基于交货期库存协调的供应链转移定价研究

在将缺货成本和延期惩罚成本纳入决策模型的前提下,对供应商—制造商两阶供应链在转移定价、安全库存,以及总成本之间的相互作用进行了探讨:首先探讨基于 Stackelberg 收益共享定价机制的最优决策;然后,以一体化供应链为参照系,探讨对供应链各成员的行为进行优化的两部制(two-part scheme)收益共享转移定价决策在实践中的运用;最后,探讨模型中部分变量变化对最优决策所产生的影响.通过分析发现,承诺的交货期、延期惩罚成本与缺货成本将会影响供应链的总成本和转移定价决策,进而影响供应商的最优库存决策,而制造商则可以通过适当的激励机制设计,激励供应商按照供应链整体最优的方式进行转移定价、安全库存及相关决策.     

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。     

供应链提前期供需联合优化库存模型研究

本文研究由生产商和客户组成的二级供应链库存联合优化问题, 假设提前期服从均匀分布, 提前期订货受生产商的生产行为和客户的订货行为的制约, 建立了以库存成本为目标函数的供应链提前期供需库存模型, 提出了联合优化解决方案, 证明了目标函数在最优订货量和最优订货次数处存在最小目标值。在此基础上, 分析了提前期对供应链库存联合优化决策的影响。数据分析表明, 联合优化决策方法的库存成本曲线始终在分散决策优化方法的成本曲线的下方, 因此联合决策方法是非常有效的。     

库存共享和服务水平限制下三级分销网络侧向转运模型与算法

侧向转运是库存共享的一种方式,一方面会减少库存成本,另一方面又会因转运而增加运输成本,针对这一问题考虑不同时间窗W^k的服务水平产生的服务水平的分级,构建三级分销网络中基于库存共享与时间服务水平限制的批量订货模型,分销网络模型中包含一个制造商、一个RDC、m个DC、n个客户。其中客户的需求相互独立且服从泊松分布,DC的订货提前期服从指数分布。首先,建立了基于时间服务水平限制的分销网络系统总成本最小化模型,并将再购点、订购批量等库存参数作为同时作为决策变量,以达到三级备件分销网络的成本优化;其次,根据问题和模型的特点对所建的模型开发了基于采用贪婪增加算法的求解思想求解问题,以便给管理者提供决策参考并通过降低利润的方式增加效益和资源利用率;最后,通过中航材集团推行的“航材共享”项目作为算例分析,将三个分销区域与机场群组成的备件分销系统进行建模分析,验证了本文模型的有效性,成本优化的显著性。研究结果表明,三级分销网络中,RDC采用连续盘点的（Q,R）补货策略、DC采用（Q,R,H）的补货策略时系统总成本与普遍采用的定期盘点策略和连续盘点中的One-for-One订货策略成本相比,DC之间的库存共享策略能有效降低整个系统的成本,例如,在案例研究中发现成本降低约为30%。     

风险质检行为下的模糊生产-库存决策

本文研究在风险质检行为下需求与生产率具有模糊属性的由一个制造商与一个零售商构成的生产-库存问题。在假设制造商生产的产品存在质量缺陷,零售商在质检过程中存在质检风险的基础上,分别建立了需求模糊下的生产-库存总成本模型,需求与生产率模糊下的总成本模型,运用符号距离法进行了逆模糊化处理,将模糊化的生产-库存总成本转化为确定性成本。证明了两类情形下的总成本均是关于最优订购量与最优缺货量的联合凸函数。数值分析结果表明：随着期望缺陷率的增加,最优订购量增加,最优缺货量减少,最优成本增加,且最优成本增加的速度越来越快。一类质检风险增大引起总成本增加,二类质检风险增大引起总成本减少。最优订购策略对质检一类风险敏感,对质检二类风险不敏感。     

