超宽带雷达的欺骗式干扰技术研究

根据超宽带雷达的工作方式和成像特点,提出基于DRFM与DDS技术对超宽带雷达实施假目标干扰的方法.通过分析超宽带雷达距离维成像的基本原理,提出利用DDS技术对转发信号实施移频调制,并根据需要产生不同特征的假目标干扰.在理论分析的基础上进行了仿真实验,仿真结果证明了这种方法的可行性.该方法可对一维和二维雷达成像进行干扰.     

推进党内民主建设 增强党的团结统一

胡锦涛同志在党的十七大报告中,将"发展党内民主"提升到了"党内民主建设"的战略高度,并对推进党内民主建设明确了目标任务和有效方略。深入学习领会、准确把握讲话的这一精神,对于开创党内民主建设新局面具有重大而深远的意义。     

具有战时随机延误与损耗的多配送中心路径优化

建立了具有战时随机延误与损耗的多配送中心配送路径安排模型,给出了基于随机模拟的蚁群算法。算法通过给定残存率、用时与置信度阈值,把多目标问题作为单目标来处理。用随机模拟的方法来求路径的置信度,并以此为基础搜索转移策略的临域与判断未遍历点的插入位置。算法设计了符合问题特点的从虚拟点出发的转移策略与对两类路段不同的信息素更新策略,确保算法的实现。最后,通过算例说明了该方法的可行性与有效性。     

50000m~3油罐壁厚设计比较与分析

油罐的壁厚设计关系到油罐的经济性和可靠性,如何把握两者的辩证关系对油罐的设计与建设具有十分重要的意义。传统上大型油罐壁厚设计采用的是变点法;以可靠度理论为基础,以可靠性分析模型为核心,提出利用可靠度法求解大型油罐壁厚的新方法。以50 000 m3油罐为例,编程分析并计算以2种方法设计的壁厚。变点法设计的底圈罐壁厚度与圈数无关;以中心点可靠度法求解壁厚,可靠性指标一定时,底圈罐壁厚度同样与圈数无关。探讨可靠度法求解壁厚的可行性,验证变点法设计壁厚的可靠性。     

基于公式法的某型水陆坦克海上火力打击效能

针对某型水陆坦克火力威力和火控系统特点,运用定性与定量分析相结合的方式,研究水陆坦克的火力打击效能。运用公式法,从分析某型水陆坦克海上射击误差入手,结合海上作战环境,研究其命中概率,并结合环境特点,分析条件毁伤概率,确定其对M60A3坦克和工事目标的火力打击的能力,得出了水陆坦克的开火距离、弹种选择等相关结果。研究结论对于分析火力打击效能提供了较为合理的方法,对于指导某型水陆坦克海上火力运用提供了借鉴作用。     

Production and transport logistics scheduling with two transport mode choices

This paper considers a new class of scheduling problems arising in logistics systems in which two different transportation modes are available at the stage of product delivery. The mode with the shorter transportation time charges a higher cost. Each job ordered by the customer is first processed in the manufacturing facility and then transported to the customer. There is a due date for each job to arrive to the customer. Our approach integrates the machine scheduling problem in the manufacturing stage with the transportation mode selection problem in the delivery stage to achieve the global maximum benefit. In addition to studying the NP‐hard special case in which no tardy job is allowed, we consider in detail the problem when minimizing the sum of the total transportation cost and the total weighted tardiness cost is the objective. We provide a branch and bound algorithm with two different lower bounds. The effectiveness of the two lower bounds is discussed and compared. We also provide a mathematical model that is solvable by CPLEX. Computational results show that our branch and bound algorithm is more efficient than CPLEX. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

Coordinating independent buyers with integer‐ratio time coordination and quantity discounts

Supply chain members can gain substantial benefits by coordinating their activities. However, a remaining challenge is to create useful coordination mechanisms when channel members are independent. This paper develops a coordination strategy with which a supplier uses quantity discounts to entice independent buyers to comply with an integer‐ratio time coordination scheme. The problem is analyzed as a Stackelberg game in which the supplier acts as the leader by announcing its coordination policy in advance and buyers act as followers by deciding their ordering decisions with this information. The strategy is compared to a coordination mechanism with quantity discounts and power‐of‐two time coordination. While both strategies are able to produce substantial benefits over simple quantity discounts, integer‐ratio time coordination provides a better coordination mechanism for a decentralized supply chain. It is shown that power‐of‐two time coordination may not be able to provide a stable equilibrium coordination strategy when buyers act independently and opportunistically. Furthermore, if this is not the case, integer‐ratio time coordination is at least equally effective. Unlike a centralized solution, under which the improvement by integer‐ratio over power‐of‐two time coordination is limited to 2% of optimality, system cost reduction from a decentralized coordination strategy could be much more significant. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Clustered coverage orienteering problem of unmanned surface vehicles for water sampling

This study investigates a clustered coverage orienteering problem (CCOP), which is a generalization of the classical orienteering problem. The problem is widely motivated by the emerging unmanned techniques (eg, unmanned surface vehicles and drones) applied to environmental monitoring. Specifically, the unmanned surface vehicles (USVs) are used to monitor reservoir water quality by collecting samples. In the CCOP, the water sampling sites (ie, the nodes) are grouped into clusters, and a minimum number of nodes must be visited in each cluster. With each node representing a certain coverage area of the water, the objective of the CCOP is to monitor as much as possible the total coverage area in one tour of the USV, considering that overlapping areas provide no additional information. An integer programming model is first formulated through a linearization procedure that captures the overlapping feature. A two-stage exact algorithm is proposed to obtain an optimal solution to the problem. The efficiency and effectiveness of the two-stage exact algorithm are demonstrated through experiments on randomly generated instances. The algorithm can effectively solve instances with up to 60 sampling sites.     

Optimal production ramp-up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp-up model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.     

Revenue maximization in two-station tandem queueing systems

We study optimal pricing for tandem queueing systems with finite buffers. The service provider dynamically quotes prices to incoming price sensitive customers to maximize the long-run average revenue. We present a Markov decision process model for the optimization problem. For systems with two stations, general-sized buffers, and two or more prices, we describe the structure of the optimal dynamic pricing policy and develop tailored policy iteration algorithms to find an optimal pricing policy. For systems with two stations but no intermediate buffer, we characterize conditions under which quoting either a high or a low price to all customers is optimal and provide an easy-to-implement algorithm to solve the problem. Numerical experiments are conducted to compare the developed algorithms with the regular policy iteration algorithm. The work also discusses possible extensions of the obtained results to both three-station systems and two-station systems with price and congestion sensitive customers using numerical analysis.