变量有界线性目标规划的对偶算法

本文讨论了变量有界的线性目标规划问题,给出了求解这类问题的一个对偶算法,此方法与变量有界线性规划问题的对偶算法相类似。文中证明了算法的有效性,并举例说明了计算过程。     

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。     

不相容矩阵方程AXB=D的反中心对称迭代解

研究了不相容矩阵方程AXB=D的反中心对称最佳逼近解,基于经典共轭梯度法思想,构造了求解这一问题的迭代算法,证明了该算法的有限终止性并给出了该方法的误差估计,最后利用具体的数值例子验证了算法的有效可行性。     

关于i=(4n+7±(6n~2+6n-11)~(1/2))/10的正整数解的快速算法

本文给出了有效信号为二次多项式时,线性一步外推点与采样点关系方程正整数解的快速求解算法。     

关于(I=4n+7±√6n2+6n-11/10)的正整数解的快速算法

本文给出了有效信号为二次多项式时,线性一步外推点与采样点关系方程正整数解的快速求解算法.     

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。     

约束最短链路不相交路径的启发式算法

文章研究约束最短链路不相交路径(CSDP(k))问题,该问题分为两类:CSDP(k)-和CSDP(k)-。首先引入问题的整数规划模型,通过拉格朗日乘子将复杂约束引入到目标函数中,接着给出了求解CSDP(k)-的一种快速启发式算法FHABIP,并给出了改进的搜索方案。算法实验结果表明该算法快速有效,能得到最优解或很好的近似最优解。     

潜艇不机动纯方位解算编队目标运动要素方法

针对单艘潜艇纯方位目标定位必须潜艇机动才能使算法收敛的问题,首次采用几何分析法研究了潜艇不机动情况下,解算编队目标运动要素的方法;然后,基于最小二乘法,给出了具体的解算模型,并给定态势,进行实例仿真。证明该算法能够满足潜艇隐蔽对编队目标定位的需要,进一步丰富了潜艇纯方位目标定位方法。     

CGCS2000大地问题解算工程运用研究

军事信息系统研制中经常涉及CGCS2000坐标系下的大地问题解算,但直接采用韦森特公式进行软件设计存在一些问题。通过对韦森特公式在工程运用中的问题进行了分析,对正解、反解算法进行了补充完善并给出了相关的计算实例供编程检查。改进后的算法简单实用,适合军事信息系统软件的实现。     

关于i＝4n＋7±√6n^2n＋6n－11／10的正整数解的快速算法

本文给出了有效信号为二次多项式时,线性一步外推点与采样点关系方程正整数解的快速求解算法。     

有容量限制的运输问题

具有容量限制的运输问题可以用有界变量的线性规划问题求解,但是问题的规模往往变得很大,给求解带来不便。本文给出求解这一问题的表上作业法。     

Variations on a cutting plane method for solving concave minimization problems with linear constraints

A cutting plane method for solving concave minimization problems with linear constraints has been advanced by Tui. The principle behind this cutting plane has been applied to integer programming by Balas, Young, Glover, and others under the name of convexity cuts. This paper relates the question of finiteness of Tui's method to the so-called generalized lattice point problem of mathematical programming and gives a sufficient condition for terminating Tui's method. The paper then presents several branch-and-bound algorithms for solving concave minimization problems with linear constraints with the Tui cut as the basis for the algorithm. Finally, some computational experience is reported for the fixed-charge transportation problem.     

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.     

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.     

A generalized-indices transportation problem

A generalized-indices transportation problem is formulated and an algorithm is presented for its solution. The algorithm is an extension of the modi-method. A theorem on the number of independent variables in the generalized-indices transportation problem is proved. An example problem is solved for the four-indices transportation problem. A computer program has been written to solve any four-indices problem.     

Adjacent vertices on transportation polytopes

To solve linear fixed charge problems with Murty's vertex ranking algorithm, one uses a simplex algorithm and a procedure to determine the vertices adjacent to a given vertex. In solving fixed charge transportation problems, the simplex algorithm simplifies to the stepping-stone algorithm. To find adjacent vertices on transportation polytopes, we present a procedure which is a simplification of a more general procedure for arbitrary polytopes.     

Optimal location of public facilities

Location of both public and private facilities has become an important consideration in today's society. Progress in solution of location problems has been impeded by difficulty of the fixed charge problem and the lack of an efficient algorithm for large problems. In this paper a method is developed for solving large-scale public location problems. An implicit enumeration scheme with an imbedded transportation algorithm forms the basis of the solution technique.     

A lagrangean relaxation algorithm for the constrained matrix problem

We present variants of a convergent Lagrangean relaxation algorithm for minimizing a strictly convex separable quadratic function over a transportation polytope. The algorithm alternately solves two “subproblems,” each of which has an objective function that is defined by using Lagrange multipliers derived from the other. Motivated by the natural separation of the subproblems into independent and very easily solved “subsubproblems,” the algorithm can be interpreted as the cyclic coordinate ascent method applied to the dual problem. We exhibit our computational results for different implementations of the algorithm applied to a set of large constrained matrix problems.