一种采用应力分离法的杂交/混合板壳单元

本文利用一种采用应力分离法的杂交/混合板壳有限元模型分析板壳结构静力学问题。分析表明,该元素在总体水平上没有多余零能模式。而且,该元素通过了薄板的闭锁试验,对薄壳算例也没有观察到闭锁现象。与参考解相比,该元素对各种壳结构均能适用,且收敛性较好,精度也较高。     

推理树概率逻辑公式集有效赋值列数计算

本文通过对概率逻辑推理树知识结构模型的研究,得出在树推理中对矩阵列消减起主要作用的相邻关系及其计算方法,给出了树推理模型的有效赋值列数的一般计算方法。     

一种系统存在有界干扰的混合自适应极点配置方案

主要讨论了确定性单输入单输出连续时间系统存在有界干扰时的混合自适应极点配置问题,给出了一种间接式混合自适应极点配置方案,并以某飞行器自动驾驶仪为例,对该方案进行了数字仿真,结果表明,该方案能有效地抑制有界干扰的不利影响。     

空地支援多目标模型

本文研究空地支援优化问题。模型的构造是基于目标规划理论。首先建立基础模型,然后根据问题条件,建立多目标模型。     

战场弹药贮供建模分析

本文运用库存论、目标规划和计算机模拟等理论与方法,分析研究了战场弹药的储备与补充问题,建立了“两级”弹药贮供数学模型,并应用实战数据在计算机上进行了模拟运算和分析。     

C3I时隙分配优化设计

用线性规划方法讨论了Ｃ３Ｉ通信网络中时隙分配的优化设计问题     

高级程序设计语言综述

为读者选择语言提供了总体思路,给出了高级语言的发展的趋势。     

科研经费管理决策支持系统若干模型

本文应用多目标决策原理和计算机技术,在分析用户需求的基础上,建立了科研任务项目的预测评价和经费分配模型,并分析了有关模型的关系和解决性质。根据这些模型和决策的特点,简要叙述了该系统的主要功能。我们相信,该系统的应用,将给管理人员提供方便,且使管理进一步科学化,让有限的经费发挥更大的作用。     

Cyclic best first search: Using contours to guide branch‐and‐bound algorithms

The cyclic best‐first search (CBFS) strategy is a recent search strategy that has been successfully applied to branch‐and‐bound algorithms in a number of different settings. CBFS is a modification of best‐first search (BFS) that places search tree subproblems into contours which are collections of subproblems grouped in some way, and repeatedly cycles through all non‐empty contours, selecting one subproblem to explore from each. In this article, the theoretical properties of CBFS are analyzed for the first time. CBFS is proved to be a generalization of all other search strategies by using a contour definition that explores the same sequence of subproblems as any other search strategy. Further, a bound is proved between the number of subproblems explored by BFS and the number of children generated by CBFS, given a fixed branching strategy and set of pruning rules. Finally, a discussion of heuristic contour‐labeling functions is provided, and proof‐of‐concept computational results for mixed‐integer programming problems from the MIPLIB 2010 database are shown. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 64–82, 2017     

Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.