运用多阶段转载货物运输模型优化军需物资调运费用

现有军需物资调运存在着调运体制不健全、调运组织不科学、成本核算不规范、经费标准不合理等弊端,不利于运输工具的回空利用和合理调度,不利于调运部门内部机制的自我约束和自我控制,不利于军需物资运输保障效益的提高。为了适应市场经济的发展、联勤保障体制和军需应急保障的要求,有必要优化军需物资调运费用,建立多阶段转载货物运输模型,以达到降低成本、提高效率、保障有力的目的     

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.     

战时单需求点的战储物资调度模型及算法

战储物资调度是战时后勤保障工作的重要环节,关系着前线部队战斗力的持续生成。考虑战时情况下战储物资调度快速性、复杂性和高危性的特点,以单目标需求为切入点,分析单需求点下战时战储物资调度的特点及决策目标,借助模糊数学中的三角模糊数描述调度过程中不确定的需求信息特征,建立以调运时间最短与调度成本最小为目标的多目标优化模型,在物资需求的约束条件下采用模糊转换和线性规划方法对模型进行求解,算例结果验证了模型的正确性和算法的有效性。     

战区多需求点装备物资紧急调运模型及算法

在战区装备保障过程中,装备物资调运是一项非常重要的工作,其好坏直接决定战区装备保障任务能否迅速并有效执行.在战时情况下,战区装备物资调运问题必须考虑复杂性、时效性、安全性等要素,基于此构建了不同时间因素限制条件下的两种多需求点装备物资紧急调运优化模型,进行了详细的逻辑分析和推导,分别给出了模型的算法,并通过实际算例验证了模型的适用性和算法的有效性.     

基于Hopfield神经网络的战时物资筹措模型

战时物资筹措是国防动员的一个重要组成部分,它实质上是一种基于约束的最短路径问题,Hopfield神经网络曾被用来解决最短路径优化问题。采用连续型Hopfield神经网络求解战时物资筹措问题,在已知供需量、通行参数等数据的情况下可快速确定物资筹措方案。这种方法针对战时物资筹措的特殊性如时间严格、道路通行受阻等具体情况具有很好的适应性。实验结果证明在问题规模较大的情况下模型具有收效速度快、计算结果准确性高等优点。     

基于蝙蝠算法的多目标战备物资调运决策优化

战备物资的调运是部队后勤工作中十分重要的一环,面对复杂多变的战场环境,战备物资调运必须进行科学合理的决策优化。然而现有战备物资调运模型的优化模型较为复杂,如果涉及较多供应点和目标要求则不易实现。针对涉及多个调运点的多目标战备物资调运问题,提出一种新的决策优化模型,利用启发式蝙蝠算法进行优化计算,发挥出蝙蝠算法易于实现、性能优越的特点,较好地解决了涉及较多供应点的多目标战备物资调运决策优化问题,并结合一个仿真算例,证明该方法相对于其他方法更具简易性和有效性。     

战区抗震救灾单需求点物资调运模型及算法研究

在综合考虑物资需求多样性、运输工具多样性及其载重、容量约束的基础上,构建了以应急响应时间最短和运输工具平均空载率最低为目标的战区抗震救灾单需求点物资调运多目标非线性整数规划模型,提出了模型求解的改进多目标粒子群优化算法。通过调整粒子编码、适应度函数和速度更新策略使得该算法适于求解本文模型的自然数解空间约束和等式约束。理论分析表明,该模型具有较强的实用性和普适性。仿真结果表明,该算法是有效的,既能维护解的多样性,又能保证解的收敛性。     

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.     

基于多智能体决策技术的远海物资保障模型

针对远海防卫后勤保障特点,基于Anylogic仿真软件,以作战力量、保障点、保障力量等为智能体模拟了远海物资保障流程,建立了远海物资保障模型,提出了两种保障方案,并以成本费用、补给时间、补给效率为指标,对两种方案进行了对比评估,得到了最优保障方案。结果表明,利用多智能体仿真技术能有效实现远海物资保障方案问题求解,降低补给时间和成本,提高补给效率,为远海物资保障方案的研究提供技术参考。     

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。     

Scheduling of depalletizing and truck loading operations in a food distribution system

This paper studies a scheduling problem arising in a beef distribution system where pallets of various types of beef products in the warehouse are first depalletized and then individual cases are loaded via conveyors to the trucks which deliver beef products to various customers. Given each customer's demand for each type of beef, the problem is to find a depalletizing and truck loading schedule that fills all the demands at a minimum total cost. We first show that the general problem where there are multiple trucks and each truck covers multiple customers is strongly NP‐hard. Then we propose polynomial‐time algorithms for the case where there are multiple trucks, each covering only one customer, and the case where there is only one truck covering multiple customers. We also develop an optimal dynamic programming algorithm and a heuristic for solving the general problem. By comparing to the optimal solutions generated by the dynamic programming algorithm, the heuristic is shown to be capable of generating near optimal solutions quickly. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

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。     

The sequencing of “related” jobs

Given n jobs and a single facility, and the fact that a subset of jobs are “related” to each other in such a manner that regardless of which job is completed first, its utility is hampered until all other jobs in the same subset are also completed, it is desired to determine the sequence which minimizes the cost of tardiness. The special case of pairwise relationship among all jobs is easily solved. An algorithm for the general case is given through a dynamic programming formulation.     

