复杂网络理论的防空反导系统网络特征

网络中心战是未来战争的主要作战样式,网络复杂性也是需要研究的重要问题之一.提出基于复杂网络的防空反导系统网络模型,并对网络模型的拓扑结构、统计特征和中心度进行了研究.针对防空反导体系结构问题,复杂网络理论是一种有效揭示网络内部特性的分析工具,对于网络模型的建立和优化具有很好的理论指导意义.     

节点缓存受限场景下车载自组织网络拥堵特性研究

车载自组织网络作为一种新型的移动自组织网络,是智能交通系统的关键组成部分.近年来,社会各界对车载自组织网络的拥堵性进行了广泛研究,但却忽略了车载自组织网络中移动节点缓存空间有限的现实问题.因此,针对节点缓存受限场景下车载自组织网络的拥堵性问题进行了研究,通过设计来优化网络中数据包的传递过程.仿真实验的结果表明,提出的多...     

复杂网络社团发现算法研究新进展

社团结构是复杂网络普遍存在的拓扑特性之一,发现复杂网络中的社团结构是复杂网络研究的基础性问题.针对非重叠社团发现和重叠社团发现两类问题,全面综述了当前复杂网络社团发现算法研究的新进展,分析了每类社团发现算法的特点,指出该领域值得进一步探索的研究方向.     

复杂指控网络的决策优势度量研究

信息优势转化为决策优势是当今世界各国军事研究的热点和前沿问题.在军事指控网络中,网络中节点的协作是决策优势综合体现.从网络冗余、无用信息和冗余信息对网络协作度的影响出发,在建立信息优势模型的基础上,分析网络复杂性对决策优势度量的影响,建立相应的网络协作度数学模型.结果表明:网络的复杂性通过信息传输过程影响网络协作度,从而影响决策优势.     

面向战术通信网络的AODV路由协议的仿真

战术通信网络能实现语音、数据、图像等的传输,是指挥控制、武器系统、侦察、保障装备互连互通的传输平台.路由是网络的基石,研究路由协议是战术通信网络的首要问题.采用AODV路由协议,在网络仿真软件OPNET基础上进行仿真,结果表明满足系统应用需求.     

基于贝叶斯网络的态势估计时间推理方法

基于贝叶斯网络的态势估计方法是目前态势估计领域中的主要方法之一,然而,传统的贝叶斯网络不具备时间语义,因此无法解决态势估计中的时间推理问题.基于此,对贝叶斯网络进行改造,研究了时间贝叶斯网络的构建方法.通过一个战场想定,说明了时间贝叶斯网络构建、推理的过程与方法,证明了提出方法的有效性.     

基于改进融合算法的交通网络节点重要性评估

交通目标体系在作战中是打击目标选择的重点,在目标选择前需要对交通网络的节点及关联关系的重要性进行准确评估.交通目标体系是一种多层复杂网络模型,目前的评估方法没有考虑到路径的权重和对网络整体价值,造成评估模型与实际网络不符合的问题.针对该问题,提出一种改进的融合算法对多层网络进行评估.该方法构建的节点关联度矩阵考虑了各层网络中边的权重和网络整体价值,再将每层的节点关联度进行融合.通过仿真分析和对比,该方法能够准确有效地评估多层交通网络节点的重要性,对目标选择具有一定实用价值.     

水下信息对抗中水声网络相关问题

水下信息对抗是海军信息化建设中需要重点考虑的内容之一.除了现有的声纳侦察之外,在未来的海战中,信息获取和信息对抗将更多地依赖水声网络的作用.因此,水声网络的相关问题必须得到重视.探讨了水声网络的一些关键技术,从信息对抗的角度分析了水声网络面临的挑战及应对措施.     

