短缺量拖后率、需求和采购价均为时变的变质物品EOQ模型

基于需求和采购价均为时变的EOQ模型,进一步考虑时变短缺量拖后率以及资金时间价值等因素对变质物品库存管理的影响,建立了相应的EOQ模型。采用Mathematica4 1对模型进行仿真寻优和主要参数的灵敏度分析,结果显示,最优解存在且库存系统的各参数对最优库存控制策略有不同程度的影响。     

补货能力影响部分短缺量拖后率的边补货边需求EOQ模型

Based on the hypothesis of time-dependent partial backlogging,the effect of the waiting time between the next replenishment and the satisfaction of the backlogging demand on the rate of partial backlog-grog was investigated in this paper. Then the backlogging rate influenced by the replenishment capacity and the total of backlogging demand was proposed. Based on this new standpoint,an EOQ model of replenishment with the replenishment capacity affecting the partial backlogging was built up.At last,an emulator was performed and the results showed:improving the replenishment capacity could reduce the replenishment times to a lower constant level;this effect may increase the waiting time before the next replenishment,but it could decrease the waiting time after the start of reple山shment;so the trend of the lostsales varying with the replenishment capacity was not very distinct at the medium level of the replenishment capacity;However,the more adequate replenishment capacity could reduce the number of lostsales and improve the rate of partial backlogging during shortage period than the less one.     

部分短缺量拖后下基于顾客敏感度的补货策略适用范围研究

顾客对缺货数量和等待时间的敏感度影响着部分短缺量拖后率。基于此,根据顾客对缺货数量和等待时间的敏感度以及成本结构,对部分短缺量拖后下不同补货策略的适用范围进行了研究,得出以下结论:(s,S)连续性检查策略适用于单位缺货和丢单成本较高的库存系统;(t,S)周期性检查策略和(t,s,S)混合策略适用于单位缺货和丢单成本较低的库存系统;在单位缺货和丢单成本较低的库存系统中,当顾客对缺货数量较敏感时,(t,s,S)混合策略的运作成本更低,否则(t,S)周期性检查策略更适用,而顾客对等待时间的敏感度对补货策略适用范围的影响不明显。     

提前期、构建成本和短缺量拖后率均可控的EOQ模型

传统库存模型通常将提前期和构建成本视为不可控制。事实上可以通过追加投资缩短提前期和降低构建成本。缺货期间,为减少订单丢失量和补偿顾客的损失,供应商会给予一定的价格折扣。现实库存系统中,容易得到需求的期望值和标准差,但较难得到其分布规律。基于此,考虑短缺量拖后率与价格折扣和缺货期间库存水平相关,提出了一种需求为任意分布且提前期和构建成本均可控的EOQ模型,证明了模型存在唯一最优解,给出了一种寻优算法。数值仿真分析表明,一般情况下,压缩提前期和降低构建成本能降低订购批量和安全库存,降低库存总成本;短缺量拖后系数和缺货概率对库存总成本影响较大,企业应尽量降低缺货概率,尤其在短缺量拖后系数较小时。     

时变需求环境下周转库存优化策略研究

本文研究了供应链管理领域的一个热点问题——库存优化问题。针对周转库存的优化问题,本文在需求量未必能被时间解析表示的条件下给出了一个能够求得局部最优解的方法。方法要求先将周转库存的优化问题转化为一个求集合最优划分的问题,然后用最快下降法求出一个带条件的最优解,最后再给出一个进一步调整这个带条件的最优解的算法。     

基于时变需求的供应链基本库存水平优化研究

在时变需求环境下,基于模型推导得到供需双方基本库存水平局部优化与整体优化决策方程,并基于算例对基本库存水平整体优化的成本绩效以及需求时变性的影响进行数据分析,得到如下重要结论:基本库存水平整体优化明显降低供应链库存成本.     

基于可控提前期和延迟供货的短生命周期产品库存模型

本文用斜坡型函数来描述短生命周期产品需求变化特征,用持有成本不断增加的形式来表示存货的无形变质所带来的损失,通过向供应商支付额外的赶工成本来缩短提前期,同时在允许缺货的情况下设置合理的缺货期价格折扣来控制订单的流失,即以价格折扣的损失来挽留住更多的顾客,使其总成本到达最优。本文在此基础上建立了短生命周期产品的库存模型,并提出了最优解的求解方法。     

基于时变需求的一对一供应链库存决策研究

随着竞争日趋激烈,需求变化节奏加快,需求时变性越加明显.在需求时变的环境下探讨了等周期补货情形下缺货时点优化以及非等周期补货情形下缺货时点与补货时点优化问题,特别着重研究了补货时点局部优化与整体优化的决策模型,并对非等周期补货与等周期补货、补货时点整体优化与补货时点局部优化作对比分析,并发现,在时变需求环境下非等周期补货与补货时点整体优化在降低库存成本方面具有明显优势.同时,分析了补货时点优化的效果与供需双方补货次数、需求时变性的关系.     

基于需求预测的短生命周期产品订货策略研究

本文针对短生命周期产品的特点,考虑与需求因子相关的库存短缺拖后和无形变质,根据缺货时间点、订货时间点与产品到达成熟期的时间点之间的关系,建立基于短生命周期产品需求预测的3种订货策略模型:乐观订货策略,悲观订货策略和中庸订货策略模型,对模型进行了分析求解,对最优解的存在性进行了证明.然后根据实际销售数据进行需求预测和算例分析,分析各因子对于总库存成本、订货量、短缺拖后量、销售损失量以及最优订货策略的影响.最后通过订货策略分析,分析各因子对于最优订货策略的影响,并将它们划分为3种订货策略影响因子,分析结果可以指导企业在不同因子水平情况下如何调整订货策略,以降低总库存成本.     

