联合火力打击弹药需求计算动态模型研究

弹药是火力打击和火力毁伤的基础.传统的火力毁伤弹药需求计算主要有两种思路,一种是不考虑对抗,单纯基于目标的幅员和弹药的毁伤概率静态计算弹药的需求,一种是考虑对抗,运用兰切斯特方程计算弹药的消耗,这样求得的预测结果与实际需求均有较大的差距.研究发现,将基于目标打击的弹药需求和在对抗条件下武器损耗因素结合起来考虑,可以有机地将两种弹药消耗的计算思路融合在一起,建立新的数学模型,所得结果反映了弹药实际需求与外部因素的内在关系,与实际作战更加相符,对战时的弹药供应决策具有重要意义.     

装备维修中备件需求率的预计方法

通过研究装备维修过程中器件的固有可靠性和维修性,着重分析了影响维修器件需求率的主要因素.利用系统建模和仿真的方法,分别针对耗损类型器件和可修复类型器件建立了相应的需求数学模型,最后给出了维修备件需求率的预计方法.     

基于BP神经网络的装备器材需求预测模型

准确预测装备器材的需求数量,是装备器材保障工作的重要内容和装备器材计划管理的前提。采用BP神经网络算法,通过对装备器材历史消耗数据进行处理,建立了装备器材需求预测模型,并结合实例,对模型进行了探讨和验证。     

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。     

A selective newsvendor approach to order management

Consider a supplier offering a product to several potential demand sources, each with a unique revenue, size, and probability that it will materialize. Given a long procurement lead time, the supplier must choose the orders to pursue and the total quantity to procure prior to the selling season. We model this as a selective newsvendor problem of maximizing profits where the total (random) demand is given by the set of pursued orders. Given that the dimensionality of a mixed‐integer linear programming formulation of the problem increases exponentially with the number of potential orders, we develop both a tailored exact algorithm based on the L‐shaped method for two‐stage stochastic programming as well as a heuristic method. We also extend our solution approach to account for piecewise‐linear cost and revenue functions as well as a multiperiod setting. Extensive experimentation indicates that our exact approach rapidly finds optimal solutions with three times as many orders as a state‐of‐the‐art commercial solver. In addition, our heuristic approach provides average gaps of less than 1% for the largest problems that can be solved exactly. Observing that the gaps decrease as problem size grows, we expect the heuristic approach to work well for large problem instances. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2008     

反渗透预处理及膜清洗方法

基于预处理和膜清洗对于反渗透RO(reverse osmosis)长期稳定运行的重要性,介绍了常规预处理方法及其改进、膜法预处理等预处理方法,以及各种新型的物理、化学膜清洗方法.     

基于维修器材离散随机需求最佳库存量确定研究

结合装备维修器材保障工作,就维修器材离散随机需求最佳库存量确定进行研究,对其建模进行系统分析,结合修理厂维修器材最佳库存量进行实例计算,以指导维修器材最佳库存量的确定.     

Impact of reseller's and sales agent's forecasting accuracy in a multilayer supply chain

We consider a three‐layer supply chain with a manufacturer, a reseller, and a sales agent. The demand is stochastically determined by the random market condition and the sales agent's private effort level. Although the manufacturer is uninformed about the market condition, the reseller and the sales agent conduct demand forecasting and generate private demand signals. Under this framework with two levels of adverse selection intertwined with moral hazard, we study the impact of the reseller's and the sales agent's forecasting accuracy on the profitability of each member. We show that the manufacturer's profitability is convex on the reseller's forecasting accuracy. From the manufacturer's perspective, typically improving the reseller's accuracy is detrimental when the accuracy is low but is beneficial when it is high. We identify the concrete interrelation among the manufacturer‐optimal reseller's accuracy, the volatility of the market condition, and the sales agent's accuracy. Finally, the manufacturer's interest may be aligned with the reseller's when only the reseller can choose her accuracy; this alignment is never possible when both downstream players have the discretion to choose their accuracy. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 207–222, 2014     

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     

An exact analysis of a joint production‐inventory problem in two‐echelon inventory systems

We consider a two‐echelon inventory system with a manufacturer operating from a warehouse supplying multiple distribution centers (DCs) that satisfy the demand originating from multiple sources. The manufacturer has a finite production capacity and production times are stochastic. Demand from each source follows an independent Poisson process. We assume that the transportation times between the warehouse and DCs may be positive which may require keeping inventory at both the warehouse and DCs. Inventory in both echelons is managed using the base‐stock policy. Each demand source can procure the product from one or more DCs, each incurring a different fulfilment cost. The objective is to determine the optimal base‐stock levels at the warehouse and DCs as well as the assignment of the demand sources to the DCs so that the sum of inventory holding, backlog, and transportation costs is minimized. We obtain a simple equation for finding the optimal base‐stock level at each DC and an upper bound for the optimal base‐stock level at the warehouse. We demonstrate several managerial insights including that the demand from each source is optimally fulfilled entirely from a single distribution center, and as the system's utilization approaches 1, the optimal base‐stock level increases in the transportation time at a rate equal to the demand rate arriving at the DC. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011