测试点的选取问题

在故障检测的过程中 ,每个测试点检测需要的时间可能不同。本文研究了如何选取一些测试点 ,使得这些测试点可以检测所有故障 ,而所需时间最少的问题。我们将其转化成整数规划问题 ,并给出一个求解算法 .最后给出一个实例对算法加以说明。     

多品种维修器材统一订购策略的库存决策模型

分析了一种多品种维修器材联合订货的库存决策模型,该模型采用连续检查的多品种统一订购库存策略。维修器材库存控制的目的是在确保维修器材服务水平的基础上,降低库存成本。根据这一思想设计了算法,该算法首先假设订货点和订购提前期为零,根据库存成本确定最大库存量,然后考虑订货点和订购提前期不为零的情况,根据维修器材满足率确定各品种维修器材的订货点,从而求得决策参数。最后通过实例证明模型和算法的有效性。     

军队人力资源培训问题的整数线性规划模型

文章研究了军队人力资源培训问题,并基于时间和费用两个指标,建立了一个满足培训时间约束且费用最省的0-1整数线性规划模型,给出了基于Lagrange松驰分解的模型求解算法。在算法中,采用一种简单可行的Lagrange乘子更新方法代替传统的次梯度法。另外,文章证明了算法获得最优解的两个充分条件,计算实例初步表明给出的算法是行之有效的。     

考虑维修时间不确定性的维修任务选择模型

由于维修时间存在不确定性,如何在考虑维修时间不确定性的情况下确定任务间隔期的维修方案是一个值得探讨的问题.给出了一种考虑维修时间不确定性的维修任务选择模型及其求解算法,以在一定置信水平下获得最佳的维修方案.首先,给出了考虑维修时间不确定性的维修任务选择问题的假设条件,并建立了一种非线形的、离散的机会约束规划.其次,给出了一种基于随机模拟的粒子群求解算法,包括粒子的表示、适应度函数、更新公式、算法框架等.最后,给出了具体实例,证明了模型与算法的有效性.该模型非常适用于管理人员在维修时间存在不确定性的情况下作出合理的维修任务选择决策.     

瓶颈指派问题的一种多项式时间算法

本文对瓶颈指派问题给出了一种新的算法,该算法不需要利用最大流算法,而类似于解经典指派问题的匈牙利算法。该算法是一个多项式时间算法,其复杂性为Ｏ（ｎ３）     

车体姿态仿真的一种算法

车辆姿态仿真是自行高炮仿真中的一个重要环节。该文给出了车辆姿态仿真的一种新算法,该算法采取了一系列措施,在基本上不降低仿真精度的情况下,使仿真时间大大减少。     

基于One-port模式的测试点选取问题

在故障诊断过程中 ,每个测试点检测故障所需的时间可能不同。对于每个测试点一次检测所有可检测故障点的问题已经获得解决。对于每个测试点一次只能检测一个故障点 ,分两种情况加以讨论。若要求检测时间之和最小 ,给出了最优算法 ;若要求最大检测时间最小 ,证明了其是NP完全问题 ,并给出近似算法。最后给出一个实例对算法加以说明     

论作战系统中时间与精度关系

在作战系统研制各阶段中,经常为时间与精度关系发生异议。为此,本文首先陈述我国国军标有关规定;其次论证了时间与精度的复杂关系;重点给出了情报指挥系统、火控系统时间与精度指标的估算法;最后给出一个有用的实例、若干注释和结论。     

基于CMAC的模糊神经网络方法

结合模糊神经网络和小脑模型连接控制CMAC理论,提出训练时间短、精度高的CMAC模糊神经网络方法,给出了网络结构、算法,并通过一个维修经费预测实例讲述了这种算法.     

