Simulation-based approximate policy iteration for dynamic patient scheduling for radiation therapy

We study radiation therapy scheduling problem where dynamically and stochastically arriving patients of different types are scheduled to future days. Unlike similar models in the literature, we consider cancellation of treatments. We formulate this dynamic multi-appointment patient scheduling problem as a Markov Decision Process (MDP). Since the MDP is intractable due to large state and action spaces, we employ a simulation-based approximate dynamic programming (ADP) approach to approximately solve our model. In particular, we develop Least-square based approximate policy iteration for solving our model. The performance of the ADP approach is compared with that of a myopic heuristic decision rule.