Artificial Intelligence Constrained Multi-Objective Optimisation of Emergency Blood Transfusion Courier Dispatch Under Variable Drone Airspace Clearance Latency: A computed Evaluation of Healthcare Haemorrhage Survival Equity

Emergency blood transfusion for haemorrhage requires courier dispatch decisions that trade off delivery speed against equity across receiving facilities, yet drone airspace clearance latency varies unpredictably and can invert the intended priorities of any fixed dispatch rule. the present analysis formulates the dispatch problem as a constrained multi-objective optimisation in which a central planner selects, for each request, a courier mode and route subject to a latency-dependent feasibility set, and evaluates the resulting Pareto frontier against an equity-weighted survival objective. Because no field data were available, the study is a design and operating-characteristics analysis: a before-after-control-impact estimator was characterised under a declared variance structure with between-cluster SD of 6.0, residual SD of 4.5 and intraclass correlation of 0.64, across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis derives a feasibility condition linking clearance latency to the survival benefit of drone dispatch, shows that the equity-weighted objective is concave in the dispatch threshold only when latency variance is bounded, and reports that 54 clusters are required to detect a 5-percentage-point effect at 80% power. The contribution is a formal characterisation of when latency-aware constrained optimisation preserves haemorrhage survival equity rather than merely improving mean delivery time.