Inventory placement problems in dynamic supply chains

Summary  In this paper a basic model for analyzing the inventory placement problem in a supply chain is developed. The problem will be studied for a capacitated, multi-stage supply chain facing a continuous, stochastic demand for a single period for a single product — a specialty item with a very short selling season. The objective is to maximize the probability of achieving a set profit level. We prove that finding an optimal stock investment at the stages entails solving a mixed binary integer linear program. We characterize properties of the stock investment, examine two interesting cases where the stock investments have a simple structure, and develop a branch and bound approach for solving the more general case.

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Zusammenfassung  In diesem Beitrag wird ein Grundmodell entwickelt, um das Problem der Lagerplatzierung in einer Lieferkette analysieren zu k?nnen. Das Problem wird für eine kapazitierte mehrstufige Lieferkette untersucht, wobei unterstellt wird, dass es um die Nachfrage nach einem einzigen Produkt für eine einzige Periode (einen Sonderartikel mit sehr kurzer Verkaufszeit) geht und diese Nachfrage stetig und stochastisch ist. Unter diesen Bedingungen soll die Wahrscheinlichkeit maximiert werden, ein vorgegebenes Gewinnniveau zu erreichen. Die Suche nach der optimalen Vorratsinvestition auf den Stufen der Lieferkette führt zur Formulierung eines gemischten ganzzahligen linearen Programmierungsproblems mit Bin?rvariablen. Die Eigenschaften der Bevorratungsinvestition werden charakterisiert, zwei interessante F?lle mit einer einfachen Struktur der Bevorratungsinvestition untersucht und ein Branch-and-Bound-Ansatz zur L?sung eines allgemeineren Problemfalls entwickelt.

     

定期信用支付条件下的多阶段货价变动型存贮问题

本文研究了定期信用支付条件下的多阶段货价变动型存贮问题,得到了最优订购策略并设计了算法以说明模型的使用。     

图书馆清产核资以后的思考

通过清产核资,暴露出对馆藏文献资源的管理、开发与利用存在着不足.在分析原因的基础上,提出几点建议.     

Optimal Ordering Policies for Inventory Problems With Dynamic Information Delays

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. In other words, the IM observes only the inventory level that belongs to an earlier period. Such situations are not uncommon, and they arise when it takes a while to process the demand data and pass the results to the IM. We introduce dynamic information delays as a Markov process into the standard multiperiod stochastic inventory problem with backorders. We develop the concept of a reference inventory position. We show that this position along with the magnitude of the latest observed delay and the age of this observation are sufficient statistics for finding the optimal order quantities. Furthermore, we establish that the optimal ordering policy is of state‐dependent base‐stock type with respect to the reference inventory position (or state‐dependent (s, S) type if there is a fixed ordering cost). The optimal base stock and (s, S) levels depend on the magnitude of the latest observed delay and the age of this observation. Finally, we study the sensitivity of the optimal base stock and the optimal cost with respect to the sufficient statistics.     

Economic Evaluation of Systems that Expedite Inventory Information

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. Such situations arise when it takes a while to process the demand data, count the inventory, and pass the results to the IM. We show that the optimal total inventory‐related cost decreases when the length of the information delay decreases. The amount of the decrease is an important datum for an IM interested in considering whether or not to invest in reducing the delay. The investment is required to finance design and acquisition of an information (collection and dissemination) system that can reduce the information delay. Such systems include phone calls, business meetings, and the use of information collection mechanisms such as radiofrequency identification tags.     

连锁经营物流配送系统的动态分析模型研究

本文提出了研究连锁经营零售商业的物流配送总体计划的大型线性目标规划数学模型,并用动态目标规划的方法来模拟和动态地分析配送系统运行性能和合理性。详细论述了模型结构,并以大型问题的实例进行了测试,证实了方法的正确性和可用性。     

变质率呈Weibull分布的一体化三级冷链库存策略研究

本文以有限期内一个生产商、多个分销商和多个零售商组成的冷链系统库存一体化决策为研究内容,建立了考虑可变运输成本、变质率服从三参数Weibull分布的三级冷链库存模型。文章从系统利润最大化的角度确定各个供应链成员的最佳补货策略,在与零售商主导的非一体化决策的对比分析中发现,库存一体化决策带来系统利润增加的同时也牺牲了部分成员的利益。与此同时,通过对变质率参数和运输成本参数的灵敏度分析发现,在运输距离一定的条件下,固定运输成本对最优库存策略的影响较大;在保鲜期一定情况下,变质率的尺度因子对系统总变质成本的影响较大。     

Building Supply Chain Resilience through Virtual Stockpile Pooling

Stockpiling inventory is an essential strategy for building supply chain resilience. It enables firms to continue operating while finding a solution to an unexpected event that causes a supply disruption or demand surge. While extremely valuable when actually deployed, stockpiles incur large holding costs and usually provide no benefits until such a time. To help to reduce this cost, this study presents a new approach for managing stockpiles. We show that if leveraged intelligently, stockpiles can also help an organization better meet its own regular demand by enabling a type of virtual pooling we call virtual stockpile pooling (VSP). The idea of VSP is to first integrate the stockpile into several locations’ regular inventory buffers and then dynamically reallocate the stockpile among these locations in reaction to the demand realizations to achieve a kind of virtual transshipment. To study how to execute VSP and determine when it can provide the most value, we formulate a stylized multi‐location stochastic inventory model and solve for the optimal stockpile allocation and inventory order policies. We show that VSP can provide significant cost savings: in some cases nearly the full holding cost of the stockpile (i.e., VSP effectively maintains the stockpile for free), in other cases nearly the savings of traditional physical inventory pooling. Last, our results prescribe implementing VSP with many locations for large stockpiles, but only a few locations for small stockpiles.     

Experimental Results Indicating Lattice‐Dependent Policies May Be Optimal for General Assemble‐To‐Order Systems

We consider an assemble‐to‐order (ATO) system with multiple products, multiple components which may be demanded in different quantities by different products, possible batch ordering of components, random lead times, and lost sales. We model the system as an infinite‐horizon Markov decision process under the average cost criterion. A control policy specifies when a batch of components should be produced, and whether an arriving demand for each product should be satisfied. Previous work has shown that a lattice‐dependent base‐stock and lattice‐dependent rationing (LBLR) policy is an optimal stationary policy for a special case of the ATO model presented here (the generalized M‐system). In this study, we conduct numerical experiments to evaluate the use of an LBLR policy for our general ATO model as a heuristic, comparing it to two other heuristics from the literature: a state‐dependent base‐stock and state‐dependent rationing (SBSR) policy, and a fixed base‐stock and fixed rationing (FBFR) policy. Remarkably, LBLR yields the globally optimal cost in each of more than 22,500 instances of the general problem, outperforming SBSR and FBFR with respect to both objective value (by up to 2.6% and 4.8%, respectively) and computation time (by up to three orders and one order of magnitude, respectively) in 350 of these instances (those on which we compare the heuristics). LBLR and SBSR perform significantly better than FBFR when replenishment batch sizes imperfectly match the component requirements of the most valuable or most highly demanded product. In addition, LBLR substantially outperforms SBSR if it is crucial to hold a significant amount of inventory that must be rationed.