RT Journal Article
T1 A CUDA approach to compute perishable inventory control policies using value iteration.
A1 Ortega, Gloria L.
A1 Hendrix, Eligius María Theodorus
A1 García-Fernández, Inmaculada
K1 Programación dinámica
AB 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.
PB Springer Nature
YR 2018
FD 2018
LK https://hdl.handle.net/10630/35172
UL https://hdl.handle.net/10630/35172
LA eng
NO 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
NO This research is partly funded by Project TIN2015-66680 financed by the Spanish Ministry and Spanish network CAPAP-H6 (TIN2016-81840-REDT). G. Ortega is a fellow of the Spanish “Juan de la Cierva Incorporación” program (Grant No. IJCI-2016-30173).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026