Going faster to see further: GPU-accelerated value iteration and
simulation for perishable inventory control using JAX
- URL: http://arxiv.org/abs/2303.10672v1
- Date: Sun, 19 Mar 2023 14:20:44 GMT
- Title: Going faster to see further: GPU-accelerated value iteration and
simulation for perishable inventory control using JAX
- Authors: Joseph Farrington, Kezhi Li, Wai Keong Wong, Martin Utley
- Abstract summary: We use the Python library JAX to implement value iteration and simulators of the underlying Markov decision processes in a high-level API.
Our method can extend use of value iteration to settings that were previously considered infeasible or impractical.
We compare the performance of the optimal replenishment policies to policies, fitted using simulation optimization in JAX which allowed the parallel evaluation of multiple candidate policy parameters.
- Score: 5.856836693166898
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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 API, and relied on this library's function transformations and
compiler to efficiently utilize GPU hardware. Our method can extend use of
value iteration to settings that were previously considered infeasible or
impractical. We demonstrate this on example scenarios from three recent studies
which include problems with over 16 million states and additional problem
features, such as substitution between products, that increase computational
complexity. We compare the performance of the optimal replenishment policies to
heuristic policies, fitted using simulation optimization in JAX which allowed
the parallel evaluation of multiple candidate policy parameters on thousands of
simulated years. The heuristic policies gave a maximum optimality gap of 2.49%.
Our general approach may be applicable to a wide range of problems in
operational research that would benefit from large-scale parallel computation
on consumer-grade GPU hardware.
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