Learning an Inventory Control Policy with General Inventory Arrival Dynamics
CoRR(2023)
摘要
In this paper we address the problem of learning and backtesting inventory
control policies in the presence of general arrival dynamics – which we term
as a quantity-over-time arrivals model (QOT). We also allow for order
quantities to be modified as a post-processing step to meet vendor constraints
such as order minimum and batch size constraints – a common practice in real
supply chains. To the best of our knowledge this is the first work to handle
either arbitrary arrival dynamics or an arbitrary downstream post-processing of
order quantities. Building upon recent work (Madeka et al., 2022) we similarly
formulate the periodic review inventory control problem as an exogenous
decision process, where most of the state is outside the control of the agent.
Madeka et al., 2022 show how to construct a simulator that replays historic
data to solve this class of problem. In our case, we incorporate a deep
generative model for the arrivals process as part of the history replay. By
formulating the problem as an exogenous decision process, we can apply results
from Madeka et al., 2022 to obtain a reduction to supervised learning. Via
simulation studies we show that this approach yields statistically significant
improvements in profitability over production baselines. Using data from a
real-world A/B test, we show that Gen-QOT generalizes well to off-policy data
and that the resulting buying policy outperforms traditional inventory
management systems in real world settings.
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关键词
general inventory arrival,inventory control policy,learning
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