A novel modeling approach for express package carrier planning

Express package carrier networks have large numbers of heavily‐interconnected and tightly‐constrained resources, making the planning process difficult. A decision made in one area of the network can impact virtually any other area as well. Mathematical programming therefore seems like a logical approach to solving such problems, taking into account all of these interactions. The tight time windows and nonlinear cost functions of these systems, however, often make traditional approaches such as multicommodity flow formulations intractable. This is due to both the large number of constraints and the weakness of the linear programming (LP) relaxations arising in these formulations. To overcome these obstacles, we propose a model in which variables represent combinations of loads and their corresponding routings, rather than assigning individual loads to individual arcs in the network. In doing so, we incorporate much of the problem complexity implicitly within the variable definition, rather than explicitly within the constraints. This approach enables us to linearize the cost structure, strengthen the LP relaxation of the formulation, and drastically reduce the number of constraints. In addition, it greatly facilitates the inclusion of other stages of the (typically decomposed) planning process. We show how the use of templates, in place of traditional delayed column generation, allows us to identify promising candidate variables, ensuring high‐quality solutions in reasonable run times while also enabling the inclusion of additional operational considerations that would be difficult if not impossible to capture in a traditional approach. Computational results are presented using data from a major international package carrier. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Optimal sensor placement for underwater threat detection

The optimization framework for optimal sensor placement for underwater threat detection has been developed. It considers single‐period and multiperiod detection models, each of which includes two components: detection algorithm and optimization problem for sensor placement. The detection algorithms for single‐period and multiperiod models are based on likelihood ratio and sequential testing, respectively. For the both models, the optimization problems use the principle of superadditive coverage, which is closely related to energy‐based and information‐based approaches. An algorithm for quasi‐regular sensor placement approximating solutions to the optimization problems has been developed based on corresponding continuous relaxations and a criterion for its applicability has been obtained. Numerical experiments have demonstrated that the algorithm consistently outperforms existing optimization techniques for optimal sensor placement.© 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

无缝GPS／INS组合导航系统的设计与实现

针对GPS卫星信号失锁条件下GPS/INS组合导航性能大幅度下降的缺点,设计了一种基于ANFIS的无缝组合导航方法,当卫星数目不低于4颗时采用伪距,伪距率的卡尔曼滤波算法,一旦卫星数目少于4颗时采用ANFIS系统估计导航误差,抑制INS的误差积累,从而实现无缝导航.动态车载实验表明,该方法切实可行,相对于传统的紧组合方法,有效地提高了组合导航系统的定位精度和抗干扰能力.     

基于自适应Kalman滤波算法的联邦滤波器

为弥补传统联邦滤波器实用性不好,缺乏对对象模型和传感器噪声的自适应估计能力的缺陷,利用自适应Kalman滤波算法的思想,结合联邦滤波器本身的算法结构,对联邦滤波器进行改进,使之具有很强的自适应性,能够自适应地计算出模型噪声和传感器噪声的协方差阵.给出了基于自适应Kalman滤波算法的联邦滤波器的计算架构,其他优秀的自适应算法均可按相同的方式加入该架构中.最后通过仿真计算验证了该算法的有效性.     

基于约束满足的多目标对地观测卫星成像调度

EOS(对地观测卫星)成像调度需要根据用户提出的成像任务需求确定卫星成像序列,是一个复杂的组合优化问题。考虑到成像侧视约束条件,建立EOS成像调度的多目标有向无环约束图模型。在此模型的基础上提出了基于SPEA2(strength pareto evolutionary algorithm 2)的多目标成像调度算法,采用约束控制技术设计遗传算子处理成像约束。经过三个实际的多目标成像调度问题测试,表明该算法可以有效地解决EOS成像调度问题。     

超燃冲压发动机燃烧室构型优化的试验研究

在直连式超燃冲压发动机试验系统上,通过调节超燃冲压发动机燃烧室壁面扩张角和燃料喷注位置,对燃烧室构型优化进行了试验研究。为了提高试验效率,燃烧室形面调节采用正交试验设计方法进行组织,每个形面进行5种喷注位置的试验,每次试验通过文氏管调节3个当量比的燃料流量。利用试验数据构造燃烧室性能关于构型参数的响应面模型,可用于燃烧室构型优化。通过两次渐进优化获得了性能更优的燃烧室构型,并根据试验数据分析了各构型参数对燃烧室性能的影响,结果表明:优化构型燃烧室的推力增益比基准构型增大了10.4%;燃烧室性能受各构型参数的强烈耦合影响。