信息融合系统中的数据标准化研究

数据标准化是信息融合系统中的一个重要环节,对后续的信息处理精度有着很大的影响。介绍了防空系统中常用的时空配准方法,并给出了具体的时间对准算法,在传感器的扫描周期不同,导致数据采样时刻不同步的情况下,基于滤波算法,提出了一种细分时间片的时间对准算法。对防空信息的进一步处理具有重要的作用。     

基于遗传算法的虚拟物流联合库存控制研究

针对随机需求条件下的虚拟物流库存控制问题进行了深入研究，提出了一种新的联合库存控制策略——（T，S，s）策略，建立了相应的库存成本模型，并构造遗传算法对模型进行求解。结果分析表明，所提出的（T，S，S）联合库存控制策略是有效的。     

Approximate decision rules for continuous review inventory systems

Constrained multi-item inventory models have long presented signifcant computational problems. This article presents a general algorithm to obtain simultaneous solutions for order quantities and safety stocks for each line item in an inventory, while satisfying constraints on average inventory investment and reordering workload. Computational experience is presented that demonstrates the algorithm's efficiency in handling large-scale applications. Decision rules for several customer service objectives are developed, with a discussion of the characteristics of the inventory systems in which each objective would be most appropriate. The decision rules are approximations, based on the assumptions commonly used in practice.     

Some approximations in multi-item,multi-echelon inventory systems for recoverable items

The optimization problem as formulated in the METRIC model takes the form of minimizing the expected number of total system backorders in a two-echelon inventory system subject to a budget constraint. The system contains recoverable items – items subject to repair when they fail. To solve this problem, one needs to find the optimal Lagrangian multiplier associated with the given budget constraint. For any large-scale inventory system, this task is computationally not trivial. Fox and Landi proposed one method that was a significant improvement over the original METRIC algorithm. In this report we first develop a method for estimating the value of the optimal Lagrangian multiplier used in the Fox-Landi algorithm, present alternative ways for determining stock levels, and compare these proposed approaches with the Fox-Landi algorithm, using two hypothetical inventory systems – one having 3 bases and 75 items, the other 5 bases and 125 items. The comparison shows that the computational time can be reduced by nearly 50 percent. Another factor that contributes to the higher requirement for computational time in obtaining the solution to two-echelon inventory systems is that it has to allocate stock optimally to the depot as well as to bases for a given total-system stock level. This essentially requires the evaluation of every possible combination of depot and base stock levels – a time-consuming process for many practical inventory problems with a sizable system stock level. This report also suggests a simple approximation method for estimating the optimal depot stock level. When this method was applied to the same two hypotetical inventory systems indicated above, it was found that the estimate of optimal depot stock is quite close to the optimal value in all cases. Furthermore, the increase in expected system backorders using the estimated depot stock levels rather than the optimal levels is generally small.     

Exact analysis of (R,s, S) inventory control systems with lost sales and zero lead time

We study an (R, s, S) inventory control policy with stochastic demand, lost sales, zero lead‐time and a target service level to be satisfied. The system is modeled as a discrete time Markov chain for which we present a novel approach to derive exact closed‐form solutions for the limiting distribution of the on‐hand inventory level at the end of a review period, given the reorder level (s) and order‐up‐to level (S). We then establish a relationship between the limiting distributions for adjacent values of the reorder point that is used in an efficient recursive algorithm to determine the optimal parameter values of the (R, s, S) replenishment policy. The algorithm is easy to implement and entails less effort than solving the steady‐state equations for the corresponding Markov model. Point‐of‐use hospital inventory systems share the essential characteristics of the inventory system we model, and a case study using real data from such a system shows that with our approach, optimal policies with significant savings in inventory management effort are easily obtained for a large family of items.     

