Platelet inventory management in hospital networks: A reinforcement learning approach

Abstract

This study proposes a reinforcement learning (RL)-based framework incorporating the Proximal Policy Optimization (PPO) algorithm to improve platelet inventory management. The proposed approach considers an inventory system with varying ordering intervals, incorporating ABO-Rh substitution decisions and hospital collaborations through transshipment. In this framework, transshipment is modeled as a fixed policy, reflecting real-world practices where blood units nearing expiration are proactively transferred from smaller local hospitals to larger hospitals, where they are more likely to be used in time. We extend our analysis by exploring several RL models, including Trust Region Policy Optimiz..