Advertisement

Background Concepts: An Introduction to the (s − 1, s) Policy under Poisson Demand

  • John A. Muckstadt
  • Amar Sapra
Chapter
Part of the Springer Series in Operations Research and Financial Engineering book series (ORFE)

Abstract

In the previous chapter we studied order-up-to policies when time was divided into periods. We will now discuss the implications of following a similar policy, called an (s–1, s) inventory policy. In this chapter we assume inventories are reviewed continuously in time. Recall that the stock level, s, measures the amount of inventory on hand plus on order minus backorders, that is, the stock level represents the inventory position for a particular location. In certain situations, we will refer to the on-order quantity as the “in resupply” quantity. This “in resupply” terminology is often used in military and aviation applications in which items fail and are repaired or are procured from an external source. When an (s–1, s) policy is followed in continuous review environments, an order is placed immediately whenever a demand occurs for one or more units of an item. The order quantity matches the size of the demand exactly. Hence, the inventory position is constant in the case where the demand process and costs are stationary over an infinite planning horizon, which is the one we will examine in some detail in this chapter.

Keywords

Item Type Stock Level Steady State Probability Demand Process Customer Order 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Operations Research and Information EngineeringCornell UniversityIthacaUSA
  2. 2.Department of Quantitative Methods and Information SystemsIndian Institute of Management BangaloreBangaloreIndia

Personalised recommendations