In this paper we investigate a stochastic appointment-scheduling problem in an outpatient clinic with a single doctor. The number of patients and their sequence of arrivals are fixed, and the scheduling problem is to determine an appointment time for each patient. The service duration of the patients are stochastic, and only the mean and co-variance estimates are known. We do not assume any exact distributional form of the service duration, and we solve for distributionally robust schedules that minimize the expectation of the weighted sum of patients' waiting time and the doctor's overtime. We formulate this scheduling problem as a convex conic optimization problem with a tractable semidefinite relaxation. Our model can be extended to handle additional support constraints of the service duration. Using the primal–dual optimality conditions, we prove several interesting structural properties of the optimal schedules. We develop an efficient semidefinite relaxation of the conic program and show that we can still obtain near-optimal solutions on benchmark instances in the existing literature. We apply our approach to develop a practical appointment schedule at an eye clinic that can significantly improve the efficiency of the appointment system in the clinic, compared to an existing schedule.

Additional Metadata
Keywords appointment scheduling, copositive programming, semidefinite programming, network flow
Persistent URL dx.doi.org/10.1287/opre.2013.1158, hdl.handle.net/1765/111459
Journal Operations Research
Citation
Kong, Q, Lee, C.Y, Teo, C.P, & Zheng, Z.C. (2012). Scheduling Arrivals to a Stochastic Service Delivery System using Copositive Cones. Operations Research, 61(3), 711–726. doi:10.1287/opre.2013.1158