Artificial Intelligence Constrained Reinforcement Learning for Antivenom Cold-Chain Replenishment Under Variable Refrigerator Power Outage Duration: A computed Healthcare Stockout Resilience Evaluation

Antivenom stockouts in low-resource health systems are driven less by average demand than by the interaction between stochastic power outages and the cold-chain constraints that make replenishment irreversible once a vial is compromised. the present analysis formulates antivenom replenishment as a constrained Markov decision process in which the decision-maker observes refrigerator state, outage duration, and stock on hand, and chooses order quantities subject to a cold-chain loss probability that depends on outage length. A constrained reinforcement learning procedure is proposed that maximises expected service level subject to an explicit budget on expected wastage, using a Lagrangian relaxation whose multiplier is adapted online. Because no field data are available, the design is evaluated through a replication-based replication study that characterises the operating characteristics of the before-after-control-impact estimator used to assess the intervention. The replication uses a between-cluster standard deviation of 6.0, a residual standard deviation of 4.5, and an intraclass correlation of 0.64, with 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 52 clusters are required to detect a 5 percentage point effect at 80 per cent power under this variance structure. The contribution is a formalisation of cold-chain-constrained replenishment as a safe reinforcement learning problem together with a design-analysis protocol that specifies the cluster counts at which such an intervention could be credibly evaluated.