协作图对系统进行描述的方法.结果表明利用UML进行系统描述,描述过程完整,易于理解和操作.