战时弹药供应协同调运模型研究

弹药协同调运是战时弹药保障工作中的重要环节,其协同调运的合理性将直接影响到弹药保障工作的顺利进行.针对弹药的调运问题,从战时技术实施与应用角度研究弹药的调运问题,以到达需求点的运输时间、弹药输送车数量以及弹药损失量为优化目标,建立一种多目标决策模型,为缩短运输时间、减少弹药输送车数量、提高安全到达需求点的弹药量提供一种实用的方法.     

MOQPSO/D算法求解不确定目标分配问题

为了增强不确定目标分配对战场态势变化的适应性,提出了一种基于分解的多目标量子行为粒子群算法(MOQPSO/D)的不确定目标分配方法.基于模糊多目标规划方法建立了不确定目标分配模型.以MOEA/D为算法框架,以QPSO算法为寻优手段提出了一种MOQPSO/D算法.通过粒子编码和非法粒子调整,将MOQPSO/D算法成功应用于求解目标分配模型.仿真结果表明:采用多目标优化方法能有效增强不确定目标分配对战场态势变化的适应性;MOQPSO/D算法在求解目标分配模型时要明显优于MOEA/D及MOEMD-CD算法.     

战时交通运输路径优化研究

以战时交通运输路径优化问题为研究对象,分析问题特点,考虑多式联运,以运输时间代价、运输费用代价、路段和运输节点的危险性代价为优化目标,建立起广义运输代价最小的运输路径优化模型,并设计了蚁群算法来求解问题模型。给出的算例表明,文中模型符合战时交通运输的特点和实际需要,可为确定战时运输路径提供决策支持,而采用的蚁群算法是求解该问题的一种有效方法。     

基于熵权多目标决策的战时物资运输方案优选研究

提出了战时物资运输方案优选问题,分析战时运输的影响因素,提出了评估战时物资运输方案的较有代表性的指标,并给出了具体计算方法.在没有指标权重的情况下,应用熵权多目标决策方法对多个合理方案进行优选评估,得出了可信度较高的优选方案.     

基于博弈论的军用油料运输路径选择研究

军用油料运输面临的风险因素很多,事故后果较为严重,安全问题突出。选择合适的运输路径是提高油料运输安全的重要途径。分析了军用油料运输路径选择的影响因素,建立了以军交运输部门和敌方打击力量为双方的博弈模型,构建各自的目标函数,并根据博弈均衡理论,选择总收益值最大的路径为军用油料运输最优路径。     

二维运动目标纯方位系统的部分可观测性

针对二维空间作匀加速运动的目标,对等速直线运动的单观测站纯方位目标运动分析问题进行了讨论.此时系统是不完全可观测的.本文证明了目标部分运动参数是可估计的,即目标与观察者的相对航向Kr,相对速度与初距离之比Vr/D0,加速度与初距离之比a/D0是可以解算的,并对可估计参数的有解条件进行了分析.     

基于MOEA/D的柔性结构燃料—时间多目标优化控制研究

利用基于分解的多目标优化算法(MOEA/D)研究了柔性航天器多目标优化的rest-to-rest机动问题.基于空间飞行器刚柔耦合动力学方程,提出了最小时间-最少耗能的多目标优化控制模型;给出了基于MOEA/D的算法框架,并对柔性飞行器空间机动问题进行了多目标优化控制的分析设计;典型算例表明该算法可有效地应用于柔性航天器姿态机动控制器的分析设计之中.     

一类基于模型的仿射代数不变量

3D目标的标识别已逐渐成为计算机视觉领域的热点问题.在研究了传统的基于几何不变量和基于3D/2D约束关系两种识别方法的基础上,提出了1类新的空间几何结构模型,并计算出了这类模型3D仿射不变量和其对应的2D仿射不变量之间的约束关系.利用这种关系,可以在目标的姿态和摄像机的参数均未知的情况下,有效利用单幅图像来识别3D目标.     

战场物资快速运输的指挥控制

针对战场快速物资运输的指挥控制问题,提出一种基于线性规划的方法。根据供求双方物质的供应和需求总量、地理空间上的位置以及运输工具的运载能力等信息,制订一个满足供求双方物资总运载量,并以运送所有物资耗费时间为最小的优化目标,改进了传统运输问题模型。仿真分析表明,该方法为实现战场物资快速运输的指挥控制提供了有价值的参考。     

基于差分进化算法的运输直升机人员装载问题

针对空中突击作战中运输直升机资源有限、人员装载需求较大的特点,在满足装载原则约束的前提下,建立了以空间利用率最大化为目标的运输直升机人员装载模型,并提出了一种动态的差分进化算法对模型进行求解.该算法对传统差分进化算法的控制参数进行调整,同时加入个体适应度参数,设计了动态精英变异算子和动态二项式交叉算子,仿真实验证明,该算法可对运输直升机人员装载问题进行有效求解.     

