战场物资运输指挥控制的对偶分析

在战场的供求双方之间实施快速物资运输的指挥控制是作战指挥控制的一个重要组成部分,根据供求双方的物资供应量和需求量、运输路径的长度和运输工具的能力等参数,制订一个使物资总运载量以及耗费时间为最小的运输指挥控制计划是战场指挥员必须解决的关键问题.一种基于线性规划及对偶分析的战场物资运输的指挥控制方法为解决此类问题提供了一种可行途径.     

联合防空作战后勤保障面临的困难及解决对策

联合防空作战后勤保障存在着保障体制不顺,技术手段落后,物资储备缺口大,战场设施不配套,指挥人才匮乏等问题。提高未来联合防空作战后勤保障能力应大力加强后勘指挥体系、战储保障体系、新型战场保障体系、保障力量体系和指挥网络建设。     

信息垄断：作战指挥的“超级保镖”

作战指挥与信息密不可分。信息是决策之源,是战场之魂。随着信息技术不对称发展,出现了某些信息技术高度发达的国家,利用对信息资源及其相关产业的垄断地位,对信息技术领域发展相对落后的国家实行信息控制。敌对双方信息能力的差异,产生非对称战场信息,其极端形式为信息垄断,战场呈现单向透明。     

夺取战场综合控制权军队指挥中的对策

作战实践表明,作战的核心问题是如何牢牢把握控制权。随着新制权理论的不断涌现,制权的重心从制海权、制空权、制信息权、制交通权等拓展至战场综合控制权。分析了夺取战场综合控制权指挥活动的特点和可能遇到问题,论述了战场维度、社会形态和科技发展对夺取战场综合控制权指挥活动带来的影响,并对夺取战场综合控制权军队指挥活动进行了初步探讨,提出夺取战场综合控制权应实施一体化联合作战指挥、形成军地联合指挥控制机制、建设完善夺取战场综合控制权的物质条件等对策性措施,对夺取战场综合控制权军队指挥活动有一定借鉴意义。     

数字化炮兵指挥控制的探讨

从分析数字化定义入手,根据数字化炮兵的特点,探讨了数字化炮兵指挥控制问题.提出了提高数字化炮兵指挥控制效能应重点解决好8个方面的问题.即:合理优化阵地配置,适应战场变化;建立高效的侦察、通信保障系统,增强指挥控制的时效性和准确性;建立一体化信息网络系统,实现动态控制,加强指挥控制的有效性;优化指挥手段,提高指挥控制的应变性;周密组织电子对抗,确保指挥控制的不间断性;提高指挥机构的战场生存能力,确保指挥控制的稳定性;加强技术保障,确保指挥控制的可靠性;着眼全局,把握关节,加强指挥控制的协调性.     

国防工程快速抢修指挥控制探究

国防工程快速抢修是信息化战争条件下保障战争体系正常运转的支撑环节,国防工程快速抢修指挥控制是制约抢修效能的重中之重.本文从信息化条件下国防工程快速抢修指挥控制信息的重要性和特点入手,从战术思想指导工程保障行动的角度,分析了指挥控制信息在引导国防工程抢修体系的构建和运行中的作用;探讨了国防工程快速抢修的主要指挥控制方式,其特点和运用时机,力争做到战场实时、精确控制,提高控制效能.     

数字化战场指挥控制系统的发展

简要介绍了数字化战场指挥控制系统的定义,并探讨了未来高科技战争对作战指挥控制所提出的新要求和产生的影响;重点介绍数字化战场指挥控制系统的描述方法及其采用的关键技术;最后着重论述国外数字化战场指挥控制系统的现状和发展趋势,特别是对未来数字化战场指挥控制系统发展的四个方向进行了重点阐述.     

试论高技术条件下空中联合作战指挥的整体效能

随着航空、航天技术的不断发展和高、精、尖新式武器在战场上的应用,我传统的空中作战指挥样式受到了极大的挑战。高科技与高科技的角逐,系统与系统的对抗,智能与智能的较量,整体与整体的拼博,已成为现代空中作战指挥中敌我双方的基本特点。面对空中作战指挥系统、指挥机构、指挥结构新的变化,指挥样式、指挥手段、指挥方式、指挥方法新的变革,我空中作战指挥诸系统,诸因素如何与之相适应,使众多的参战力量真正形成整体作战能力,使全维战场空间得到有力的控制,使战略决策、战略目标、战略目的最终得到实现。笔者认为,最关键是要…     

砺兵秣马驭沙场

日前,吉林某预备役炮兵师组织全员额、全装备、全物资、全过程的实兵快速集结演练,在实战背景下全面检验预备役部队的快速集结和战斗能力。演练中,他们立足现有装备,结合自行设计研制的信息化指挥系统,不断改进指挥手段,使系统理论与模拟训练形成一体化,在高强度、高逼真的“战场”环境下,不断强化指挥效能,进一步提高了预备役部队信息化建设水平。     

远海非战争军事行动物资采购保障组织指挥体系的构建

文章结合当前远海非战争军事行动物资采购保障组织指挥存在的问题,从促进保障力量融合、规范物资资源使用、优化组织指挥流程等方面论述了远海非战争军事行动物资采购保障组织体系构建的目标,提出要建立军民融合、内外一体的物资采购保障组织指挥体系,同时要完善上下联动、快速响应和互惠共赢等配套机制。     

The bottleneck transportation problem

The bottleneck transportation problem can be stated as follows: A set of supplies and a set of demands are specified such that the total supply is equal to the total demand. There is a transportation time associated between each supply point and each demand point. It is required to find a feasible distribution (of the supplies) which minimizes the maximum transportaton time associated between a supply point and a demand point such that the distribution between the two points is positive. In addition, one may wish to find from among all optimal solutions to the bottleneck transportation problem, a solution which minimizes the total distribution that requires the maximum time Two algorithms are given for solving the above problems. One of them is a primal approach in the sense that improving fcasible solutions are obtained at each iteration. The other is a “threshold” algorithm which is found to be far superior computationally.     

