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.     

基于Petri网的多状态运输网络端端可靠度计算方法

经典的网络可靠性问题认为网络和部件只存在完全工作或完全故障两种状态。但在运输网络中,节点和弧可能处于某种中间状态,即存在多状态特性。描述了一种基于Petri网计算多状态运输网络端端可靠度的方法,节点和弧的能力可以服从任何分布,传输时间是与其当前能力和运输需求有关的的随机值。能力随机有色Petri网被用于模拟系统行为,通过仿真估算多状态运输网络的端端可靠度,同时可以确定可靠度最高的最优路径,最后给出了一些计算实验。     

基于Floyd算法的常规导弹连续波次作战运输规划

为提高常规导弹连续波次作战效能，对常规导弹连续波次作战运输规划问题进行研究。以Floyd算法为基础，首先生成了作战机动区域的交通网络最短路径、距离矩阵；其次将常规导弹连续波次作战运输过程分解为不同阶段，以运输过程中的整体暴露时间最短为目标，构建了初始规划方案0-1整数规划模型；然后考虑道路通行量及地域容量限制，对初始规划方案中存在的地域容量超过限制及单行道路中会车、超车情况进行逐步循环优化，以得到最佳运输规划方案；最后选择了一个作战案例想定，通过Matlab编程对案例进行了求解，得到了针对此案例的最佳运输规划方案，验证了模型的正确性和算法的有效性。     

配套装备器材的库存与运输集成优化研究

针对我军配套装备器材订货的特点，在考虑库存容量空间限制与整套装备的最低期望满足率两种约束条件下，建立了配套装备器材的库存与运输优化模型，并应用改进的动态规划方法进行求解。结果表明：应用库存与运输的优化模型，在保障军事目标实现的前提下，有效地降低了物流成本。     

军队油料运输网络安全优化研究

军队油料运输路径选择受交通运输网络和运输线路风险的影响和制约。战时,交通运输网络是敌方破坏的重点,油料运输面临很大风险,安全形势非常严峻。合理优化交通运输网络,选择合适的运输路径是避免敌方破坏、保障油料运输安全的重要途径。从不同决策角度出发,选择以总后勤部军事交通运输部为代表的网络规划者和以各级军事代表处及运油分队为代表的运输路径选择者为两级决策者,以实现总体风险均衡和单条运输路径目标最优为出发点,建立双层规划模型,对运输网络进行优化,选择最优运输路径。该法既可降低不同性质的风险,又能兼顾各种风险的相互影响。     

浅析储油罐安全容量的确定计算

由于油料具有热膨胀性，在运输和储存期间，如果油罐装油超过安全容量，当温度升高时，罐内油料体积膨胀，就可能导致溢油事故的发生，不仅造成油料数量的损失，甚至会引发火灾事故。正确确定不同油罐的安全容量，对油料的安全具有重要意义。文章阐述了油罐安全容量的概念，分析了安全容量计算不准确可能造成的危害，提出了油罐安全容量的计算方法。     

军事物流运输中的最小风险最大流模型构建及求解

文章综合考虑运输风险、弧的容量和网络流量等因素,基于最小费用最大流问题,构建最小风险最大流模型并进行求解,研究军事物流运输中的最小风险路径选择和增广链流量调整问题。实例分析结果表明,最小风险最大流模型,可以较好地应用于军事物流运输路线的选择上。通过获得最优的运输路线,可以为军事物流运输的决策提供理论支持。     

基于火力打击的装备物资配送中心选址模型

战时装备物资配送中心的选址优劣与否对作战效果影响很大,有时甚至会影响到战斗的结果.针对战时装备配送中心选址的安全性要求,深入剖析了现代战争条件下装备物资配送中心选址模型的影响要素,建立了基于火力打击的费用最少和时间最短的装备物资配送中心选址模型,以及同时考虑时间和费用的装备物资配送中心的综合权衡模型,并给出了时间、费用最小模型的求解流程.     

多参数带约束限制的风险运输网络优化研究

对赋有四个参数（可靠度、时间、费用、容量）的运输网络进行优化分析,给出求解最优路线的算法。     

《海军工程大学学报》2006年总目次

第18卷第1期(总第126期)一阶超对策模型中结局偏好认知信息集结的模式识别法………………………宋业新董青(1)一类带配送中心运输问题的容量扩张模型研究………………………薛强董鹏罗朝晖(6)多尺度法的设解形式之探讨……………………………………………任爱娣张琪昌张良欣(11     

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.     

Machine scheduling with an availability constraint and job delivery coordination

In this paper we study the scheduling problem that considers both production and job delivery at the same time with machine availability considerations. Only one vehicle is available to deliver jobs in a fixed transportation time to a distribution center. The vehicle can load at most K jobs as a delivery batch in one shipment due to the vehicle capacity constraint. The objective is to minimize the arrival time of the last delivery batch to the distribution center. Since machines may not always be available over the production period in real life due to preventive maintenance, we incorporate machine availability into the models. Three scenarios of the problem are studied. For the problem in which the jobs are processed on a single machine and the jobs interrupted by the unavailable machine interval are resumable, we provide a polynomial algorithm to solve the problem optimally. For the problem in which the jobs are processed on a single machine and the interrupted jobs are nonresumable, we first show that the problem is NP‐hard. We then propose a heuristic with a worst‐case error bound of 1/2 and show that the bound is tight. For the problem in which the jobs are processed on either one of two parallel machines, where only one machine has an unavailable interval and the interrupted jobs are resumable, we propose a heuristic with a worst‐case error bound of 2/3. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Benders' partitioning scheme applied to a new formulation of the quadratic assignment problem

In this paper we present a new formulation of the quadratic assignment problem. This is done by transforming the quadratic objective function into a linear objective function by introducing a number of new variables and constraints. The resulting problem is a 0-1 linear integer program with a highly specialized structure. This permits the use of the partitioning scheme of Benders where only the original variables need be considered. The algorithm described thus iterates between two problems. The master problem is a pure 0-1 integer program, and the subproblem is a transportation problem whose optimal solution is shown to be readily available from the master problem in closed form. Computational experience on problems available in the literature is provided.     

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.     

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     

多旋翼无人机在战术后勤中的应用分析

旋翼无人机技术发展迅猛,在军用领域展现出极强的发展潜力,部分军事强国已将旋翼无人机技术引入后勤,对后勤运输产生了深刻影响。旋翼无人机的技术特点使其更适应恶劣条件下战术后勤任务,是对陆、海、空运输方式的良好补充。旋翼无人机实现快速运输与精准分发平稳换挡接力,给原有的运输、投送、分发后勤运输体系带来一系列新的变化,有助于建设立体化现代后勤。     

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.     

A fluid queueing model for link travel time moments

We analyze the moments of the random time required for a vehicle to traverse a transportation network link of arbitrary length when its speed is governed by a random environment. The problem is motivated by stochastic transportation network applications in which the estimation of travel time moments is of great importance. We analyze this random time in a transient and asymptotic sense by employing results from the field of fluid queues. The results are demonstrated on two example problems. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

一种基于神经网络的彩色图像分割方法

提出了适于彩色图像分割的加强型径向基函数网络方法,采用在线自适应聚类学习算法确定隐层节点的数目、激活函数中心值;通过Hebb学习算法迅速将隐层节点中心值分为目标颜色聚类中心和背景颜色聚类中心两类;输出层用竞争规则将目标与背景分开。通过对多幅彩色图像进行分割处理验证了该方法的有效性。