Approximation algorithms for general one‐warehouse multi‐retailer systems

Logistical planning problems are complicated in practice because planners have to deal with the challenges of demand planning and supply replenishment, while taking into account the issues of (i) inventory perishability and storage charges, (ii) management of backlog and/or lost sales, and (iii) cost saving opportunities due to economies of scale in order replenishment and transportation. It is therefore not surprising that many logistical planning problems are computationally difficult, and finding a good solution to these problems necessitates the development of many ad hoc algorithmic procedures to address various features of the planning problems. In this article, we identify simple conditions and structural properties associated with these logistical planning problems in which the warehouse is managed as a cross‐docking facility. Despite the nonlinear cost structures in the problems, we show that a solution that is within ε‐optimality can be obtained by solving a related piece‐wise linear concave cost multi‐commodity network flow problem. An immediate consequence of this result is that certain classes of logistical planning problems can be approximated by a factor of (1 + ε) in polynomial time. This significantly improves upon the results found in literature for these classes of problems. We also show that the piece‐wise linear concave cost network flow problem can be approximated to within a logarithmic factor via a large scale linear programming relaxation. We use polymatroidal constraints to capture the piece‐wise concavity feature of the cost functions. This gives rise to a unified and generic LP‐based approach for a large class of complicated logistical planning problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

小波变换在漏磁检测信号处理中的应用

在对军用装备进行漏磁无损检测时,漏磁信号中含有大量噪声,针对这种情况,介绍了小波变换消噪理论.在数字化实现中,离散小波变换是利用Mallat分解与重构算法来完成.仿真试验中,选取二阶样条小波为小波函数,对从漏磁检测装置中采集到的漏磁信号进行消噪.结果表明将小波变换应用于炮管漏磁检测中,能提高检测信号的信噪比和抗干扰能力.为漏磁信号分析、特征提取和缺陷漏磁反演打下了基础.     

基于二层遗传算法的导弹航路选优问题研究

在多批次多数量的反舰导弹从多个方向对敌舰艇进行饱和攻击的决策过程中，如何选择出最优的航路组合是提高反舰导弹作战效能的关键问题。在综合分析反舰导弹航路选优问题的基础上，建立了导弹航路选择的优化模型，并针对导弹航路选优问题，对基本遗传算法进行了改进，提出了二层遗传算法。仿真分析了多火力单元多目标导弹攻击的航路组合优化问题，得出了有效的导弹攻击航路。通过验证，说明了二层遗传算法在导弹航路规划中的有效性。     

基于遗传算法的结构测试数据自动生成

生成测试数据是软件测试的主要过程,将遗传算法引入到测试数据的自动生成中,重点介绍其生成过程,并对遗传操作进行改进,在一定程度上降低算法的过早收敛性.最后构建出基于遗传算法的测试数据生成模型.并用实例证明算法的高效性.     

求解不确定型车辆路径问题的弱鲁棒优化方法

为降低鲁棒优化模型最优解的保守性,以最小化违约车辆数和总惩罚成本为目标,建立针对旅行时间不确定的开放式车辆路径问题的弱鲁棒优化模型。对于不确定数据集的每个取值,该模型的最优解可以使其目标函数值始终不超过某数值,进而改善最优解的保守性。为提高启发式算法发现最优解的概率,提出一种自设计遗传算法对模型进行求解,其主要思想是利用粒子群算法搜索出可使遗传算法预期产生最好解的算法要素,并将其进行组合,从而产生新的遗传算法。采用新产生的遗传算法对模型继续求解,输出最好解。计算结果表明:与以往的鲁棒优化方法相比,弱鲁棒优化方法的最优解的保守性显著降低。     

基于神经网络和遗传算法对反装甲目标威胁度评估研究

针对威胁度估计问题和装甲车辆在战场上的实际情况,运用神经网络和遗传算法理论探索反装甲目标威胁度评估问题。基于BP神经网络模型的算法,利用神经网络良好的自适应能力和遗传算法强大的全局搜索能力,通过样本数据训练,提高了威胁度估计的准确性和适应性。经过验证该算法能够有效反应出反各种反装甲目标的威胁度,其稳定性、精确性也比较高。     

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.     

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%。     

基于多目标优化的任务计划建模及方法

针对任务计划在进行多目标优化时采用进化算法求解效率较低的问题,设计了一种结合分组策略的非支配排序遗传(NSGA-Ⅱ)算法,可以快速有效地得到合理的分组结果。基于分组结果,调整NSGA-Ⅱ算法的步骤,灵活地进行种群初始化,使最终分配结果各优化的目标有了明显的改善,提高了算法的效率。通过实验分析,验证了所提方法的可行性和有效性。     

风扇蜗壳排气段结构优化研究

为有效降低车辆封闭式冷却风道中风扇蜗壳的空气流动阻力,提出了计算流体力学分析与遗传算法相结合的蜗壳排气段结构优化方法,建立了蜗壳结构优化模型,开发了优化算法可执行程序Control CFD.EXE。对某车辆的风扇蜗壳排气段进行了结构优化。优化后蜗壳排气段的空气流动阻力比优化前减小了10.1%,空气质量流量增大了1.70%。