基于蒙特卡洛法的随机型库存系统仿真技术的研究

库存是物流管理中各种资源储备的客观现象,通常库存的总费用包括订货费、存货费和缺货费。本文针对运筹学存储论中传统存储模型所不便求解的多周期随机型离散问题,通过引入蒙特卡洛法来建立适应于随机型库存系统的仿真模型,并将随机性的因素引入其中,这些因素包括：商品的需求量和订货提前期。最后,模型以存储系统的周平均费用作为指标,来评价库存策略的优劣。     

考虑多种安全库存策略的选址-库存问题研究

考虑多种安全设置策略的物流网络的选址-库存问题,不仅是选址、订货、运输和库存的集成优化,还需要考虑多种不同的安全库存设置和转运策略。因此,本文深入讨论了二级物流网络中的六种安全库存设置策略,构建了六种考虑不同安全库存设置的选址-库存模型。在考虑集中设置安全库存时,集中安全库存需要通过转载运输实现,因此需要将转载运输成本引入选址-库存模型之中,使新的选址-库存模型更加科学合理。另外,针对六种新的选址-库存模型,提出了基于个体成本差异分配的遗传算法,迭代搜索选址、分配、库存设置策略的优化组合。最后,通过数据实验验证了模型的有效性：（1）安全库存与转载运输之间存在此消彼长的背反关系;（2）安全库存设置和转载运输策略对总成本的影响取决于两种费率权重情况。本文的研究可以为二级物流网络的选址、订货和安全库存策略集成优化决策提供参考依据。     

浅谈控制库存成本的方法

库存成本的降低意味着企业经济效益的提高,企业可通过正确确定库存物料、减少不可用库存、加强双向信息沟通,建立供应链预警机制等方法来降低库存,但在压缩时应在追求物流总成本最低的前提下展开。     

VMI模式下供应商之间的生产进度信息共享研究

当制造商采用供应商管理库存策略时,供应商之间的生产进度信息共享一般能减少供应商的缺货和存货持有成本。针对供应商的生产提前期的不同情况,建立了供应商库存成本的优化模型。分析了供应商之间的生产提前期变动大小比较对供应商生产进度信息共享选择的影响,指出在绝大多数情形下生产进度信息共享是有利的,但在某些情形下共享不一定有利。无论信息共享与否,供应商中某一方存在的生产提前期的不确定都会增加供应商的最小期望成本,并对供应商如何实现期望成本最小提出了建议。     

供应商—零售商联合库存的供应链库存成本分析

联合库存是商业零售企业改善供应链库存管理的有效途径。本文通过对传统供应模式与联合库存模式下库存成本模型的比较分析,发现联合库存模式能够很好地降低整个供应链的库存成本;通过算例分析证明了联合库存降低供应链成本的现实意义。     

Point‐of‐Use Hybrid Inventory Policy for Hospitals

Modern point‐of‐use technology at hospitals has enabled new replenishment policies for medical supplies. One of these new policies, which we call the hybrid policy, is currently in use at a large U.S. Midwest hospital. The hybrid policy combines a low‐cost periodic replenishment epoch with a high‐cost continuous replenishment option to avoid costly stockouts. We study this new hybrid policy under deterministic and stochastic demand. We develop a parameter search engine using simulation to optimize the long‐run average cost per unit time and, via a computational study, we provide insights on the benefits (reduction in cost, inventory, and number of replenishments) that hospitals may obtain by using the hybrid policy instead of the commonly used periodic policies. We also use the optimal hybrid policy parameters from the deterministic analysis to propose approximate expressions for the stochastic hybrid policy parameters that can be easily used by hospital management.      