考虑批量滞后支付的变质物品EOQ模型

考虑订货商在信用期间将货款用于投资,提出了一种批量滞后支付且短缺量部分拖后的变质物品EOQ模型.证明了单位时间利润函数在一定条件下是订货期长度的上凸函数.仿真寻优和灵敏度分析表明,滞后支付对单位时间利润的影响主要来自投资收益的变化,服务水平、变质系数和短缺量拖后系数对订货商物料成本影响较大,对其它成本及单位时间利润影响较小,且短缺量拖后系数比服务水平和变质系数的影响更大.     

一种需求和采购价均为时变的EOQ模型

本文提出了一种需求和采购价均为时变的EOQ模型,证明了该模型的总库存成本目标函数在给定条件下为凸函数,给出了寻求最佳采购次数及服务水平的算法,并对该模型进行了数值仿真和灵敏度分析。     

存货影响销售率且理论需求为线性时变函数时的EOQ模型

本文建立了理论需求率为线性时变递增函数且无短缺量拖后和短缺量完全拖后时的EOQ模型,并对无短缺量拖后时模型最优解的存在及唯一性进行了讨论,为库存系统的管理决策提供了理论依据。     

单位保管和短缺成本均为时变的生产库存模型

基于短缺量拖后率不相同时的边生产边需求库存模型,进一步考虑单位保管和缺货成本与时间呈线性或非线性关系,建立了相应的生产库存模型.运用Mathematica5.0对模型进行仿真寻优和主要参数的灵敏度分析,结果表明,单位保管和缺货成本对库存系统各项成本影响较大,在制定库存控制策略时必须重点考虑这两个因素.     

有限时域内部分缺货的变质品生产-定价策略研究

研究有限时域内对变质产品同时安排生产计划和制定定价策略的问题。有限时域被划分成同等长度的多个周期,产品以固定的生产率间歇生产,并以固定的速率发生变质。需求同时依赖于时间和产品价格,在每个周期内都允许缺货且短缺量部分延迟订购。目的是要寻求一个使有限时域内系统的平均利润最大化的生产计划和定价策略。在算例中,分别讨论了需求随时间增大和减小两种不同的需求模式,采用Box复合算法,通过数值计算求得相应的最优生产计划和定价策略。分析表明：对于成长期或销售季初的产品,系统应采用“小批量多批次”的生产方式;而对于衰退期或销售季末的产品,应采用“大批量小批次”的生产方式。此外,不同类型的产品在不同需求模式下的定价策略有所不同：对于有限时域较短或变质率较高的产品,处于衰退期或销售季末的定价要低于成长期或销售季初的定价;相反,对于有限时域相对较长或变质率较低的产品,在衰退期或销售季末的最优定价要高于在成长期或销售季初的定价。     

Inventory Commitment and Prioritized Backlogging Clearance with Alternative Delivery Lead Times

We propose a model where customers are classified into two groups: short lead‐time customers who require the product immediately and long lead‐time customers to whom the supplier may deliver either immediately or in the next cycle. Unmet orders are backlogged with associated costs. Specifically, the supplier faces two problems: how the on‐hand inventories should be allocated between the two classes of customers and how the backlogged orders should be cleared when replenishments arrive. We treat the former as an inventory commitment problem and handle the latter with priority rules. We characterize and compare the inventory commitment policies with three priority rules in clearing backlogs. We also explore the optimal inventory replenishment decision and evaluate the performance of each priority rule.     

动态定价与允许需求延迟订货批量模型的联合决策

基于MRP的经典订货批量模型往往忽略了产品的售价对订货策略的影响。本文针对离散时间多周期情形,研究了允许需求延迟的动态定价和订货批量的集成问题。建立了二次规划数学模型。提出了一种基于动态规划的精确算法,可以在多项式时间内求解原问题的最优定价策略和最优订货计划。实验结果表明,算法可以有效地求解动态定价和订货批量的集成问题;与串行决策机制相比,并行决策机制可以给订货商带来更多的利润。通过固定订货费用的灵敏度分析,揭示了固定订货费用对利润和订货计划的显著影响,表明了允许需求延迟的联合决策模型优于不允许需求延迟的联合决策模型。     

A Dynamic Model of Demand for Houses and Neighborhoods

This paper develops a dynamic model of neighborhood choice along with a computationally light multi‐step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non‐marketed amenities—neighborhood air pollution, violent crime, and racial composition—in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.     

The Dynamic Newsvendor Model with Correlated Demand

The classic newsvendor model was developed under the assumption that period‐to‐period demand is independent over time. In real‐life applications, the notion of independent demand is often challenged. In this article, we examine the newsvendor model in the presence of correlated demands. Specifically under a stationary AR(1) demand, we study the performance of the traditional newsvendor implementation versus a dynamic forecast‐based implementation. We demonstrate theoretically that implementing a minimum mean square error (MSE) forecast model will always have improved performance relative to the traditional implementation in terms of cost savings. In light of the widespread usage of all‐purpose models like the moving‐average method and exponential smoothing method, we compare the performance of these popular alternative forecasting methods against both the MSE‐optimal implementation and the traditional newsvendor implementation. If only alternative forecasting methods are being considered, we find that under certain conditions it is best to ignore the correlation and opt out of forecasting and to simply implement the traditional newsvendor model.