Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Pharmaceuticals & Biotechnology, Operations & Process Management · Depth: Expert, short

Summary

A study introduces a hybrid deep reinforcement learning (DRL) algorithm, specifically an asynchronous advantage actor critic distributed proximal policy optimization (A3C DPPO) algorithm, to optimize dynamic inventory management in pharmaceutical supply chains (PSCs). PSCs face challenges from unpredictable demand, variable lead times, and finite product shelf lives, creating a complex optimization problem. The research formulates this as a Markov decision process and tailors the A3C DPPO algorithm to handle the continuous action space inherent in inventory management. Numerical results demonstrate that the proposed algorithm adaptively updates replenishment strategies under dynamic scenarios, achieving lower inventory costs compared to various benchmarks. Validation using real-world pharmaceutical inventory data confirms its practical feasibility, aiming to maximize PSC profitability while maintaining high patient service levels.

Key takeaway

For Operations Professionals managing pharmaceutical supply chains, consider implementing hybrid deep reinforcement learning solutions like A3C DPPO. This approach can dynamically optimize inventory replenishment, significantly reducing costs and improving patient service levels despite unpredictable demand and variable lead times. You should explore integrating DRL models validated with real-world data to enhance profitability and minimize waste from finite shelf-life products.

Key insights

A hybrid DRL algorithm effectively optimizes pharmaceutical inventory replenishment by adapting to stochastic demand and variable lead times.

Principles

• Inventory management is a complex optimization problem.
• DRL can handle continuous action spaces.
• Real-world data validates DRL for PSCs.

Method

Formulate inventory management as a Markov decision process. Apply a hybrid A3C DPPO algorithm, tailored for continuous action spaces, to adaptively update replenishment strategies.

In practice

• Implement A3C DPPO for dynamic inventory.
• Use DRL to balance stock and waste.
• Validate DRL with real-world supply chain data.

Topics

• Pharmaceutical Supply Chains
• Inventory Management
• Deep Reinforcement Learning
• A3C DPPO Algorithm
• Markov Decision Process
• Supply Chain Optimization

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.