Dynamic Robust Multi-Class Advance Patient Scheduling

Abstract

In this work, we study an advance patient scheduling problem where patients of different classes have different service times and incur different waiting costs to the system. It is known in the literature that multi-class advance dynamic patient scheduling is a challenging problem due to the high variability in the daily arrival process of patients, as well as the high dimensionality of the problem. To overcome these challenges, we develop a novel dynamic optimization framework where the multi-class advance scheduling problem can be approximately decomposed to multiple single-stage stochastic programs. Furthermore, we develop a distributionally robust formulation and quantify uncertainty in arrivals by applying a risk-averse optimization approach. Exploiting patient-level offline data, we develop a data-driven algorithm to minimize the worst-case outcome that may happen due to the high variability in arrivals. We examine the performance of the proposed robust algorithm by leveraging the MRI data from hospitals in Ontario and show that the dynamic robust model outperforms the dynamic stochastic approach significantly. We also observe that the proposed robust model performs well compared to an offline policy, which is based on the full knowledge of the future arrivals.