Multiperiod capacity expansion and shipment planning with linear costs

In this paper we consider a multiperiod deterministic capacity expansion and shipment planning problem for a single product. The product can be manufactured in several producing regions and is required in a number of markets. The demands for each of the markets are non-decreasing over time and must be met exactly during each time period (i.e., no backlogging or inventorying for future periods is permitted). Each region is assumed to have an initial production capacity, which may be increased at a given cost in any period. The demand in a market can be satisfied by production and shipment from any of the regions. The problem is to find a schedule of capacity expansions for the regions and a schedule of shipments from the regions to the markets so as to minimize the discounted capacity expansion and shipment costs. The problem is formulated as a linear programming model, and solved by an efficient algorithm using the operator theory of parametric programming for the transporation problem. Extensions to the infinite horizon case are also provided.     

An easy solution for a special class of fixed charge problems

The fixed charge problem is a mixed integer mathematical programming problem which has proved difficult to solve in the past. In this paper we look at a special case of that problem and show that this case can be solved by formulating it as a set-covering problem. We then use a branch-and-bound integer programming code to solve test fixed charge problems using the setcovering formulation. Even without a special purpose set-covering algorithm, the results from this solution procedure are dramatically better than those obtained using other solution procedures.     

Mathematical control theory solution of an interactive accounting flows model

In this paper we have applied the mathematical control theory to the accounting network flows, where the flow rates are constrained by linear inequalities. The optimal control policy is of the “generalized bang-bang” variety which is obtained by solving at each instant in time a linear programming problem whose objective function parameters are determined by the “switching function” which is derived from the Hamiltonian function. The interpretation of the adjoint variables of the control problem and the dual evaluators of the linear programming problem demonstrates an interesting interaction of the cross section phase of the problem, which is characterized by linear programming, and the dynamic phase of the problem, which is characterized by control theory.     

Linear programming and the reliability of multicomponent systems

Several problems in the assignment of parallel redundant components to systems composed of elements subject to failure are considered. In each case the problem is to make an assignment which maximizes the system reliability subject to system constraints. Three distinct problems; are treated. The first is the classical problem of maximizing system reliability under total cost or weight constraints when components are subject to a single type of failure. The second problem deals with components which are subject to two types of failure and minimizes the probability of one mode of system failure subject to a constraint on the probability of the other mode of system failure. The third problem deals with components which may either fail to operate or may operate prematurely. System reliability is maximized subject to a constraint ori system safety. In each case the problem is formulated as an integer linear program. This has an advantage over alternative dynamic programming formulations in that standard algorithms may be employed to obtain numerical results.     

On n/1/F̄ dynamic deterministic problems

We consider sequencing of n jobs which will arrive intermittently and are to be processed on a single machine; the arrival and the processing times of each jobs are assumed known. A schedule is to be developed that minimizes the mean flow time. Two models are considered: (i) when no pre-emption or inserted idle time is allowed in the schedule, and (ii) when pre-emption is allowed but the jobs follow a pre-empt-repeat discipline We illustrate that Cobham's and Phipp's SPT dispatching rule does not guarantee the optimum F? even for the non-preemptive model We propose a branch and bound algorithm for both models and discuss our computational experience We also examine the relative performances of the optimum nonpre-emptive sequence, and the optimum pre-empt-repeat sequence over that resulting from SPT dispatching rule on a large number of sets of jobs of varying sizes and tightness.     

Algorithms for solving the conditional covering problem on paths

Consider the conditional covering problem on an undirected graph, where each node represents a site that must be covered by a facility, and facilities may only be established at these nodes. Each facility can cover all sites that lie within some common covering radius, except the site at which it is located. Although this problem is difficult to solve on general graphs, there exist special structures on which the problem is easily solvable. In this paper, we consider the special case in which the graph is a simple path. For the case in which facility location costs do not vary based on the site, we derive characteristics of the problem that lead to a linear‐time shortest path algorithm for solving the problem. When the facility location costs vary according to the site, we provide a more complex, but still polynomial‐time, dynamic programming algorithm to find the optimal solution. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

AN ALGORITHM FOR THE SEGREGATED STORAGE PROBLEM

The segregated storage problem involves the optimal distribution of products among compartments with the restriction that only one product may be stored in each compartment. The storage capacity of each compartment, the storage demand for each product, and the linear cost of storing one unit of a product in a given compartment are specified. The problem is reformulated as a large set-packing problem, and a column generation scheme is devised to solve the associated linear programming problem. In case of fractional solutions, a branch and bound procedure is utilized. Computational results are presented.