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oai:riuma.uma.es:10630/351722026-02-03T11:33:26Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Ortega, Gloria L. author Hendrix, Eligius María Theodorus author García-Fernández, Inmaculada author 2018 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 prob- lems. 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. Ortega, G., Hendrix, E.M.T. and Garcia, I. (2019), A CUDA approach to compute perishable inventory control policies using value iteration, Journal of Supercomputing, 75, 3, 1580–1593 https://hdl.handle.net/10630/35172 10.1007/s11227-018-2692-z Programación dinámica A CUDA approach to compute perishable inventory control policies using value iteration.