Neuro-dynamic programming approach to retailer inventory management

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

Research output: Contribution to journalConference articlepeer-review

97 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)4052-4057
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA
Duration: Dec 10 1997Dec 12 1997

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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