This paper studies an appointment scheduling problem under schedule-dependent patient no-show behavior. The problem is motivated by our studies of independent datasets from countries in two continents which identify a significant time-of-day effect on patient show-up probabilities. We deploy a distributionally robust model, which minimizes the worst case total expected cost of patient waiting and service provider's idle and overtime, by optimizing the scheduled arrival times of patients. We show that this model under schedule-independent patient show-up behavior can be reformulated as a copositive program and then be approximated by semidefinite programs. These formulations are obtained by a new technique that uses a completely positive program to equivalently represent a linear program with uncertainties present in both the objective function and the right-hand side of the constraint sets. To tackle the case when patient no-shows are endogenous on the schedule, we construct a set of dual prices to guide the search for a good schedule and use the technique iteratively to obtain a near optimal solution. Our computational studies reveal a significant reduction in total expected cost by taking into account the time-of-day variation in patient show-up probabilities as opposed to ignoring it.

Additional Metadata
Keywords Distributionally Robust Optimization, Copositive Program, Appointment Scheduling, Patient No-shows
Persistent URL hdl.handle.net/1765/106439
Journal Management Science
Citation
Kong, Q, Li, S, Liu, N, Teo, C.P, & Yan, Z. (2018). Appointment Scheduling under Patient Schedule-Dependent No-Show Behavior. Management Science. Retrieved from http://hdl.handle.net/1765/106439