Aggregate constrained inventory systems with independent multi-product demand: control practices and theoretical limitations

In practice, inventory managers are often confronted with a need to consider one or more aggregate constraints. These aggregate constraints result from available workspace, workforce, maximum investment or target service level. We consider independent multi-item inventory problems with aggregate constraints and one of the following characteristics: deterministic leadtime demand, newsvendor, basestock policy, rQ policy and sS policy. We analyze some recent relevant references and investigate the considered versions of the problem, the proposed model formulations and the algorithmic approaches. Finally we highlight the limitations from a practical viewpoint for these models and point out some possible direction for future improvements

[[alternative]]Ordering Strategy of the Inventory System for Considering the Effect of Controllable Lead Time, Time Value of Money and Inflation

計畫編號：NSC91-2416-H032-005研究期間：200208~200307研究經費：394,000[[sponsorship]]行政院國家科學委員

[[alternative]]Lead Time and Setup Cost Reductions in Continuous Review Inventory Model When the Amount Received Is Uncertain

計畫編號：NSC90-2416-H032-028研究期間：200108~200207研究經費：256,000[[sponsorship]]行政院國家科學委員

A Spreadsheet Model that Estimates the Impact of Reduced Distribution Time on Inventory Investment Savings: What is a Day Taken out of the Pipeline Worth in Inventory?

In most of the literature dealing with inventory problems, either with a deterministic or probabilistic model, lead time is viewed as a prescribed constant or a stochastic variable that is not subject to control. But in many practical situations, lead time can be reduced by an extra crashing cost; in other words, it is controllable. This study proposes a repeatable spreadsheet optimization model that estimates the impact of reduced replenishment lead time on inventory investment savings at forward and strategic locations to motivate decision makers to support enterprise-wide distribution process improvement. The study provides users with a means of automatically calculating inventory control parameters such as safety stocks and reorder points, and automatically estimating the savings caused by lead time mean or variability reduction. A trade-off analysis can be done to determine whether reducing lead time would override the lead time crashing cost. First, the model finds the optimal safety factor of an item based on a fill rate goal using Excel Solver. Then, Excel\u27s VBA automates the process of finding safety factors for other items before and after lead time reduction. Finally, the model is applied to three different supply support activities to illustrate its superior features, which include allowing the user to change and upgrade it for future research

[[alternative]]A Study of Some Stochastic Inventory Models for Controllable Lead Time

計畫編號：NSC89-2213-E032-013研究期間：199908~200007研究經費：196,000[[sponsorship]]行政院國家科學委員

An Integrated Single Vendor-Buyer Stochastic Inventory Model with Partial Backordering under Imperfect Production and Carbon Emissions

This paper develops an integrated single vendor single buyer inventory model with imperfect quality and environmental impact. The demand during lead time is assumed to be stochastic and follows the normal distribution. An integrated system with controllable lead time and logarithmic investment to reduce the defective percentage is discussed in this model.100% error-free screening process is adopted by the buyer to separate defective and non-defective items. We assume that shortages are allowed and are partially backordered at the buyer’s end. Logistics management is the component of supply chain management that focusses on how and when to get raw materials, intermediate products and finished goods from their respective origins to their destinations.Thus, transportation play a major role in supply chain. As transportation increases, it affects the weather by the matter of carbon emission.The fixed and variable carbon emission cost for both vendor and buyer is considered. The prime motive is to determine the optimal policies regarding optimal order quantity, reorder point, lead time and the number of lots delivered in a production run by minimizing the expected total cost of the system. Finally, a numerical example is provided to demonstrate the model