| Summary: | Drug shortage is always a critical issue of inventory management in healthcare systems since it potentially invokes several negative impacts. In supply chain management, optimization goes hand-in-hand with inventory control to address several issues of the supply, management, and use of drugs. However, it is difficult to determine a shortage situation in a hospital due to multiple unpredictable reasons, such as manufacturing problems, supply and demand issues, and raw material problems. To avoid the shortage problem in a hospital, efficient inventory management is required to operate the system in a sustainable way and maximize the profit of the organization in the Hospital Supply Chain (HSC). In this work, we study a drug refilling optimization problem, a general model for drug inventory management in a hospital. We then investigate a Deep Reinforcement Learning (DRL) model to address this problem under an online solution that can automatically make a drug refilling decision in order to prevent a drug shortage. We further present a numerical result to verify the performance of the proposed algorithm, which outperforms the baselines (e.g., over-provisioning, ski-rental, and max-min) in terms of the refilling cost and the shortage rate.
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