Reinforcement Learning Provides a Flexible Approach for Realistic Supply Chain Safety Stock Optimisation
View / Open Files
Authors
Kosasih, EE
Brintrup, A
Publication Date
2022Journal Title
IFAC-PapersOnLine
ISSN
2405-8963
Publisher
Elsevier BV
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Kosasih, E., & Brintrup, A. (2022). Reinforcement Learning Provides a Flexible Approach for Realistic Supply Chain Safety Stock Optimisation. IFAC-PapersOnLine https://doi.org/10.1016/j.ifacol.2022.09.609
Abstract
Although safety stock optimisation has been studied for more than 60 years, most
companies still use simplistic means to calculate necessary safety stock levels, partly due to
the mismatch between existing analytical methods’ emphases on deriving provably optimal
solutions and companies’ preferences to sacrifice optimal results in favour of more realistic
problem settings. A newly emerging method from the field of Artificial Intelligence (AI), namely
Reinforcement Learning (RL), offers promise in finding optimal solutions while accommodating
more realistic problem features. Unlike analytical-based models, RL treats the problem as a
black-box simulation environment mitigating against the problem of oversimplifying reality.
As such, assumptions on stock keeping policy can be relaxed and a higher number of problem
variables can be accommodated. While RL has been popular in other domains, its applications in
safety stock optimisation remain scarce. In this paper we investigate three RL methods, namely,
Q-Learning, Advantage Actor-Critic and Multi-agent Advantage Actor-Critic for optimising
safety stock in a linear chain of independent agents.We find that RL can simultaneously optimise
both safety stock level and order quantity parameters of an inventory policy, unlike classical
safety stock optimisation models where only safety stock level is optimised while order quantity
is predetermined based on simple rules. This allows RL to model more complex supply chain
procurement behaviour. However, RL takes longer time to arrive at solutions, necessitating
future research on identifying and improving trade-offs between the use of AI and mathematical
models are needed.
Identifiers
External DOI: https://doi.org/10.1016/j.ifacol.2022.09.609
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337046
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.