基于随机提前期的备件携行量计算方法

舰艇出海执行任务期间,需要携行一定种类和数量的物资自给。文中研究了这种单级供应条件下,如何确定备件最优携行量的问题。建立了以服务水平为目标,以资源为约束的目标规划模型,模型考虑了随机提前期和备件重要度。提出一种基于边际效费比的增量法用于求解这个模型。最后,给出一个算例并得到了一些结论。     

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     

Inventory control and periodic price discounting campaigns

This paper develops an inventory model that determines replenishment strategies for buyers facing situations in which sellers offer price‐discounting campaigns at random times as a way to drive sales or clear excess inventory. Specifically, the model deals with the inventory of a single item that is maintained to meet a constant demand over time. The item can be purchased at two different prices denoted high and low. We assume that the low price goes into effect at random points in time following an exponential distribution and lasts for a random length of time following another exponential distribution. We highlight a replenishment strategy that will lead to the lowest inventory holding and ordering costs possible. This strategy is to replenish inventory only when current levels are below a certain threshold when the low price is offered and the replenishment is to a higher order‐up‐to level than the one currently in use when inventory depletes to zero and the price is high. Our analysis provides new insight into the behavior of the optimal replenishment strategy in response to changes in the ratio of purchase prices together with changes in the ratio of the duration of a low‐price period to that of a high‐price period. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007.     

Should firms conceal information when dealing with common suppliers?

A firm making quantity decision under uncertainty loses profit if its private information is leaked to competitors. Outsourcing increases this risk as a third party supplier may leak information for its own benefit. The firm may choose to conceal information from the competitors by entering in a confidentiality agreement with the supplier. This, however, diminishes the firm's ability to dampen competition by signaling a higher quantity commitment. We examine this trade‐off in a stylized supply chain in which two firms, endowed with private demand information, order sequentially from a common supplier, and engage in differentiated quantity competition. In our model, the supplier can set different wholesale prices for firms, and the second‐mover firm could be better informed. Contrary to what is expected, information concealment is not always beneficial to the first mover. We characterize conditions under which the first mover firm will not prefer concealing information. We show that this depends on the relative informativeness of the second mover and is moderated by competition intensity. We examine the supplier's incentive in participating in information concealment, and develop a contract that enables it for wider set of parameter values. We extend our analysis to examine firms' incentive to improve information. © 2014 Wiley Periodicals, Inc. 62:1–15, 2015     

应急器材柔性采购定价建模与求解

由于当前应急器材储备依然存在“多储”或“少储”风险,为提高储备效益,以企业经济效益与军队采购成本为优化目标,提出了应急器材柔性采购策略。通过建立企业储备策略与军队采购定价最优化模型,求解得到了在不同战争爆发概率及器材现货市场价格区间等外部环境因素下的军队最佳柔性定价及对应的企业最优储备策略,并通过实例分析验证了得出的决策结论。结果表明,最优策略的实施将有利于军队与企业共担应急器材数量储备风险。     

航天测控软件共用技术研究与实现

软件共用是航天测控软件的发展趋势,分析了航天测控软件共用的可行性,并选取火箭遥测软件作为原型验证系统,提出了解决方案和设计方法,采用ICE中间件和构件技术实现了该原型验证系统,经测试验证,分布式部署的原型验证软件各项指标满足测控任务软件的技术要求,对航天测控软件共用研究具有重要的借鉴和指导意义。     

具有适应值预测机制的遗传算法

针对适应值计算费时的优化问题,提出一种具有适应值预测机制的遗传算法:为了有效控制预测适应值的准确度和预测频率,建立了一个基于可信度概念的适应值预测模型,引入可信度流失机制以减少预测误差的传播和累积,引入冗余个体剔除机制以减少计算消耗。利用3个基准函数对算法进行收敛性和有效性的测试,测试结果表明算法对于3个测试函数均能获得满意的最优解,并且都能减少60%以上的真实适应值计算次数。     

共享态势认识的效用

态势认识是指挥控制过程在认知域中的重要环节,认识共享是改进态势认识、减少认识偏差的重要手段。文中在分析态势认识的基础上,提出战术相关要素的发现概率、态势认识中的理解程度和理解偏差的概念,建立了用于态势认识的定量评估的数学模型,分析了认识共享对战场态势认识的改进程度,导出了团队合作时战术相关要素发现概率与个体数量的关系式,给出理解偏差与个体数量的联系方程并求出极值条件。     

A sequential dynamic pricing model and its applications

Consider a sequential dynamic pricing model where a seller sells a given stock to a random number of customers. Arriving one at a time, each customer will purchase one item if the product price is lower than her personal reservation price. The seller's objective is to post a potentially different price for each customer in order to maximize the expected total revenue. We formulate the seller's problem as a stochastic dynamic programming model, and develop an algorithm to compute the optimal policy. We then apply the results from this sequential dynamic pricing model to the case where customers arrive according to a continuous‐time point process. In particular, we derive tight bounds for the optimal expected revenue, and develop an asymptotically optimal heuristic policy. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Resource allocation with lumpy demand: To speed or not to speed?

In the classical EPQ model with continuous and constant demand, holding and setup costs are minimized when the production rate is no larger than the demand rate. However, the situation may change when demand is lumpy. We consider a firm that produces multiple products, each having a unique lumpy demand pattern. The decision involves determining both the lot size for each product and the allocation of resources for production rate improvements among the products. We find that each product's optimal production policy will take on only one of two forms: either continuous production or lot‐for‐lot production. The problem is then formulated as a nonlinear nonsmooth knapsack problem among products determined to be candidates for resource allocation. A heuristic procedure is developed to determine allocation amounts. The procedure decomposes the problem into a mixed integer program and a nonlinear convex resource allocation problem. Numerical tests suggest that the heuristic performs very well on average compared to the optimal solution. Both the model and the heuristic procedure can be extended to allow the company to simultaneously alter both the production rates and the incoming demand lot sizes through quantity discounts. Extensions can also be made to address the case where a single investment increases the production rate of multiple products. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.