Inventory Management for Customers with Alternative Lead Times

It is common for suppliers operating in batch‐production mode to deal with patient and impatient customers. This paper considers inventory models in which a supplier provides alternative lead times to its customers: a short or a long lead time. Orders from patient customers can be taken by the supplier and included in the next production cycle, while orders from impatient customers have to be satisfied from the on‐hand inventory. We denote the action to commit one unit of on‐hand inventory to patient or impatient customers as the inventory‐commitment decision, and the initial inventory stocking as the inventory‐replenishment decision. We first characterize the optimal inventory‐commitment policy as a threshold type, and then prove that the optimal inventory‐replenishment policy is a base‐stock type. Then, we extend our analysis to models to consider cases of a multi‐cycle setting, a supply‐capacity constraint, and the on‐line charged inventory‐holding cost. We also evaluate and compare the performances of the optimal inventory‐commitment policy and the inventory‐rationing policy. Finally, to further investigate the benefits and pitfalls of introducing an alternative lead‐time choice, we use the customer‐choice model to study the demand gains and losses, known as demand‐induction and demand‐cannibalization effects, respectively.     

Optimal lot sizing under continuous price decrease

An important characteristic of high-tech industries is decreasing component prices over time. In the personal computer industry, some component prices decline at a rate of 1% per week. This paper develops an inventory model for products experiencing continuous decrease in unit price. We develop an accurate closed-form approximate solution to the model. Our results indicate that declining prices lead to substantial decrease in the optimal cycle time and much frequent ordering. This explains the heavy emphasis on just-in-time inventory management practiced by successful companies in high-tech industries. While previous models attributed the success of just-in-time policies to reduced holding cost and improved quality, under declining prices a substantial source of savings becomes lower costs of raw materials which is significant part of cost in these industries. We illustrate the results of the model with a numerical example and perform sensitivity analysis.     

Inventory control with seasonality of lead times

The practical challenges posed by the seasonality of lead times have largely been ignored within the inventory control literature. The length of the seasons, as well as the length of the lead times during a season, may demonstrate cyclical patterns over time. This study examines whether inventory control policies that anticipate seasonal lead-time patterns can reduce costs. We design a framework for characterizing different seasonal lead-time inventory problems. Subsequently, we examine the effect of deterministic and stochastic seasonal lead times within periodic review inventory control systems. We conduct a base case analysis of a deterministic system, enabling two established and alternating lead-time lengths that remain valid through known intervals. We identify essential building blocks for developing solutions to seasonal lead-time problems. Lastly, we perform numerical experiments to evaluate the cost benefits of implementing an inventory control policy that incorporates seasonal lead-time lengths. The findings of the study indicate the potential for cost improvements. By incorporating seasonality in length of seasons and length of lead times within the season into the control models, inventory controllers can make more informed decisions when ordering their raw materials. They need smaller buffers against lead-time variations due to the cyclical nature of seasonality. Reductions in costs in our experiments range on average between 18.9 and 26.4% (depending on safety time and the probability of the occurrence of stock out). Therefore, inventory control methods that incorporate seasonality instead of applying large safety stock or safety time buffers can lead to substantial cost reductions.     

Inventory Models with Continuous and Poisson Demands and Discounted and Average Costs

We develop a new, unified approach to treating continuous‐time stochastic inventory problems with both the average and discounted cost criteria. The approach involves the development of an adjusted discounted cycle cost formula, which has an appealing intuitive interpretation. We show for the first time that an (s, S) policy is optimal in the case of demand having a compound Poisson component as well as a constant rate component. Our demand structure simultaneously generalizes the classical EOQ model and the inventory models with Poisson demand, and we indicate the reasons why this task has been a difficult one. We do not require the surplus cost function to be convex or quasi‐convex as has been assumed in the literature. Finally, we show that the optimal s is unique, but we do not know if optimal S is unique.     

DECENTRALIZED MULTIECHELON INVENTORY CONTROL

This paper considers a model for decentralized control of an inventory system consisting of 1 central warehouse and a number of retailers. The cost structure includes holding costs at both echelons and shortage costs proportional to the time until delivery at the retailers. We analyze a procedure for coordinated but still decentralized control of the system. The procedure is based on a simple approximation, in which the stochastic lead times perceived by the retailers are replaced by their correct averages. The approximation enables us to decompose the considered multiechelon inventory problem into a number of single echelon problems, 1 for each installation. The information about how a certain decision at the warehouse affects the retailers is conveyed through the marginal cost increase with respect to a change of the expected lead time. This information about the retailer costs is used as a shortage cost at the warehouse. We show that a coordination procedure based on this information can be used for finding near-optimal reorder points for the system and provide bounds for the approximation errors.     

