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     

基于神经网络的舰船建造费用预测分析

采用了一种基于神经网络的舰船建造费预测方法 .计算结果表明 ,这种方法与传统的参数法相比较 ,有更好的估算精度 ,因而该方法可以作为研究此类问题的新途径     

一种无人战斗机系统使用保障费用分析方法

基于无人战斗机系统在使用保障上的特点,通过对有人战斗机使用保障费用模型进行合理改造,使其能适用于无人战斗机的使用保障费用分析。经过改造的模型具有结构清晰、概念明确的特点。适合在飞机总体设计阶段用于全寿命周期的费用估算以及飞机的费效分析和综合优化设计。通过与有人战斗机的使用保障费用进行对比计算表明,无人战斗机系统的使用保障费用具有明显的经济优势。     

Effectiveness of (R,Q) policies in one‐warehouse multiretailer systems with deterministic demand and backlogging

Consider a distribution system with a central warehouse and multiple retailers. Customer demand arrives at each of the retailers continuously at a constant rate. The retailers replenish their inventories from the warehouse which in turn orders from an outside supplier with unlimited stock. There are economies of scale in replenishing the inventories at both the warehouse and the retail level. Stockouts at the retailers are backlogged. The system incurs holding and backorder costs. The objective is to minimize the long‐run average total cost in the system. This paper studies the cost effectiveness of (R, Q) policies in the above system. Under an (R, Q) policy, each facility orders a fixed quantity Q from its supplier every time its inventory position reaches a reorder point R. It is shown that (R, Q) policies are at least 76% effective. Numerical examples are provided to further illustrate the cost effectiveness of (R, Q) policies. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 422–439, 2000     

试论武器装备的效费比分析

运用价值工程(VE)方法分析武器装备,称效费比为价值。按照杜佩(Dupuy)的方法,把武器装备的效能转化为“战斗效能值”(OLI),又计算出了武器装备的全寿命费用,然后将2者加以对比,获得效能—费用的最佳组合,同时提出了选择武器装备系统的定量分析方法。这些对于提高我国国防经济效益,具有一定的指导和借鉴意义。     

Optimal switchover times between two activities utilizing the same resource

The “gold‐mining” decision problem is concerned with the efficient utilization of a delicate mining equipment working in a number of different mines. Richard Bellman was the first to consider this type of a problem. The solution found by Bellman for the finite‐horizon, continuous‐time version of the problem with two mines is not overly realistic since he assumed that fractional parts of the same mining equipment could be used in different mines and this fraction could change instantaneously. In this paper, we provide some extensions to this model in order to produce more operational and realistic solutions. Our first model is concerned with developing an operational policy where the equipment may be switched from one mine to the other at most once during a finite horizon. In the next extension we incorporate a cost component in the objective function and assume that the horizon length is not fixed but it is the second decision variable. Structural properties of the optimal solutions are obtained using nonlinear programming. Each model and its solution is illustrated with a numerical example. The models developed here may have potential applications in other areas including production of items requiring the same machine or choosing a sequence of activities requiring the same resource. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 186–203, 2002; DOI 10.1002/nav.10008     

The multiplicative representation for multiple objectives optimization with an application for arms procurement

Multiple Objectives Optimization is much seen in combination with linear functions and even with linear programming, together with an adding of the objectives by using weights. With distance functions, normalization instead of weights is used. It is also possible that together with an additive direct influence of the objectives on the utility function a mutual utility of the objectives exists under the form of a multiplicative representation. A critical comment is brought on some representations of this kind. A full‐multiplicative form may offer other opportunities, which will be discussed at length in an effort to exclude weights and normalization. This theoretical approach is followed by an application for arms procurement. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 327–340, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10014     

A search game on a cyclic graph

There is a finite cyclic graph. The hider chooses one of all nodes except the specified one, and he hides an (immobile) object there. At the beginning the seeker is at the specified node. After the seeker chooses an ordering of the nodes except the specified one, he examines each nodes in that order until he finds the object, traveling along edges. It costs an amount when he moves from a node to an adjacent one and also when he checks a node. While the hider wishes to maximize the sum of the traveling costs and the examination costs which are required to find the object, the seeker wishes to minimize it. The problem is modeled as a two‐person zero‐sum game. We solve the game when unit costs (traveling cost + examination cost) have geometrical relations depending on nodes. Then we give properties of optimal strategies of both players. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Oligopolistic contracting: Channel coordination under competition

We study contracts between a single retailer and multiple suppliers of two substitutable products, where suppliers have fixed capacities and present the retailer cost contracts for their supplies. After observing the contracts, the retailer decides how much capacity to purchase from each supplier, to maximize profits from the purchased capacity from the suppliers plus his possessed inventory (endowment). This is modeled as a noncooperative, nonzero‐sum game, where suppliers, or principals, move simultaneously as leaders and the retailer, the common agent, is the sole follower. We are interested in the form of the contracts in equilibrium, their effect on the total supply chain profit, and how the profit is split between the suppliers and the retailer. Under mild assumptions, we characterize the set of all equilibrium contracts and discuss all‐unit and marginal‐unit quantity discounts as special cases. We also show that the supply chain is coordinated in equilibrium with a unique profit split between the retailer and the suppliers. Each supplier's profit is equal to the marginal contribution of her capacity to supply chain profits in equilibrium. The retailer's profit is equal to the total revenue collected from the market minus the payments to the suppliers and the associated sales costs.