A neuro-dynamic programming approach to retailer inventory management

Author

Van Roy, Benjamin ; Bertsekas, Dimitri P. ; Lee, Yuchun ; Tsitsiklis, John N.

Author_Institution

Unica Technol. Inc., Lincoln, MA, USA

Volume

4

fYear

1997

fDate

10-12 Dec 1997

Firstpage

4052

Abstract

We discuss an application of neuro-dynamic programming techniques to the optimization of retailer inventory systems. We describe a specific case study involving a model with thirty-three state variables. The enormity of this state space renders classical algorithms of dynamic programming inapplicable. We compare the performance of solutions generated by neuro-dynamic programming algorithms to that delivered by optimized s-type (“order-up-to”) policies. We are able to generate control strategies substantially superior, reducing inventory costs by approximately ten percent

Keywords

dynamic programming; learning (artificial intelligence); neural nets; stock control; control strategies; inventory costs; neuro-dynamic programming approach; order-up-to policies; retailer inventory management; s-type policies; Approximation algorithms; Artificial intelligence; Artificial neural networks; Buffer storage; Computational modeling; Costs; Dynamic programming; Inventory management; Laboratories; Transportation;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.652501

Filename

652501