Inventory‐Based Dynamic Pricing with Costly Price Adjustment

We study an average‐cost stochastic inventory control problem in which the firm can replenish inventory and adjust the price at anytime. We establish the optimality to change the price from low to high in each replenishment cycle as inventory is depleted. With costly price adjustment, scale economies of inventory replenishment are reflected in the cycle time instead of lot size—An increased fixed ordering cost leads to an extended replenishment cycle but does not necessarily increase the order quantity. A reduced marginal cost of ordering calls for an increased order quantity, as well as speeding up product selling within a cycle. We derive useful properties of the profit function that allows for reducing computational complexity of the problem. For systems requiring short replenishment cycles, the optimal solution can be easily computed by applying these properties. For systems requiring long replenishment cycles, we further consider a relaxed problem that is computational tractable. Under this relaxation, the sum of fixed ordering cost and price adjustment cost is equal to (greater than, less than) the total inventory holding cost within a replenishment cycle when the inventory holding cost is linear (convex, concave) in the stock level. Moreover, under the optimal solution, the time‐average profit is the same across all price segments when the inventory holding cost is accounted properly. Through a numerical study, we demonstrate that inventory‐based dynamic pricing can lead to significant profit improvement compared with static pricing and limited price adjustment can yield a benefit that is close to unlimited price adjustment. To be able to enjoy the benefit of dynamic pricing, however, it is important to appropriately choose inventory levels at which the price is revised.     

SETUP REDUCTION AND MACHINE AVAILABILITY

This paper is an extension of Billington, who used the framework of the economic production quantity (EPQ) to model setup cost reduction. In the present paper, we use the EPQ model as a starting point to investigate the nature of setup costs and the effect of setup time reduction on the increase in available capacity. Reducing setup is vital to a company's success because a lengthy changeover of machinery is expensive: it demands long production runs to justify its cost, and these, in turn, lead to excessive inventory and to a slow response to customer needs. As in Billington, setup reduction is modeled as a function of an annual amortized investment. The paper examines the behavior of the setup time, the inventory cost, the lot size, and the freeing up of machine time in the face of a capacity constraint. A solution algorithm is provided to find setup times that minimize the sum of setup and holding cost, subject to a constraint on machine availability. The analysis sheds light on the true nature of setup cost and on the opportunity cost of not reducing setups. In the constrained optimization, the Lagrangian multiplier gives an estimate of the marginal value of adding one time unit of machine capacity, or, alternatively, of reducing one unit of setup time.     

Inventory Rationing for Multiple Class Demand under Continuous Review

We consider how a firm should ration inventory to multiple classes in a stochastic demand environment with partial, class‐dependent backlogging where the firm incurs a fixed setup cost when ordering from its supplier. We present an infinite‐horizon, average cost criterion Markov decision problem formulation for the case with zero lead times. We provide an algorithm that determines the optimal rationing policy, and show how to find the optimal base‐stock reorder policy. Numerical studies indicate that the optimal policy is similar to that given by the equivalent deterministic problem and relies on tracking both the current inventory and the rate that backorder costs are accumulating. Our study of the case of non‐zero lead time shows that a heuristic combining the optimal, zero lead time policy with an allocation policy based on a single‐period profit management problem is effective.     

A demand classification scheme for spare part inventory model subject to stochastic demand and lead time

In this study, we aim to develop a demand classification methodology for classifying and controlling inventory spare parts subject to stochastic demand and lead time. Using real data, the developed models were tested and their performances were evaluated and compared. The results show that the Laplace model provided superior performance in terms of service level, fill rate (FR) and inventory cost. Compared with the current system based on normal distribution, the proposed Laplace model yielded significant savings and good results in terms of the service level and the FR. The Laplace and Gamma optimisation models resulted in savings of 82 and 81%, respectively.