Stock Optimization in Emergency Resupply Networks under Stuttering Poisson Demand

We consider a network in which field stocking locations (FSLs) manage multiple parts according to an (S-1,S) policy. Demand processes for the parts are assumed to be independent stuttering Poisson processes. Regular replenishments to an FSL occur from a regional stocking location (RSL) that has an unlimited supply of each part type. Demand in excess of supply at an FSL is routed to an emergency stocking location (ESL), which also employs an (S-1,S) policy to manage its inventory. Demand in excess of supply at the ESL is backordered. Lead time from the ESL to each FSL is assumed to be negligible compared to the RSL-ESL resupply time. In companion papers we have shown how to approximate the joint probability distributions of units on hand, units in regular resupply, and units in emergency resupply. In this paper, we focus on the problem of determining the stock levels at the FSLs and ESL across all part numbers that minimize backorder, and emergency resupply costs subject to an inventory investment budget constraint. The problem is shown to be a nonconvex integer programming problem, and we explore a collection of heuristics for solving the optimization problem

Inventory models with lateral transshipments : a review

Lateral transshipments within an inventory system are stock movements between locations of the same echelon. These transshipments can be conducted periodically at predetermined points in time to proactively redistribute stock, or they can be used reactively as a method of meeting demand which cannot be satised from stock on hand. The elements of an inventory system considered, e.g. size, cost structures and service level denition, all in uence the best method of transshipping. Models of many dierent systems have been considered. This paper provides a literature review which categorizes the research to date on lateral transshipments, so that these dierences can be understood and gaps within the literature can be identied

A stochastic dynamic programming approach-based yield management with substitution and uncertainty in semiconductor manufacturing

AbstractYield management is important and challengeable in semiconductor industry for the quality uncertainty of the final products. The total yield rate of the semiconductor manufacturing process is uncertain, each product is graded into one of several quality levels according to performance before being shipped. A product originally targeted to satisfy the demand of one product may be used to satisfy the demand of other products when it conforms to their specifications. At the same time, the products depreciate in allocation periods, which mainly results from technical progresses. This paper studies the semiconductor yield management issue of a make-to-stock system with single input, multi-products, multi-demand periods, upward substitution and periodic depreciation. The whole time horizon of the system operation process can be divided into two stages: the production stage and the allocation stage. At the first stage, the firm invests in raw materials before any actual demand is known and produces multiple types of products with random yield rates. At the second stage, products are classified into different classes by quality and allocated in numbers of periods. The production and allocation problem are modeled as a stochastic dynamic program in which the objective is to maximize the profit of the firm. We show that the PRA (parallel allocation first, then upgrade) allocation policy is the optimal allocation policy and the objective function is concave in production input. An iterative algorithm is designed to find the optimal production input and numerical experiments are used to illustrate its effectiveness

Hybrid Lateral Transshipments in a Multi-Location Inventory System

In managing networks of stock holding locations, two approaches to the pooling of inventory have been proposed. Reactive transshipm nts respond to stockouts at a location by moving inventory from elsewhere within the network, while proactive redistribution of stock seeks to minimise the chance of future shocks. This paper is the first to propose a hybrid approach in which transshipments are viewed as an opportunity for stock redistribution. We adopt a quasi-myopic approach to the development of a strongly performing hybrid transshipment policy. Numerical studies which utilise dynamic programming and simulation testify to the benefits of using transshipments proactively. In comparison to a purely reactive approach to transshipment, service levels are improved while a reduction in safety stock levels is achieved. The aggregate costs incurred in managing the system are significantly reduced, especially so for large networks facing high levels of demand.

Dynamic demand fulfillment in spare parts networks with multiple customer classes

We study real-time demand fulfillment for networks consisting of multiple local warehouses, where spare parts of expensive technical systems are kept on stock for customers with di??erent service contracts. Each service contract specifies a maximum response time in case of a failure and hourly penalty costs for contract violations. Part requests can be fulfilled from multiple local warehouses via a regular delivery, or from an external source with ample capacity via an expensive emergency delivery. The objective is to minimize delivery cost and penalty cost by smartly allocating items from the available network stock to arriving part requests. We propose a dynamic allocation rule that belongs to the class of one-step lookahead policies. To approximate the optimal relative cost, we develop an iterative calculation scheme that estimates the expected total cost over an infinite time horizon, assuming that future demands are fulfilled according to a simple static allocation rule. In a series of numerical experiments, we compare our dynamic allocation rule with the optimal allocation rule, and a simple but widely used static allocation rule. We show that the dynamic allocation rule has a small optimality gap and that it achieves an average cost reduction of 7.9% compared to the static allocation rule on a large test bed containing problem instances of real-life size

Benefits of hybrid lateral transshipments in multi-item inventory systems under periodic replenishment

Lateral transshipments are a method of responding to shortages of stock in a network of inventory-holding locations. Conventional reactive approaches only seek to meet immediate shortages. The study proposes hybrid transshipments which exploit economies of scale by moving additional stock between locations to prevent future shortages in addition to meeting immediate ones. The setting considered is motivated by retailers who operate networks of outlets supplying car parts via a system of periodic replenishment. It is novel in allowing non-stationary stochastic demand and general patterns of dependence between multiple item types. The generality of our work makes it widely applicable. We develop an easy-to-compute quasi-myopic heuristic for determining how hybrid transshipments should be made. We obtain simple characterizations of the heuristic and demonstrate its strong cost performance in both small and large networks in an extensive numerical study