Going faster to see further: graphics processing unit-accelerated value iteration and simulation for perishable inventory control using JAX.

ABSTRACT: Value iteration can find the optimal replenishment policy for a perishable inventory problem, but is computationally demanding due to the large state spaces that are required to represent the age profile of stock. The parallel processing capabilities of modern graphics processing units (GPUs) can reduce the wall time required to run value iteration by updating many states simultaneously. The adoption of GPU-accelerated approaches has been limited in operational research relative to other fields like machine learning, in which new software frameworks have made GPU programming widely accessible. We used the Python library JAX to implement value iteration and simulators of the underlying Markov decision processes in a high-level interface, and relied on this library's function transformations

SUBMITTER: Farrington J 

PROVIDER: S-EPMC12350524 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature