Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains
Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with restocking. This complexity is further compounded by the finite shelf lives of pharmaceutical products, which necessitate a delicate balance between adequate stock and minimal waste. These intertwined factors create a complex optimization problem that requires sophisticated inventory strategies to ensure both product availability and PSC efficiency. This study aims to develop an optimal inventory replenishment policy for pharmaceutical products that can handle the stochasticity arising from uncertain demand and variable PSC conditions. Th...
| Authors | Amandeep Kaur, Gyan Prakash |
|---|---|
| Primary category | cs.AI |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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