用于态势估计的贝叶斯网络方法

态势估计系统需要对大量的不确定性知识进行处理,不确定性知识表示和推理是态势估计中的研究热点.利用贝叶斯网络技术,可以实现对不确定性知识的处理.为此讨论了贝叶斯网络理论,详细分析了态势估计的功能模型,提出事件检测是态势估计的核心和起点,研究了用于态势估计的贝叶斯网络的构建方法.该方法充分考虑贝叶斯网络的几项基本要素,可以解决态势估计领域中贝叶斯网络的构建问题.贝叶斯网络技术在态势估计领域具有广阔的前景,将极大地推动态势估计系统的发展.     

对基层官兵网络借贷问题的几点思考

随着经济社会与互联网技术快速发展,网络借贷悄无声息地融入每位官兵的日常生活,在给官兵带来便利的同时,也诱发了不少问题隐患。习主席指出,基层是部队全部工作和战斗力的基础。面对来势迅猛的网络借贷问题,高质量地抓好防控工作就显得尤为重要。一、基层官兵网络借贷易诱发的问题(一)扰乱官兵训练生活。网络借贷平台利用极具诱导性的宣传方式,使一些警惕性不高的官兵看不透网络借贷看似利率低.     

Geometric programming with multiplicative slack variables

A posynomial geometric programming problem formulated so that the number of objective function terms is equal to the number of primal variables will have a zero degree of difficulty when augmented by multiplying each constraint term by a slack variable and including a surrogate constraint composed of the product of the slack variables, each raised to an undetermined negative exponent or surrogate multiplier. It is assumed that the original problem is canonical. The exponents in the constraint on the product of the slack variables must be estimated so that the associated solution to the augmented problem, obtained immediately, also solves the original problem. An iterative search procedure for finding the required exponents, thus solving the original problem, is described. The search procedure has proven quite efficient, often requiring only two or three iterations per degree of difficulty of the original problem. At each iteration the well-known procedure for solving a geometric programming problem with a zero degree of difficulty is used and so computations are simple. The solution generated at each iteration is optimal for a problem which differs from the original problem only in the values of some of the constraint coefficients, so intermediate solutions provide useful information.     

The dynamic transportation problem: A survey

The dynamic transportation problem is a transportation problem over time. That is, a problem of selecting at each instant of time t, the optimal flow of commodities from various sources to various sinks in a given network so as to minimize the total cost of transportation subject to some supply and demand constraints. While the earliest formulation of the problem dates back to 1958 as a problem of finding the maximal flow through a dynamic network in a given time, the problem has received wider attention only in the last ten years. During these years, the problem has been tackled by network techniques, linear programming, dynamic programming, combinational methods, nonlinear programming and finally, the optimal control theory. This paper is an up-to-date survey of the various analyses of the problem along with a critical discussion, comparison, and extensions of various formulations and techniques used. The survey concludes with a number of important suggestions for future work.     

Optimal flowshop schedules with no intermediate storage space

In this paper the problem of finding an optimal schedule for the n-job, M-machine flowshop scheduling problem is considered when there is no intermediate space to hold partially completed jobs and the objective function is to minimize the weighted sum of idle times on all machines. By assuming that jobs are processed as early as possible, the problem is modeled as a traveling salesman problem and solved by known solution techniques for the traveling salesman problem. A sample problem is solved and a special case, one involving only two machines, is discussed.     

Solution algorithms for the parallel replacement problem under economy of scale

We consider the parallel replacement problem in which machine investment costs exhibit economy of scale which is modeled through associating both fixed and variable costs with machine investment costs. Both finite- and infinite-horizon cases are investigated. Under the three assumptions made in the literature on the problem parameters, we show that the finite-horizon problem with time-varying parameters is equivalent to a shortest path problem and hence can be solved very efficiently, and give a very simple and fast algorithm for the infinite-horizon problem with time-invariant parameters. For the general finite-horizon problem without any assumption on the problem parameters, we formulate it as a zero-one integer program and propose an algorithm for solving it exactly based on Benders' decomposition. Computational results show that this solution algorithm is efficient, i.e., it is capable of solving large scale problems within a reasonable cpu time, and robust, i.e., the number of iterations needed to solve a problem does not increase quickly with the problem size. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 279–295, 1998     

D运输问题在物资调度中的应用

D运输问题是含离散目标约束的目标规划问题.首先将此多目标问题转化为单一目标问题,使之简化为传统的运输问题,然后使用一种相对简单的方法求解.最后结合一个实例,讨论了D运输问题在物资调度中的应用.     

