Drone pre-positioning and scheduling for emergency response in ports

In recent years, ports have become increasingly congested and operationally complex with the expansion of global maritime trade, making effective emergency response to frequent low-severity incidents critical. Drones offer a promising solution for rapid response in large-scale container terminals by enabling fast access and reducing surveillance blind spots. Motivated by this potential, we study a drone-based pre-positioning and scheduling problem for port emergency response under uncertainty. To address stochastic accident arrivals and service times, we develop an entropy-based robust satisficing model that jointly determines pre-positioning and operational scheduling decisions within a P-Queue framework. Numerical experiments on a container terminal, including a case drawn from Nansha Port in China, demonstrate that the proposed approach consistently outperforms benchmark methods in out-of-sample performance.