# HMART

Hospital delivery robots must decide both which request to take and in what order to visit stops, while STAT and routine items carry different clinical deadlines. HMART puts that joint decision into a shared graph-attention encoder with two sequential decoders, bounds the tail of weighted tardiness with a conditional value-at-risk constraint, and coordinates the shared elevators through a model predictive control layer. In a discrete-event simulation of a four-floor hospital with three elevators and twelve robots, the engineered-decoder instantiation lowers mean total weighted tardiness from 2359±214 to 29±6 against the strongest dispatching rule and raises the STAT on-time rate from 0.925±0.011 to 0.969±0.007.

Hospital delivery robots must decide both which request to take and in what order to visit stops, while STAT and routine items carry different clinical deadlines. HMART puts that joint decision into a shared graph-attention encoder with two sequential decoders, bounds the tail of weighted tardiness with a conditional value-at-risk constraint, and coordinates the shared elevators through a model predictive control layer. In a discrete-event simulation of a four-floor hospital with three elevators and twelve robots, the engineered-decoder instantiation lowers mean total weighted tardiness from 2359±214 to 29±6 against the strongest dispatching rule and raises the STAT on-time rate from 0.925±0.011 to 0.969±0.007.