DocumentCode :
2286080
Title :
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
Link To Document :
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