A neuro-dynamic programming approach to retailer inventory management

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 inapplicabl...

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Bibliographic Details
Published inProceedings of the 36th IEEE Conference on Decision and Control Vol. 4; pp. 4052 - 4057 vol.4
Main Authors Van Roy, B., Bertsekas, D.P., Lee, Y., Tsitsiklis, J.N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Approximation algorithms
Artificial intelligence
Artificial neural networks
Buffer storage
Computational modeling
Costs
Dynamic programming
Inventory management
Laboratories
Transportation
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Summary: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.
ISBN:0780341872
9780780341876
ISSN:0191-2216
DOI:10.1109/CDC.1997.652501