Inventory Planning over Multiple Time Periods: Linear-Cost Case

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


The discussion in the previous chapter focused on decision making when the planning horizon consisted of a single period. The assumption was made that decisions in one period would not affect those made in other periods. But what happens when a decision at one point in time does affect decisions in subsequent time periods? Hence, when is it necessary to consider other time periods when making these inventory decisions? Additionally, what policies are best for managing inventories in a dynamic environment? The focus of this chapter is on answering these and other related questions. Specifically, we will again restrict ourselves to the case where the fixed cost of placing an order is negligible. We will begin by showing that a base-stock or order-up-to policy is optimal in a number of different environments. Next we will develop a dynamic programming model that can be employed to find optimal stocking decisions. On the basis of this model, we will then establish properties that optimal solutions possess. The discussion of how capacities on production affect stocking decisions follows. The chapter concludes with a discussion of multi-echelon systems, and how the role of each location within a supply chain differs.


Lead Time Optimal Policy Planning Horizon Stock Level Inventory Planning 
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.


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