Two‐machine flow shop scheduling with common due window to minimize weighted number of early and tardy jobs

This article studies two due window scheduling problems to minimize the weighted number of early and tardy jobs in a two‐machine flow shop, where the window size is externally determined. These new scheduling models have many practical applications in real life. However, results on these problems have rarely appeared in the literature because of a lack of structural and optimality properties for solving them. In this article, we derive several dominance properties and theorems, including elimination rules and sequencing rules based on Johnsos order, lower bounds on the penalty, and upper bounds on the window location, which help to significantly trim the search space for the problems. We further show that the problems are NP‐hard in the ordinary sense only. We finally develop efficient pseudopolynomial dynamic programming algorithms for solving the problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

应科学规范地组织实施心理行为训练

心理行为训练在武警部队得到了越来越广泛的应用,但是它专业技术性比较强,有一套系统科学的训练程序和规范要求。如果不按科学规律办事,缺乏严密的组织,不仅达不到心理行为训练的预期效果,还可能导致受训人员出现心理反应。因此,要提高心理行为训练的有效性,就必须科学规范地组织施训。     

对教学管理与课程建设的思考

课程建设是一项复杂的系统工程,本文分析了教学管理在促进课程建设工作中所起的作用。进一步加强教学管理,促进课程建设,就必须提高教学管理水平,为课程建设作好服务工作。     

潜射导弹出水过程空化流数值计算

运用Zwart-Gerber-Belamri空化模型,采用动网格和复合网格技术,提出复杂外形潜射导弹出水过程中空化流数值计算方法。对头肩部、舵面及突起物的空化生成、演化及影响因素进行分析。仿真结果表明:出水过程初期空化数降低使附体空泡迅速扩大,随后对空泡脱落与振荡的影响将更为显著;肩部以15 m/s的速度出水时产生空化并随速度上升而迅速增长,5°~10°攻角时肩空化非对称性显现;舵面空化受出水速度影响较小,攻角小于等于5°时空化面积小于全舵面积的10%,但攻角大于5°后空化面积迅速增长至50%以上;减小突起物尺寸有利于避免空化产生,突起物空化受攻角影响较小,但在高速下可诱导临近弹体产生空化。     

Making defense planning meaningful: the evolution of strategic guidance in the Hungarian Ministry of Defense

Hungary, a former communist state, adapted a Western-style defense planning system during the 1990s and 2000s. Although on the surface the elements of this planning system were similar to the planning programming budgeting system (PPBS) developed by the US Department of Defense, strategic guidance for defense planning has not been properly developed until recently. Thus, albeit PPBS-based defense plans were developed in the Hungarian Ministry of Defense (Hungarian MoD) regularly, they lacked both an expression of clear priorities and strategic focus. This article delineates the evolution of strategic guidance in the Hungarian MoD concentrating on current developments, and introduces the newly elaborated analytical concepts and tools, which helped to create needed strategic guidance in Hungary.     

基于协方差矩阵估计的稳健Capon波束形成算法

常规Capon波束形成器性能对模型误差或失配非常敏感,尤其是当期望信号包含在训练数据中,导向矢量失配将引起性能急剧下降。为解决这一问题,提出了一种采用干扰噪声协方差矩阵和导向矢量联合估计的稳健波束形成算法。该方法通过对Capon空间谱在非目标信号的方位区域内的积分,实现对干扰噪声协方差矩阵的估计,解决数据协方差矩阵包含有目标信号时引起信号自相消问题;其次为了克服导向矢量失配的影响,通过最大化输出功率,并增加二次型约束防止估计的导向矢量接近于干扰导向矢量,实现对导向矢量的估计。仿真实验表明:该算法能获得近似最优的输出信干噪比,与现有算法相比稳健性更强。     

配套装备器材的库存与运输集成优化研究

针对我军配套装备器材订货的特点,在考虑库存容量空间限制与整套装备的最低期望满足率两种约束条件下,建立了配套装备器材的库存与运输优化模型,并应用改进的动态规划方法进行求解。结果表明：应用库存与运输的优化模型,在保障军事目标实现的前提下,有效地降低了物流成本。     

流编程模型下的存储一致性模型

在流编程模型下建立了一个新的存储一致性模型--流一致性模型,它比传统的释放一致性模型更加松弛.讨论了流一致性模型对程序设计和系统设计的要求,给出了一个正确的系统实现,并且指出流一致性模型的编程和实现并不比现有的一致性模型复杂.     

Optimal capacity in a coordinated supply chain

We consider a supply chain in which a retailer faces a stochastic demand, incurs backorder and inventory holding costs and uses a periodic review system to place orders from a manufacturer. The manufacturer must fill the entire order. The manufacturer incurs costs of overtime and undertime if the order deviates from the planned production capacity. We determine the optimal capacity for the manufacturer in case there is no coordination with the retailer as well as in case there is full coordination with the retailer. When there is no coordination the optimal capacity for the manufacturer is found by solving a newsvendor problem. When there is coordination, we present a dynamic programming formulation and establish that the optimal ordering policy for the retailer is characterized by two parameters. The optimal coordinated capacity for the manufacturer can then be obtained by solving a nonlinear programming problem. We present an efficient exact algorithm and a heuristic algorithm for computing the manufacturer's capacity. We discuss the impact of coordination on the supply chain cost as well as on the manufacturer's capacity. We also identify the situations in which coordination is most beneficial. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008