An adaptive forecasting algorithm and inventory policy for products with short life cycles

Products with short life cycles are becoming increasingly common in many industries, such as the personal computer (PC) and mobile phone industries. Traditional forecasting methods and inventory policies can be inappropriate for forecasting demand and managing inventory for a product with a short life cycle because they usually do not take into account the characteristics of the product life cycle. This can result in inaccurate forecasts, high inventory cost, and low service levels. Besides, many forecasting methods require a significant demand history, which is available only after the product has been sold for some time. In this paper, we present an adaptive forecasting algorithm with two characteristics. First, it uses structural knowledge on the product life cycle to model the demand. Second, it combines knowledge on the demand that is available prior to the launch of the product with actual demand data that become available after the introduction of the product to generate and update demand forecasts. Based on the forecasting algorithm, we develop an optimal inventory policy. Since the optimal inventory policy is computationally expensive, we propose three heuristics and show in a numerical study that one of the heuristics generates near‐optimal solutions. The evaluation of our approach is based on demand data from a leading PC manufacturer in the United States, where the forecasting algorithm has been implemented. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

A time dependent continuous review inventory model with compound poisson demand

We discuss a time dependent optimal ordering policy for a finite horizon inventory system for which the provision of service is essential and thus no stockout is allowed. It is assumed that the system can place an order at any point in time during the horizon when it cannot meet the customer's demand and that lead time is negligible. The demand is considered to be distributed as a compound Poisson process with known parameters and the functional equation approach of dynamic programming is used to formulate the objective function. An algorithm has been developed to obtain the solution for all the cases. In addition, analytical solutions of the basic equation under two limiting conditions are presented.     

初始和终止库存不为零的生产与库存问题

针对初始库存和终止库存不为零的生产与库存问题,通过适当设置需求量,将其化为初始库存和终止库存为零的问题,以便应用重生性质进行求解。     

A nonlinear continuous time optimal control model of dynamic pricing and inventory control with no backorders

In this paper, we present a continuous time optimal control model for studying a dynamic pricing and inventory control problem for a make‐to‐stock manufacturing system. We consider a multiproduct capacitated, dynamic setting. We introduce a demand‐based model where the demand is a linear function of the price, the inventory cost is linear, the production cost is an increasing strictly convex function of the production rate, and all coefficients are time‐dependent. A key part of the model is that no backorders are allowed. We introduce and study an algorithm that computes the optimal production and pricing policy as a function of the time on a finite time horizon, and discuss some insights. Our results illustrate the role of capacity and the effects of the dynamic nature of demand in the model. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Dynamic inventory management with cash flow constraints

In this article, we consider a classic dynamic inventory control problem of a self‐financing retailer who periodically replenishes its stock from a supplier and sells it to the market. The replenishment decisions of the retailer are constrained by cash flow, which is updated periodically following purchasing and sales in each period. Excess demand in each period is lost when insufficient inventory is in stock. The retailer's objective is to maximize its expected terminal wealth at the end of the planning horizon. We characterize the optimal inventory control policy and present a simple algorithm for computing the optimal policies for each period. Conditions are identified under which the optimal control policies are identical across periods. We also present comparative statics results on the optimal control policy. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2008     

Performance evaluation of a production/inventory system with periodic review and endogenous lead times

This paper considers a discrete time, single item production/inventory system with random period demands. Inventory levels are reviewed periodically and managed using a base‐stock policy. Replenishment orders are placed with the production system which is capacitated in the sense that there is a single server that sequentially processes the items one at a time with stochastic unit processing times. In this setting the variability in demand determines the arrival pattern of production orders at the queue, influencing supply lead times. In addition, the inventory behavior is impacted by the correlation between demand and lead times: a large demand size corresponds to a long lead time, depleting the inventory longer. The contribution of this paper is threefold. First, we present an exact procedure based on matrix‐analytic techniques for computing the replenishment lead time distribution given an arbitrary discrete demand distribution. Second, we numerically characterize the distribution of inventory levels, and various other performance measures such as fill rate, base‐stock levels and optimal safety stocks, taking the correlation between demand and lead times into account. Third, we develop an algorithm to fit the first two moments of the demand and service time distribution to a discrete phase‐type distribution with a minimal number of phases. This provides a practical tool to analyze the effect of demand variability, as measured by its coefficient of variation, on system performance. We also show that our model is more appropriate than some existing models of capacitated systems in discrete time. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007