基于NSGA-Ⅱ的WTA多目标优化(英文)

将多目标遗传算法NSGA-(改进的非支配排序遗传算法)应用于求解武器-目标分配(WTA)问题。首先,针对以往在建立防空型WTA问题的优化模型上的片面性,把WTA问题看做多目标优化问题,建立了综合考虑作战效能和防御效能的WTA双目标优化模型。然后在此基础上,研究和应用了NSGA-来求解WTA问题。最后由仿真算例验证了NSGA-在WTA问题中的应用可行性,表明了NSGA-可以快速地搜索到WTA多目标优化的Pareto最优解集,从而为求解WTA问题提供了一条有效途径。     

基于遗传算法的多目标虚拟装配路径规划

针对以往求解路径规划问题中以路径最短为目标的局限性以及随机选取初始路径的缺陷,引入合适的目标函数和遗传算子,提出了一种基于遗传算法的多目标虚拟装配路径规划方法,利用大范围初始化的方式产生具有代表性的初始群体,设计了具有启发作用、适合路径规划的遗传算子,能够同时得到不同特点的多条路径,优化了装配路径.     

Algorithms for the minimax transportation problem

In this paper, we consider a variant of the classical transportation problem as well as of the bottleneck transportation problem, which we call the minimax transportation problem. The problem considered is to determine a feasible flow x_ij from a set of origins I to a set of destinations J for which max_(i,j)εIxJ{c_ijx_ij} is minimum. In this paper, we develop a parametric algorithm and a primal-dual algorithm to solve this problem. The parametric algorithm solves a transportation problem with parametric upper bounds and the primal-dual algorithm solves a sequence of related maximum flow problems. The primal-dual algorithm is shown to be polynomially bounded. Numerical investigations with both the algorithms are described in detail. The primal-dual algorithm is found to be computationally superior to the parametric algorithm and it can solve problems up to 1000 origins, 1000 destinations and 10,000 arcs in less than 1 minute on a DEC 10 computer system. The optimum solution of the minimax transportation problem may be noninteger. We also suggest a polynomial algorithm to convert this solution into an integer optimum solution.     

Solving generalized transportation problems via pure transportation problems

This paper investigates certain issues of coefficient sensitivity in generalized network problems when such problems have small gains or losses. In these instances, it might be computationally advantageous to temporarily ignore these gains or losses and solve the resultant “pure” network problem. Subsequently, the optimal solution to the pure problem could be used to derive the optimal solution to the original generalized network problem. In this paper we focus on generalized transportation problems and consider the following question: Given an optimal solution to the pure transportation problem, under what conditions will the optimal solution to the original generalized transportation problem have the same basic variables? We study special cases of the generalized transportation problem in terms of convexity with respect to a basis. For the special case when all gains or losses are identical, we show that convexity holds. We use this result to determine conditions on the magnitude of the gains or losses such that the optimal solutions to both the generalized transportation problem and the associated pure transportation problem have the same basic variables. For more general cases, we establish sufficient conditions for convexity and feasibility. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 666–685, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10034     

一类带配送中心运输问题的容量扩张模型研究

运输问题一般采用表上作业法来解决,考虑一类带配送中心的运输问题,若仍采用表上作业法,会使问题复杂化.文中采用一种构造辅助网络的方法:在运输网络中将每个配送中心均拆分成两个点,连接两点形成新弧,构造出新的网络,并给每条弧赋予参数,将此类运输问题转换为最小费用流模型来解决,可以使问题模型和运算简单化.在此基础上,考虑运输网络中配送中心和边的容量扩张问题.     

火力分配多目标优化的IBACO算法

应用蚁群优化算法(Ant Colony Optimization)求解多目标优化问题已经引起广泛关注,多目标火力分配问题的目标是求出一个合适的武器目标分配方案,使满足决策需要。建立了多目标火力分配的数学模型,提出一种基于指标的蚁群优化算法Indicator-Based Ant Colony Optimization),给出了算法的具体步骤。IBACO的核心思想是利用二元性能指标来引导人工蚂蚁进行搜索,由于该算法中的信息素是根据指标的值来更新的,通过奖励信息素可以强化最优解。仿真实验证明了该算法的有效性,在解决火力分配问题上,所提算法和蚁群优化算法相比具有较好的收敛性。     

The one-period,N-location distribution problem

This paper studies the one-period, general network distribution problem with linear costs. The approach is to decompose the problem into a transportation problem that represents a stocking decision, and into decoupled newsboy problems that represent the realization of demand with the usual associated holding and shortage costs. This approach leads to a characterization of optimal policies in terms of the dual of the transportation problem. This method is not directly suitable for the solution for large problems, but the exact solution for small problems can be obtained. For the numerical solutions of large problems, the problem has been formulated as a linear program with column generation. This latter approach is quite robust in the sense that it is easily extended to incorporate capacity constraints and the multiproduct case.     

Solving incremental quantity discounted transportation problems by vertex ranking

Logistics managers often encounter incremental quantity discounts when choosing the best transportation mode to use. This could occur when there is a choice of road, rail, or water modes to move freight from a set of supply points to various destinations. The selection of mode depends upon the amount to be moved and the costs, both continuous and fixed, associated with each mode. This can be modeled as a transportation problem with a piecewise-linear objective function. In this paper, we present a vertex ranking algorithm to solve the incremental quantity discounted transportation problem. Computational results for various test problems are presented and discussed.     

The pure fixed charge transportation problem

The pure fixed charge transportation problem (PFCTP) is a variation of the fixed charge transportation problem (FCTP) in which there are only fixed costs to be incurred when a route is opened. We present in this paper a direct search procedure using the LIFO decision rule for branching. This procedure is enhanced by the use of 0–1 knapsack problems which determine bounds on partial solutions. Computational results are presented and discussed.