需求可拆分的应急物资调度问题的蚁群算法

应急物资调度问题是个典型的需求可拆分的车辆路径问题,区别于传统的车辆路径问题,将每个需求节点只能由一辆车访问的约束去除,允许需求节点由多辆车进行访问。针对应急物资调度问题的特点,建立相应的多目标车辆路径数学规划模型(SDVRP),并根据模型特点设计改进蚁群优化算法。最后,进行相应的算例分析,验证了该模型和算法的有效性。     

Algorithms for the constrained maximum-weight connected graph problem

Given a positive integer R and a weight for each vertex in a graph, the maximum-weight connected graph (MCG) problem is to find a connected subgraph with R vertices that maximizes the sum of the weights. The MCG problem is strongly NP-complete, and we study a special case of it: the constrained MCG (CMCG) problem, which is the MCG problem with a constraint of having a predetermined vertex included in the solution. We first show that the Steiner tree problem is a special case of the CMCG problem. Then we present three optimization algorithms for the CMCG problem. The first two algorithms deal with special graphs (tree and layered graphs) and employ different dynamic programming techniques, solving the CMCG problem in polynomial times. The third one deals with a general graph and uses a variant of the Balas additive method with an imbedded connectivity test and a pruning method. We also present a heuristic algorithm for the CMCG problem with a general graph and its bound analysis. We combine the two algorithms, heuristic and optimization, and present a practical solution method to the CMCG problem. Computational results are reported and future research issues are discussed. © 1996 John Wiley & Sons, Inc.     

Time minimizing flows in directed networks

An important class of network flow problems is that class for which the objective is to minimize the cost of the most expensive unit of flow while obtaining a desired total flow through the network. Two special cases of this problem have been solved, namely, the bottleneck assignment problem and time-minimizing transportation problem. This paper addresses the more general case which we shall refer to as the time-minimizing network flow problem. Associated with each arc is an arc capacity (static) and a transferral time. The objective is to find a maximal flow for which the length (in time) of the longest path carrying flow is minimized. The character of the problem is discussed and a solution algorithm is presented.     

指派问题的降阶优化算法

指派问题是运筹学中特殊线性规划中的一类问题。在现实生活中,指派问题非常普遍,常常可以见到各种各样的指派问题。通过对指派问题的数学模型进行分析,提出了与以往方法不同的求解指派问题的一种新的思路,通过对几个定理的研究,给出了一种新的求解方法——降阶优化算法。对求解指派问题提供了一种新的途径,在运筹学等领域有着较好的应用前景。     

A combination of Lagrangian relaxation and column generation for order batching in steelmaking and continuous‐casting production

This article considers the order batching problem in steelmaking and continuous‐casting production. The problem is to jointly specify the slabs needed to satisfy each customer order and group all the slabs of different customer orders into production batches. A novel mixed integer programming model is formulated for the problem. Through relaxing the order assignment constraints, a Lagrangian relaxation model is then obtained. By exploiting the relationship between Lagrangian relaxation and column generation, we develop a combined algorithm that contains nested double loops. At the inner loop, the subgradient method is applied for approximating the Lagrangian dual problem and pricing out columns of the master problem corresponding to the linear dual form of the Lagrangian dual problem. At the outer loop, column generation is employed to solve the master problem exactly and adjust Lagrangian multipliers. Computational experiments are carried out using real data collected from a large steel company, as well as on large‐scaled problem instances randomly generated. The results demonstrate that the combined algorithm can obtain tighter lower bound and higher quality solution within an acceptable computation time as compared to the conventional Lagrangian relaxation algorithm. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011