A mean cost approximation for transportation problems with stochastic demand

Among the many tools of the operations researcher is the transportation algorithm which has been used to solve a variety of problems ranging from shipping plans to plant location. An important variation of the basic transportation problem is the transportation problem with stochastic demand or stochastic supply. This paper presents a simple approximation technique which may be used as a starting solution for algorithms that determine exact solutions. The paper indicates that the approximation technique offered here is superior to a starting solution obtained by substituting expected demand for the random variables.     

Transshipment through crossdocks with inventory and time windows

A major challenge in making supply meet demand is to coordinate transshipments across the supply chain to reduce costs and increase service levels in the face of demand fluctuations, short lead times, warehouse limitations, and transportation and inventory costs. In particular, transshipment through crossdocks, where just‐in‐time objectives prevail, requires precise scheduling between suppliers, crossdocks, and customers. In this work, we study the transshipment problem with supplier and customer time windows where flow is constrained by transportation schedules and warehouse capacities. Transportation is provided by fixed or flexible schedules and lot‐sizing is dealt with through multiple shipments. We develop polynomial‐time algorithms or, otherwise, provide the complexity of the problems studied. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

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.     

A stock redistribution model

Multi-depot supply systems are subject to stock distribution imbalances; i. e., the fraction of total system stock located at a depot may be too small to support the fraction of system demand expected to be placed on it. In the supply system of concern, a cutomer is always satisfied if there is stock anywhere in the system. Stock redistributions to correct imbalances may reduce both transportation costs and customer waiting times. A model for determining optimum redistribution quantities is formulated, and a practical method of solution for the two depot case is described. Selected numerical illustrations are given.     

Outbound supply chain network design with mode selection and lead time considerations

We present a large‐scale network design model for the outbound supply chain of an automotive company that considers transportation mode selection (road vs. rail) and explicitly models the relationship between lead times and the volume of flow through the nodes of the network. We formulate the problem as a nonlinear zero‐one integer program, reformulate it to obtain a linear integer model, and develop a Lagrangian heuristic for its solution that gives near‐optimal results in reasonable time. We also present scenario analyses that examine the behavior of the supply chain under different parameter settings and the performance of the solution procedures under different experimental conditions. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Transportation type problems with quantity discounts

It is known to be real that the per unit transportation cost from a specific supply source to a given demand sink is dependent on the quantity shipped, so that there exist finite intervals for quantities where price breaks are offered to customers. Thus, such a quantity discount results in a nonconvex, piecewise linear functional. In this paper, an algorithm is provided to solve this problem. This algorithm, with minor modifications, is shown to encompass the “incremental” quantity discount and the “fixed charge” transportation problems as well. It is based upon a branch-and-bound solution procedure. The branches lead to ordinary transportation problems, the results of which are obtained by utilizing the “cost operator” for one branch and “rim operator” for another branch. Suitable illustrations and extensions are also provided.     

Assessing the value of demand sharing in supply chains

We consider the problem of assessing the value of demand sharing in a multistage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast of the leadtime demand and uses it to determine the order quantity via a periodic review myopic order‐up‐to policy. We demonstrate how a typical supply chain player can determine the extent of its available information in the presence of demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. The retailer's demand is driven by the random shocks appearing in the autoregressive moving average representation for its demand. Under the assumptions we will make in this article, to the retailer, knowing the shock information is equivalent to knowing the demand process (assuming that the model parameters are also known). Thus (in the event of sharing) the retailer's demand sequence and shock sequence would contain the same information to the retailer's supplier. We will show that, once we consider the dynamics of demand propagation further up the chain, it may be that a player's demand and shock sequences will contain different levels of information for an upstream player. Hence, we study how a player can determine its available information under demand sharing, and use this information to forecast leadtime demand. We characterize the value of demand sharing for a typical supply chain player. Furthermore, we show conditions under which (i) it is equivalent to no sharing, (ii) it is equivalent to full information shock sharing, and (iii) it is intermediate in value to the two previously described arrangements. Although it follows from existing literature that demand sharing is equivalent to full information shock sharing between a retailer and supplier, we demonstrate and characterize when this result does not generalize to upstream supply chain players. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full information shocks (FIS) with an adjacent upstream player as quasi‐ARMA in—quasi‐ARMA out. We also provide a convenient form for the propagation of demand in a supply chain that will lend itself to future research applications. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 515–531, 2014     

Inventory management with advance capacity information

An important aspect of supply chain management is dealing with demand and supply uncertainty. The uncertainty of future supply can be reduced if a company is able to obtain advance capacity information (ACI) about future supply/production capacity availability from its supplier. We address a periodic‐review inventory system under stochastic demand and stochastic limited supply, for which ACI is available. We show that the optimal ordering policy is a state‐dependent base‐stock policy characterized by a base‐stock level that is a function of ACI. We establish a link with inventory models that use advance demand information (ADI) by developing a capacitated inventory system with ADI, and we show that equivalence can only be set under a very specific and restrictive assumption, implying that ADI insights will not necessarily hold in the ACI environment. Our numerical results reveal several managerial insights. In particular, we show that ACI is most beneficial when there is sufficient flexibility to react to anticipated demand and supply capacity mismatches. Further, most of the benefits can be achieved with only limited future visibility. We also show that the system parameters affecting the value of ACI interact in a complex way and therefore need to be considered in an integrated manner. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

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.