A CUDA approach to compute perishable inventory control policies using value iteration

G. Ortega, E.M.T. Hendrix*, I. García

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Dynamic programming (DP) approaches, in particular value iteration, is often seen as a method to derive optimal policies in inventory management. The challenge in this approach is to deal with an increasing state space when handling realistic problems. As a large part of world food production is thrown out due to its perishable character, a motivation exists to have a good look at order policies in retail. Recently, investigation has been introduced to consider substitution of one product by another, when one is out of stock. Taking this tendency into account in a policy requires an increasing state space. Therefore, we investigate the potential of using GPU platforms in order to derive optimal policies when the number of products taken into account simultaneously is increasing. First results show the potential of the GPU approach to accelerate computation in value iteration for DP.

Original languageEnglish
Pages (from-to)1580-1593
Number of pages14
JournalJournal of Supercomputing
Issue number3
Early online date16 Nov 2018
Publication statusPublished - Mar 2019


  • CUDA
  • GPU
  • Inventory control
  • Value iteration

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