Artificial Intelligence Distributionally Robust Optimisation of Community Pharmacy Oral Rehydration Salt Replenishment Under Variable Road Network Floodability: A computed Healthcare Shortage Equity Evaluation

Community pharmacies in flood-prone regions face replenishment decisions for oral rehydration salts (ORS) under road-network floodability that is uncertain and heterogeneous across settlements. the present analysis formulates ORS replenishment as a distributionally robust optimisation problem in which the ambiguity set is defined over road-segment floodability probabilities, and it evaluates shortage equity through a computed healthcare shortage framework. The formulation combines a two-stage stochastic program with a Wasserstein ambiguity set, a budgeted replenishment policy, and an equity-weighted shortage objective that penalises shortfalls in communities with limited alternative care access. Because no field data are available, the study is a methodological and replication-based evaluation: it specifies the model, derives tractable reformulations, and characterises the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells. The contribution is a formal, equity-aware robust replenishment model for ORS in community pharmacy networks, together with a replication design that makes the equity–efficiency trade-off explicit and auditable.