Optimal Inventory Policies when the Demand Distribution is not Known

This paper analyzes the stochastic inventory control problem when the demand distribution is not known. In contrast to previous Bayesian inventory models, this paper adopts a non-parametric Bayesian approach in which the firm's prior information is characterized by a Dirichlet process prior. This provides considerable freedom in the specification of prior information about demand and it permits the accommodation of fixed order costs. As information on the demand distribution accumulates, optimal history-dependent (s,S) rules are shown to converge to an (s,S) rule that is optimal when the underlying demand distribution is known.
Publication date: November 2000
ISBN: 9781451859300
$15.00
Add to Cart by clicking price of the language and format you'd like to purchase
Available Languages and Formats
English
Prices in red indicate formats that are not yet available but are forthcoming.
Topics covered in this book

This title contains information about the following subjects. Click on a subject if you would like to see other titles with the same subjects.

Inventory models , Non-parametric Bayesian learning , Dirichlet process , inventory , probability , statistics , equation , probabilities

Summary