  Intensity-modulated radiation therapy (IMRT) allows for the design of
customized, highly-conformal treatments for cancer patients. Creating IMRT
treatment plans, however, is a mathematically complex process, which is often
tackled in multiple, simpler stages. This sequential approach typically
separates radiation dose requirements from mechanical deliverability
considerations, which may result in suboptimal treatment quality. For patient
health to be considered paramount, holistic models must address these plan
elements concurrently, eliminating quality loss between stages. This combined
direct aperture optimization (DAO) approach is rarely paired with uncertainty
mitigation techniques, such as robust optimization, due to the inherent
complexity of both parts. This paper outlines a robust DAO (RDAO) model and
discusses novel methodologies for efficiently integrating salient constraints.
Because the highly-complex RDAO model is difficult to solve, an original
candidate plan generation (CPG) heuristic is proposed. The CPG produces rapid,
high-quality, feasible plans, which are immediately clinically viable, and can
also be used to generate a feasible incumbent solution for warm starting the
RDAO model. Computational results obtained using clinical patient datasets with
motion uncertainty show the benefit of incorporating the CPG, both in terms of
first incumbent solution and final output plan quality.
