This work is motivated by the need for the Australian Defence Force to produce the right number of trained aircrew in the right place at the right time. This necessitates the development of optimal recruitment strategies while sustaining squadron capability within some risk tolerance. The challenge is that Defence Aircrew training environments typically have highly variable failure rates and relatively small numbers of students. We investigate three receding horizon strategies, each of which use inflated notional targets with some deterministic assumptions to mitigate risk. The first strategy back-fills expected demand given fixed targets; the second strategy dynamically chooses targets using Monte Carlo simulations; and the third strategy incorporates Integer Linear Programming for partial solutions. We show that the first two strategies scale well and maintain steady states, and that the second strategy successfully incorporates the risk tolerance, resulting in an efficient and highly scalable strategy for the recruitment problem.